Impact of Climate Change in Health in Colombia and Recommendations for Mitigation and Adaptation Impact of Climate Change in Health in Colombia and Recommendations for Mitigation and Adaptation September 2023 Task Team Leaders Jeremy Veillard and Gabriel Aguirre Martens Component 1 Samuel Osorio, Rodrigo Sarmiento, Nelson Alvis Zakzuk, Jean Carlo Pineda, and María de los Ángeles Carrasquilla Component 2 Mersedeh Tariverdi, Miguel Nuñez del Prado, and Daniel Clark Thompson Component 3 Mikhael Iglesias, Marcela Portocarrero, and Luisa Castellano Component 4 Eduardo Alfonso-Sierra Editing: Katherine Ward Graphic design and illustration: Danielle Willis iii © 2023 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of the staff of The World Bank with external contributions. 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Cover illustration: Danielle Willis All photos used with permission, and any reuse requires permission of the copyright holder.  iv CONTENTS COMPONENT 3............................................................................ 81 Biodiversity and Climate Change: Implications for Human Health in Colombia ACKNOWLEDGMENTS..................................................................x Key Messages............................................................................................. 81 INTRODUCTION............................................................................. 1 Biodiversity, Climate Change, and Health............................................ 82 Drivers of Biodiversity Loss......................................................................87 COMPONENT 1...............................................................................9 Interaction of Environment and Human Health in Colombia............ 94 Nature as an Ally for Public Health..........................................................97 Burden of Disease Attributable to Non-Optimal Temperature in Colombia and Its Costs: 2010–2019 and Future Projections Governance in Environmental and Human Health.............................102 Conclusions and Recommendations....................................................109 Objective...................................................................................................... 13 Methods....................................................................................................... 13 COMPONENT 4........................................................................... 111 Results......................................................................................................... 16 Towards a Roadmap of Interventions to Address Climate Conclusions and Recommendations..................................................... 39 Change in Colombia’s Health Sector COMPONENT 2............................................................................ 41 Background................................................................................................ 111 Methods......................................................................................................113 People at the Heart of Resilience-Informed Health System Investments: Colombia Hazard Risk Assessment using Results........................................................................................................117 Artificial Intelligence Discussion................................................................................................. 125 Introduction................................................................................................ 44 ANNEXES.................................................................................... 127 Methodology ..............................................................................................47 Results.........................................................................................................57 ANNEX 1. COMPONENT 1..........................................................128 Conclusion and Recommended Actions................................................75 A. Methodological Annex Systematic Literature Review................................................................128 Methodology of Descriptive Analysis of Temperature and IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION  v Demographic Structure.......................................................................143 FIGURES Estimation of Disease Burden Attributable to Suboptimal Temperatures........................................................................................144 COMPONENT 1...............................................................................9 Economic Burden Attributable to Suboptimal Temperatures..........146 Figure 1. Flowchart: Methodology for Estimating Disease Burden B. Results Attachments......................................................................151 Attributable to Non-Optimal Temperatures................................................................. 14 Description of Temperatures and Demographic Structure in Figure 2. Spatial Distribution of Daily Temperature per Pixel, Mean of Daily Colombia.................................................................................................151 Temperatures 2015–2019....................................................................................................17 Figure 3. Evolution of Average Temperature, Analysis by Geographical Units C. Limitations and Methodological Considerations....................... 190 2010–2019............................................................................................................................... 19 C. Limitations and Methodological Considerations....................... 190 Figure 4. Distribution of Mortality by the 17 Causes Analyzed, 2010–2019............. 20 Figure 5. Structure of Preventable Deaths in Colombia............................................... 21 ANNEX 2. COMPONENT 2.........................................................192 Figure 6. Population Pyramids of Colombia, 2010–2050..............................................22 Figure 7. Percentage of Deaths Attributable to Heat and Cold by Age Group Boundaries ...............................................................................................192 and Sex in Colombia, 2010–2019.....................................................................................23 Population Maps.......................................................................................192 Figure 8. Interannual Averages of Mortality Rates Attributable to Cold and Hazard Maps.............................................................................................193 Heat, by Department, Colombia 2010–2019................................................................24 Population Exposure to Hazards ..........................................................194 Figure 9. Average Interannual Mortality Rate Attributable to Non-Optimal Temperatures, Colombia 2010–2019..............................................................................25 Infrastructure Exposure to Hazards .................................................... 197 Figure 10. Year-on-Year Average Mortality Rates Attributable to Heat and Facility Prioritizations ............................................................................ 206 Cold in Colombian Departments, 2010–2019............................................................. 26 Artificial Intelligence-Based Integrated Climate-Sensitive Risk Figure 11. Annual Mortality Rates Attributable to Cold (blue) and Heat (red), Index: Method Details..........................................................................214 2010–2019...............................................................................................................................27 Figure 12. Economic Burden Attributable to Heat, Cold, and Suboptimal Temperature (Total) in Colombia, 2010–2019.............................................................. 28 ANNEX 3. COMPONENT 3 ........................................................ 217 Figure 13. Rate of Productive Years of Life Potentially Lost and Economic Burden Attributable to Heat and Cold in Colombia, 2010–2019............................ 29 REFERENCES.............................................................................218 Figure 14. Productive Years of Life Potentially Lost and Proportion Attributable to Suboptimal Temperatures in Colombia, by Age Group, 2010–2019.............................................................................................................................. 30 Figure 15. Economic Burden Due to Suboptimal Temperatures in Colombia, 2010–2019.............................................................................................................................. 30 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION  vi Figure 16. Proportion of Productive Years of Life Potentially Lost Attributable Figure 31. Population and Health Facilities Exposed to Landslide Risks, to Suboptimal Temperatures over Total PYPLL, by Departments in Colombia.31 Department Level............................................................................................................... 65 Figure 17. Indirect Costs (GDP Per Capita Scenario), by Population Figure 32. Health Facility Prioritization, National Level: Top Ten PHCs and Attributable to Premature Mortality from Heat and Cold in Colombia, Hospitals for Flooding........................................................................................................ 66 2010–2019...............................................................................................................................32 Figure 33. Health Facility Prioritization, Department Level: Top Three PHCs Figure 18. Relationship between Rate of PYPLL Attributable to Suboptimal and Hospitals per Department for Flooding................................................................67 Temperatures and GDP Per Capita, by Departments in Colombia, 2010–2019.32 Figure 34. Health Facility Prioritization, National Level: Top Ten PHCs and Figure 19. Temperature Projections by Shared Socioeconomic Trajectories Hospitals for Landslides Risks.........................................................................................70 Scenarios, Colombian Regions, 2020–2100............................................................... 34 Figure 35. Health Facility Prioritization, Department Level: Top Three PHCs Figure 20. YLL Cold-Attributable Annual Rates for Five Climate Scenarios, and Hospitals for Landslide Risks...................................................................................70 Colombia 2020–2050 (per 100,000 Inhabitants)....................................................... 36 Figure 36. Average Accessibility Time of Population to Their Preferred Figure 21. YLL Heat-Attributable Annual Rates for Five Climate Scenarios, Health Services in Bogotá and Most Affected Communities by Indirect Colombia 2020–2050 (per 100,000 Inhabitants)....................................................... 36 Impact of Floods ..................................................................................................................74 Figure 22. Temperature Difference Between 2020 and 2050 for Five Climate Figure 37. Critical Road Segments in Bogotá Essential to Ensure Scenarios................................................................................................................................37 Accessibility to Health Services......................................................................................74 Figure 23. Costs by AMW and GDP Per Capita Projection Scenario for Five Climate Scenarios, Colombia 2020–2050................................................................... 38 COMPONENT 3............................................................................ 81 Figure 38. Ecosystem Services, Biodiversity, and Implications for Human COMPONENT 2............................................................................ 41 Well-Being ............................................................................................................................ 83 Figure 24. Climate and Disaster Risk Management For Health Systems Figure 39. Effects of Biodiversity Loss and Degradation on the Climatic and Prioritization......................................................................................................................... 46 Ecological Balance.............................................................................................................. 85 Figure 25. Geotagged Population Density (A) and Geolocated Health Facilities (B) .......................................................................................................................... 50 COMPONENT 4........................................................................... 111 Figure 26. Schematic Process of Calculating Climate-Sensitive Risk Index ........ 53 Figure 40. Quantifying the Costs of Inaction: Sub-Optimal Temperature..............114 Figure 27. Department-level Climate and Health Risks................................................ 58 Figure 41. CHEVT: The Tool in Brief....................................................................................115 Figure 28. Compound Hazards Climate-Sensitive Integrated Risk Levels............ 58 Figure 42. CHEVT Results, Colombia................................................................................118 Figure 29. Population and Healthcare Facilities Exposed to Floods and Landslide Risks.................................................................................................................... 59 Figure 43. Climate-Aware Integrated Health Surveillance Implementation Costs (10-year horizon).....................................................................................................124 Figure 30. Population and Health Facilities Exposed to Floods, Department Level........................................................................................................................................ 60 Figure 44. Health Care Providers’ Adaptation Implementation Costs (10-year horizon).................................................................................................................125 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION  vii ANNEX 1. COMPONENT 1..........................................................128 Figure A2.4. Colombian Population Exposed to Floods and Fault Lines as Proxy of Landslides............................................................................................................194 Figure A1.1. Flowchart Search Result Burden of Disease............................................130 Figure A2.5. Population Exposed to Floods, Department Level...............................194 Figure A1.2. Flowchart Search Results Risk Factors.....................................................133 Figure A2.6. Population Exposed to Fault Lines as Proxy of Landslides at Figure A1.3. Steps Review Economic Literature............................................................138 Department Level..............................................................................................................196 Figure A1.4. PRISMA from the Literature Review of Studies of Economic Figure A2.7. Exposed Healthcare Facilities to Floods and Fault Lines as Burden Due to Temperature Changes.........................................................................138 Proxy of Landslides, Where (A) Depicts the Exposed Primary Healthcare Figure A1.5. Cost Taxonomy for Estimating the Economic Burden of Disease..... 147 Facilities, and (B) and (C) Illustrate the Exposed Hospitals .................................. 197 Figure A1.6. Caldas-Lang Colombia Index (IDEAM)......................................................154 Figure A2.8. Exposed Primary Health Care Facilities to Floods, by Department.198 Figure A1.7. Monthly Temperature Behavior by Regions in Colombia.....................154 Figure A2.9. Hospitals Exposed to Floods by Department.........................................198 Figure A1.8. Average Temperature by Colombian Regions, 2010–2019................... 157 Figure A2.10. Primary Health Care Facilities Exposed to Landslides by Figure A1.9. Average Monthly Temperature by Colombian Cities, 2010–2019.......159 Department.........................................................................................................................201 Figure A1.10. Temperature Trends by Cities of Colombia for 2010–2019.................160 Figure A2.11. Hospitals Exposed to Landslides by Department................................201 Figure A1.11. Bland-Altman Graphs of Temperature by Department and Cities....161 Figure A2.12. Cosine Similarity Between the Ideal Vector I and a Vector X Figure A1.12. Departmental Concentration of the Colombian Population Representing Two Districts.............................................................................................215 2010–2050............................................................................................................................163 Figure A1.13. Evolution of Summary Exposure Values (SEV) in Colombia ANNEX 3. COMPONENT 3 ........................................................ 217 2010–2019..............................................................................................................................171 Figure A3.1. Results of Performance Measurements at the Departmental Figure A1.14. Average SEV by Cause of Death................................................................ 172 Level in Colombia, 2021 ................................................................................................... 217 Figure A1.15. Indirect Costs per Capita Attributable to Suboptimal Temperatures in Colombia, 2010–2019: Scenario of Annual Minimum Wage (AMW) and GDP per Capita, without Discount...............................................181 Figure A1.16. Relationship between the Rate of YLL Attributable to Cold and Heat and GDP per Capita by Departments in Colombia, 2010–2019.................182 ANNEX 2. COMPONENT 2.........................................................192 Figure A2.1. Colombia Political Divisions ........................................................................192 Figure A2.2. Geolocated Population Representation Examples...............................192 Figure A2.3. Examples of Floods and Fault Line as Proxy to Landslides in Colombia..............................................................................................................................193 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION  viii TABLES COMPONENT 4........................................................................... 111 Table 12. Indicators and Information Sources, Colombia Analysis............................115 INTRODUCTION............................................................................. 1 ANNEX 1. COMPONENT 1..........................................................128 Table 1. Health Impacts Due to Climate Change...............................................................4 Table A1.1. Categories of Analysis of Data Extracted from the Review.....................131 Table A1.2. Categories of Analysis of Data Extracted from the Review...................134 COMPONENT 1...............................................................................9 Table A1.3. Categories of Analysis of Data Extracted from the Review...................136 Table 2. Shared Socioeconomic Trajectories by Emission Scenarios...................... 15 Table A1.4. Characteristics of the Articles Included in the Review of Studies of Economic Burden of Temperature Variation in the World.................................139 COMPONENT 2............................................................................ 41 Table A1.5. Descriptive Statistics Temperature by Department in Colombia 2010–2019..............................................................................................................................151 Table 3. Climate, Population, and Health System Determinants ............................. 48 Table A1.6. Linear Regression Temperature Evolution by Year and Table 4. Analysis of Exposure to Floods and Related Impacts.................................... 61 Department, Colombia 2010–2019...............................................................................155 Table 5. Analysis of Exposure to Landslides and Related Impacts........................... 63 Table A1.7. Overview of Monitoring Stations 10 Cities in Colombia..........................156 Table 6. Health Facility Prioritization, National Level: Top Ten PHCs for Table A1.8. Data Representativeness Monitoring Stations 10 Cities Flood Intervention ...............................................................................................................67 Colombia 2010–2019.........................................................................................................156 Table 7. Health Facility Prioritization, National Level: Top Ten Hospitals for Table A1.9. Measures of Central Tendency and Variability Monthly Average Flood Intervention............................................................................................................... 68 Temperature Monitoring Stations 10 Cities in Colombia 2010–2019.................. 157 Table 8. Health Facility Prioritization, National Level: Top Ten PHCs for Table A1.10. Linear Regression Temperature Evolution by Year Colombia Landslides Intervention.......................................................................................................71 2010–2019.............................................................................................................................160 Table 9. Health Facility Prioritization, National Level: Top Ten Hospitals for Table A1.11. Description of Mortality by the 17 Causes Covered by the Study.......162 Landslides Intervention.......................................................................................................71 Table A1.12. Main Suboptimal Temperature Events, Exposure by Department....164 Table 10. Recommended Actions to Improve Health System Climate Table A1.13. Burden of Disease Attributable to Heat, by Department Resiliency in Colombia.......................................................................................................78 Colombia 2010–2019.........................................................................................................166 Table A1.14. Burden of Disease Attributable to Cold, by Department COMPONENT 3............................................................................ 81 Colombia 2010–2019.........................................................................................................169 Table 11. Annual Indicated Values of Particulate Matter (PM 10 and PM2.5) Table A1.15. Summary Exposure Values Antioquia Exhibition and Coffee to Protect Public Health, according to the WHO guideline value, Axis 2010–2019.................................................................................................................... 172 intermediate goals, and national regulations of Colombia (Resolution Table A1.16. Summary Exposure Values Central Andean Zone Exhibition 2244 of 2017) ........................................................................................................................ 95 2010–2019............................................................................................................................. 173 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION  ix Table A1.17. Summary Exposure Values Exhibition Eastern Zone 2010–2019........ 173 Table A2.7. Health Facility Prioritization at Department Level, Top Three Table A1.18. Summary Exposure Values Western Zone Exhibition 2010–2019...... 174 PHCs for Flooding.............................................................................................................206 Table A1.19. Summary Exposure Values Exhibition Caribbean Region 2010–2019.175 Table A2.8. Health Facility Prioritization at Department Level, Top Three Hospitals for Flooding .....................................................................................................209 Table A1.20. Summary Exposure Values Orinoquia Exhibition 2010–2019............. 176 Table A2.9. Health Facility Prioritization at Department Level, Top Three Table A1.21. Summary Exposure Values Amazon Exhibition 2010–2019................. 177 PHCs for Landslides...........................................................................................................211 Table A1.22. Productive Years of Life Potentially Lost and Total Economic Table A2.10. Health Facility Prioritization at Department Level, Top Three Burden of the 17 Causes Studied and Attributable to Non-Optimal Hospitals for Landslides..................................................................................................213 Temperatures in Colombia, 2001–2019........................................................................ 178 Table A2.11. Targets of Different Factor Variables..........................................................216 Table A1.23. PYPLL and Economic Burden Attributable to Heat and Cold by Colombian Departments, 2010–2019, without Discount........................................ 178 Table A1.24. Economic Burden Attributable to Heat and Cold by Colombian ANNEX 3. COMPONENT 3 ........................................................ 217 Departments, 2010–2019, with Discount....................................................................180 Table A1.25. Average Temperature Projections According to Scenarios by Department.........................................................................................................................183 Table A1.26. Vector of Change (2050–2100) Expected According to Scenarios by Department...............................................................................................185 Table A1.27. Economic Burden Projections Attributable to Heat and Cold by Colombian Departments, 2020-2050, without Discount....................................... 187 ANNEX 2. COMPONENT 2.........................................................192 Table A2.1. Exposed Population to Floods at Department Level Order by Percentage of Exposed Population..............................................................................195 Table A2.2. Exposed Population to Landslides at Department Level, Ordered by Percentage of Exposed Population........................................................196 Table A2.3. Healthcare Infrastructure Exposed to Floods at Department Level, Ordered by Percentage of Exposed Population...........................................199 Table A2.4. Healthcare Infrastructure to Exposed Landslides at Department Level, Ordered by Percentage of Exposed Population..................202 Table A2.5. Information Used for Clustering Departments Exposed to Floods..204 Table A2.6. Information Used for Clustering Departments Exposed to Fault Line as Proxy for Landslides..........................................................................................205 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ACKNOWLEDGMENTS x ACKNOWLEDGMENTS This report was made possible with the financial support from the Agence Française de Développement (AFD)1 and the Japan- Bank Program for Mainstreaming Disaster Risk Management in Developing Countries.2 1  Agence Française de Développement (AFD) implements France’s policy on international development and solidarity. Through its financing of NGOs and the public sector, as well as its research and publications, AFD supports and accelerates transitions towards a fairer, more resilient world. It also provides training in sustainable development (at AFD Campus) and other awareness-raising activities in France. 2  The Japan-Bank Program for Mainstreaming Disaster Risk Management in Developing countries is financed by the Government of Japan and receives technical support from the World Bank Tokyo Disaster Risk Management Hub and is supported by World Bank Staff in Global Facility for Disaster Reduction and Recovery (GFDRR). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION 1 INTRODUCTION Climate change has been called the most important threat to human health in the 21st century. It is estimated that if the temperature rises and its impact on the other climatic variables continues unchanged, it will kill more than 83 million people (1 percent of the world’s population) in the next 80 years (Watts et al. 2020)—13 times the toll of the COVID-19 pandemic (World Health Organization 2023). Historically, only pandemics or world wars have posed such threats to human health. As a result, the issue has aroused unprecedented attention. In 2021, the World Health Organization (WHO) declared climate change the greatest health threat facing humanity (WHO 2021). Now, more than 195 governments have included climate change mitigation and adaptation as pillars in their multi-year plans,3 and government health sectors have been developing plans to measure and respond to the impact of climate change on health. However, recognition of the links between climate change and health remains nascent, so these efforts have not yet been accompanied by strategic and actionable approaches to measure the impacts and ground the responses. This report contributes to 3  In 2015, 195 countries signed the Paris Agreement. The agreement aims to limit global warming to well below 2o Celsius (C), preferably 1.5oC, compared to pre-industrial levels. To achieve this goal, countries have committed to reducing their greenhouse gas emissions. INTRODUCTION 2 addressing that gap by providing a framework for understanding the impact of climate change on human health in Colombia and by outlining the most effective actions to mitigate the threat. In Colombia, a health sector response to climate change is especially urgent due to geographic vulnerabilities which impact the health risk to its people. Most people in Colombia live in the Andes region, which is prone to landslides and floods. Floods account for 45 percent of all natural hazards in the country and landslides account for 19 percent. Melting glaciers and rising temperatures have increased the frequency of these Photo: Chris Ford ‘Rolling Hills’ (CC BY-NC 2.0) dangers. Droughts are also increasing—now occurring 2.2 times more often than in previous years and generating direct health changes in rainfall patterns may further spread vector-borne consequences as well as indirect ones through impacts such as diseases such as dengue fever and malaria to higher elevations lower agricultural production. In addition, the El Niño Southern in the country and cities such as Bogotá, which is home to more Oscillation phenomenon causes abnormal weather conditions, than 7 million people. For all these reasons, there is an urgent such as more intense droughts or extreme rainfall patterns. For need for a schematic analysis and a comprehensive response to example, the 2010–2011 La Niña floods caused 470 deaths due to the threats of climate change to health in Colombia. the proliferation of waterborne diseases such as diarrhea, as well as economic losses of COP 3.4 billion. In total, between 1998 and Although there are global and regional level efforts to address 2011, weather-related disasters constituted around 90 percent this threat, its complexity has made analysis and development of reported emergencies in Colombia. Significant risks also exist of comprehensive strategies difficult. While the direct impacts due to many hazards that have not yet materialized but are likely of climatic variables are limited to increases in temperature, in the future. For example, communities along the Caribbean humidity, and precipitation, these variables also have numerous and Pacific coasts are at risk from sea level rise, storm surge, indirect impacts on health. Reporting by groups such as the and temperature extremes. In addition, rising temperatures and Intergovernmental Panel on Climate Change (IPCC) has IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION INTRODUCTION 3 highlighted not only direct risks to health, but also impacts on When evaluating the impact of climate change on health, it must infrastructure, productive activities, biodiversity, and ecosystems, be taken into account that we are dealing with a highly complex which in turn affect health. For example, these variables can phenomenon. Simply put, the relationship between climate and precipitate geological disasters that can damage health or affect health is very complicated. First, climate projections have a high the infrastructure necessary to ensure continuity of care. In turn, degree of uncertainty, which then carries over to adaptation and these impacts increase pressure on budget management, effective mitigation plans that rely on those projections. Second, climate implementation of policies and programs, and identification of critical components are networked and interdependent, so a variation in actors essential for timely responses to climate change hazards. one component affects the entire system. Third, the relationship between causes and effects is not linear, so small changes can have big impacts. For example, an increase of 1°C (Celsius) in global temperature can intensify the water cycle, which increases the probability of droughts and floods that threaten food security and displace people. On top of this, there is uncertainty about the long- term effects of climate change on health (Sarmiento-Suárez 2016). Further complicating the picture is the fact that climate change is only one of the global environmental changes and challenges associated with the Anthropocene (our current era in which human activity is the dominant force in changes to how Earth systems operates). Other examples include the loss of biodiversity and desertification, both of which trigger a cascade of direct, indirect, and ecosystem-mediated effects on health (McMichael et al. 1998). Finally, the picture is even more complex because these changes are all inter-related, so there are both additive and multiplicative impacts on health. Table 1 attempts to summarize this complicated picture by presenting these impacts, the global environmental changes associated with them, and the driving forces linked to the causal mechanism. Photo: © Scott Wallace / World Bank ‘Woman in flooding area. Colombia’ (CC BY-NC-ND 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION INTRODUCTION 4 Table 1. Health Impacts Due to Climate Change Driving forces and human pressures on the environment: Global environmental changes: economic growth, use of fossil fuels, greenhouse effect, population climate change, depletion of the stratospheric ozone layer, loss of biodiversity, growth, and deforestation depletion of freshwater reserves, desertification, and land degradation Direct health impacts Indirect health impacts Health impacts caused by ecosystem changes Floods Population displacements/ Changes in the dynamics of climate refugees vector-borne diseases Heat waves Loss of livestock Higher exposure to animal contact because of changes to the Increased exposure to species’ ecological niche ultraviolet radiation Conflict UV Emergence of new Landslides Damage to health infectious diseases infrastructure Exposure to pollutants (forest Droughts and crop decline fires, urban air quality) Inadequate mitigation and adaptation strategies Malnutrition Increased burden of chronic diseases Inequities Loss of natural medicines Increase in skin and Poverty respiratory allergies Urban blight IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION INTRODUCTION 5 Due to the inherent complexity of the relationship between Conceptual frameworks such as Planetary Health and One environment and health, environmental epidemiology has Health are useful to understand the interactions amongst developed different models to address it. The most widely used human health, climate change, and biodiversity. These models are (i) the model of socio-environmental determinants of frameworks highlight three key challenges for climate change and health developed by the WHO; (ii) critical epidemiology; and (iii) the health: (i) protecting health from the full range of climate impacts; Ecosocial theory of disease distribution. Different strategies have (ii) building resilient and sustainable health systems for the 21st emerged from these models, such as the ecosystem approach century; and (iii) promoting the health co-benefits of climate to health, with variations, including Ecohealth, the One Health action. This report responds to these three challenges by providing approach, and the Planetary Health movement (Lebel 2005; data and tools useful for mapping vulnerability and sensitivity WHO 2017; Planetary Health Alliance n.d.). These initiatives are all to temperature across Colombia, and for developing adaptation based on shared paradigm shift in the approach to public health: strategies focused on vulnerable populations and subnational moving from an anthropocentric (human-centered) approach to areas. It also considers alternative pathways such as ecosystems a biocentric one that treats human, animal, and environmental and biodiversity, and the economic implications of those impacts. health as inextricably linked. For example, the Ecohealth approach is centered on analysis of the determinants of health This flagship report is focused on providing a holistic, in socio-ecosystems, multidisciplinary approaches to develop actionable analysis of this complex but urgent issue—analysis a deeper understanding of environmental health problems, and rooted in methodologies specifically designed to assess the the participation of civil society in the process. These principles impact of climate change on health in Colombia, considering focus on sustainability, social and gender equity, and translating the complexity the factors involved and the particularities of knowledge into action. In recent years, academia and scientific the Colombian context. It is particularly designed to support societies in Latin America have made advances in environmental policymakers in Colombia. Specifically, it lays the groundwork for health understanding on the conceptual and methodological policy action in Colombia and provides a starting point for World levels, but research funding is very low compared to scale of the Bank collaboration with the government in understanding the needs and problems. Within this rubric, the relationship between phenomenon and taking concrete actions to tackle it, thereby climate change and health is one of the priority research topics improving health outcomes for all Colombians, and contributing (Rodríguez-Villamizar 2015). to improvements in healthcare all around the world in the face of a IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION INTRODUCTION 6 Four Components of Climate Change and Health in Colombia global threat. It also offers insights for policymakers and academics around the world seeking ideas and toolkits to confront this daunting challenge. Using an actionable approach for policymakers that considers Biodiversity the complexity of the Colombian context, this report divides the interaction of climate change and health into four components. 2 Natural disasters and health system vulnerabilities 3 impacts and One Health factors Component 1 looks at the direct impact of climate change on human health by analyzing the effect of temperature increases on mortality at the subnational level. Component 2 expands 1 upon Component 1 and analyzes climate change-induced natural Rising temperatures disasters, such as landslides and floods, and their potential impact and mortality on human population, health infrastructure, and access to health services. Component 3 addresses indirect interactions between climate change and health, with a focus on the One Health approach and particularly the biodiversity implications for human health given their importance in Colombia. Finally, Component 4 brings the first three lines of impact analysis together in a common monetary analysis to illustrate health economic benefits and to facilitate investment and policy decisions. Each of these four components is described in more detail below. 4 Comparative analysis IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION INTRODUCTION 7 Component 1 addresses one of the major challenges Colombia Component 2 assesses climate change-induced natural disasters faces in designing and implementing a policy to protect human and how they affect health systems and vulnerable populations. health against climate change: namely, quantifying the impact. It prioritizes resilience investments using developed compound and To build effective adaptation strategies, it is crucial that the integrated risk indices at both national and departmental levels. government can quantify the health impacts of specific climate Health facilities are further ranked based on criteria such as staff, hazards and their distribution in each region of the country. This supplies, infrastructure, climate vulnerabilities, and the communities is a top priority for the government as a vulnerability mapping tool they serve, both nationally and at the departmental level. For to build adaptation strategies. To address this need, Component 1 Bogotá, it identifies communities most affected by disruptions in analyzes daily temperature-related mortality and its costs at the health services during extreme events. To ensure uninterrupted subnational level, by sex, age group, and cause of death, for the supply chains and service access, critical road segments requiring 2010–2019 period and 2050 Shared Socioeconomic Pathways cross-sectoral coordination and investment are also identified. The (SSP) scenarios. The analysis uses the Global Burden of Disease primary goal of these efforts is to offer guidance to policymakers, (GBD) methodology, one of the most comprehensive and widely considering the diverse realities across regions of the country used approaches; this methodology also allows us to make and the specific climate-induced challenges. The impacts and comparisons with other countries. recommendations presented in this analysis align with existing legal frameworks, ensured through employing Frontline Rapid Scorecard to evaluate climate-related laws, regulations, and procedures across sectors. Finally, this data-informed deep dive employs cutting-edge approaches, including Artificial Intelligence and mathematical modeling, for prioritizations. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION INTRODUCTION 8 as Colombia, paying attention to the interactions amongst health, biodiversity and climate change is essential. Lastly, Component 3 identifies and analyzes national and subnational governance structures and capacities—a key step for operationalizing effective policies and interventions addressing the interactions between biodiversity and human health. Component 3 focuses on biodiversity—a particularly important Component 4 supports decision-making by integrating the area of indirect climate impacts on health in Colombia. It assesses findings from Components 1–3 into a comparative analysis the the drivers of biodiversity loss, emphasizing the interactions costs of intervention versus the costs of inaction. To this end, between biodiversity and air quality, and outlines the governance Component 4 integrates the findings of the economic burden mechanisms being used to address environmental and climate- associated with non-optimal temperature (Component 1), the related health risks. Component 3 focuses on the crucial role that implications for the cost of reconstruction of infrastructure climate and biodiversity play as determining factors that shape not affected by extreme events (Component 2), and indirect effects only the environment but also the ecological and social processes of climate change on health (Component 3) and complements that take place within them. For example, changes in vegetation it with an estimate of the costs associated with mortality and can increase global greenhouse gas emissions and impact capacity morbidity of six selected outcomes, using the World Bank’s for absorbing carbon dioxide. Biodiversity can also contribute to Climate and Health Economic Valuation Tool (CHEVT). In addition, adaptation efforts and reduce the risks of climate-related hazards the component identifies climate change adaptation interventions to human health. In Colombia, water and air pollution constitute that have been proposed in the health sector and documents their key environmental risks for biodiversity and health, increasing the implementation costs. Assessing the cost of inaction contributes burden of disease in the country. Drivers of biodiversity loss such to raising awareness and stimulating policy development about as habitat loss and land use changes due to deforestation (which current and future health-related challenges that deserve is the primary source of greenhouse gas (GHG) emissions in the attention. Highlighting the policy interventions from the health country) threatens biodiversity, increasing channels of exposure for sector and their costs, contributes to cementing a roadmap of zoonotic diseases. In the context of a megadiverse country such interventions to tackle the challenges of climate change. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION 9 COMPONENT 1 Burden of Disease Attributable to Non-Optimal Temperature in Colombia and Its Costs: 2010–2019 and Future Projections Colombia is highly vulnerable to climate change (National Planning Department (DNP) 2011). Global warming increases the frequency and severity of extreme hydro-meteorological events such as floods, hurricanes, heat waves, and droughts, that generate health effects— as observed with the overflowing of the Mocoa, Mulatos, and Sangoyaco rivers in 2017 and with Hurricane Iota in 2020 (Fajardo López and Reyes Burgos 2018; Government of Colombia 2020). The burden of disease due to climate change has a broad spectrum of effects, including direct impacts of natural disasters such as trauma and injuries, and also indirect impacts produced by climate variability (i.e., changes in temperature, humidity, and rainfall), such as the spread of infectious diseases (e.g., malaria, dengue, cholera, and respiratory infections) and increases in chronic diseases (e.g., cardiovascular diseases, respiratory diseases, diabetes mellitus, malnutrition, and mental COMPONENT 1 10 disorders) (Sarmiento-Suárez 2016). Added to all this is potential infrastructure damage that can impair or entirely interrupt the provision of health services. It is important to note that climate change affects mortality due to not only high temperatures but also low temperatures. This is because climate change increases both the global average temperature and also the range of the temperature variance. This affects the oscillations in climate or climatic variability that can occur in different time scales: seasonal, intra-seasonal, between years, and over the course of a decade (Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM n.d.). In fact, the displacement of the Intertropical Convergence Zone in a northerly direction as a result of greenhouse gases has altered the seasonal pattern of rainfall and has increased the occurrence of climatic anomalies in relation to the El Niño-Niña-Southern Oscillation cycle (ENSO phenomenon). On the other hand, the increase in the energy of the atmosphere intensifies the water cycle, which contributes to increased precipitation in the rainiest areas and less in the driest areas. As a result of all of these factors, extreme cold and heat events occur more frequently. The temperature at which mortality is lowest is known as the “minimum mortality temperature” (MMT) and it coincides with the lowest point on a temperature/mortality curve that generally has a U-shape (Lee et al. 2017). “Suboptimal temperatures” are ones that Photo: ©2010CIAT/NeilPalmer ‘Shadow 1’ (CC BY-SA 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 11 move away from the MMT. According to the Institute for Health Recent methodological advances in environmental epidemiology Metrics and Evaluation (IHME), in Colombia, the attributable move away from traditional epidemiology because phenomena risk for mortality due to high temperature is 3.43 percent characterized by the nonlinearity of associations must be studied. due to drowning, 2.21 percent due to interpersonal violence, Several studies have described the associations of temperature 1.85 percent due to traffic accidents, and 1 percent due to lower with mortality using distributed lag nonlinear models (DLNLM) and respiratory infections. Meanwhile, the fraction attributable to low have validated their use as a standard methodology to study the temperatures is 4.73 percent for chronic obstructive pulmonary effects of temperature (Gasparrini 2010). On the other hand, the disease (COPD), 3.39 percent for lower respiratory infections, development of methods to estimate the burden of disease using 3.26 percent for ischemic heart disease, 2.92 percent for stroke, Bayesian methods has allowed the evaluation of the impact of 2.34 percent for chronic kidney disease, and 1 percent for different risk factors, such as suboptimal temperatures, on specific diabetes mellitus (IHME n.d.). causes of mortality—finding evidence of association for 17 different events (e.g., cardiovascular diseases, diabetes, suicide, violence, These growing effects of climate change on the health of the and respiratory diseases) (Burkart et al. 2021). However, there is Colombian population make it necessary to identify strategies still no consensus on the appropriate method for assessing the and policies to mitigate and adapt to its impact. Due to the impact of the burden of disease at global, regional, and local levels geographical and cultural diversity of the country, local-level due to exposure to changes in global temperature values. interventions are required to better adapt to climate change. Therefore, it is crucial to have disaggregated information to support On the other hand, the economic impact of these temperature decision-making. In fact, climate change scenarios predict that changes (mainly in the form of rising temperatures due to climate temperature increases will be uneven across Colombia, with the change) is increasing and is relevant for world economies. Callahan departments of Arauca, Vichada, Vaupés, and Norte de Santander and Mankin (2022) estimated that cumulative losses from 1992 to having the most pronounced increases (+2.6ºC for 2070–2100) 2013 from anthropogenic extreme heat could range from COP 5 (IDEAM 2023). trillion to COP 29.3 trillion worldwide. These amount to 6.7 percent of gross domestic product (GDP) per capita per year for regions The complexity of the relationship between climate change and in the lowest income decile, but only 1.5 percent for regions in the health means that robust methods are required to quantify impact. highest income decile (Callahan and Mankin 2022). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 12 Several high-income countries have quantified the economic In Colombia, measurements of the economic impact of climate costs of the health hazards caused by high temperatures. For variability are scarce. There are approximations towards the example, in the United States, estimated economic losses due to estimation of the economic burden by environmental risk heat-related deaths ranged from approximately USD 4.2 million factors. For 2016, the National Institute of Health (INS) calculated to USD 5.1 billion (Knowlton et al. 2011; Gronlund et al. 2019; and the productive years of life potentially lost (PYPLL) due to Chen et al. 2022). Similarly, a study in Spain on heat-related deaths environmental risk factors of air, water, and others, and the indirect showed that hospitalization costs for these deaths amounted to costs they represented (INS 2018). The deaths caused by the EUR 426,087 in 2002–2006 (Roldán et al. 2015). In Australia, for nine diseases analyzed by the INS analysis represented a total of 2013 and 2014, the additional economic burden due to reduced 169,136 PYPLL and COP 2.7 trillion of economic burden, of which labor productivity caused by heat stress was USD 6.2 billion per 34,524 PYPLL and COP 585 billion were due to environmental year (Chen et al. 2022; Zander et al. 2015). risk factors. In other words, 21 out of every 100 Colombian pesos of GDP loss in Colombia due to people who died prematurely from the nine diseases explored were caused by exposure to the environmental risk factors analyzed by the INS (INS 2018). Because of all these factors, the research presented in this 21 out of every 100 Colombian pesos report estimates the burden of disease related to suboptimal of GDP loss in Colombia due to people temperatures in each department of Colombia for 2010–2019 who died prematurely from the nine and 2020–2050. It also estimates the effect of these changes in temperatures on the economic burden of disease due to diseases explored were caused by premature mortality in Colombia. exposure to the environmental risk factors analyzed by the INS (INS 2018) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 13 Objective attributable burden analysis available for Colombia and other countries (Roque et al. 2021). The objective of the analysis presented in this section (Component 1) is to estimate the disease and economic burden of mortality attributable to suboptimal temperature in Colombia, by Methods departments, for 2010–2019 and 2020–2050. The study used environmental, demographic, epidemiological, and economic sources of information. The environmental Methods sources included the daily temperature per grid of 0.25 degrees from the ERA52 satellite reanalysis; the Institute of Hydrology, Scope Meteorology and Environmental Studies (IDEAM) monitoring network for 10 Colombian cities; and the temperature projections This is an ecological disease burden study that used the for climate scenarios (source: Climate Change Knowledge Portal). methodology previously published by the GBD-2019 (GBD 2019 Demographics sources included data from population projections Diseases and Injuries Collaborators 2020).1 The study included a and retroprojections (Colombian National Department of Statistics review of published scientific literature on economic burden and (DANE)), vital statistics (DANE), and spatial distribution of the disease studies to identify the methodological approaches used to population (WorldPOP). The epidemiological sources used estimate disease burden by suboptimal temperature (see Annex were the exposure-response curves for 17 causes of mortality on Methodology for a descriptive analysis of temperature and associated with temperature variation and the minimum mortality demographic structure). It also explored national and international temperatures by climatic zone (TMRELs) of the GBD-2019 for sources of information on temperatures, disease burden, vital Colombia. For the economic analysis, the study used data on statistics, demographics, and socio-economic factors. the labor market (DANE), the annual minimum wage (AMW), and The disease burden estimate and projections are framed in the average productivity measured in GDP per capita. non-optimal temperature approach; this is different from the extreme temperatures approach, and is comparable to the GBD 2  ERA5 is the fifth generation European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate covering January 1  GBD = Global Burden of Diseases, Injuries, and Risk Factors Study. 1940 to the present. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 14 Figure 1. Flowchart: Methodology for Estimating Disease Burden Attributable to Non-Optimal Temperatures IDEAM/ Temperature World Bank projections Fixed Sensitivity IDEAM monitoring analysis stations A ributable Zone burden Environmental Temperature ERA5 temperature by projection to estimates departments 2050 Population Grid a ributable fraction Data (PAF) and Summary population Exposure Value (SEV) Sources WorldPop estimate Theoretical minimum risk Demography Exposure exposure level and health response (TMREL) GBD curves RR by cause for Non-optimal each daily and average annual temperature Daily temperature a ributable DANE mortality referenced to the burden TMREL Departmental population and YLL estimates mortality projections IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 15 To estimate the burden of disease attributable to suboptimal Table 2. Shared Socioeconomic Trajectories by Emission Scenarios temperatures, the study calculated the daily mortality risks (2010–2019) for 17 causes based on the response exposure curves Shared socioeconomic Scenario for each pixel and then aggregated the risks by climatic zone and trajectories department. It also obtained values of prevalence of temperature Very low greenhouse gas emissions: CO2 exposure and proportions attributable to temperature per day, SSP 1-1.9 emissions reach zero by 2050. climatic zone, and department. Finally, the study described deaths and determined attributable YLL (years of life lost). For economic Low greenhouse gas emissions: CO2 emissions SSP 1-2.6 burden, it calculated and economically valued productivity loss due reach zero by 2075. to attributable premature mortality (PYPLL) using two scenarios Median greenhouse gas emissions: CO2 (AMW and GDP per capita). SSP 2-4.5 emissions remain the same until 2050 and fall but not reach zero by 2100. The estimated disease and economic burden projected for 2020–2050 was calculated using five greenhouse gas emission High greenhouse gas emissions: CO2 emissions scenarios in the Shared Socioeconomic Pathways (SSP) of the SSP 3-7.0 double by 2100. Intergovernmental Panel on Climate Change (IPCC) (Table 2) to simulate attributable mortality rates using linear departmental Very high greenhouse gas emissions: CO2 SSP 5-8.5 emissions triple by 2100. regression models for the sum of the causes of death considered. Source: Adapted from International Panel on Climate Change (2019) Note: CO2 = carbon dioxide; SSP = Shared Socioeconomic Pathways IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 16 Results Key points This section presents the temperatures and changes in Colombia’s • To our knowledge, this is the first study reporting non-optimal population structure for 2010–2019. It also presents estimates temperature effects disaggregated by mortality cause, sex, of disease burden attributable to suboptimal temperatures for age group, and department in Colombia. the 17 causes, disaggregated by sex, age group, and department. The section then provides the attributable economic burden • This study’s calculations found that 0.43 percent of total calculations and, finally, the projections of the disease and mortality (1.05 percent of 17 mortality causes analyzed) in attributable economic burden for 2020–2050. It presents the 2010–2019 was attributable to non-optimal temperatures: most important results for decision making, due to their impact 24.3 percent was attributable to heat and 75.7 percent to cold. on human health, economics, and novelty: showing the impacts • Most of the attributable burden occurred in men in the 70+ according to the demographic structure and disaggregation by sex age group—a group expected to increase in the future due to for each department to support making local decisions targeted to population demographic transitions. specific population groups. • Colombia’s heat-attributable mortality rate will surpass its cold-attributable mortality rate in 2040 for the climate change projection scenario SSP5 (8.5°C), or in 2049 for SSP3 (7.0°C). • The economic burden due to cold and heat variations varied from COP 0.26–1.5 and COP 0.38–1.2 trillion, respectively, and it was concentrated in 15–44 year-old people. • The cumulative economic losses due to non-optimal temperature related with premature mortality ranged from 0.1 to 0.4 percent of the 2019 GDP in Colombia. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 17 Temperatures, Mortality, and Demographics, Figure 2. Spatial Distribution of Daily Temperature per Pixel, Mean of Daily Temperatures 2015–2019 2010–2050 Temperatures Satellite temperature analysis. For 2010–2019, Sucre was the department with the highest average temperature (28.18°C, SD=±1.26) and Bogotá, D.C. recorded the lowest (13.08°C, SD=±2.52). Temperatures varied greatly in most departments in the Andean region, particularly those in the Magdalena River Valley (Huila, Tolima, Cundinamarca, Caldas, Boyacá, Antioquia, and Santander). Temperatures were more homogeneous in the Caribbean region, except for departments containing the Sierra Nevada de Santa Marta (Magdalena, La Guajira, and Cesar). Throughout the departments of Orinoquía and Amazonia, temperatures were fairly constant (Figure 2). Table A1.5 in the methodological annex to this report presents the statistics by department. Source: ERA5 (Hersbach et al. 2020) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 18 Temperature changes by regions and zones (Figure 3). Temperatures varied greatly in both the Andean zone (Central and Eastern) and in the Cafetera region and Antioquia. The Western region also had geographical variability in temperatures, except Most areas have in most of Chocó which has predominately a super-humid warm temperatures climate and the highest temperatures in the region. On the other that vary greatly hand, the Caribbean region has mostly dry warm temperatures. and have various But it is also home to some microclimates such as the extreme microclimates. cold of the Sierra Nevada de Santa Marta; the humid warm climate of the archipelago of San Andrés, Providencia, and Santa Catalina; and the semi-humid climate in the south of Bolívar and in the southern municipalities of Córdoba. In the Orinoco and Amazon regions, warm and humid temperatures prevailed with the exception of the Andean piedmont which has temperate and cold climates. Temperatures in all departments of these regions follow a similar pattern, with slightly higher temperatures in Vichada. Much higher temperatures Much lower temperatures Predominantly humid climates Predominantly dry climates IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 19 Figure 3. Evolution of Average Temperature, Analysis by Geographical Units 2010–2019 Source: European Centre for Medium-Range Weather Forecasts (n.d.) (ERA5). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 20 Mortality and demographic structure Figure 4. Distribution of Mortality by the 17 Causes Analyzed, 2010–2019 Mortality Between 2010–2019, a total of 2,147,531 deaths were reported in Colombia. The causes of death included in the present analysis Ischemic heart Homicide accounted for 884,628 deaths, which is 41.1 percent of the total disease for the period. These deaths occurred mostly in men, in people 70 years and older, and in the departments with the most populous cities such as Cali, Medellín, and Bogotá, D.C. (see Table A1.11 in the annexes for full data results by location and cause of death). Causes of death. The main causes of death in this study were ischemic heart disease (39.4 percent), homicide (15.6 percent), and chronic obstructive pulmonary disease (14.3 percent) (Figure 4). This study identified 583,117 deaths from cardiorespiratory causes Chronic Stroke Traffic Hypertensive between 2010 and 2019, with ischemic heart disease accounting for obstructive accidents heart disease the largest share of these deaths (59.7 percent). Cardiorespiratory pulmonary mortality causes included stroke, hypertensive heart disease, disease (COPD) ischemic heart disease, chronic obstructive pulmonary disease (COPD), lower respiratory tract infection (LRTI), cardiomyopathy, Kidney disease Suicide Diabetes Drowning Mechanical and myocarditis (Methodological Annex, Table A1.11). mellitus injuries Disasters Mortality demographics. As shown in Figure 5, nationwide in Animal- related Colombia, preventable mortality is higher in men; relationship that Drowning Respiratory decreases over time, especially amongst males between 10 and 20 infections Myocarditis Drowning years of age. Also, preventable mortality in older adults increased between 1990 and 2030. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 21 Figure 5. Structure of Preventable Deaths in Colombia increase to an estimated 61.9 million and the distribution by age group will have shifted significantly as the average age increases as part of the demographic transition Figure 6. (For details of the population distribution by departments in Colombia, see the section “Annex to the description of the demographic structure” in Annex 1.) 1990 Age 2020 2030 25 20 15 10 5 0 5 10 15 20 Proportional distribution (%) Source: Vega (2023) Demographic structure In 2010, Colombia’s population was 44 million (51 percent men), with 39.1 percent concentrated in only three departments (Bogotá, D.C., Antioquia, and Valle del Cauca) which are home to the country’s most populous cities. By 2050, the total population will IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 22 Figure 6. Population Pyramids of Colombia, 2010–2050 2010 2010 2050 2050 >80 >80 75-79 75-79 70-74 70-74 65-69 65-69 60-64 60-64 55-59 55-59 50-54 50-54 45-49 45-49 40-44 40-44 35-39 35-39 30-34 30-34 25-29 25-29 20-24 20-24 15-19 15-19 10-14 10-14 5-9 5-9 0-4 0-4 6 4 2 0 2 4 6 4 3 2 1 0 1 2 3 4 5 % % Men Hombres Women Mujeres Hombres Women Men Mujeres Source: DATA 2030 2030 >80 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5-9 0-4 5 4 3 2 1 0 1 2 3 4 5 % Men Hombres Women Mujeres IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 23 Historical Analysis: Disease Burden Attributable Figure 7. Percentage of Deaths Attributable to Heat and Cold by Age Group and Sex in Colombia, 2010–2019 to Suboptimal Temperatures, 2010–2019 Suboptimal temperature disease burden Between 2010–2019, the 17 causes studied accounted for 884,628 deaths, of which 9,472 (1.07 percent) were attributable to suboptimal temperatures. Of these deaths, 2,332 (24.6 percent) were attributed to heat and 7,141 (75.4 percent) to cold. Gender and age distribution. Of 9,283 deaths attributable to suboptimal temperature (5,510 were men and 3,962 were women. The average mortality rate attributable to this risk factor was 20.41 per 1 million, with a slightly higher risk amongst men (24.27 per 1 million) than women (16.72 per 1 million). Cold-related deaths (average rate: 15.41 per 1 million) were much higher than Cold deaths Heat deaths heat-related deaths (average rate: 5.00 per 1 million). Suboptimal Note: F = female; M = male temperature was associated with a total of 172,870 YLL and a rate of 37.28 YLL per 100,000 population. During 2010–2019, YLL statistics related to low temperatures (109,313, YLL rate=23.62 Geographic distribution. The geographic distribution of the burden per 100,000) were also higher than those associated with high of disease by temperature, according to the variables analyzed, temperatures (63,557, YLL rate=13.66 per 100,000). including sex, age group, and cause of death, is described below. According to the distribution by sex, the burden was higher in men, The highest mortality rates due to suboptimal temperature were in especially related to heat, while the burden related to cold is more Quindío (37.45 deaths per 1 million population; confidence interval homogeneous and tends to be concentrated in those over 70 (CI) 95%:34.81–40.23), Tolima (33.47 deaths per 1 million people; (Figure 7). CI95%: 30.96–36.08), and Caldas (30.71 deaths per 1 million people; CI95%:28.31–33.22). The highest rates of YLL for suboptimal IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 24 temperature were observed in Arauca (63.49 years per 100,000 Figure 8. Interannual Averages of Mortality Rates Attributable to people; CI95%: 60.05–67.09), Quindío (53.60 years per 100,000 Cold and Heat, by Department, Colombia 2010–2019 people; CI95%: 50.43–56.90), and Tolima (53.05 years per 100,000 people; CI95%: 49.90–56.34). Figure 8 shows the interannual averages for mortality rates attributable to cold (marked in blue) and heat (in red), disaggregated by department. Higher rates of cold-related mortality were observed for Quindío, followed by Caldas, Risaralda, Tolima, and Boyacá; while higher rates for heat were observed for Sucre, followed by Córdoba, Atlántico, and Arauca. The average interannual mortality rates attributable to suboptimal Departments/states temperatures for 2010–2019 can be seen in Figure 9. The departments with the highest rates were found in the center of the country and in Antioquia and Sucre. The Caribbean region has a large percentage of the heat disease burden with 64 percent of YLL, followed by the Eastern Andean region with 9.2 percent and the Antioquia region and the Coffee Axis with 8.9 percent. The departments with the highest mortality rate from heat exposure were Sucre (21.17 per 1,000.00; CI95%: 19.18–23.26), Córdoba (16.69 per 1,000,000; CI95%:14.95–18.59) and Arauca (16.47 per 1,000,000; CI95%:14.72–18.32). The highest rates of YLL were in Arauca (55.40 per 100,000; CI95%: 52.18–58.76), Sucre (49.80 per 100,000; CI95%: 46.75–52.99), and Córdoba (44.18 Deaths per million inhabitants per 100,000; CI95%:41.33–47.21). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 25 Figure 9. Average Interannual Mortality Rate Attributable to The causes associated with a higher burden of heat mortality Non-Optimal Temperatures, Colombia 2010–2019 were homicides, traffic accidents and ischemic heart disease. In fact, heat-related YLL for homicides were 30,045, with higher YLL in Atlántico (4,850), Bolívar (3,891), and Córdoba (3,720). Traffic accidents were responsible for 10,937 YLL, with the highest figures in Córdoba (1,226), Cesar (1,109), and Atlantico (1,095). Ischemic heart disease caused 9,408 YLL, with higher numbers in Atlántico (1,923), Bolívar (1,311), and Córdoba (1,237) (see Annex 1, Table A1.13 for details). The events with the highest population attributable fraction (PAF) due to high temperatures were homicides (0.76 percent), traffic accidents (0.64 percent), and diabetes mellitus (0.50 percent). On the other hand, the outcomes with the highest PAF associated with low temperatures were drowning (1.85 percent), COPD (1.11 percent), and lower respiratory infection (1.03 percent). In relation to the cold-related burden, the highest YLL were in the Central Andean region (38 percent of total YLL), followed by the Antioquia region and the Coffee Axis (28 percent), and the Western region (20 percent). The departments with the highest mortality rate due to cold exposure were Quindío (37.45 per 1 million; CI95%: 34.81– 40.23), Tolima (28.39 per 1 million; CI95%:26.11–30.83), and Caldas (27.89 per 1 million; CI95%:25.63–30.31). The highest rates of YLL were in Quindío (53.60 per 100,000; CI95%: 50.43–56.90), Risaralda (42.28 per 100,000; CI95%: 39.49–45.24), and Tolima (40.34 per 100,000; CI95%:37.61–43.23) (Figure 10). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 26 Figure 10. Year-on-Year Average Mortality Rates Attributable to The causes that generated a greater impact on cold mortality Heat and Cold in Colombian Departments, 2010–2019 were ischemic heart disease, COPD, and stroke (cerebrovascular disease). Ischemic heart disease caused 48,906 YLL, with the highest numbers in Bogotá (9,039), Antioquia (8,531), and Valle (6,099). The cold-related YLL for COPD was 18,723, with higher numbers in Bogotá (4,394), Antioquia (3,675), and Cundinamarca (1,997). Stroke was responsible for 10,372 YLL, with the highest figures in Bogotá (2,719), Antioquia (1,593), and Valle (1,081) (see Annex 1, Table A1.14 for details). According to the annual trend of mortality rates attributable to cold and heat, a peak in the heat mortality rate was observed in 2015, which may correspond to the El Niño Southern Oscillation (ENSO) phenomenon in its Niño phase, which is characterized by an increase in dry and warm periods in the country (Figure 11). As Figure 11 shows, the different scales of climate variability, in particular the ENSO phenomenon, had an impact on fluctuations in heat-attributable mortality rates in 2010–2019. This also would be expected to contribute to different climatic anomalies such as heat waves in the future. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 27 Figure 11. Annual Mortality Rates Attributable to Cold (blue) and Economic Burden Results Heat (red), 2010–2019 Between 2010–2019, deaths in Colombia caused by the 17 causes analyzed represented a total loss of 7,293,919 PYPLL, meaning on average 21.3 productive years lost for each death of productive age. In this period, the PYPLL of these causes generated an economic burden that ranged between COP 88–522 trillion. Of the total for the 17 diseases, 52,887 PYPLL (0.7 percent) and between COP 0.6–3.3 trillion were attributable to non-optimal temperatures (Table A1.22, Figure 12). Of the total attributable economic burden, Rate * 1 mill the related to cold and heat ranged between COP 0.26–1.5 trillion and COP 0.38–2 trillion, respectively. Of the total PYPLL, 40.3 percent was attributable to cold (21,331 PYPLL) and 59.6 percent to heat (31,556 PYPLL). Likewise, the loss in productive years was equivalent to 30 percent of the loss of years of life due to premature death (PYPLL/YLL) attributable to suboptimal temperatures. Year IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 28 Figure 12. Economic Burden Attributable to Heat, Cold, and The PYPLL rate fluctuated during 2010–2019. Fluctuations were Suboptimal Temperature (Total) in Colombia, 2010–2019 especially notable for heat, with a decrease in 2011 followed by an increase until 2015, the year with the highest rate of loss of productivity due to premature mortality (8.57 per 100,000). The rate of cold PYPLL experienced a constant decline over the decade (Figure 13). The economic valuation of these PYPLL is shown in Figure 13b. In the floor scenario (valued with the AMW), economic losses due to premature mortality attributable to suboptimal temperatures are relatively constant, with the largest loss in 2011 (COP 75 billion; COP 19 billion discounted) and the smallest in 2012 (COP 51.6 billion; COP 13 billion discounted). In the scenario of the valuation with GDP per capita these values ranged between COP 286–428 billion (COP 53–81 billion discounted) for 2012 and 2016, respectively. This burden adjusted for the time discount can be seen in Figure 13b. Source: World Bank Note: Values in million COP IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 29 Figure 13. Rate of Productive Years of Life Potentially Lost and (B) Economic burden in billions of pesos for suboptimal temperatures, Economic Burden Attributable to Heat and Cold in Colombia, with and without temporary discount 2010–2019 (A) PYPLL rate per 100,000 Note: GDPpc = gross domestic product per capita The distribution of the 50,233 PYPLL attributable to suboptimal temperatures is presented in Figure 14, by age group, for 2010–2019. The analysis observed that between 0–9 years and in people over 44 years of age, the attributable PYPLL are higher for cold. On the other hand, between 10 and 44 years of age, they are higher for heat (Figure 14a). The proportions of PYPLL attributable to heat and cold follow the same trend (Figure 14b). The mortality structure presents as a U-shape, expressed in the proportional increase of PYPLL by cold at the beginning of life and at the end of productive life (Figure 14b), and due to the impact of heat on homicides in young men. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 30 Figure 14. Productive Years of Life Potentially Lost and Proportion Causes of death. Homicides, drowning, traffic accidents, and Attributable to Suboptimal Temperatures in Colombia, by Age ischemic heart disease were the causes that generated the Group, 2010–2019 highest indirect costs associated with premature mortality due (A) PYPLL attributable to heat and cold to suboptimal temperatures (Figure 15). For example, during 2010–2019, economic losses from homicides ranged between COP 0.3–1.6 trillion, losses from drowning ranged between COP 68–642 billion, and from traffic accidents ranged between COP 99–592 billion pesos (Annex 1, Table A1.22, Economic Burden Due to Suboptimal Temperatures in Colombia, 2010–2019; Figure 15 below). Figure 15. Economic Burden Due to Suboptimal Temperatures in Colombia, 2010–2019 (B) Proportion of PYPLL attributable to heat and cold IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 31 Economic burden by departments. According to the PYPLL national GDP per capita for the period. Note that the departments findings, the departments with the highest proportion of PYPLL with higher GDP per capita, which are usually in the zone of cold attributable to suboptimal temperatures over the total PYPLL of the temperatures, report higher rates of PYPLL due to cold. 17 causes were La Guajira, Sucre, Bolívar, Córdoba, and Atlántico; the territories with the lowest proportion were Nariño and Cauca Figure 16. Proportion of Productive Years of Life Potentially Lost Attributable to Suboptimal Temperatures over Total PYPLL, by (Figure 16). The heat-related economic burden was greatest in Departments in Colombia the Caribbean region, specifically Atlántico (COP 54–249 billion), Bolívar (COP 46–204 billion), and Cesar (COP 25–126 billion); and the cold-related one was mainly concentrated in Antioquia (COP 2.5 attributable/APVPP (%) (%) 59–324 billion), Valle del Cauca (COP 51–251 billion), and Bogotá, attributable/PYPLL 2.0 D.C. (COP 31–212 billion) (Annex 1, Table A1.23). Figure 17 shows these values in relative terms (per inhabitants) for cold and heat, in the loss 1.5 valuation scenario with GDP per capita. (See Annex 1, Figure A1.15 for a depiction the AMW scenarios and the economic burden for 1.0 non-suboptimal temperatures in both scenarios.) APVPP PYPLL 0.5 A negative and statistically significant association (p<0.05) can be 0.0 observed between GDP per capita and PYPLL rates attributable to La Guajira Sucre Bolivar Atlantico Cesar Magdalena Arauca Casanare Norte de Santander Guaviare Meta Cordoba Vichada Vaupes Caqueta Choco Santander Putumayo Boyaca Tolima Guainia Huila Antioquia San Andrés, Py ST. Amazonas Caldas Risaralda Cundinamarca Narino Quindio Valle del Cauca Bogota, D.C. Cauca suboptimal temperatures. Association with heat and cold. La Guajira, Sucre, Córdoba, Cesar, Bolívar, and Atlántico have the highest rates of PYPLL due Note: PYPLL: Productive year of life potentially lost. to heat and also levels of well-being measured by GDP per capita below the national average (Figure 18). On the other hand, the departments of Valle, Quindío, Risaralda, and Antioquia, which have the highest rates of cold PYPLL, are also below the average IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 32 Figure 17. Indirect Costs (GDP Per Capita Scenario), by Population Figure 18. Relationship between Rate of PYPLL Attributable to Attributable to Premature Mortality from Heat and Cold in Suboptimal Temperatures and GDP Per Capita, by Departments in Colombia, 2010–2019 Colombia, 2010–2019 PYPLL per 100,000 habitants GDP per capita IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 33 A negative and statistically significant association (p<0.05) can be Temperature and Disease and Economic Burden observed between GDP per capita and PYPLL rates attributable to Projections suboptimal temperatures. Temperature projections, 2020–2100 Association with heat and cold. La Guajira, Sucre, Córdoba, According to the five scenarios of the Shared Socioeconomic Cesar, Bolívar, and Atlántico have the highest rates of PYPLL due Trajectories (SSP), the Caribbean, Orinoco, and Amazon regions to heat and also levels of well-being measured by GDP per capita will have above-average temperatures. In the SSP5-8.5 scenario, below the national average (Figure 18). On the other hand, the the average temperature in these three regions would be above departments of Valle, Quindío, Risaralda, and Antioquia, which 30ºC by 2100, while in the SSP3-7.0 scenario, only the Caribbean have the highest rates of cold PYPLL, are also below the average region would exceed the 30ºC threshold. On the other hand, in national GDP per capita for the period. Note that the departments the SSP1.19 scenario, the average temperature would remain with higher GDP per capita, which are usually in the zone of cold relatively stable throughout the century. The following figures temperatures, report higher rates of PYPLL due to cold. present the time series for each scenario and by region between 2020 and 2100. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 34 Figure 19. Temperature Projections by Shared Socioeconomic Trajectories Scenarios, Colombian Regions, 2020–2100 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 35 In the scenarios of high greenhouse gas emissions, temperature YLLs attributable to cold compared to the other scenarios, since projections predict the highest averages will be in the departments this scenario is associated with an increase in daily temperature of Cesar, Magdalena, Atlántico, Vichada, and Sucre (Annex 1, Table variability, so there would be a greater likelihood of extreme A1.25). cold days (Figure 20). With respect to heat, there is a growing divergence in YLL rates between the very low emissions scenario In the scenarios of low greenhouse gas emissions the temperature (SSP1-1.9) and the very high emissions scenario (SSP5-8.5), even would remain stable, with low fluctuations and even decrease seeing a decrease over time in SSP1-1.9 (Figure 21). in some places (SSP1-1.9 scenario). In contrast, high-emission scenarios forecast temperature increases between 1 and 2°C Projected heat-attributable YLL rates in the country could vary by 2050 and between 3 and 5°C by 2100 (SSP3-70 scenarios significantly in different socio-economic and greenhouse gas and SSP5-8.5 scenarios). In the SSP5-8.5 scenario for 2100, the production scenarios. For example, there would be a 14 percent departments with a greater gradient of change are part of the decrease in the attributable YLL rate between 2020 and 2050 for Orinoco region and Arauca (+5ºC), Casanare (+4.9ºC), Vichada SSP1-1.9. On the other hand, the rate could increase by 63 percent (+4.9ºC), Guainía (+4.7ºC), and Meta (+4.7ºC) (Annexes, Table A1.26). for SSP5-8.5 (Figure 20 and Figure 21). This shows that, in terms of climate change mitigation, which occurs when fewer greenhouse Disease burden projections, 2020–2050 gases are produced (e.g., as in the SSP1-1.9 scenario, compared According to the scenario analysis, the heat attributable burden to the SSP5-8.5 scenario), there would be health co-benefits of a could decrease by 14 percent between 2020 and 2050 in the very significant magnitude for YLL rates attributable to heat. low greenhouse gas emissions scenario (SSP1.19). In the other There will be an important turning point: effects of cold are expected scenarios it would increase, ranging from 28 percent (SSP1.26) to to be outweighed by heat effects in SSP5.85 by 2040, and, in SSP3.70 63 percent (SSP5.85). By 2050, YLL will almost double for scenario in 2049 (Figure 20 and Figure 21). At this point, the landscape of health SSP5.85 (YLL 44,703) compared to scenario SSP1.19 (YLL=25,493). effects would change, giving less weight to respiratory diseases, Going forward, the YLL rates attributed to cold tend to decrease which are more related to the cold, and more weight to cardiovascular in all scenarios. However, the scenario involving high emissions effects, aggressions, and traffic accidents, which are more related (SSP5-8.5) would have a paradoxical effect on the annual rate of to the heat. This would occur in a future where there would be many more susceptible individuals due to an aging population. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 36 Figure 22 shows the temperature change in degrees Celsius Figure 21. YLL Heat-Attributable Annual Rates for Five Climate between 2020 and 2050 for different climate change scenarios, Scenarios, Colombia 2020–2050 (per 100,000 Inhabitants) by department. In the SSP1-1.9 scenario the greatest changes will be in departments of the Atlantic coast, Antioquia, Chocó, and Guainía, but in those magnitudes will be smaller than in the other scenarios. On the other hand, for the SSP5-8.5 scenario, the most important changes in temperatures will be seen in the departments of the plains, Bogotá, Boyacá, and the Santanderes. Figure 20. YLL Cold-Attributable Annual Rates for Five Climate Scenarios, Colombia 2020–2050 (per 100,000 Inhabitants) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 37 Figure 22. Temperature Difference Between 2020 and 2050 for Economic burden projections, 2020–2050 Five Climate Scenarios According to the projection analysis of economic burden scenarios, it is expected that by 2050 there will be a reduction in cold-related costs in all greenhouse gas emissions analysis scenarios: with reductions ranging between 11.3 percent (SSP5.85 with AMW) and 17 percent (SSP1.19 with GDP per capita) compared to the year 2020. The analysis also shows that cold-related costs will have a tendency to decrease each year. Meanwhile, the economic burden of heat for 2050 could increase compared to 2020 in three of the of greenhouse gas emissions scenarios, namely SSP5.85, SSP3.70, and SSP2.45 (dropping 32 percent, 12 percent, and 1.5 percent, respectively). However, in two emission reduction scenarios, which are very low greenhouse gas emission scenarios (SSP1.19, SSP1.26), a reduction in indirect costs attributable to heat would occur during the 2020–2050 period. A higher projected economic burden resulted in the SSP5.85 scenario, which simulates greenhouse gas emissions similar to those currently observed, with an increase of up to 8.3 percent compared the estimate for 2020. Source: World Bank 2023 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION Trillion pesos Trillion pesos COMPONENT 1 20 20 20 20 20 20 22 22 20 20 24 24 20 20 26 26 20 20 28 28 SSP_126 SSP_585 20 20 30 30 20 20 32 32 20 20 34 34 20 20 3 3 20 6 20 6 38 38 Cold: GDP Cold: AMW 20 20 SSP_119 SSP_370 40 40 20 20 42 42 20 20 44 44 20 20 46 46 20 20 48 48 20 20 20 20 SSP_245 Trillion pesos Trillion pesos 20 20 20 20 20 20 22 22 20 20 24 24 20 20 26 26 SSP_126 SSP_585 20 20 28 28 20 20 30 30 20 20 32 32 20 20 34 34 20 20 3 3 20 6 20 6 SSP_119 SSP_370 38 38 Heat: GDP Heat: AMW 20 20 40 40 20 20 42 42 20 20 44 44 20 20 46 46 20 20 SSP_245 48 48 20 20 20 20 Trillion pesos Trillion pesos 20 20 20 20 20 20 22 22 20 20 24 24 20 20 26 26 SSP_126 SSP_585 20 20 28 28 20 20 Figure 23. Costs by AMW and GDP Per Capita Projection Scenario for Five Climate Scenarios, Colombia 2020–2050 30 30 20 20 32 32 20 20 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION 34 34 20 20 3 3 20 6 20 6 SSP_119 SSP_370 38 38 20 20 40 40 20 20 42 42 20 20 44 44 20 20 46 46 Non-optimal temperature: GDP Non-optimal temperature: AMW 20 20 48 48 SSP_245 20 20 20 20 38 COMPONENT 1 39 Conclusions and Recommendations 6. The different scales of climate variability, in particular the ENSO phenomenon, had an impact on fluctuations in Conclusions heat-attributable mortality rates in 2010–2019, and would be expected to contribute to different climatic anomalies such 1. For 2010–2019, the events with the greatest impact on the as heat waves in the future. rate of deaths attributable to heat were homicides, traffic accidents, and ischemic heart disease, while the events with 7. The cumulative economic losses due to premature mortality a greater impact on the rate of deaths attributable to cold due to suboptimal temperature amounted to 0.1–0.4 percent were ischemic heart disease, COPD, and stroke. of GDP in 2019 in Colombia. 2. The departments with the highest heat mortality rates were 8. Most of the economic burden due to heat occurred in Sucre, Córdoba, and Atlántico, while the highest cold rates young adults due to external causes such as homicide, were observed in Quindío, Caldas, and Risaralda. transportation-related injuries, and drowning. 3. The departments of Atlántico, Bolívar, and Córdoba had the 9. The effects of cold are expected to be outweighed by the highest number of premature deaths attributable to heat, effects of heat in SSP5.85 by 2040, and, in SSP3.70 in 2049. while the departments of Antioquia, Bogotá, and Valle had the 10. Projected heat-attributable YLL rates in the country would highest number of premature deaths attributable to cold. decrease by 14 percent between 2020 and 2050 for SSP1-1.9 4. The Caribbean region has had the greatest impact due to heat or could increase by 63 percent for SSP5-8.5. This shows (67 percent of total YLL attributable to heat) while the Central that, in terms of climate change mitigation, there would be Andean region has had the greatest impact due to cold on the health co-benefits of a significant magnitude for mortality burden of YLL (38 percent of total YLL due to cold). rates attributable to heat. 5. The highest proportion of attributable mortality occurs 11. The higher projected economic burden resulted in the in men, particularly those over 70 years of age, although SSP5.85 high emissions scenario with an increase of up to 8.3 costs are concentrated in young men due to the impact of percent compared to what was estimated for 2020. However, homicides on this population. there is a potential for greenhouse gas emission reduction of up to 23 percent if policies are adopted to achieve low greenhouse gas emissions in the SSP1-1.9 scenario. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 1 40 Recommendations 7. The approach used here to estimate deaths associated with temperature exposure should also be used to assess 1. This study can be used as a baseline to carry out health the burden of disease related to morbidity; that will be impact assessments of different adaptation and mitigation necessary step in any potential health system reform in measures in the different climate change scenarios. Colombia. 2. This subnational analysis is useful for mapping vulnerability 8. Future analyses should include the estimation of direct at regional and local levels. It can help identify strategies to medical costs and direct non-medical costs for the diseases counteract the impact of climate change and design heat studied. This would complement the economic burden of early warning systems. premature mortality from the perspective of costs incurred 3. An approach focused on noncommunicable disease (NCD) by society. preventive programs for elderly people is needed to improve the ability of the elderly to adapt to future heat increases. 4. Developing strategies to reduce the economic burden of heat on external causes of mortality such as homicides and traffic accidents in young adults is highly recommended. 5. The use of temperature with data from the ERA5 satellite re-analysis gives greater representativeness to regions where there are no fixed monitoring stations and shows greater consistency in the data at the temporal level, so this approach is recommended for future similar analyses. 6. Studies at the municipal and province level must also be conducted. This would help reduce uncertainty and better capture the spatial variability of temperatures, mortalities, and sociodemographic characteristics. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION 41 COMPONENT 2 People at the Heart of Resilience- Informed Health System Investments: Colombia Hazard Risk Assessment using Artificial Intelligence Scope • This set of analyses quantifies the adverse effects of climate change and extreme events on the vulnerable populations and on the health system. It prioritizes departments for resilience-sensitive investments based on a compound (multi-hazard) and integrated (multi-factorial) risk index. Priority health facilities for investment are further identified, nationally and in each department, and ranked based on medical staff, supplies and equipment, infrastructure condition, and climate vulnerabilities, as well as health system design and people who they serve. For a high-resolution analysis of the city of Bogotá, it identifies communities most affected by disruptions, directly and indirectly, in accessing health services due to extreme events. To minimize disruptions in health supply chains and service access, the analyses also identify critical road segments for cross-sectoral coordination and investment needs. COMPONENT 2 42 • The principal goal of the study aims prioritize resilience Methods measures that can strengthen overlapping areas of vulnerability in the population and health system to • We used the Frontline Rapid Scorecard (Thompson et climate-induced hazard impacts, including slow-onset and al. 2023) to assess climate related laws, regulations, and extreme events. This analysis focused on flooding and procedures for the health sector and others (i.e. transport, landslide events. An analysis of existing legal frameworks energy) that support health system functionality in and policies helped to translate these priorities into routine care and emergencies. The high-level assessment actionable recommendations. informed the design and depth of the country data informed deep dive. • Data-informed deep dive: Prioritizations are conducted by employing Artificial Intelligence and Mathematical modeling approaches. Data includes poverty, households’ characteristics, maternal and child health, population climate related health risks, access to basic utilities, health system capacity, and health infrastructure. Methods identify risks to climate change and extreme events for: (i) populations with direct exposure to hazards; and (ii) primary health care (PHC) centers and higher tier hospitals (categories II and III) which are directly and indirectly affected by extreme climatic events. Photo: ©butforthesky.com ‘Flood in Santa Marta’ (CC BY-NC 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 43 Findings and livelihoods. Valle del Cauca has the highest number of vulnerable people facing direct risks: 2.4 million (42.71 • The Government of Colombia has worked intensively on percent of its population). policy and legislative instruments for climate change. It has put in place data mandates and collection efforts • About 2.3 million inhabitants are directly exposed to for various determinants of climate and health risks, and landslides, which can cause high mortality due to trauma conducted quantitative risk assessments on which this or suffocation by entrapment and injuries. In addition work is built. to service disruptions, landslide damage to lifeline infrastructure also leads to loss of access to essential • Risk exposure of PHC facilities potentially affect primary services, water-borne diseases, electrocution, and care services to 17 million and 25 million people facing lacerations from debris (World Health Organization 2023). flood and landslide risks, respectively, and exposure of hospitals to flood and landslide risks affects services for 16 • About 1 out of 5 health facilities are directly exposed million and 23 million people, respectively. to disruptive floods. This includes 4,416 primary care facilities and 143 hospitals. Indirect impacts of floods are • The departments of Vaupes, Cundinamarca, Boyacá, also substantial due to disruption to water, power, and and Arauca should be prioritized for all of the risks communication networks, as well as impaired access to assessed. Cauca and Norte de Santander should primarily facilities. be monitored for flood risks, and Tolima and Cordoba for landslide risks. • 549 primary care facilities and 20 hospitals are directly exposed to higher landslide risks, including the possibility • Approximately a quarter of Colombia’s population (15.7 of partial or full collapse. Adequate preparedness measures million people) is directly exposed to low flood level must be prioritized in these facilities to ensure patient and risks (5 cm depth). This means they face higher risks of health workforce safety and to minimize service disruptions. skin, acute respiratory infections, and diarrhea, in addition to wider socioeconomic impacts of floods on well-being • We provide healthcare facility prioritization to highlight the top facilities at a national level and in each department IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 44 for subnational health service delivery planning. Most Introduction climate-vulnerable facilities, serving the most vulnerable In addition to the increased climate-related morbidity and populations, in areas with lower health system presence mortalities discussed in Component 1, the last few years have are prioritized. Regarding PHC centers, the departments in seen increased impacts from climatic extreme events in Colombia, the north have more at risk of floods, while most affected such as coastal and river floods and landslides, which affect hospitals are in the west and north. Notably, Bolivar has five people’s well-being and essential services, such as healthcare of the 10 nationally prioritized PHC centers; Bolivar and Valle and transport. Extreme climatic hazards are expected to worsen del Cauca have PHC centers prioritized for all risks; and Valle severely due to climate change (World Bank 2021). Floods affect del Cauca and Antioquia have prioritized hospitals for all peoples’ health, livelihood, and well-being (Walker-Springett, risks. Butler, and Adger 2017). They also have a significant impact on • Impacts of extreme climatic events on transport health services and patient care delivery (McGlown and Fottler networks slow access to health services and the effects 1996) through structural damage to buildings (McGlown and vary, as shown in the analysis. Northwestern areas of Fottler 1996); interruption of care (Yusoff, Shafii, and Omar 2017); Bogotá are severely affected by floods, with average access loss of lighting, heating, and cooling (Martin 2019); extensive time to all health services increasing from 15 to 80 minutes. contamination of building structures, equipment, and supplies Delayed access to care is associated with significant costs with microorganisms (CDC 2013); exposure to toxic chemicals and for patients due to higher risks of morbidity and mortality. infectious waste (Martin 2019); and fire hazards due to erosion Underserved populations further from health systems are of electrical systems or equipment (Martin 2019). Furthermore, less likely to seek care putting them at even higher risks. damage to health facilities and the utility services they rely upon Disruptions in accessibility also affect supply chain and (e.g., water, electricity, communication lines), directly and indirectly service availability. impacts access to essential services and quality of care (World Health Organization (WHO 2023); International Federation of Red Cross and Red Crescent Societies 2023; Centers for Disease Control and Prevention (CDC) 2018). Injury or illness in the affected population (WHO 2023; CDC 2018) and well-being losses also IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 45 include and lead to adverse mental health impacts (Akpinar-Elci wt al. 2018; Kennedy et al. 2015). Climate change impacts will also likely be exacerbated by human development through land use changes and the built environment, particularly in areas with laxer regulations and enforcement (Campos Garcia 2011). Colombia has taken significant steps to address climate change in national policies, but further research is necessary to enhance the understanding of climate change impacts on vulnerable populations and on the health system to strengthen evidence-based decision-making. The government has introduced legislation (Ministry of Environment and Sustainable Development 2017; Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) 2016; IDEAM et al. 2016; Ministry of the Environment 2014; Congress of Colombia 2018; Ministry of Environment and Sustainable Development 2023) and developed technical documents on risk assessment for floods (Pérez et al. 2018; Güiza 2013; Sedano Cruz 2017; Vargas et al. 2018; Villegas González 2020), ground mass movements (Cerquera Gómez Photo: ©butforthesky.com ‘Flood in Santa Marta’ (CC BY-NC 2.0) and Novoa García 2021; Aristizábal 2019; Ruiz Peña 2017; Moreno et al. 2006; Gáfaro Duarte 2015; Cobos Romero and Salamanca Extreme climatic hazards are expected to Pira 2021), climate change (Mendoza 2011; Cuartas and Méndez worsen severely due to climate change. 2016; Cardona et al. 2020), and the impacts on health (Rodríguez- Floods affect peoples’ health, livelihood, Pacheco, Jiménez-Villamizar, and Pedraza-Álvarez 2019; and well-being. World Bank 2012a), which have been reviewed and taken into consideration for study design and recommendations. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 46 A high-level assessment of the health system’s adoptive capacity to Figure 24. Climate and Disaster Risk Management For Health climate hazards was conducted using the Climate and Disaster Risk Systems Prioritization Management Frontline Rapid Scorecard (Frontline Rapid Scorecard) (Thompson et al. 2023). The results highlighted how Colombia’s Health System Analyze Enabling Key Sectors health system has benefited from investment in preparedness 1. Health Foundations 4. Integrated Emergency Response essential workers, and critic tions, measures at the departmental and municipal levels and from policy 2. Resilient Health po pula s’, and infrastructures’ ex al pe rson 5. Resilient Care Facilities risk facilitie nel Infrastructure At- on , posu and legislative strengthening. Notable investments in the health 3. Resilient Network of Health Facilities 1 Po pu la ti yst e m a cce ss ib il ity dis ruptions re to h aza 2 lth s rds system include additional planning measures at the healthcare Hea ency c oordination, preparedn 3 r-a g ess, Inte a Sco re cr iti c al c om ponen n d facility and system levels, such as mandating risk assessments structu res Infra rec 4 ts po ns ard 5 for hospitals under the Disaster Risk Management Plan for Public Multi-Level Approach em National / Sub-National ec and Private Entities (PGRDEPP), along with enhanced medical ha TOOLS AND nism INTERVENTIONS supply and distribution networks. These investments have been s PRIORITIZATION bolstered by improvements in the country’s wider emergency Frontline response sector, which has strengthened its financing and funding Vulnerability Multisectoral Assessment Approach Vulnerable Vulnerable measures for disaster mitigation, response, and recovery over the People Systems Health Systems: staff, stuff, last two decades, including for extreme climatic events and health Poverty space, system, social support, sustainability Gender emergencies. Over the last decade, the government has focused Disabilities Elderly Emergency Response: fire department, emergency HAZARDS management, military more on its response to health emergencies, complimented by the Care deserts Female head of household Lifeline Infrastructure: transport, electricity, 2012–2021 Public Health Plan, which identified infectious disease water/wastewater, ITC control as a critical mission—an important factor in responding to climate-induced incidents. Through the National Institute of Health The scorecard assessment complemented the development of a (INS), surveillance and medical supply measures were prioritized data-informed, climate-sensitive risk index that combines wider and later accelerated through response measures during and in health system characteristics with population-level health and the aftermath of COVID-19. From a legislative and administrative sociodemographic information. To this end, the study acquired and perspective, departments and municipalities spearheaded much of processed related determinants, in consultation with stakeholders, the planning and work on response capabilities. and developed an Artificial Intelligence algorithm for the compound IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 47 and integrated risk index. Compound refers to multiple hazards Methodology and integrated refers to consideration of population and health This section summarizes the methodology used to calculate the system climate vulnerabilities simultaneously. The risk-informed Artificial Intelligence-based Integrated Climate Risk Index (Climate- prioritizations empower policymakers to: Sensitive Risk Index) (Nunez-del-Prado, Tariverdi, and Barrera 1. Identify, locate, and target more vulnerable populations 2023) for the populations and health systems affected by climate directly exposed to climate risks and indirectly impacted change, extreme events, and natural hazards. The section explains by climatic hazards; the computational framework used to determine a department’s direct exposure to risks. The section also includes the prioritization 2. Identify and locate PHC centers and hospitals that are at criteria for health facilities used to inform climate resilient higher risk of climate-induced hazards and essential for investments and the methodology for the high-resolution analysis continuity of care; and of Bogotá. 3. Prioritize mitigation, preparedness, and response interventions in departments based on the most Table 3 lists the climate, geospatial, demographic, and health significant climate risks. system risk determinants that were used for the model. These determinants were selected based on established methodologies The climate risks and climatic hazards included in the analysis are in related fields, along with consultations with country stakeholders recurring floods and landslides, as well as the population health and experts in the country. The latest data for each determinant risks estimated in Component 1 described in greater detail on page were acquired, cleaned, harmonized, and, where possible, also 51 (p. 51). These hazards were selected based on considerations on validated with other sources. All process data are available as Colombia’s natural hazard exposure risk profile and data availability part of the outputs of this work. Notably, Geotagged Health and quality for systematic analysis. An economic analysis of the Facility Master List for Colombia was created through a process impacts of extreme events on populations and health systems is developed specifically for this analysis. It is based on official data being developed and will be provided as a follow-up note to this and open-source information though online platforms (i.e., Open report based on the findings herein (Thompson et al. 2023). Street Map, Google), and geotagged using data mining techniques. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 48 Table 3. Climate, Population, and Health System Determinants Factors Variable Reference Geographic availability Year Female-headed households National Administrative with no spouse and children Department of Statistics Department - municipality 2018 under the age of 18 (DANE) (2018) No access to reliable electricity DANE (2018) Department - municipality 2018 Socioeconomic No sewerage system at dwelling DANE (2018) Department - municipality 2018 No access to internet DANE (2018) Department - municipality 2018 Dwelling with more than two DANE (2018) Department - municipality 2018 families Native and indigenous Cubillos, Matamoros, and Perea Demographic vulnerability Department 2019 population (2020) Prevalence of protein intake Herrera et al. (2015) Department 2015 deficiency by department Chronic malnutrition in children Herrera et al. (2015) Department 2015 0 to 4 years old Mortality rate due to malnutrition per 100,000 Herrera et al. (2015) Department 2015 Health vulnerability inhabitants, females Mortality rate due to malnutrition per 100,000 Herrera et al. (2015) Department 2015 inhabitants, males Climate-induced mortality and Comp. 1 Department 2 morbidity Average distance to primary This analysis Department - municipality 2015 Access to health services health care (km) Average distance to hospitals (km) This analysis Department - municipality 2015 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 49 Factors Variable Reference Geographic availability Year Percentage of exposed primary This analysis Department - municipality 2015 healthcare Health system Climate Percentage of exposed hospital This analysis Department - municipality 2022 vulnerability category II Percentage of exposed hospital This analysis Department - municipality 2022 category III Number of beds per Ministry of Health and Social Department 2022 department* Protection (2022) Number of physicians per Ministry of Health and Social Department 2018 department* Protection (2018) Number of nurses per Ministry of Health and Social Department 2018 Health system department* Protection (2018) Location of hospitals Ministry of Health (2022a) Latitude and longitude 2022 Location of primary healthcare Integrated Social Protection Latitude and longitude 2022 facilities Information System. (2023) Primary healthcare and hospital Ministry of Health (2022b) National level 2020 buildings quality state Floods (1 in 100 years return Fathom (2022b) National level period) Hazards maps Higher risk of landslides through Geological Service of Colombia National level 2020 fault lines (2022) OpenStreetMap Contributors Road network Bogotá, D.C. 2017 Urban data (2017) Population Bondarenko (2020) National level 2020 * The consulted document reports statistics for the year 2016. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 50 People and Health Systems Figure 25. Geotagged Population Density (A) and Geolocated Health Facilities (B) Vulnerability to climate change varies widely across Colombia (within and across regions). Therefore, knowledge about where people live and health services’ locations are critical in identifying risks. As a result, the first step is to identify where people live, what characteristics they exhibit that might identify them as vulnerable, and the type(s) of health facilities that they could access (as a proxy for the complexity and types of health services available). Figure 25 provides an overview; further details are available in Nunez-del- Prado, Tariverdi, and Barrera (2023). Proximity to facilities $ Income Services Gender o ered # of # of $ beds sta (A) Population density map $ $ Proximity to hazards Age $ (B) Geolocated health facilities master list $ Note: spatial resolution = 100m (meters) * 100m. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 51 Even in a single location, not every person or facility is affected in of coverage and representative thresholds and were therefore the same way. For example, senior citizens are more vulnerable to excluded from this analysis. extreme temperatures, and poorer households have fewer means to seek health services during and after extreme events. In health Health systems: assessed based on services and facilities facilities, well-prepared and trained facilities and staff can handle Our analysis collates and superimposes official information demand surges whereas less resilient facilities and providers might on health facilities’ infrastructure and services to identify the see their capacity drop or collapse entirely. Thus, the analysis locations, types, capacities and capabilities. Health system uses country specific sociodemographic characteristics and information such as number of beds, health workers, services, features to represent these vulnerabilities. It also addresses equity quality, and other indicators are linked to geo-tagged (located) health considerations for access to health services and reflects income facilities for facility prioritization analysis. Figure 25b depicts PHC inequality and other measures of social exclusion that are closely facilities, and category II (secondary referral) and category III (tertiary related to poverty, as climate events disproportionately affect the referral) hospitals mapped in the country. Furthermore, population poor (Hallegatte et al. 2020). coverage for analyses was modified to reflect areas where services such as remote consultation or mobile clinics are used. People at the center: gender, age, and sociodemography To identify at-risk populations, sources were first used for urban Exposure of people and health systems to extreme climate and rural population settlements from WorldPop (Bondarenko events 2020) as illustrated in Figure 25a. The estimates are based on In addition to morbidity and mortality risks in Component 1, the population numbers from the Colombian statistics department study focused on two natural hazards based on Colombia’s risk (DANE) and other sources, which were updated to reflect the profile and consultation with stakeholders: (i) low to moderate population in 2020. Demographic and socioeconomic factors floods from river and rainfalls (Fathom 2022a) and (ii) fault were captured through sources listed in Table 3. The Large lines integrated with risk of heavier rains (Geological Service of Integrated Household Survey (GEIH) for 2021 and Measuring Colombia 2022) as a proxy for high-risk of landslide (see Annex Monetary Poverty and Inequality survey (2021) were processed Figure A2.3). The flood risk was captured by flood events with a but did not pass the national and department level standards 100-year return period (i.e., a 10 percent chance per decade). These IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 52 types of flood events are expected to increase in frequency and Climate-Sensitive Risk Index intensity due to climate change (Tabari 2020) and thus relevant for The Climate-Sensitive Risk Index has been formulated for health system planning for near future. individual selected hazards, as well as for a compound scenario, To estimate population and health infrastructure exposure, the encompassing the combined risk of multiple hazards. The hazard- information on population and health system locations have been specific index serves as a foundation for tailored interventions merged with the data on direct hazard exposure. The information addressing each hazard’s unique characteristics. In contrast, the was then aggregated to the desired political boundaries (Annex, composite risk index guides overarching climate and disaster risk Figure A2.1) for the target unit of analysis (e.g., department or management strategies that transcend specific hazards. municipality). Indirect exposure to extreme climate events is equally important. A rain-induced landslide that blocks a road might not directly affect any populations or health facilities, but could make it harder for people to access health services and it might disrupt supply chains. As shown in Annex Figure A2.10, the study has captured the negative impacts of hazards on accessibility to health services in detail in the urban context of Bogotá. It is important to note that exposure data may underestimate future impacts of flooding, as land use and other human development are exacerbating the effects of flooding in Colombia (Campos Garcia 2011). Photo: © European Union, 2020/D. Membreño (CC BY-ND 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 53 Figure 26. Schematic Process of Calculating Climate-Sensitive Risk Index Climate Extreme change HAZARDS climate events AI identifies Population Health ! factors system factors Subnational integrated risk Determinants affecting population, index infrastructure, climate, and DRM risk • Poverty • Access to • Health • Households basic utilities infrastruture characteristics • Access to • Population • Maternal and health services exposed to child health • Health system shocks • Population capacity • Infrastructure health risk • Exposures exposed to shocks Source: World Bank IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 54 For instance, this could involve implementing flood protection selected, and each department is classified according to its scores. measures for primary care facilities using the flood risk index, while Note that in this method, not every risk level has the same number simultaneously devising risk communication strategies for high- of departments. Details of the AI model are explained in Annex 2 and risk departments regardless of the specific hazards they face. in Nunez-del-Prado, Tariverdi, and Barrera (2023). Departments are grouped together based on their overall risks. There are over 20 determinants that inform the risk directly (Table Health Facility Prioritization 3) in addition to population health risks that were presented in To improve health system resiliency to climate risks, the study Component 1 at the department level. This analysis uses multiple has identified priority facilities for each prominent climate hazard models for clustering, a class of machine learning algorithms (a to inform resilient investments at the facility level nationally and subcategory of Artificial Intelligence models), which combines sub-nationally. This prioritization informs recommendations. determinants to render a single index for each department. Indices The prioritization of facilities considers additional information are then grouped. Clustering helps to band similar departments available at the facility level, compare to risk index, namely: together based on their shared risk profiles. Figure 26 is a schematic representation of this process. The analysis uses normative targets for all determinants. Normalization is defined based on the “gap” to a target threshold for each determinant in each department. For example, if the health system plan is aiming for nine nurses per 10,000 inhabitants, the corresponding difference of each department’s present number of nurses to this target is Priority 1 defined as the gap. These estimated gaps are then used in the AI for grouping departments together (“clustering”). Under the normative Prioritize facilities that: analysis, the further a region is from the established target for a 1) support vulnerable people determinant, the riskier it is. In other words, regions with similar 2) have more exposure to hazards 3) have less backup systems points share more or less similar risk levels. Four risk levels are IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 55 construction or remodeling year and government assessments on the current state of the buildings as a proxy for infrastructure quality, construction material (as fragility to floods and landslides differ by building material), medical equipment at facilities; and catchment population and their health and sociodemographic information. We prioritize facilities based on (i) exposure to climate risks, (ii) the vulnerability of the population in its catchment area, as defined by the determinants, (iii) the current state of the building infrastructure, and (iv) health system reach in each department. For example, if two facilities are serving populations with similar vulnerability profiles, the facility that has fewer back-up options in the region is prioritized. A lack of redundancy can hinder patient referrals and patient care- path alteration in the system. PHC, Category II, and Category III facilities are compared both nationally and at department level to identify the priority facilities for action. Indirect and Direct Impacts of Climate Hazards on Accessibility to Health Services: Bogotá The study used a recently developed network analysis tool (Tariverdi et al. 2023) to estimate the average duration that individuals in Bogotá require to access their various health services and preferred health facilities (hospitals). The analysis refocuses attention on people’s needs. Within this model, individuals’ decisions within every city block regarding healthcare facilities were taken into account: the inclination to opt for a larger, Photo: ID 476186. 11/06/2011. Cartagena, Colombia. UN Photo/Evan Schneider. www.unmultimedia.org/photo/ (CC BY-NC-ND 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 56 better-equipped facility with insurance coverage as opposed to a smaller, nearer one. Both alternatives were deliberated with their respective probabilities. The road conditions and travel times due to climate-induced disruptions are considered and effect of people’s choices are estimated as well. This is part of a broader Average time to reach approach to make health systems more resilient to hazards, like based on patient all ser 's ch vic oice es natural disasters or pandemics, as discussed in the World Bank Report Frontline: preparing healthcare systems for hazards from disasters to pandemics (Rentschler et al. 2021). The results identify Compromised areas where accessibility times to health services are very high and travel time by # of # of beds sta illustrate how accessibility times change in the event of climate- hazard induced hazards like floods and landslides. The most affected communities and at-risk assets are identified, and delays and Travel time to reach access loss are estimated, which inform increased morbidity and a selected family mortality risks (Battle et al. 2016; Alegana 2017; Manongi 2014). Results are presented in the next section. It’s important to note that the alterations in access time don’t pertain to a single facility; hence, each minute disparity signifies notable overall divergence. Furthermore, the travel time assumptions are established considering off-peak travel times, accentuating the magnification of longer journeys during working days. (Battle et al. 2016; Alegana 2017; Manongi 2014). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 57 Results Results Roadmap Given that interventions for public health and facilities need to be customized according to the type of hazard they intend to mitigate, the results section is structured based on distinct hazards. It Organized by begins with an overview of the climate-sensitive integrated risk hazard index for each department, followed by the prioritization list of primary healthcare facilities and hospitals at both the national and department levels. It then provides a high-resolution analysis of Risk Bogotá, which includes indirect impacts of disasters and climate Climate-sensitive Very high hazards (accessibility and service disruptions). Detailed interim integrated risk High by department outputs of population and health sector determinants that were index analyzed Medium estimated through this work are available digitally and samples are Low provided in Annex 2. Climate-Sensitive Risk Index Facilities Figure 27a and 27b illustrate the departments grouped in four PHCs levels of risk ranging from very high, high, medium, to low risks. Category II Based on all climate risk factors included in this report, Vaupes, Prioritization national zoom in on Cundinamarca, Boyacá, and Arauca are the highest priority determined Category II departmental Bogotá for action. Cauca and Norte de Santander should be observed Category III national for flood risks primarily, and Tolima and Cordoba for landslide Category III risks. Looking into multivariate characteristics of departments departmental for very high risk departments (Annex Table A2.5 and Table A2.6), they share population and health system risks, such as high chronic malnutrition, shortages of staffed hospital beds, higher poverty IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 58 rates, and lack of access to internet services. For hazard-agnostic Figure 28. Compound Hazards Climate-Sensitive Integrated Risk interventions, a compound risk index is computed as shown in Figure Levels 28. The central areas of the country clearly should take priority for health system strengthening and climate resiliency disregard of hazards. Examples are risk communication strategies, disaster risk management and surge trainings for health workforce, etc. The risk index for departments is included in Annex Table A2.5 and Table A2.6. Figure 27. Department-level Climate and Health Risks Risk Very high High Medium Low (A) Floods Details of the intermediate information from processed data for population and health sector determinants estimated through this work, are being provided in digital outputs for reference. (B) Landslides Risk Very high High Medium Low IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 59 Exposure Analysis Figure 29. Population and Healthcare Facilities Exposed to Floods and Landslide Risks Colombia’s population and healthcare system are subject to substantial and direct exposure to climate hazards (Figure Population PHCs 30 30 29). However, the effects of these hazards differ based on the specific nature of the hazard. It is important to highlight that the 25 25 Percent of exposed population consequences of moderately frequent floods differ significantly Percent of exposed PHCs 20 20 from those of potential landslides, for example. As a result, it is imperative to interpret the findings that accounts for these 15 15 variations. Tables 4 and 5 summarize the findings of the analysis 10 10 of the risk exposure to floods and landslides for people and health 5 5 systems, including adverse disruptive and destructive impacts. 0 0 Category II hospitals Category III hospitals 30 30 Percent of exposed category III hospitals Percent of exposed category II hospitals 25 25 20 20 15 15 10 10 5 5 0 0 Risk Floods Landslides IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 60 Floods Exposure risks to floods vary significantly at the department level as indicated in Figure 30. Table 4 provides a comprehensive overview of the findings and outlines implications for individuals, the healthcare system, and critical infrastructure that supports the delivery of quality services. The table encompasses various aspects, such as the population directly exposed to floods and associated health risks, followed by an analysis of the ensuing disruptions of transport on healthcare service delivery. (B) PHC Figure 30. Population and Health Facilities Exposed to Floods, Department Level (C) Category II (D) Category III (A) Population IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 61 Table 4. Analysis of Exposure to Floods and Related Impacts Category Impact Almost one quarter of the population (15.7 million people) is directly exposed to low flood level risks (5 cm depth). Populations exposed to floods can face higher risks of disease outbreaks due to contaminated food and water and vector-borne diseases, and injuries such as skin infections, acute respiratory infections, and diarrhea. They also face broader socioeconomic risks of impacts Health on well-being and livelihoods. Exposure to flood risk can disproportionately harm vulnerable populations, such as people with chronic disease or disabilities. It can also impact their access to health services. Clean-up and reconstruction efforts can also expose workers to additional health risks. Annex Table A2.1 contains detailed exposure analysis. It shows that among Colombia’s People departments, Vaupes, Amazonas, and Arauca have the highest percentage of their populations directly exposed to floods. Poor people tend to live in flood-prone areas (where education levels and income levels are lower). Valle del Cauca, with a poverty rate of 29.7 percent, has the highest number vulnerable people directly exposed to floods (2.4 million people / 42.71 percent of Socioeconomic its population). Floods can further exacerbate poverty by affecting sources of income and/or leading to an increased health and education expenses, resulting in long-term poverty. About 1 out of 5 health facilities are directly exposed to disruptive floods. This includes 4,416 primary care facilities and 143 hospitals. Indirect impacts on health infrastructure are also substantial due to disruption to water, power, and communication networks, as well as impaired accessibility to facilities. Guaviare, Chocó, and Amazonas are the departments with the most healthcare infrastructure exposed to floods (Table A2.3). Figures A2.8 and A2.9 show the percentage of primary healthcare Built Environment infrastructure and hospitals exposed to floods at the department level. The inundation of buildings and parking areas by floodwaters can drastically reduce functionality and close facilities in some cases. Highlighted adverse effects of Hospital is listed under “Indirect Impacts on Accessibility: Zoom In on Bogotá” and an economic analysis of exposed health facilities to flooding in (Thompson, Tariverdi, and Nunez forthcoming). Health System Flooding inundation also can compromise critical medical equipment, lifeline functions (e.g., water, electricity), disrupting or halting services and costing millions in damages. The expected inundation levels (5 cm) are unlikely to damage beyond repair most Equipment medical and other equipment in major facilities. However, the 5 cm depth used in the analysis is an average value of potential flood level; actual levels can vary significantly. Higher flood levels should be studied in areas with heavier rainfall zones and urban areas with weaker drainage systems. Flooding also can lead to health impacts from mold growth in health facilities, along with other impacts outlined above. These People impacts are also addressed, in part, through the results of Component 1. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 62 Category Impact Inundation from floodwaters can slow or stop emergency transport, supply chain, health facility personnel, and voluntary Transport admissions in a hospital’s catchment area. Transport impacts in the city of Bogotá, are outlined in the section “Indirect Impacts on Accessibility: Zoom In on Bogotá”. Damage from the flooding of electricity assets can disrupt electrical power to residences, exacerbating the conditions outlined above on impact on population and resulting in other risks due to loss of power (e.g., powering of at-home medical devices). Power Electricity disruptions also can impair or cease health facility functioning, particularly for facilities without regularly tested redundancies. Collecting data on the existence of electricity and fuel backup in health facilities from nationally mandated risk assessments could enable a targeted assessment of power risks to health systems. Lifeline Infrastructure Inundation from floodwater can contaminate potable water, which will impact populations via consumption of contaminated waters or reduced access to potable water. Like power, disruptions to water/wastewater networks from flooding can limit or halt basic Water/ Wastewater hospital functions due to a lack of usable water, especially for hospitals with insufficient redundancy measures. Collecting data on the existence of water and wastewater backup in health facilities from nationally mandated risk assessments could enable a targeted assessment of water/wastewater risks to health systems. Flooding can damage some ICT infrastructure at or near ground level. This can limit emergency, interagency, and other Information communication and coordination, especially in agencies and health facilities without redundancies for digital or cellular Communications communication. Collecting data on backup communications in health facilities could enable a targeted assessment such Technology (ICT) disruptions to health systems. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 63 Table 5. Analysis of Exposure to Landslides and Related Impacts Category Impact About 5 percent of the population (2.2 million people) is directly exposed to landslides, as shown in Figure A2.4. Risaralda, Tolima, Health and Norte de Santander are the departments with the most population directly exposed to landslides (Table A2.2). In addition to injury and death, landslides can cause respiratory issues from dust and debris. Poorer people are more likely to live in weakly built housing made from lower-quality building materials, making them more People vulnerable to landslides. As such, they may face displacement or significant rebuilding costs after a landslide, which would worsen the cycle of sustained poverty. Landslides can also disrupt local infrastructure, such as roads and highways. This can Socioeconomic impact all facets of life: market access, education, and health services, with longer lasting effects for the most vulnerable parts of the society (e.g., in the form of higher dropout rates after disasters). To overcome these challenges, vulnerable people may spend all their savings or be forced to sacrifice other necessities for survival. In Colombia, 3.5 percent of the healthcare infrastructure is directly exposed to landslides risks. This encompasses, 549 primary healthcare facilities and 20 hospitals (Figure 31). As shown in Table A2.4, Tolima, Risada, and Valle del Cauca are the three departments with the highest healthcare infrastructure exposure to landslides. The impacts of landslides on the built Built Environment environment can vary from inconsequential to complete destruction, with the level in part depending on a building’s design (e.g., codes, construction materials) and on the type of landslide (e.g., gradient of the slope, type of material, size of the landslide). Consequently, economic, and functionality losses can vary from minimal to complete loss. An economic estimate of exposed assets to landslides can be found in an addendum to this report (Thompson, Tariverdi, and Nunez forthcoming). Health System Landslide impacts can result in partial or complete destruction of equipment, depending on the severity of the event. Equipment Equipment located outside of hospital facility buildings are particularly vulnerable, including backup power generators, fuel storage, and water supplies. In addition to mortality and severe injuries, landslides can cause respiratory issues from resulting dust and debris. (The impacts People of service disruption on catchment area of facilities are investigated in Thompson, Tariverdi, and Nunez (forthcoming). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 64 Category Impact Landslides often cause significant disruptions to roadways by blocking roadways with debris, damaging them, or destroying Transport individual sections. Landslide-related transport delays are often more pronounced than other hazard-induced disruptions, like floods, but they also tend to be more localized. Landslides often disrupt the power infrastructure by damaging electricity and gas lines. Damage to major electrical infrastructure, in particular, can impact people and health systems far beyond the landslide area and exacerbate conditions outlined in the Power People section in this table. Electricity and gas outages to hospitals without power redundancies can disrupt health facilities’ functionality. Collecting data on the existence of electricity and fuel backup in health facilities from nationally mandated risk assessments could enable a targeted assessment of power risks to health systems as also highlighted in our recommendations. Lifeline Landslides can produce similar damage to water/wastewater infrastructure as it does to power infrastructure. Prolonged water/ Infrastructure wastewater disruptions can cause health facilities to limit or cease functioning, especially in facilities without redundancies for these systems. Disruptions also can exacerbate conditions outlined in the people section, along with creating new problems due Water/ Wastewater to a lack of water. Collecting data on the existence of water and wastewater backup in health facilities from nationally mandated risk assessments could enable a targeted assessment of water/wastewater risks to health systems as also highlighted in our recommendation section. Landslides can damage ICT infrastructure, which is often particularly vulnerable to damage, since ICT infrastructure is often located in elevated areas that are more prone to landslides. Damages to ICT infrastructure could disrupt standard ICT communications systems, particularly in more remote areas with potentially devastating impacts due to extended delays and resource shortages. Collecting data on backup communication systems in health facilities could enable a targeted assessment of ICT risks to health systems. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 65 Figure 31. Population and Health Facilities Exposed to Landslide Risks, Department Level (B) PHC (A) Population (C) Category II (D) Category III IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 66 Resilience-Sensitive Health Facility Facility prioritization for flood interventions Prioritization Figure 32. Health Facility Prioritization, National Level: Top Ten PHCs and Hospitals for Flooding Facility exposure information, though informative, is insufficient for prioritization. In the present analysis, the facility exposure information to hazards was also integrated with population and health system characteristics to establish a ranking of the top ten healthcare facilities (PHCs and category II and III hospitals) that should be prioritized at the national level and within departments separately as defined in the methodology. The analysis showed regions of Bolivar with five out of ten PHCs at risk (Figure 32a and Table 6). The hospital analysis identified Valle del Cauca and Magdalena as the only departments with two category III hospitals at risk of floods (Figure 32b and Table 7). Additionally, Figure 33 presents the three healthcare facilities for each region with the highest exposure to floods. Regarding PHCs, the analysis indicates that the departments in the north have more PHCs at risk of floods, while most affected hospitals are in the western and northern regions of Colombia, as described in Annex Figure A2.7 and Figure A2.8, respectively. Bolivar and Valle del Cauca (A) National top ten have PHCs prioritized for floods and landslide risks, while Valle PHCs to prioritize del Cauca and Antioquia have prioritized hospitals for both hazards. Lastly, comparing the exposure maps (REF) with priority figures (Figure 30) and priority maps (Figure 32 and Figure 33), clearly reveals the value of added information on population’s (B) National top ten characteristics in the facilities catchment area and health system. hospitals to prioritize IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 67 Figure 33. Health Facility Prioritization, Department Level: Top Table 6. Health Facility Prioritization, National Level: Top Ten PHCs Three PHCs and Hospitals per Department for Flooding for Flood Intervention PHC ID Name Category Department 135490035501 -- 1 Bolívar E.S.E Hospital San Nicolas De 135490009501 1 Bolívar Tolentino E.S.E Hospital San Nicolas De 135490009504 1 Bolívar Tolentino 110010101031 -- 1 Bogotá, D.C. E.S.E Hospital San Nicolas de 135490009506 1 Bolívar Tolentino 257540380801 -- 1 Cundinamarca Valle del 763640375607 -- 1 Cauca (A) Top three PHC to prioritize per Valle del department 763641109802 -- 1 Cauca E.S.E Hospital San Nicolas de 135490009503 1 Bolívar Tolentino (B) Top three hospitals 810010053901 -- 1 Arauca to prioritize per department * In cases in which the name of PHC is unavailable or in which two PHCs share the same name, each facility is identified through its unique identifier code. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 68 Table 7. Health Facility Prioritization, National Level: Top Ten Economic exposure and disruption analysis from flooding Hospitals for Flood Intervention The integrated risk model was used as the basis to estimate the value of exposed health facilities to floods in a forthcoming T Hospital ID Name Category Department analysis (Thompson et al. 2023). The report found that cost of 760010511501 Hospital Isaias Duarte Cancino 2 Valle del direct exposure of facilities to floods (1 in 100-year flooding) is Empresa Social del Estado Cauca approximately USD 170.8 million (2023), with aggregate exposure E.S.E Hospital La Divina of PHC facilities (USD 149.4 million) constituting the majority of 134680049204 Misericordia Sede San Juan de 2 Bolívar this exposure. Category II and III facilities exposed to flooding face Dios higher levels of financial exposure per facility, which is reflected in 51540220101 E.S.E. Hospital Cesar Uribe 2 Antioquia their size, quality, and other factors. The departments of Vaupés, Piedrahita San Andres, Vichada, Amazonas, Guainía, Guaviare, and La Guajira Empresa Social Del Estado possess a greater proportion of exposed category II and category 472450024901 2 Magdalena Hospital La Candelaria III facilities. Notably, in many of these departments, the exposed Nueva Empresa Social del category II and III facilities serve as the principal referral facilities. 270010116901 Estado Hospital Departamental 2 Chocó San Francisco de Asis A comparison of disrupted patient days within different departments suggests that for category II and III facilities in Arauca, Bolívar, E.S.E. Hospital Departamental Valle del 768340465201 2 Chocó, Magdalena, and Sucre to achieve a comparable impact Tomas Uribe de Tulua Cauca to the disruption of an average PHC facility in their respective Hospital Universitario Julio 470010065001 Mendez Barreneche 3 Magdalena departments, facility restoration times would need to be over 10 times faster, all else being equal. It’s important to note that this 810010007701 E.S.E. Hospital San Vicente 2 Arauca analysis likely underestimates the speed required for parity, given 680810079701 E.S.E. Hospital Regonal del 2 Santander that category II and III facilities offer more advanced levels of care. Magdalena Medio Unidad de Servicios de Salud Mitigating risks of 5 cm flood depths through the construction 110013029640 3 Bogotá, D.C. San Bernardino of external mitigation measures, such as a stormwater drainage IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 69 system, indicate varying levels of vulnerability reduction. Assuming facilities would have a more pronounced impact on reducing patient that the constructed flood mitigation measure would completely days disrupted than similar interventions in category one facilities. eliminate flooding in the facility, modeling flood reduction measures in the top 20 most financially exposed health facilities Simulating El Niño events based on historical or anticipated would result in a USD 14 million reduction in asset exposure. This rainfall models could likely result in significantly greater damages finding is complemented by an analysis of disrupted services due due to higher levels of inundation depth in certain facilities. to flooding, which suggests that mitigating disruptions at these This consideration warrants further attention in future planning and mitigation efforts against flood events (Thompson et al. forthcoming). Facility prioritization for landslides interventions Figure 34a and Figure 34b show the top ten PHCs and hospitals at the greatest risk of landslides at the country level. These healthcare facilities are located mainly in the country’s center. Facilities in southern Colombia are less exposed to landslides (see Figure 31). Tables 8 and 9 detail the health facilities most at risk and show that the departments of Tolima and Antioquia have the highest number of PHCs and hospitals among the top ten most exposed facilities. Figure 35 also shows the top three healthcare facilities that should be prioritized in each department. A corresponding list of the respective health facilities can be found in Table A2.9 for PHCs and Table A2.10 for category II and III hospitals. Photo: © European Union, 2020/D. Membreño (CC BY-ND 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 70 Figure 34. Health Facility Prioritization, National Level: Top Ten Figure 35. Health Facility Prioritization, Department Level: Top PHCs and Hospitals for Landslides Risks Three PHCs and Hospitals for Landslide Risks (A) National top ten (A) Top three PHCs PHCs to prioritize to prioritize per department (B) National top ten (B) Top three hospitals to hospitals to prioritize prioritize per department IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 71 Table 8. Health Facility Prioritization, National Level: Top Ten PHCs Table 9. Health Facility Prioritization, National Level: Top Ten for Landslides Intervention Hospitals for Landslides Intervention PHC ID Name Category Department Hospital ID Name Category Department 852790042217 Red Salud Casanare E.S.E. 1 Casanare Centro de Rehabilitación En Valle del 760010360902 2 Salud Mental – CRESM Cauca Valle del 760011261801 -- 1 Cauca E.S.E. Hospital Departamental Valle del 760010360901 Psiquiatrico Universitario del 2 50010210903 -- 1 Antioquia Cauca Valle 730010297401 -- 1 Tolima Hospital Federico Lleras Acosta 730010104701 3 Tolima Ese E.S.E. Hospital San Jose de E.S.E. 413590042402 1 Huila Isnos E.S.E Hospital San Vicente de 51290214601 2 Antioquia Hospital Centro E.S.E. de Paul de Caldas 735550103101 1 Tolima Planadas 661700027803 Puesto de Salud Frailes 2 Risaralda 182560200203 E.S.E. Sor Teresa Adele 1 Caquetá Ese Hospital San Juan de Dios 50040547802 2 Antioquia CXAYU’CE JXUT Empresa Social de Abriaqui 198210004001 1 Cauca del Estado Centro de Atención Ambulatorio 661700027804 2 Risaralda 682550239101 -- 1 Santander Japìn 136700007601 E.S.E. Hospital Local San Pablo 1 Bolívar E.S.E Hospital La Merced de 51010213901 2 Antioquia Ciudad Bolivar 176530064609 Centro de Salud San Felix 2 Caldas Empresa Social del Estado Norte de 544980054701 Hospital Emiro Quintero 2 Santander Cañizares IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 72 Economic exposure and disruption analysis from landslides A retrospective analysis of historical landslide deposit heights yielded two distinct scenarios occurring within the landslide risk zones identified by the climate-sensitive integrated risk model. The landslide deposit heights for the scenarios measured 1.56 meters and 3.96 meters, respectively. Based on economic exposure models, health facilities facing exposure to the 1.56-meter scenario amounted to USD 416.4 million (2023), while those exposed to the 3.96-meter scenario totaled USD 656.4 million. Notably, structures within the 3.96-meter scenario are anticipated to experience extensive damage, ultimately leading to complete loss. The disruptions arising from both scenarios are estimated to persist for years, as all impacted facilities would necessitate reconstruction in the aftermath of such an event. Likewise, comprehending and strategizing the distribution of health services from a non-operational facility among functioning ones could mitigate the repercussions of functional losses. These strategies can be further enhanced by implementing monitoring systems that offer advanced alerts to health facilities (Thompson et al. forthcoming). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 73 Indirect Impacts on Accessibility: Zoom In on potentially affect population health as to travel long distances to Bogotá reach health facilities have been shown to decrease the likelihood of seeking health services (Battle et al. 2016; Alegana et al. 2017; To illustrate the secondary effects of floods on people’s ability Manongi et al. 2014). Flood exposure information for Bogotá in to access health services in a timely manner, a Network greater detail is available in digital outputs. Analysis approach (meso-simulation, methodology in Tariverdi et al. (2023)) for Bogotá, D.C was used. The nework approach models climate-induced disruptions to transport networks that cause accessibility delays and isolated communities (complete accessibility loss to care). The model considers peoples’ preferences for specific health facilities based on number of beds and services offered (type of facility) as described earlier in Methodology section of this chapter. The analysis estimates average expected travel times to seek different health services. As shown in Figure 36, the average travel time for all citizens increases from 30 to 45 minutes as a result of moderate floods. Note while the times reported here are average travel times, some communities suffer much higher delays. Northwestern Photo: © Hembo Pagi (CC BY-NC 2.0) areas of Bogotá are the most affected by floods, with average travel times increasing five-fold (from 15 minutes to 80 minutes), Northwestern areas of Bogotá are the indicating a severe delay in access to potentially life-saving most affected by floods, with average travel services. Delayed access to healthcare services is associated times increasing five-fold (from 15 minutes with significant costs for patients due to higher risks of morbidity and mortality from treatable conditions in addition to service to 80 minutes), indicating a severe delay in disruptions due to health workers access to facilities and supply access to potentially life-saving services. chain issues. Such events in higher frequency and caused delays, IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 74 Figure 36. Average Accessibility Time of Population to Their Preferred Health Services in Bogotá and Most Affected Communities by Indirect Impact of Floods Figure 37. Critical Road Segments in Bogotá Essential to Ensure Accessibility to Health Services IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 75 Figure 37 complements the accessibility time metric by highlighting Conclusion and Recommended Actions critical routes to be maintained to ensure accessibility to the The impact of climate change and hazards on population health health system at times of disruption. The criticality of roads is exacerbated by social inequalities, poverty, overwhelmed is defined based on routes that serve the largest number of healthcare facilities, and a lack of evidence-based public policies people seeking access to all the health services of their choice. (Palmeiro 2023). To better understand the situation in Colombia The higher the criticality, the more important the road and the (including variations in different regions), this analysis identified greater the need to be prioritized by the governing agency for risk vulnerable populations and overlayed that data with health mitigation and preparedness interventions (e.g., prepositioning system vulnerabilities. The results indicate a significant and debris removal, flood protections). Note that these road sections direct impact of floods and landslides on both Colombia’s are crucial for continuity of care in health. Using this metric, the population and its health system, particularly where health analysis shows that western routes are the most essential to be resources are more limited and where they serve more maintained and kept open for floods; while for landslides, eastern vulnerable populations. routes becomes more essential for access to health services. It is important to note that exposure data may underestimate future In this regard, the Climate-Sensitive Risk Index can be a starting impacts of flooding, as land use and other human development are point for prioritization, as it systematically classifies risk based exacerbating flooding in Colombia (Campos Garcia 2011). on a normalized and representative set of information. It considers climate-sensitive risk determinants for populations and the health system simultaneously to identify departments, communities, and facilities at the forefront of climate change- related health risks. It directly supports decision making by providing timely key performance indicators and data analysis to prioritize departments, as shown in the results, while also generating detailed lists of vulnerable communities and priority health facilities (PHCs, hospital category II, and hospital category III) for targeted interventions. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 76 The risk assessment results highlight several potential actions. leverage existing structures. This includes further integrating the Mitigation strategies are strongly recommended, preparedness health system’s disaster risk management efforts with actors such interventions, and policies to reduce risks from floods and as the military, civil protection, and community groups, with clearly landslides, starting in the departments with the highest risks, defined roles and mandates for crisis response, including more as identified in the results and detailed in the annex. Mitigation frequent hazards such as floods. and preparedness measures are critical for the identified departments due to the extensive exposures detailed in the Concerning preparedness for floods and impact mitigation, findings. Prioritization is key, given the limited resources and vast the Climate-Sensitive Risk Index can support local agencies potential impacts; it impossible to immediately equip every facility to determine flood risk locations in each area and develop a to the highest standard to minimize disruption of health services to detailed preparedness and response plan accordingly (Wood the most vulnerable parts of the population. 2018). Department-level exposure maps can be used directly. Additionally, the index can be used to prioritize the creation of the Enhanced communication channels, data-driven approaches for evacuation protocols and multi-purpose shelter locations needed coordinated service delivery, resource allocation, and targeted for critical hospitals this analysis has identified as priority facilities. deployment of mobile clinics can help to meet surge demand Tailored response plans also need to be developed for each area. through a system-level and regionally coordinated response. Table 10 lists the recommendations; they are aligned with previous We suggest adding information on long-term care facilities and government of Colombia policy measures to reduce the impact strengthening service delivery for areas with a higher proportion of climatic and non-climatic hazards, including seismic and some of seniors and for people with jobs directly exposed to weather, volcanic activity. While government ministries in Colombia agree such as construction and agriculture. Further assessments should about the vulnerability of the healthcare system due to climate be conducted to identify and prioritize the concrete needs and change, to date, more work is needed. To address this, it is to tailor policy interventions according to community needs and recommended that the emergency preparedness of the country’s routine health service delivery specific to the region’s need and health systems regarding extreme climatic events should be disease profiles. closely coordinated with the overall emergency management and disaster response systems, to increase efficiency and IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 77 Lastly, multi-sectoral investment involving the health, the structural quality of buildings and infrastructure (highlights transport, power, water, and communication sectors is in Table 10). Building capacity, particularly in under-resourced recommended to strengthen the health system and enhance areas, will become increasingly important to minimize the losses its climate resilience. As part of flood and landslide mitigation to the health system and to population health due to climate measures, transport upgrades should prioritize identified critical change, which is contributing to increased risk from hazards. It is corridors for health service delivery. Measures could include important to note that many recommendations require multi-level maintaining critical routes and prepositioning response equipment. governance and multi-sectoral collaborations involving the health In addition, modes of alternative service delivery, such as telehealth, sector and other sectors, particularly emergency response and should be considered for highly impacted communities. However, lifeline infrastructure. This reflects the importance of a unified doing so depends on the availability and strengthen of internet and approach in governance to strengthening health and other key cellular coverage in some areas of the country. It may also be useful systems from hazard impacts (Rentschler et al. 2021). to revisit land-use zoning to reduce risks to health services and populations in high-risk departments (Habitat for Humanity 2023). In order to strengthen data-driven policy interventions and to enable more granular analyses at the municipality level (administrative level 2), increased access to sampling and coverage for the Large Integrated Household Surveys and Measuring Monetary Poverty and Inequality survey would be useful. Such granularity could enable efficient microtargeting. In addition, collecting data on how internally displaced population facing these hazards seek health, would greatly enhance any future analysis. Recommendations—based on the results of this analysis and supporting research—include specific actions for planning, disaster preparedness, staff training, capacity building, and IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 78 Table 10. Recommended Actions to Improve Health System Climate Resiliency in Colombia Observation Potential Action Action Type Reference(s) According to the Climate-Sensitive Risk Prioritize in-depth risk assessments of facilities focused Health infrastructure; Campos Garcia et al. (2011); Index, flooding impacts approximately 20% of on hazards and health services being provided through Health system planning Bennett and Iossa (2006); healthcare facilities, and landslide risks affect various modalities (i.e. remote consultations, telehealth) Government of Colombia approximately 3.5% of healthcare facilities. in areas with vulnerable populations (e.g., Valle del (2021); California Hospital Many of the most exposed facilities are in Cauca) in line with national laws and guidance, such as Association. 2023 areas with high percentages of vulnerable Decree 2157 of 2017 (2.3.1.5.2.1). populations. Floods and landslides disrupt lifeline Invest and maintain redundancies as outlined in Health infrastructure Rentschler et al. (2021) infrastructure, like electricity and water mandated risk assessments (e.g., Decree 2157 of 2017) supplies; this reduces or cripples health or equivalent assessments (e.g., hospital safety index). facilities’ ability to function. Consider green solutions like solar panels that address climate and hazard mitigations together. The size and scope of flooding and landslide Conduct assessments of building, retrofitting, and Health infrastructure Campos Garcia et al. (2011); threats highlight the importance of data-driven maintaining health infrastructure based on the risk Habitat for Humanity prioritizing of healthcare investment in new index to determine how to prioritize investments (2023); Edmonds et al. building, retrofitting, and maintenance. in these areas. To this end, Installing or improving (2020); Thompson et al. built environment flood mitigation measures can be (forthcoming) prioritized through a cost-benefit analysis. Mitigating flooding and landslide threats to Use the Climate-Sensitive Risk Index and climate Health system planning; Rentschler et al. health facilities and other health infrastructure exposure and disruption analysis to help prioritize Contingency planning (2021); Thompson et al. should be prioritized in alignment with investment in flood and landslide mitigation measures (forthcoming) available resources. for health facilities and other related infrastructure. That includes preparedness and evacuation planning for both hazards, which includes securing multi-sectoral support (e.g., enough evacuation vehicles, cross-agency training) to execute these plans. Additionally, implementing monitoring systems in hazard-prone and high-impact areas would improve preparedness. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 79 Observation Potential Action Action Type Reference(s) Climate change, coupled with changes in Continue to be proactive in terms of health system Health system planning; World Bank (2008) the built environment, may make flood and planning. Align plans with interventions that incentivize Development and built landslide zones exceedingly dangerous to or mandate that people in highest risk landslides and environment inhabitants and responders. These areas may flood zones relocate/mitigate, based on precedents be too expensive to rehabilitate after the hazard. adopted for volcano zones. Urban and rural development can change Include this consideration in health system planning and Health system planning; Campos Garcia et al. (2011); and exacerbate flood inundation areas health system modality optimizations. High risk areas Development and built World Bank (2012b) and landslide flows, potentially impacting identified here can inform such decisions as starting environment people, health service delivery, and lifeline point Areas at high climatic risk, with currently lower infrastructure. accessibility to services, can benefit from mobile clinics and alternative solutions. Flood and landslides disrupt transportation Promote multisectoral investment between health and Multisectoral Hallegate, Rentschler, and networks, particularly in some regions (e.g., the transport to leverage potential synergies in investment Rozenberg (2019); Edmonds western part of Bogotá), limiting or delaying goals. Consider increasing the health services in areas et al. (2020) accessibility to health services. with very high baseline access times. Information from the analysis concerning the Continue to increase coordination in planning and Multisectoral Rentschler et al. (2021); Díaz- exposure of vulnerable populations, lifeline preparedness between the health and emergency Tamayo (2022) infrastructure, and health infrastructure response sectors, including civil protection, the military, highlights the importance of coordination and other designated response groups. Strengthen across the national, departmental, and multi-level governance. municipal levels. More data on vulnerable populations can help Increase or mandate geospatial data collection for (i) Data capabilities and Wood (2018); Habitat for refine prioritization related to health sector and vulnerable populations, particularly internally displaced planning Humanity (2023); World Bank multisectoral investment, planning, drills and populations, and (ii) other vulnerability indicators at the (2021); Sipe and Dale (2003); simulation, response, and recovery, including departmental and municipal levels, in alignment with Díaz-Tamayo (2022) mitigating some of the risks outlined by the previous efforts by the Instituto Geográfico Agustín Climate-Sensitive Risk Index. Codazzi (IGAC). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 2 80 Observation Potential Action Action Type Reference(s) One of the barriers in using data or data-driven Incentivize or mandate the inclusion of geospatial and Health service delivery; Sipe and Dale (2003) tools, like the Climate-Sensitive Risk Index, in other data-driven tools in planning for health system decision-making is an absence of integration of planning and delivery—including service modality plans, Data capabilities and these data or tools in planning. shelter-in-place directives, and alternative patient care- planning paths through health system. Climate change likely will change Update the assessments based on the latest climate Data capabilities and Füssel (2007) hydrometeorological hazards in Colombia modeling (e.g., rainfall predictions, wind speed) when planning and will impact other hazards, increasing the possible. Collect data and monitor risk mitigation importance of using the most current hazard and preparedness interventions to adjust the risks data to understand and plan for future impacts. accordingly. As shown in priority facility section above, vulnerabilities must be considered side-by-side with exposures. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION 81 COMPONENT 3 Biodiversity and Climate Change: Implications for Human Health in Colombia Key Messages • Human health is interlinked with biodiversity and climate change. • Colombia is the third most biodiverse country in the world, but 88 percent of its ecosystems are at risk. In a megadiverse country like Colombia, analyzing the human health impacts this raises requires a comprehensive understanding of the drivers of biodiversity loss, such as deforestation, and the interaction with climate change. • In Colombia, mortality attributed to environmental risk factors accounted for 17,549 deaths in 2016, of which 15,681 were associated with poor air quality and 1,209 with poor water quality. • It is critical to outline and spotlight the benefits of maintaining biodiversity to tackle human health problems caused by interaction of climate change and air pollution. COMPONENT 3 82 • Deepening efforts to address interactions will increase Biodiversity, Climate Change, and Health demand on governance mechanisms and require Climate is a factor affecting the planet’s environment and strengthened multisectoral and multilevel arrangements. ecosystems, with direct and indirect implications for human health • Given the impacts of biodiversity loss and climate change (Bonebrake et al. 2018). Rises in greenhouse gas emissions due on human health, the health sector in Colombia must to anthropic (human) activities have increased the temperature have a more central role in developing policies and on the planet, generating changes in ecosystems and their programs to prevent and respond to health risks related biodiversity. As the climate changes, so does the performance to biodiversity loss and climate. of ecosystems and the way they fulfill their ecosystem functions • In urban planning, health risks should be integrated as (Lindley et al. 2019). key determinants for land use planning and decision- making. Biodiversity operates as an underlying requirement for the optimal functioning of ecosystems, and the provision of • The Ministry of Health would benefit from scaling various ecosystem services that in turn affect health and up nature-based solutions, which could have large well-being. Ecosystem functions include physicochemical and co-benefits for health and climate. biological processes that help maintain and regulate life on earth, such as air purification, climate regulation, crop pollination, and seed dispersal. These functions are vital for ecosystem services, such as producing food, maintaining environmental conditions that ensure water and air quality, and other elements supporting mental health—all of which depend on healthy natural capital (Figure 38) (Costanza 2012; Kremen 2005; Millennium Ecosystem Assessment 2005). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 83 Figure 38. Ecosystem Services, Biodiversity, and Implications for of more than 80 and 50 percent of the biomass of wild mammals Human Well-Being and plants, respectively (Pörtner et al. 2023). CONSTITUENTS OF WELL-BEING In this sense, climate change-induced temperature increases ECOSYSTEM SERVICES Security have added to biodiversity loss; these two factors are among the PERSONAL SAFETY Provisioning FOOD FRESH WATER SECURE RESOURCE ACCESS SECURITY FROM DISASTERS most complex threats to the functional integrity of ecosystems (García et al. 2018). Climate change affects biodiversity and WOOD AND FIBER FUEL ... Basic material for good life Freedom Supporting Regulating ADEQUATE LIVELIHOODS SUFFICIENT NUTRITIOUS FOOD of choice and action ecosystems at different levels. At the level of populations and SHELTER species, it affects the life cycles of organisms, their behavior, and NUTRIENT CYCLING CLIMATE REGULATION ACCESS TO GOODS OPPORTUNITY TO BE SOIL FORMATION FLOOD REGULATION ABLE TO ACHIEVE PRIMARY PRODUCTION DISEASE REGULATION WHAT AN INDIVIDUAL WATER PURIFICATION their geographical ranges. At the ecosystem level, climate change ... VALUES DOING ... Health AND BEING STRENGTH FEELING WELL Cultural AESTHETIC ACCESS TO CLEAN AIR AND WATER impacts primary production, the interaction between species, SPIRITUAL EDUCATIONAL RECREATIONAL Good social relations and adaptive capacities in species such as their vulnerability to ... SOCIAL COHESION MUTUAL RESPECT ABILITY TO HELP OTHERS biological threats and extreme weather events that affect their LIFE ON EARTH - BIODIVERSITY ARROW’S COLOR ARROW’S WIDTH resilience (Weiskopf et al. 2020).3/4 Potential for mediation by Intensity of linkages between ecosystem socioeconomic factors services and human well-being Low Medium Weak Medium Just as climate change affects biodiversity, biodiversity can also High Strong exert changes in climate. The quantity and variability of vegetation can contribute to increased or decreased capture and storage of Source: Millennium Ecosystem Assessment 2005 carbon dioxide (CO2), both in vegetation and soil, consequently Changes in the use and overexploitation of natural resources 3  Almost all life on Earth depends on primary producers. Primary producers use abiotic energy sources such as sunlight to produce compounds that can degrade ecosystem services. These changes are largely due to be used later by other organisms. These photosynthetic organisms are the deforestation and changes in land use for livestock and crops, as basis of most food systems. They also produce most of the Earth’s oxygen and well as use of natural resources (e.g., mining and dam construction) regulate important components of the carbon cycle and carbon dioxide (CO2) sequestration. In terrestrial ecoregions, primary producers are mainly plants, (Loh et al. 2015). Human activities have modified an estimate 75 while in aquatic ecoregions, algae they carry out this role. percent of the planet’s land surface (Ellis and Ramankutty 2008; 4  The resilience of a system refers to its ability to cope with change and maintain IPBES 2019), and 66 percent of its ocean area, resulting in the loss fundamental control of its structure and function. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 84 affecting temperature variability (Korn et al. 2019). In different terrestrial biomes, plant species diversity plays an important role in ecosystem productivity. This richness, in turn, acts as a bulwark against temperature variability. In other words, the greater the richness of plants, the greater the stability of temperature and ecosystems (Oliveira et al. 2022).5 Consequently, climate and biodiversity act as determining conditions that shape the environment, as well as the ecological and social processes that occur in them (Lindley et al. 2019). The loss of biodiversity increases the risks and impacts of climate change. Biodiversity loss and climate change are interrelated, as are other drivers of biodiversity loss. However, understanding of these interrelationships and their synergies is still limited. Both are increasingly influenced by human activity, resulting in increased risks to social systems. The effects of one also exacerbate the effects of the other (IPCC 2014; Korn et al. 2019). In this sense, biodiversity and climate change are both drivers and consequences, resulting in negative impacts on parts of the natural world that affect people’s health and well-being, and the functioning of society (Pörtner et al. 2023; Jaureguiberry et al. 2022; Cohen-Shacham et al. 2016). These effects can be biophysical, such as the degradation of air and water quality, as well as socioeconomic and cultural (e.g., impacts on crop and food production) (Figure 39). 5  Richness reflects the number of species in a given community or ecosystem (Goteli and Colwell 2001). Photo: © Matt Zimmerman ‘Slash and burn agriculture in the Amazon’ (CC BY 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 85 Figure 39. Effects of Biodiversity Loss and Degradation on the Climate change and the degradation and use of natural resources Climatic and Ecological Balance are complex problems that directly threaten the health and well- being of communities in Colombia; they are also closely related to • Water and food security systems • Air quality the drivers of biodiversity in the country.6 In Colombia, drivers of Biodiversity loss • Availability of natural resources for the biodiversity loss act synergistically with climate change, creating production of pharmaceuticals • Natural resources for recreational purposes conditions for an ecological imbalance that directly or indirectly and mental health and well-being affects human health. Colombia is the third most biodiverse • Subsistence means • Displacement of populations country in the world (Box 1). Yet only 12 percent of its ecosystems Ecological and climatic systems degradation • Social conflicts are not at risk: more than 44 percent of its ecosystems are ranked • Natural disasters • Effects on diseases’ patterns as threatened; 27 percent are in critical status; and 17 percent are in danger (Etter et al. 2017). Colombia is also vulnerable to climate change, ranked 89 of 181 countries in terms of climate risk according to the ND-GAIN INDEX (Notre Dame Global Adaptation Initiative 2021), and 47 percent of its territory is in the high and very high-risk categories (IDEAM (Institute of Hydrology, Meteorology, and Environmental Studies) et al. 2019).7 Colombia is highly vulnerable to extreme events, in particular flooding due to the La Niña phenomenon. Critically vulnerable areas include the Caribbean and Andes regions, affecting key sectors such as housing, transport, energy, agriculture, and health (World Bank 6  The drivers of biodiversity loss are agents that act directly or indirectly on the same, generating changes in the balance of ecosystems as a result of unsustainable anthropic activities. 7  The index ND-GAIN ranks 181 countries using a score that calculates a country’s vulnerability to climate change, other global challenges, and its readiness to improve resilience. Colombia’s ranking is due to a combination of Source: Pörtner et al. 2023 political, geographical, and social factors (World Bank 2021). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 86 2021) Taken together, its high species richness, high number coastlines and seas; (ii) direct overexploitation of organisms; of degraded ecosystems, and climatic vulnerability mean that (iii) the introduction of invasive species; (iv) climate change; and Colombia has a greater exposure risk to emerging diseases, as (v) pollution. All these drivers acting together exacerbate negative these factors drive more human-animal-environment interface. impacts on nature and humans and are driving biodiversity and its associated ecosystem services to a point of no return The loss of ecosystem functions is rapidly influenced by drivers (Jaureguiberry et al. 2022; IPBES 2019). of biodiversity loss, which cause a chain of changes in ecological patterns and processes, with important consequences for human health. In Colombia, biodiversity loss is affected by five main drivers, which in turn impact environmental, animal, and human health: (i) changes in land use and changes in Box 1. Colombia: A Megadiverse Country Due to its high species richness and endemism (native species found only in its territory), Colombia is listed Group Plants Animals Fungi Mammals Birds Reptiles Amphibians Fish as one of the 17 megadiverse countries on the planet, ranked as country with the third-greatest diversity Total 37,290 31,676 4,709 737 2,363 761 895 4,128 species (after Brazil and Indonesia). One of ten of all species that exist on the planet are found in Colombia (SIB Endemic 5,374 505 99 50 79 - - 349 species Colombia 2022; Like Minded Megadiverse Countries 67,000 recorded species including plants, animals, fungi, and microorganisms 2002). Colombia has 37,290 species of plants and 31,676 registered animals and is the country with the greatest wealth of birds, butterflies, and orchids in the world. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 87 Drivers of Biodiversity Loss of 207,054 hectares per year due to increases in the amount of land used for raising livestock. The highest forestation loss rates were in Land Use Changes the departments of Caquetá and Meta (59,834 ha and 58,492 ha, respectively) (SINCHI 2023). Land use change, through construction and the expanding use of land for agriculture, leads to deforestation, soil degradation, Land-use change, such as the conversion of natural covers and loss or damage to wildlife habitats as the agricultural in agricultural or urban areas, influences the risk of emerging frontier is expanded.8 Between 2001 and 2021, Colombia lost zoonotic diseases in humans, which can lead to global and more than 3.2 million hectares of forest (Ministry of Environment systematic effects (Gibb et al. 2020). These transformations and Sustainable Development 2022). In 2021, 174,103 hectares were deforested—a 1.5 percent increase in deforestation compared to 2020. The areas with the greatest changes in natural forest cover in 2021 were concentrated in the Amazon (64.8 percent), Andean (17.2 percent), Pacific (7.7 percent), Caribbean (5.5 percent), and Orinoquia regions (4.8 percent) (IDEAM and Ministry of Environment and Sustainable Development 2022). The average annual rate of forest loss (TMAPB) for the Colombian Amazon in 2020–2022 was 142,204 hectares (ha) per year, with the departments of Meta and Caquetá having the highest rates of loss (41,692 ha and 40,119 ha, respectively) (Amazon Institute of Scientific Research (SINCHI) 2023). During 2020–2022, habitat loss in the Amazon region of Colombia occurred at an average rate 8  According to Colombia’s Rural Agricultural Planning Unit (UPRA), the agricultural frontier is the boundary of rural land that separates areas where agricultural activities are carried out from areas where agricultural activities are excluded by law. Photo: © Counter Culture Coffee ‘Alvarado Castillo’ (CC BY-NC-ND 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 88 can generate changes in the habitat of disease-transmitting leukemia, brain, and prostate cancer, among others (Bassil et species, such as rodents, which, in turn, increase the channels of al. 2007). On the other hand, the reduction or elimination of transmission and risk of humans contracting zoonotic diseases from vertebrate species and vector-controlling invertebrates increases wild populations. This is because, despite damage to their natural the risk of contracting communicable diseases such as dengue, habitats, rodents are adaptable, able to colonize human-centered malaria, and enteric diseases (Müller et al. 2019). environments and even increase their populations in human- centered environments and in lands converted to agricultural use The use of land for intensive food production—involving the (Gibb et al. 2020; Mendoza et al. 2020). In Colombia, rodents serve continuous use of fertilizers and pesticides, the overexploitation as the main hosts for the zoonotic diseases of toxoplasmosis, of aquifers, and excessive grazing predominate—increases leptospirosis, and hemorrhagic viral fevers.9 the salinization and desertification of the soil, depleting the land of nutrients and organic material necessary for food Land use changes made to establish planting monocultures cultivation. This renders land unusable for food production, which and raise livestock homogenize the landscape abruptly reduce is particularly worrying given overpopulation and high demand biodiversity and affect the ecosystem roles of different species. for food. In addition, deforestation to clear land for agricultural Population decreases amongst important species or natural use is a major source of CO2 emissions, the greenhouse gas that predators of pests and herbs (e.g. microorganisms and insects) contributes most to climate change (IDEAM et al. 2017). in crops, are caused by the increasing use of pesticides. This leads to greater exposure to pesticides for the people who handle Overexploitation of Species them and in food for human consumption. This then creates risks for human health, since it has been established that direct and Overexploitation of natural resources also drives biodiversity prolonged exposure to pesticides is associated with neuronal and loss and has repercussions for human health. In Colombia, reproductive problems and genotoxic effects (Sanborn et al. 2007), the overexploitation of fishery resources and other wildlife as well as with some types of cancer such as Hodgkin’s lymphoma, populations due to hunting or illegal trafficking of species, as well as illegal logging, has caused the decline of species that 9  Rodents are mammals with early sexual maturity and high reproduction may well be important sources of food or raw materials for the rates. They reproduce frequently and have several offspring per birth. Zoonotic production of medicines (e.g., bioactive compounds of plants diseases are diseases that can spread from animals to humans. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 89 and fungi). These factors endanger the country’s food security, Species loss can also impact both prevention and treatment in terms of food availability and quality, leading to potential in human health care. More than half of today’s synthetic malnutrition problems. For example, overfishing in the Colombian medicines come from plant, fungal, and wild animal species. Amazon—including unsustainable management practices and And components of different species are already being used or failure to respect closures and minimum catch sizes— has led studied for use in treating various types of cancer, high blood to a decline in species populations, particularly large catfish pressure, HIV (human immunodeficiency virus), and malaria, and (Agudelo-Córdoba 2015). As a result, an estimated 62 percent of for use as antibacterial and antifungal treatments (MacKinnon et the fish marketed in the Amazon River are below the regulatory al. 2020; Erwin et al. 2010). size, as are 47 percent of the marketed fish from the Putumayo River (Agudelo-Córdoba et al. 2012). Invasive Species Invasive species also drive biodiversity loss, to the extent that they drive out native species and degrade ecosystems. Invasive animal species may reproduce rapidly and consume a wide range of foods, which allows them to colonize more quickly in new habitats. In Colombia, there are 508 identified exotic species of fauna and flora. However, data are available for 74 percent (378 species), and within this subset, 22 species have been identified as invasive (SIB 2022; World Wildlife Foundation 2022; and Ministry of Environment, Housing, and Territorial Development Colombia 2008).10 For example, the giant African snail threatens the agricultural production, as it quickly and effectively colonizes any habitat. Its presence has been reported in parts of Putumayo, Meta, Tolima, Vaupés, Casanare, Arauca, and Valle del Cauca. 10  There are a total of 23 officially recognized invasive species in Colombia (Minambiente 2022). Photo: © Louis Vest ‘Fishing, Cartagena’ (CC BY-NC 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 90 Moreover, this snail is host to the nematodes Angyostrongylus Climate change-induced changes in temperatures and weather cantonensis and Angyostrongylus costaricensis, which cause patterns alter and degrade ecosystems and their biodiversity. abdominal meningoencephalitis and angiostrongylosis, The impacts of associated changes and decreases in vegetation respectively (Alburqueque et al. 2008). Meningoencephalitis can are manifest in poor air quality and temperature increases, due to be marked by severe headache, nuchal rigidity, nausea, vomiting, the absence and/or degradation of vegetation that helps eliminate and paresthesias (González-Aguilera and Arias-Ortiz 2019). For its pollutants and cool the air. This conjunction of pollution with higher part, the lionfish can cause injuries in humans who step on their poisonous barbs. The lionfish also threatens food security, since it feeds on native species, such as snapper, grouper, and lobster, which are important in the diets of local communities. It has been reported on beaches near Santa Marta, Taganga, and the Tayrona National Natural Park, in the department of Magdalena (Ministry of Environment and Sustainable Development 2017). Overall, there is limited information on the environmental and economic costs of the diverse invasive species in the country. Climate Change Climate change is both a driver and a consequence of biodiversity loss. Drastic changes in rainfall and temperatures patterns alter the habitats species, affecting how species function and develop. It can also change where a species lives, sometime forcing them out of long-established territory or facilitating their movement into new areas. This may lead to an increased risk of vector-borne diseases, such and dengue and malaria which are transmitted by Aedes and Anopheles mosquitoes (Müller et al. 2019). Photo: © Iván Erre Jota Medellín - Tormenta (CC BY-SA 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 91 temperatures poses a risk to human health, since it increases Contamination respiratory complications, as well as morbidity and mortality from Pollution generated by human activities is a driver of cerebrovascular and cardiac diseases, both in rural and urban biodiversity loss, has pressing consequences for human health, areas, while the situation in cities is more complex due to the and is closely related to the deterioration of natural resources. presence of heat islands (Lindley et al. 2019).11 Air pollution (gaseous or particulate matter) from the combustion The impacts of climate change together with changes in land of fossil fuels for industrial and automotive purposes, as well as use affect the presence and stability of strategic ecosystems, particulate matter from forest fires, represents a risk to the health such as mangroves, riparian forests, wetlands, and moors, of Colombians and is associated with respiratory diseases such which buffer and regulate water cycles and availability. The as asthma, bronchitis, and rhinitis. This is directly related to the deterioration or removal of these ecosystems translates into an increase in polluting sources, the loss of tree cover, and impacts increased risk of injuries and deaths from floods, landslides, and on natural ecosystems located in urban and peri-urban areas, as storms, as well as impaired access to health facilities caused by these ecosystems are burdened with more air pollution and have such events. The regions with the highest number of municipalities less vegetation to remove air pollutants through absorption (World in the high and very high categories of climate risk are the Andean Health Organization (WHO) and Secretariat of the Convention region (36), the Amazon region (31), and the Pacific region (25). on Biodiversity (CBD) 2015). By 2016 in Colombia, the respiratory At the departmental level, the five departments with the highest diseases with the highest incidence in mortality related to poor air climatic risk are San Andrés, Vaupés, Amazonas, Guainía, and quality are ischemic heart disease (IHD) and chronic obstructive Atlántico. The 20 departments most at risk represented 69 percent pulmonary disease (COPD), with 7,230 and 3,873 attributable of the national gross domestic product (GDP) in 2016 and are home deaths, respectively (National Institute of Health (INS) 2018).12 to 57 percent of the country’s population (IDEAM et al. 2017). Overall, 15,681 deaths were attributed to air quality-related risks (619.78 per 100,000) in 2016, out of 17,549. 11  Urban heat islands are a phenomenon in which urban areas experience higher temperatures compared to surrounding non-urban areas. Heat islands increase 12  Gaseous pollutants such as ozone (O3), sulfur dioxide (SO2), carbon monoxide temperature in cities, energy consumption, and air pollution, and, in general, they (CO), and nitrogen dioxide (NO2) are eliminated through the stomata of tree leaves. decrease in the quality of life for urban populations. Therefore, the less vegetation cover, the less air cleanliness. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 92 Acting together, high temperatures and poor air quality increase exposure to respiratory diseases and affect the vascular, cardiac, and neurological systems (e.g., impacts on learning, memory, and behavior). They also cause worsening health in people with pre-existing conditions (e.g., heart disease, asthma, emphysema, and diabetes), with older adults and children the populations most at risk (WHO and CBD 2015). This is because high temperatures tend to increase the concentration of particulate matter (PM), and because the climate directs the way in which pollutant particles are transported and dispersed in the air. Notably, in Colombia the proportion of the total burden of disease attributed to PM2.5 (fine particulate matter) stands at 15.8 percent, with the greatest concentrations observed in Quindío, Córdoba, and Antioquia (INS 2018). The contamination of natural resources by different means threatens natural ecosystems, their biodiversity, and public health. The contamination of freshwater, marine or terrestrial, due Photo: © Mariusz Kluzniak ’smog over Bogotá’ (CC BY-NC-ND 2.0) to domestic or industrial activities such as improper management of solid and liquid waste, oil spills, the use of fertilizers on High temperatures tend to increase the agricultural land (including products with nitrogen and/or concentration of particulate matter (PM), phosphorus), and products associated with illegal mining such as mercury, represents a serious risk to human health. In Colombia, and the climate directs the way in which an estimated 1,209 deaths are attributable to environmental risk pollutant particles are transported and factors associated with poor water quality, of which 593 are due to dispersed in the air. acute diarrheal disease (INS 2018). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 93 Soils and water bodies can be highly contaminated by pesticide gold, discharged into water bodies, and handled and inhaled directly by use, which can cause health complications. An example is the the people who carry out this activity. Upon contact with water bodies, use of herbicides such as glyphosate, which has been used in mercury passes through different trophic levels, reaching fish that Colombia for aerial spraying to eradicate illicit crops. The use are consumed both by local river communities and by people in large of herbicides such as glyphosate has been associated with four cities. Mercury contamination is associated with fetal malformations types of cancer: liver, kidney, lymphatic, and pancreatic (WHO such as polydactyly, cognitive and learning difficulties, and affectations and CBD 2015). It has also been associated with miscarriages associated with fetal neuropathies. This situation is worrisome since and dermatological diseases. These pollutants, when applied to different studies in Colombia show levels of mercury that exceed what the land, are spread through runoff water, filtration, and leaching is permissible by the WHO in human tissue samples, with the local into groundwater and surface bodies of water, and through soil riverine populations being the most affected. For example, studies in erosion—all of which affect biodiversity. Pesticides can also be indigenous communities of the Colombian Amazon reveal that of 1,875 transported to other areas by evaporation. In addition, fertilizer hair samples taken, 1,525 had mercury levels above the WHO limit components such as nitrogen and phosphorous place too much (2015), corresponding to 1 part per million (ppm) for hair (Foundation of these elements in aquatic ecosystems and cause degradation.13 for Conservation and Sustainable Development 2022). Likewise, in the The presence of pollutants affects the ecosystem function of filter- indigenous community of Bocas de Taraira (Yaigojé Apaporis National feeding aquatic organisms (i.e., mollusks and bivalves), which are Natural Park), mercury values were found in hair between 2.3 and key in carrying out a water purification role in marine, freshwater, 34.9 ppm—the highest value reported for an indigenous community and wastewater environments (WHO and CBD 2015). in Latin America, according to published research (Valdemar-Villegas and Olivero-Verbel 2019). On the other hand, medical consultations in Another pollutant of natural resources in Colombia is mercury (Hg). Colombia of people who received some care associated with mercury Mercury contamination of water bodies and fish species is worrying contamination between 2015 and 2022, totaled 1,101 cases, with the in Colombia and represents a public health problem that urgently largest number registered in the departments of Antioquia (321), needs to be addressed. Mercury is used in the illegal exploitation of Atlántico (114), Chocó (153), Bogotá (87), Bolívar (73), and Córdoba (72) (Information System for the Monitoring of the Quality of Water for 13  Eutrophication is nitrogen and phosphorous overload in aquatic ecosystems, Human Consumption (SIVICAP) 2023; Integrated Social Protection which leads to uncontrolled phytoplankton proliferation, biodiversity imbalances, and anoxia conditions. Information System (SISPRO) 2023). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 94 Interaction of Environment and Human diseases of high occurrence in Colombia: ischemic heart disease, Health in Colombia stroke, obstructive pulmonary disease (COPD), acute respiratory infections, lung cancer, acute diarrheal disease, and chronic kidney In Colombia three main factors account for the burden of disease (INS 2018). environmental disease in the country and that impact the health of the Colombian people:14 Similarly, mortality related to environmental degradation translates into associated costs of COP 16.6 billion annually,  Poor urban air quality (air pollution in cities) corresponding to 2.08 percent of the country’s GDP in 2015. Poor air quality is the greatest factor in this result, where urban  Poor indoor air quality (burning solid fuels for cooking) air pollution represents an approximate cost of COP 12.2 billion   oor water quality (industrial, agricultural, or domestic P per year (1.5 percent of GDP in 2015). About 8,000 deaths occur wastes that pollute water sources, which is related annually in relation to poor air quality, representing a cost of COP to deficiencies in access to drinking water and basic 10.6 billion (DNP 2018).15 sanitation) In 2022, the national average rate in Colombia of air pollution In Colombia, exposure to poor quality air and water results due to PM2.5 was 15.5 μg/m3, which was higher than the 2021 in 17,549 deaths per year, which corresponds to 8 percent of average of 14.1 μg/m3. This increase is explained by the increase the total annual mortality in the country; of that total, 15,681 in forest fires in the Colombian Amazon during the dry season of deaths are attributed to poor air quality (INS 2018). The gradual 2022. Almost 30 times more forest fires were reported in January increase in poor air quality in Colombia’s cities began as a result of 2022 compared to January 2021. Consequently, the PM2.5 (fine automotive growth and increased industrial activity, factors that particulate matter) that comes from the fires can travel suspended have been exacerbated by population growth (National Planning Department (DNP) 2018). These factors are associated with seven 15  This economic assessment of environmental degradation in Colombia was based on the effects on human health of the main environmental risk factors for the country (urban air pollution, indoor air pollution, and poor water quality related 14  Colombia’s National Institute of Health (INS) has studied the burden of to deficiencies in access to drinking water and basic sanitation). Mortality and disease in the country associated with environmental risk factors; this is known as morbidity associated with environmental degradation were estimated, and their the environmental burden of disease (EBD). economic value subsequently calculated (DNP 2018). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 95 in the air long distances and pollute the air of major cities such as Table 11. Annual Indicated Values of Particulate Matter (PM 10 and Bogotá or Medellín. The cities of Bogotá, Cota (Cundinamarca), PM2.5) to Protect Public Health, according to the WHO guideline Guane (Santander) and Medellín (Antioquia), registered the highest value, intermediate goals, and national regulations of Colombia (Resolution 2244 of 2017) average values in a period of 5 years, between 2018 and 2022 (Air Quality 2021). Particulate WHO Colombia’s Matter Guideline WHO Intermediate Targets Guideline In the municipalities of Sabaneta, Medellín, and Bogotá, the (2021) As of From highest values of particulate matter smaller than 2.5 microns Goal 2 Goal 3 Goal 4 2018 2030 (PM2.5) were recorded for the year 2021; these rates were above PM 10 (μg/m3) 20 50 30 20 50 30 the annual norm for Colombia (25 μg/m3 (micrograms per cubic PM 2.5 (μg/m3) 10 25 15 10 25 15 meter)) and exceed the annual WHO guideline (5 μg/m3) by several units (Table 11). Regarding particulate matter smaller than 10 microns The levels indicated in the guideline values are evidence-based, WHO (PM10), by 2021 seven air quality monitoring stations located in recommendations and reflect a systematic review of the evidence demonstrating adverse health effects caused by particulate matter with a the departments of Antioquia (municipalities of Itagüí and Amaga), diameter of 2.5 microns or less (PM2.5) and with a diameter of 10 microns or Cundinamarca (Bogotá and the municipality of Soacha), and less (PM10) (WHO 2021). Magdalena (municipality of Ciénaga) recorded values that exceed the national standard and the WHO guideline (IDEAM 2021). The WHO intermediate targets serve to guide reduction measures towards achieving the guideline levels. Fulfilling the intermediate goals would represent Air pollution and climate change are closely interrelated. High a health benefit (WHO 2021). temperatures act as a chemical catalyst and can convert existing Colombia’s Guideline — Resolution 2444 (2017) of the Ministry of Environment elements in the air into tropospheric ozone (or ground-level ozone), and Sustainable Development is the standard that establishes the permissible which is a pollutant gas and key component of smog. Tropospheric values of pollutants for a healthy environment and to minimize the risk to ozone is a lung irritant, and chronic exposure to it is linked to human health in Colombia. It also establishes maximum permissible levels for premature deaths from respiratory diseases and heart attacks 2030. (United States Environmental Protection Agency 2022; IQAir 2022). Note: μg = microgram; m3 = cubic meter; PM = particulate matter; WHO = World Health Organization. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 96 In Colombia, research has identified common sources of A congruence has been found between GHGs and air pollutant greenhouse gas emissions and air pollutants, mainly in the emissions in the national and global results, which denotes transport, agriculture, and residential sectors. The sources of the link between these substances during the development of emission of both air pollutants and greenhouse gases (GHG) come those activities (Grisales Vargas 2021). GHGs in Colombia are from anthropogenic (human) activities, such as industrial activities, mainly related to the agriculture, livestock, forestry, and other automotive traffic, agriculture, and the production and burning of land use (AFOLU) sector.16 This sector has historically generated fossil fuels and biomass (Grisales Vargas 2021; World Bank 2022). the largest percentage of total emissions, with 71 percent of the average historical total, followed by the energy sector with a historical average of 23 percent (IDEAM 2017). Reducing overall GHG emissions in the country would bring co-benefits for health. Different national and international actors have highlighted that Nationally Determined Contributions (NDCs) commitments under the Paris Agreement would save many lives. For example, one of the NDC mitigation scenarios would result in 896,000 morbidity episodes averted by 2030—a 10 percent reduction relative to the usual scenario, without additional climate mitigation effort beyond current legislation (Ministry of Health and Social Protection (MSPS) 2022a). 16  Short-lived climate pollutants (SLCP) are referred to as such because they remain in the atmosphere less time than carbon dioxide (CO2). SLCPs include black carbon (soot), methane gas (CH4), tropospheric ozone (O3), and hydrofluorocarbons (HFCs). Photo: © Adam Cohn ‘Bucaramanga Colombia Traffic’ (CC BY-NC-ND 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 97 Nature as an Ally for Public Health The relationship between climate change, air pollution, and public health invites us to address these problems in a comprehensive manner. The health sector has begun to incorporate mechanisms to adapt to climate change (Watts et al. 2015), but it is also necessary to integrate measures that reflect the influence and contributions of biodiversity (Marselle et al., 2019). In this sense, it is essential to think about joint climate change and air pollution mitigation actions, since the co-benefits would be represented in lower GHG emissions, better air quality, and related positive repercussions for human health and ecosystems (Grisales Vargas 2021). Climate policies for air pollutant emissions could reduce global warming by 0.5°C and save the lives of 2.4 million people per year (Clean Air Fund 2022). However, as of 2021, only 7 percent of countries included short-lived climate pollutants (SLCP) in their national climate action plans. In addition, it is estimated that between 2015 and 2021, only about 2.2 percent of global public resources—coming from international development efforts aimed at Paris Agreement efforts—go directly to air quality globally; and only 0.3 percent of those resources are designated to Latin America and the Caribbean (Clean Air Fund 2022). The health sector should consider and integrate the benefits that nature brings both to health and to climate change mitigation and air quality improvement. Trees perform important Photo: © Jorge Láscar ‘Tayrona Walk - Calabazo to Pueblito’ (CC BY 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 98 services of temperature cooling, air cleaning, absorbing CO2, and averted, 670,000 fewer incidences of acute respiratory symptoms, maintaining biodiversity, which also contributes to ecosystem 430,000 fewer asthma exacerbation events, and 200,000 fewer health (WHO and CBD 2015). Better understanding and school days lost (Nowak et al. 2014). management of biodiversity also allows better management of vectors that can trigger zoonotic diseases, which has important Large cities in Colombia such as Bogotá have poorer ratios of implications for preparedness for future pandemics. trees per person than WHO-recommended rate of half a tree per inhabitant (Botanical Garden of Bogotá 2019). The rates for other Forests have the potential to serve as robust thermal buffers, cities in Colombia vary considerably. The localities with the best effectively minimizing the occurrence of strong and extreme values are Santa fé, Chapinero (1.71 trees per three inhabitants) heat stress days. In a global review of 714 paired temperature and Teusaquillo (1.20 trees per three inhabitants), while localities data points, De Frenne et al. (2019) discovered that tree canopies with high poverty rates and high population densities such as provide a buffering effect to the forest floors, effectively regulating Bosa and Ciudad Bolívar, have values of 0.15 and 0.21 trees per both high and low temperatures for the macroclimate (the overall three inhabitants respectively (Chamber of Commerce of Bogotá climate in a large geographical area). On average, the understory 2020). This highlights inequities in human health, environment, temperatures were cooler than the macroclimate temperatures by and quality of life. In the Aburrá Valley (Medellín, Caldas, Estrella, approximately 1.7±0.3°C, with a maximum temperature difference Sabaneta, Envigado, Itagui, Bello, Copacabana, Giradota, and of 4.1±0.5°C. These findings underscore the value of increasing Barbosa) in the department of Antioquia, it is estimated that forest coverage as a nature-based solution, not only in regulating there is only one tree for every seven inhabitants (Metropolitan temperatures but also in providing additional health benefits such Area of the Aburrá Valley 2019). as improved air quality, reduced stress, and enhanced physical activity (Gillerot 2022). However, the effects of forest coverage While the WHO recommends that cities and towns have a on temperature extremes are influenced by the forest structure, minimum green area of nine square meters (m2) of green area tree species composition, and geographical location. On the other per inhabitant, in Latin America, the proportion is 3.5 m2 per hand, in 2010, trees and forests in the U.S. were also estimated to inhabitant. Deficits are also seen in cities in Colombia such as have removed 17.4 million tons of air pollution, with an estimated Bogotá, where 80 percent of the population lives with a deficit of human health cost savings of USD 6.8 billion, as well as 850 deaths green areas (Greenpeace 2020). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 99 Deficits of green spaces in cities due to their omission in urban planning lead people to reduce their physical activity. This, in turn, Box 2. Health Naturally in Parks Initiative causes, among other things, increased stress and obesity, as well as physical, emotional, and behavioral instability (e.g., nature deficit The Health Naturally in Parks initiative of National Natural disorder).17 On the other hand, including green and natural and Parks of Colombia has its origins in the Healthy Parks manmade waterbodies in and between cities and maintaining them Healthy People initiative in the Australian province of as part of a network with larger surrounding protected areas helps Victoria, which has been very well received by the community with disease prevention and treatment by generating important and has provided additional benefits for protected areas. benefits for both physical and mental health (Box 2). These spaces The National Natural Parks of Colombia has 23 protected do not necessarily have to be large. Small green spaces can areas with ecotourism packages that offer services and contribute and be sufficient to sustain biodiversity (e.g., microbial activities to visitors. diversity), contribute to efforts to build networks with larger rural green areas, and provide benefits associated with health. The Health Naturally in Parks Program aims to consolidate the Colombian National Natural Parks as environments that Studies of green spaces and the biodiversity associated with provide health benefits by promoting healthy lifestyles and them report optimal relationships with respect to the increase well-being for children, youth, and adults who visit them and promotion of physical activity (Kaczynski and Henderson (National Natural Parks of Colombia (PNN) 2017). 2007; Coutts and Hahn 2015). This aspect is particularly important with regard to noncommunicable diseases such as diabetes and cardiovascular diseases, for which physical activity is a tool for both prevention and treatment (Cook et al. 2019). In this sense, green spaces are regulators of physiological functions, removing 17  The term “nature deficit disorder” was coined by Richard Louv (2005), to describe different behavioral problems, such as decreased use of the senses, Photo: © F Delventhal ‘Parque Del Japon’ (CC BY 2.0) attention difficulties, and higher rates of physical and emotional illness resulting from less time outdoors. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 100 or reducing the impact of possible stressors, especially in large Box 3. Biodiver-cities Initiatives in Colombia: Improving and overcrowded cities (Hartig et al. 2014; Coutts and Hahn 2015). Environmental and Human Health In addition, performing physical activity in urban green spaces Barranquilla Biodiver-city is potentially healthier, since the healthier green environment avoids greater exposure to harmful levels of air pollution and the The city of Barranquilla is part of the Cities4Forest program, consequent effects on respiratory and cardiovascular systems a reforestation initiative to increase green spaces and better (Lindley et al. 2019). prepare for flooding and coastal erosion (Cities4Forest With this in mind, different “Biodiver-cities” initiatives are being 2023). Following the Guide for the Ecological Restoration of developed in Colombia, to counteract the effects of air pollution Mangroves for Colombia and the recommendations of the and other factors and to improve the quality of life and health of its local community, 22,100 red mangrove seedlings were planted, inhabitants. Examples include the cities of Barranquilla (Atlántico with a survival rate of 87 percent at the beginning of 2023. department) and Leticia (Amazonas), as well as the RAMSAR- Likewise, for 10 years the city has been executing the Todos designated Urban Wetlands Complex of the Capital District of al parque initiative, which has created more than 1.5 million Bogotá (Box 3). green areas, improving the local economy, safety, and outdoor physical activity (Mayor’s Office of Barranquilla 2023). For of all these achievements, Barranquilla was recognized by the World Resources Institute in February 2023 as the city with the most innovative sustainable urban transformation project in the world. It has also been recognized on four occasions by the Tree Cities of the World program of the Arbor Day Foundation and the United Nations Food and Agriculture Organization— recognition that highlights the cities that best manage their urban forest systems (Tree Cities of the World 2023). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 101 Important initiatives led by SINCHI such as the Bioempaques Amazonas and the Bioabonos project, as well as activities to characterize urban wetlands, educate the population in sustainable consumption habits, and fish farming with native species, guide efforts to consolidate Leticia as a Biodiver-city, involving citizens in the development of a more sustainable city. Leticia is part of a group of 14 cities in the country, which, together with the Ministry of Environment and Sustainable Development, began projects aimed at recovering urban biodiversity and improving the human- nature relationship (Ministry of Environment and Sustainable Development 2020; SINCHI 2023). Photo: © Juanerre ‘Barranquilla’ (CC BY-NC-ND 2.0) Leticia Biodiver-city. The city of Leticia in the Colombian Amazon is also on its way to becoming a Biodiver-city. In 2020, within the framework of the Leticia Pact for the Amazon, the memorandum of understanding Leticia Biodiverciudad was signed between different entities (the Ministry of Environment and Sustainable Development, the Amazon Institute of Scientific Research (SINCHI), Corpoamazonia, the Government of the Amazon, and the Mayor’s Office of Leticia), with the purpose of jointly guiding the city towards the sustainable management and use of its natural, cultural, and ethnic wealth. Photo: © Eli Duke ‘Colombia: Boat from Leticia’ (CC BY-SA 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 102 Urban Wetlands Complex of the Capital District of Bogotá Governance in Environmental and The city of Bogotá has a complex of wetlands that have Human Health received the RAMSAR* designation. RAMSAR wetlands are The governance of countries and territories plays a central role ecosystems that, due to their biological, hydrological, and in addressing the relationships between human health and ecological characteristics, are considered of high importance environmental health. These mechanisms allow for the generation for their conservation at the international level. The city’s and implementation of relevant public policies and direct RAMSAR wetland complex is made up of 11 of the capital’s decision-making. Therefore, a dynamic, effective, and intersectoral 17 wetlands. These ecosystems are permanent bodies of governance approach is imperative for the management of fresh water that are home to different species, some of them environmental and human health in Colombia. endemic to the high Andean zone of the country. This wetland Although in the decades prior to the year 2000 Colombia had complex is an important water regulator of the rivers of the instruments touching on the integrated management of human Bogotá savannah; in the rainy season it helps regulate and and environmental health (e.g., Decree-Law 2811 of 1974 and the cushion floods. It also helps with building a green network in National Sanitary Code 1979), these aspects were developed in a the city, since it constitutes the main ecological connector disjointed manner. However, with the emergence of two important of the urban and rural territory of the Bogotá River basin and laws in the Colombian health system in 2007 and 2011, the first real crosses the city from east to west, also providing recreation steps are beginning to be taken to integrate environmental health for inhabitants. The Bogotá RAMSAR wetland complex has a as an important governance issue. The first of these was Law 1122 management plan that was consolidated in March 2023, to map which formulates the National Public Health Plan (PNSP) for 2007– actions that help with its conservation and contribute to the 2010. It incorporates environmental factors as part of the approach environmental health of the capital (Ministry of Environment to the social determinants of health (Balladelli et al. 2007). The and Sustainable Development 2023; Environmental second is Law 1438 of 2011, mapping out the implementation Observatory of Bogotá 2023). plan for the Ten-Year Public Health Plan 2012–2021 (PDSP), which *RAMSAR refers to the Convention on Wetlands which was prioritizes environmental health, establishing specific objectives, signed in Ramsar, India. goals, and strategies. One of the environmental health goals is the IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 103 creation of national and territorial mechanisms for the formulation, approval, and dissemination of a comprehensive environmental health policy (Ministry of Health and Social Protection 2013). Resolution 1035 of 2022 created the Ten-Year Public Health Plan for 2022–2031, which outlines environmental health as the basis of public health, recognizing the right to a healthy environment, stable climate, and the availability of food. It also proposes the implementation of policies, plans, and programs to reduce outdoor and indoor air pollution. Notably, the resolution proposes special considerations for vulnerable populations such as the indigenous peoples and communities of Colombia, victims of the armed conflict, the Roma people, and the black, Afro-Colombian, Raizal, and Palenquera populations (Ministry of Health and Social Protection 2022b). Colombia actively participates in efforts to address challenges at the intersection of health and environment. This work is led by the Ministry of Health’s Department of Environmental Health, which is also leading strategies to address health risks related to climate change. The country emphasizes the importance of environmental health as a determinant of general well-being, coordinating efforts through the National Intersectoral Technical Commission on Environmental Health (CONASA), which promotes the efforts of the National Committee on Economic and Social Policy (CONPES) 3550 and the creation of the Comprehensive Photo: © 2019 European Union (Photographer N. Mazars) (CC BY-NC-ND 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 104 Environmental Health Policy. These initiatives recognize that implementation, including climate change, are the responsibility environmental factors, including climate change, biodiversity loss, of the Ministry of Environment and Sustainable Development and deforestation, have direct and indirect impacts on individual (Minambiente). The Ministry of Environment, together with the and collective quality of life. Colombia’s commitment to promoting National Planning Department (DNP), has been at the forefront of environmental health reflects its dedication to sustainable coordinating efforts to address climate-related challenges. development and improved public health outcomes.18 Colombia has also been energetic and timely in developing The Government of Colombia has demonstrated its awareness documents that highlight key areas in different sectors that of the possible negative impacts of climate change on health need to be addressed to reduce the country’s carbon footprint and the environment and has acted by establishing strategic reduction and adaptation to climate-related risks. The country guidelines policies, as well as laws to address the impacts has developed a National Climate Change Policy (2017), the of climate change and lay the groundwork for collaboration Climate Action Law (Law 2169 of 2021), a National Climate Change and developing concrete actions. Since Colombia signed the Adaptation Plan (2012), and several tools to monitor and track the Paris Agreement in 2016 and ratified it 2018, the government has progress of programs and policies to address climate change, demonstrated its political commitment and acted to address the including the National Climate Change Information System challenges of climate change by implementing related legislative (SNICC); the Integrated Vulnerability, Risk and Adaptation System frameworks and strategies, programs, and activities. In Colombia, (SIIVRA); the Forest and Carbon Monitoring System (SNByC); the environmental policies and related legislative frameworks and their Climate Action Toolbox (HaC); and the National Climate Change System (SISCLIMA). In addition, the Intersectoral Commission on 18  The Intersectoral Commission on Environmental Health (CONASA) is Climate Change (CICC) plays a crucial role in establishing policies composed of the following institutions: Ministry of Environment and Sustainable Development, Housing, and Territorial Development; Ministry of Health and Social and actions to achieve Colombia’s climate change objectives, Protection; Ministry of Agriculture and Rural Development; Ministry of Trade, serving as the main intersectoral mechanism to promote mitigation Industry, and Tourism; Ministry of National Education; Ministry of Mines and and adaptation strategies. Energy; Ministry of Transport; National Planning Department (DNP); Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM); National Institute for Drug and Food Surveillance (INVIMA); National Institute of Health (INS); Colombian Agricultural Institute (ICA); and Administrative Department of Science, Technology, and Innovation (Colciencias). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 105 Colombia also has developed the National Policy for the Target 14, Strategic Objective D. “Improve Benefits for All from Integrated Management of Biodiversity and its Ecosystem Biodiversity and Its Ecosystem Services”). Services (PNGIBSE) (2012) and a related action plan for 2016–2030. Both documents use a socio-ecological approach, Addressing the interaction of biodiversity, health, and climate recognizing the direct relationship between biodiversity and change poses a challenge that goes beyond technical and human health. The PNGIBSE policy (Ministry of Environment scientific analysis, and that translates into a need for effective and Sustainable Development 2012) includes targets to restore for multisectoral and multilevel governance mechanisms the benefits that ecosystems bring to human health (see, e.g., in Colombia. This includes: (i) multisectoral and multilevel coordination; (ii) capacity strengthening for local governments to implement public policy and execute resources; and (iii) integration of surveillance systems. These are key areas in developing strategies and interventions that address the interactions of environment, climate change, and health. Multisectoral and Multilevel Coordination The National Climate Change System (SISCLIMA) is an intersectoral commission that coordinates policies, instruments, and tools for climate change. SISCLIMA, in turn, is made up of nine regional climate change nodes that allow the articulation of efforts under collaborative governance models involving the national government, departmental governments, and the different nongovernmental actors in each region. Each node works to coordinate with actors from different subnational governments, public utilities, regional autonomous corporations, civil society, the private sector, and academia. SISCLIMA has four committees: Photo: © Alianza por la Solidaridad, 2020. (CC BY-ND 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 106 (i) a technical committee to advise the CICC; (ii) a technical and goals, taking into account the risks and variables at the intersection scientific information committee on climate change, focused on the between biodiversity, health, and climate change. production and management of technical information; (iii) a financial management committee; and (iv) an international affairs committee. Local Government Capacity In the field of environmental health, CONASA (the National Local governments capacities are critical for the effective Intersectoral Technical Commission on Environmental implementation of programs and interventions at the Health) has the Territorial Councils for Environmental Health intersection of climate change, health, and biodiversity. As of (COTSAs). The territorial councils are designed to support the 2021, local budget sources for this work remained inadequate. decision-making process on environmental health, intersectoral Approximately 19 percent of the departments exhibited poor management for the management of social and environmental budget execution, indicating that they used less than 50 percent determinants that affect quality of life, and to act as implementers of the revenues they generated. This implies that a significant of the Comprehensive Environmental Health Policy (PISA). There portion of their own income remained unspent or unallocated, are currently 41 COTSAs that were established by administrative hindering their ability to effectively implement interventions and act. Of these COTSAs, 32 are at the departmental and district policies. Departments such as Vaupés, Vichada, Amazonas, and levels, and six are at the municipal level. Sucre depended on transfers from the central government to support more than 80 percent of their relevant activities. Similarly, Despite having mechanisms that promote a collaborative in 2021, most departments showed an average performance governance model bringing together the central government and rate of 50 percent for disaster risk management, indicating the territories, in both climate change and environmental health, that they completed only half of the tasks or strategies outlined there is no information on the actual interaction between the within their annual plan. On the other hand, technical capabilities two government levels. It is also not clear what roles each actor and implementation for climate-related threat preparedness assumes and what plans or strategies are being implemented under intervention are limited and not prioritized. In 2022, progress these collaborative arrangements at the territory level. Similarly, it is in implementing environmental and sustainable development unknown how the interaction between COTSAs and regional nodes plans remained an issue. The four departments with lowest is working and if there are redundant efforts to achieve common IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 107 implementation rates were La Guajira (11.11 percent), Amazonas (12.63 percent), Arauca (17.65 percent), and Magdalena (18.18 percent) (DNP 2021). (See Annex 1 for details.) Nongovernmental actors in Colombia with extensive experience in collaborative mechanisms with local governments and indigenous leaders are essential in addressing environmental challenges. This includes organizations such as Doctors Without Borders, Aida, De Justicia, Sinergias, and others. Importantly, these organizations have a higher level of trust among the community and better knowledge of the context and the relevant actors. They are also able to channel funds from international organizations directly to vulnerable regions and can partner with local governments on joint implementation. As a result, coordination involving the national government, local governments, and nongovernmental actors can result in greater technical capacities or financing mechanisms for local governments. Information and Surveillance Systems In Colombia, environmental health is framed in light of determinants of health, such as social, economic, and environmental aspects that affect the health system and population health. These factors interact with the health system and can generate poor living conditions, environmental risks, and changes in lifestyles. For example, factors such as water Photo: © Alliance of Bioversity International and CIAT ‘Colombia Floods8’ (CC BY-SA 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 108 access and quality, as well as air pollutants, are key determinants living conditions, food, medicines, and substances that are a risk of health in Colombia. The result is observable changes in life to humans. Initially conceived from the perspective of the natural expectancy and the appearance of diseases, disabilities, and environment, SUISA has evolved towards a broader vision of the deaths, impacting the well-being of the population. In this sense, environment, considering the natural, physical, economic, and monitoring and surveillance of the determinants of health are social environment. In this sense, there are about 35 fragmented critical for decision-making, intervention prioritization, and information systems managed by different ministries, which resource allocation to improve health outcomes for Colombians. address different social, economic, or environmental determinants. The fragmentation of information hinders the surveillance of Since 2001, public health in Colombia has adopted the health diseases affected by social determinants, as well as the capacity situation analysis (ASIS) model. This model incorporates health of national and local governments to develop programs and indicators, health system performance, and social determinants, interventions that could reduce health risks. especially demographic, socioeconomic, and environmental determinants. Although research centers and the Ministry of Health have done analytical studies on social and environmental determinants, the different databases that address the different determinants are fragmented and there is no integration of different sources of information that would enable the creation of predictive analytical models, showing results in real time; nor is it possible to estimate compound risks related to environmental, climatic, health, and health system variables. Colombia has been developing a unified environmental health information system (SUISA) since 2010 through the National Technical Commission for Environmental Health (CONASA). SUISA integrates various information systems on health and environmental determinants, such as water, air, public services, IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 3 109 Conclusions and Recommendations In the context of Colombia’s abundant biodiversity, understanding the drivers of biodiversity loss becomes a pressing concern for assessing associated health risks. Ranking as the third most biodiverse country globally, Colombia faces significant risks, with 88 percent of its ecosystems being threatened. It is essential to acknowledge that climate change acts as both a driver and a consequence of biodiversity loss, as outlined in this component. To address the challenges at hand, it is crucial to delve deeper into understanding the drivers of biodiversity loss and their implications for health. This includes acknowledging the dual role of climate change as both a driver and consequence of biodiversity loss, particularly in relation to deforestation. Further scientific investigation is necessary to examine the intricate interaction between biodiversity loss and health, guiding the formulation of effective public policies, decision-making processes, and resource allocation at the subnational level. It is essential to highlight the benefits of preserving biodiversity to combat health issues stemming from the combined impact of climate change and air pollution. This requires intensified efforts to address these interactions, which will necessitate stronger governance mechanisms and enhanced cross-sectoral and cross- level arrangements. Notably, the health sector in Colombia should play a more central role in developing policies and programs to Photo: © Alliance of Bioversity International and CIAT ‘NP_Rice Emissions10’ (CC BY-SA 2.0) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 110 prevent and respond to health risks associated with biodiversity e. Scale up nature-based solutions within the health loss and climate change. ministry’s initiatives to capitalize on their potential for delivering significant co-benefits for both health and In the light of the interaction of health, climate change, climate and biodiversity, Colombia can benefit from strengthening collaborative arrangements in order to harness policies and programs that would bring health co-benefits. Important steps could include the following: a. Develop models that estimate the benefits and economic costs of inaction and action on biodiversity to tackle health problems caused by interaction of climate change and air pollution b. Deepen efforts to address those interactions; this will increase demand on governance mechanisms and require strengthened multisectoral and multilevel arrangements c. Integrate health risks as key determinants in urban planning and land use decision-making processes, ensuring that health considerations are central to land use planning and development d. Strengthen the role of the health sector in Colombia as central actor in the development of policies and programs to prevent and respond to health risks associated with biodiversity loss and climate change IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION 111 COMPONENT 4 Towards a Roadmap of Interventions to Address Climate Change in Colombia’s Health Sector Background In recent decades, climate change has gained increasing attention due to mounting evidence of its negative impacts on ecosystems and society. This has resulted in a series of calls for action on climate change, reflecting a growing understanding of the threat it poses to our planet and the urgent need to address it. These calls from scientists, social movements, and international organizations already have a long history with some significant milestones. The Intergovernmental Panel on Climate Change (IPCC), established in 1988, has played a critical role in raising awareness and calls for action. The IPCC periodic reports have provided a solid scientific foundation on climate change and its consequences, leading to greater understanding and awareness of the need for urgent action (IPCC 2014b). In 1992, the United Nations 4 Comparative analysis Framework Convention on Climate Change (UNFCCC) was created, establishing the framework for global cooperation in the fight against climate change (UNFCCC 1992). Since then, various COMPONENT 4 112 conferences of the parties have been held to discuss and negotiate represents a significant threat to health worldwide, as it directly international agreements, with the aim of reducing greenhouse gas affects health systems, food security, access to drinking water, and emissions and limiting global warming. One of the most important disease patterns (WHO 2018). The effects include increased risk of milestones in the calls for action was the adoption of the Paris vector-borne diseases such as malaria and dengue fever, as well Agreement in 2015. This agreement, ratified by the vast majority as respiratory and cardiovascular diseases linked to air pollution. of countries, established the commitment to limit the increase in Health impacts also include heat stress, malnutrition, foodborne global temperature to below 2 degrees Celsius, and strove for a illness, and increased natural disasters such as floods and limit of 1.5 degrees Celsius (UNFCCC 2015). In addition, the Paris droughts. In addition, the IPCC has highlighted that climate change Agreement urged countries to submit Nationally Determined can exacerbate health inequalities, disproportionately affecting Contributions (NDCs) with specific measures to reduce emissions the most vulnerable populations, such as children, the elderly, the and adapt to the effects of climate change. These calls to action poor, and those living in remote regions (IPCC 2014a). have generated increased awareness and mobilization around the world. Civil society, scientists, and nongovernmental organizations These calls to action around the strong links between health and have played a key role in pushing for more ambitious action climate change have been echoed and expressed in international (IPCC 2018). commitments such as those established in the Paris Agreement and in the development of commitments for NDCs. However, Health is a central issue in the debate on climate change, and despite the advances and commitments of nations, the pace at climate change has been characterized by some as the greatest which these actions are being taken may not be fast enough. In the threat to global health in the 21st century (WHO 2015). The health sector in particular, it has been noted that while progress effects of global warming have significant direct and indirect has been made and the new iterations of several NDCs include consequences on people’s health, and all of the evidence supports healthcare, action is “too slow if any” (Hartinger et al. 2023). the need to take urgent action to address climate change from a health perspective. Numerous studies and organizations recognize This component seeks to integrate the results of the first three the close interconnection between climate change and health, components of this study into an analysis that can inform and which has led to increased concern and calls for action. According facilitate decision-making in the face of the challenges of climate to the World Health Organization (WHO), climate change change in the health sector. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 113 Methods identifies actions to face these challenges, and for each of these elements, it defines the indicators to be quantified. In this sense, The methodological approach is guided by elements developed the conceptual framework emphasizes the need to integrate in frameworks for the analysis of costs and consequences of knowledge from different disciplines into the analysis and climate change in the health sector (WHO 2013; WHO 2023), which highlights that it is essential to spotlight the role of assessing the propose a comparative analysis between the costs of inaction in cost of inaction in raising awareness of current and future health- the face of climate change and the potential costs and benefits related challenges that deserve attention from public policy. This of intervening to mitigate or adapt to the (partially unavoidable) becomes a critical element of the process, since it is expected to consequences of climate change. stimulate political intervention (WHO 2023). To do this, on the one hand, this analysis identifies and estimates Thus, this component integrates the findings of other components the impact of climate change on health. On the other hand, it of this study that point to the costs of inaction and incorporates them in a comparative analysis alongside the costs of interventions. For this reason, the dimensions of the analysis start with the components of the study—in particular, the economic burden associated with non-optimal temperature—and This component integrates the findings of complements them with an estimate of the associated costs other components of this study that point to mortality and morbidity, using a tool to assess the effects of to the costs of inaction and incorporates climate on health (the Climate Change and Health Economic Valuation Tool discussed below). them in a comparative analysis alongside the costs of interventions. In terms of the conceptual framework, the disease and economic burden due to non-optimal temperature evidences the costs of inaction and assesses the economic cost of premature mortality associated with non-optimal temperature (Figure 40; see also methodological details in Component 1). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 114 Figure 40. Quantifying the Costs of Inaction: Sub-Optimal Climate Change and Health Economic Temperature Valuation Tool Temperature-sensitive The Climate Change and Health Economic Valuation Tool diseases (CHEVT) helps to quantify the economic cost of inaction and is Effect on Mortality, attributable articulated with the framework described above, applying the health fraction, burden of disease steps of quantifying the impact of climate change on health and Economic subsequently valuing that impact in economic terms. Human capital loss burden Premature mortality To estimate the impact, the tool uses models proposed in the Inaction literature (Aström et al. 2012; WHO 2014) that describe the al climate-health relationship for the following outcomes: dengue, a se tim s re op re malaria, malnutrition, diarrheal disease, health outcomes Inc sub- ratu in pe Action/Mitigation associated with temperature extremes, and health outcomes m te associated with extreme weather events. The models associate climate information, along with other demographic, economic, Additionally, as noted above, the effects of climate change on and health indicators, to quantify the number of cases and the health are not limited to temperature. There are also important number of deaths associated with each of the selected events, effects through changes in ecosystems that have, for example, thus producing a quantitative measure of the impact of climate substantial implications for diseases transmitted by vectors or for change on morbidity and mortality. Subsequently, it combines this diseases related to the quality and availability of water. Therefore, information with cost data and uses the cost of illness method to to complement the analysis with a vision of the costs of inaction value morbidity and the value of a statistical life method to value in other dimensions, the tool developed by Metroeconomica and mortality in order to estimate the economic value of that effect the World Bank for the economic valuation of the effects of climate (Figure 41). For the climate data (temperature and precipitation) change on health is used (Metroeconomica‑World Bank 2022). The the tool uses the SSP3–RCP7.0 scenario from Coupled Model methodology of the tool is described below. Intercomparison Project Phase 6 (CMIP6). The tool quantifies and IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 115 values deaths and cases attributable to climate change under two Table 12. Indicators and Information Sources, Colombia Analysis different scenarios—with and without climate change.19 Indicator Source Figure 41. CHEVT: The Tool in Brief National GDP per capita at market prices SSP Database National GDP per capita in terms of SSP Database Step 1: Quantify Step 2: Value purchasing power parity Cover sheet Summary and charts data template National population (2020) SSP Database Quantify climate-related Put a monetary value on the number of cases and/or Dengue cases (2020) SIVIGILA number of cases and deaths using peer-review-dose- deaths using the cost of Direct cost per dengue episode Literature and own calculations response function (e.g., illness (includes productivity proposed in WHO 2014) loss) and/or the VSL Indirect cost per dengue episode Literature and own calculations Total cost per dengue episode Literature and own calculations 1. DR function sheet 1. Analytical model sheet Malaria case fatality rate per 1,000 cases IHME 2. Climate data sheet 2. Mortality values sheet 3. Economic data sheet 3. Morbidity values sheet Number of malaria cases nationwide (2020) IHME 4. Demographic data sheet 5. Health data sheet Direct cost per malaria episode (2014–2016, Literature and own calculations indexed to 2020) 6. No. of cases and/or deaths 4. Cost of cases and/or deaths Indirect cost per malaria episode (2014– Literature and own calculations (Step 1 output sheet) (Step 2 output sheet) 2016, indexed to 2020) Total cost per malaria episode (2014–2016, Literature and own calculations indexed to 2020) Source: adapted from Metroeconomica‑World Bank (2022). Direct cost per episode of diarrhea (2014– Literature and own calculations Note: CHEVT = Climate and Health Economic Valuation Tool. 2016, indexed to 2020) Indirect cost per episode of diarrhea Literature and own calculations To use the tool, it is then necessary to calculate the indicators with (2014–2016, indexed to 2020) the data for Colombia. The list of indicators and the related sources of Total cost per episode of diarrhea (2014– Literature and own calculations 2016, indexed to 2020) information used for the present analysis presented below (Table 12). Note: IMHE = Institute for Health Metrics and Evaluation; GDP = gross domestic product. 19  RCPs are scenarios developed by the research community “to provide information on possible development trajectories for the main forcing agents of climate change” (see van Vuuren et al. 2011). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 116 Estimation of cost indicators are a combination of literature sources and authors’ calculations. For each cost indicator, a The cost of each of the cases was literature search for sources reporting data on these outcomes estimated using individual health records was conducted in English and Spanish. Regarding the items identified, the cost information per case was extracted and (claims data) to characterize all the health updated at 2020 prices, using the growth in the legal minimum care services provided to patients that wage (considering that several of the calculations are based on meet the selected diagnoses. prices of the rates used in Colombia, several of which are indexed to the growth of the minimum wage). To estimate the financial cost, generalized linear models (gamma We also did our own calculations of the cost for each of the cases, with log link) and nonparametric models (XGBoost and Random using individual health records (claims data) to characterize all the Forests) were used. First, the data was split into a training set health care services provided to patients that meet the selected and a test set. Then, the estimation worked with the training diagnoses. The analysis includes all services related to the set using resampling techniques (10-fold cross-validation) to episode, including those provided during the first identified point choose between alternative model specifications and to fit of contact, as well as additional services for patients transferring hyperparameters. After having a small number of candidate to other healthcare settings (e.g., hospital care or intensive care models in the training set, the analysis used the test set to units). The analysis used data on the monetary value paid by health evaluate competing models and choose the best model using insurance companies for these services to estimate the financial predictive performance indicators. The best selected model was cost to the health system and out-of-pocket payments were used used to estimate the cost per episode. In the case of out-of- for the same services to estimate the direct cost borne by patients. pocket payments, a generalized linear models with a Tweedie The analysis also estimated the number of days (using admission distribution and a log link function was used. To estimate the date and discharge date) that patients have limited ability to work number of days with limited ability to work per episode, a negative and valued those days using different wage scenarios (minimum binomial model was used and followed the same procedure wage, median earnings) to estimate indirect cost. explained above. Then, each day was valued using different using different scenarios for the reference salary. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 117 Identifying Interventions Results To identify interventions that have the potential to address the challenges of climate change in the health sector, a two-step The Cost of Climate Change Impacts approach was used. First, a literature review was conducted The CHEVT results indicate that the economic (social) cost of to identify interventions that have been proposed and/or mortality and morbidity arising from malaria, dengue, diarrhea, implemented internationally, and a review was conducted of stunting and extreme heat is estimated to increase from COP regulatory developments and other initiatives that have been 7.1 trillion in 2020 to COP 31.5 trillion in 2050. This economic proposed in Colombia as strategies to deal with climate change. cost represents 0.7 percent of GDP in 2020 and is estimated to increase to 1.6 percent of GDP in 2050. Not all of this increase in For the literature review, a scoping review was conducted with the economic cost is attributed to climate change. Changes in the aim of systematically mapping the available literature on total population, changes in the structure of the age pyramid, and interventions related to climate change and the health sector. changes in gross domestic product also explain the significant An exhaustive bibliographic search was carried out in electronic increase in health costs. In Colombia, CHEVT estimates indicate databases (PubMed, Scopus, and Web of Science), using that 46 percent of the health costs in 2050 are directly attributed search terms related to climate change, the health sector, and to climate change. Thus, climate change will contribute 0.8 percent interventions. Studies published in English and Spanish were of GDP to health costs in 2050. included. Inclusion criteria were initially applied by reviewing titles and abstracts, and then relevant studies were selected The CHEVT results indicate that among selected outcomes, for full review. Key data was extracted from the selected dengue represents the greatest burden, with an estimated cost of studies, such as author, year of publication, country of origin, COP 4.7 trillion in 2020. This is followed by stunting, with a cost of interventions evaluated, and whether data on the effectiveness COP 580 billion and extreme heat with COP 122.9 billion. Climate of the intervention and its costs are reported. The findings are change does not uniformly affect all outcomes. The increase in presented in a descriptive manner, highlighting the identified cost due to climate change is primarily explained by heat and interventions and grouping them thematically. vector-borne diseases (dengue, malaria), but less so by diarrhea or stunting, where the cost difference between scenarios with and without climate change is relatively small. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 118 Figure 42. CHEVT Results, Colombia (A) Dengue (B) Malaria IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 119 (C) Diarrhea (D) Stunting IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 120 Interventions Identified Through the Literature Review An important group of interventions are those based on improving knowledge of the health climate relationship, where three key interventions play a leading role in the literature: climate-aware health surveillance systems, early warning systems, and efforts to assess vulnerability and risks. Surveillance systems help identify and understand the specific health impacts of climate change. This information is essential for guiding targeted interventions and resource allocation, as well as for evaluating the effectiveness of interventions over time. Without accurate and up-to-date surveillance data, it would be challenging to identify emerging health risks, allocate resources effectively, and develop evidence- based strategies to protect public health. Early warning systems play a crucial role in forecasting and alerting communities and Photo: © James Gathany healthcare providers about these events, allowing for timely preparedness and response. By providing advance notice, early warning systems help mitigate the adverse health impacts of climate-related disasters, such as injuries, waterborne diseases, and mental health effects. They enable the implementation of preventive measures, evacuation plans, and the allocation of resources to ensure a swift and effective response. Assessing vulnerability and risks associated with climate change is essential for understanding which populations and regions are most at risk and need targeted interventions. Vulnerability assessments IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 121 evaluate the susceptibility of individuals, communities, and in the health sector. They aim to provide a structured and healthcare systems to climate-related health hazards based comprehensive approach to guide actions and initiatives to on factors such as socioeconomic status, geographic location, safeguard public health in the face of changing climatic conditions. and existing health conditions. Risk assessments, on the They establish a clear vision and direction for addressing climate other hand, identify and evaluate the potential health hazards change impacts on health. They also outline overarching goals and consequences of climate change, enabling proactive and objectives, highlighting the specific areas of focus and planning and preparedness. These assessments help prioritize priority actions. By setting a strategic direction, these frameworks interventions, allocate resources appropriately, and ensure that enable policymakers and stakeholders to align their efforts and the most vulnerable populations receive the necessary support allocate resources effectively. Policy frameworks establish the and protection. regulatory and governance mechanisms necessary to support the implementation of strategies and plans. They provide a legal and Another important group of interventions comprise preparedness institutional framework for decision-making, resource allocation, and response plans. By establishing protocols, guidelines, and and coordination among various stakeholders. Policy frameworks resources in advance, these plans ensure timely and coordinated also enable the integration of climate change considerations into actions during emergencies, leading to the efficient allocation existing health policies, ensuring that climate-related risks and of resources, effective response efforts, and the protection of vulnerabilities are adequately addressed. individuals and communities. Moreover, they promote awareness, knowledge, and readiness—empowering communities to cope with The literature frequently studies interventions individually crises, adapt to changing circumstances, and recover more swiftly, considered. For example, in the context of heat-related diseases, and ultimately contributing to the overall safety and well-being of interventions such as the increase in the use of air conditioning, populations. In the literature, preparedness and response plans public awareness campaigns on personal protection measures, are usually associated with specific risks, hazards, or diseases. and cooling centers, among others, are discussed. One salient characteristic of this set of interventions is the diversity. This is There seems to be a growing consensus that holistic strategies, the result of the multi-faceted complexity of the problem and plans, and policy frameworks are important in addressing the the multiple pathways of risk exposure and impact. It is also a complex and far-reaching challenges posed by climate change consequence of the fact that, ultimately, many actions should be IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 122 taken at the local level, tackling the locally relevant issues with the and mitigation efforts to achieve sustainable health outcomes resources available. in a changing climate. Furthermore, a review by Frumkin et al. (2019) highlighted the significance of adopting a systems-thinking The interventions discussed above can contribute to creating approach that considers the interdependencies between the a roadmap of interventions to cope with the consequences of environment, human health, and social factors. A comprehensive climate change in the health sector. The review also pinpointed strategy is essential for effectively addressing climate change in two key messages from the literature: (i) there is no silver bullet the health sector. Academic research emphasizes the need to intervention to deal with climate change in the health sector, adopt integrated approaches and systems-thinking to effectively instead, countries should embrace comprehensive and integrated mitigate risks, strengthen health systems, and safeguard public approaches to deal in the short and long term with climate change; health in the face of climate change challenges. By taking a holistic and (ii) action at the local level is key, and although national approach, policymakers can ensure sustainable and resilient strategies are indispensable, if they do not translate into action at health outcomes for present and future generations. the local level, they can hardly be effective. Action at the local level plays a pivotal role in addressing the Even though many studies focus on a single intervention, there consequences of climate change in the health sector. Local seems to be consensus that single interventions fall short in governments and communities are at the forefront of responding to dealing with the complexities of climate change. It is crucial to the immediate and long-term health impacts of climate change, as adopt a comprehensive strategy rather than focusing solely on they possess a deep understanding of local contexts, vulnerabilities, individual interventions. A comprehensive approach considers and resources. Schramm et al. (2020), for example, using a number the complex and interconnected nature of climate change of case studies, shows how local data and expertise can drive and its effects on health. By implementing a broader strategy, effective programs tailored to specific needs and illustrates how policymakers and stakeholders can effectively address the one-size-fits-all models would not work for climate and health multiple dimensions of climate change, mitigate risks, and build adaptation. It also highlights some barriers to developing local resilience within healthcare systems. For instance, a study by climate-health solutions, including a lack of needed data and Watts et al. (2018) emphasized the need for integrated approaches capacity or expertise. that combine adaptation measures, health system strengthening, IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 123 Additionally, a study by Patz et al. (2015) underscores the role of Interventions in the Colombian Context local-level interventions in reducing the health risks posed by In Colombia, significant efforts have been made to address climate climate change. The authors argue that targeted local initiatives, change. A central milestone was Colombia’s signing of the Paris such as improving access to healthcare, enhancing emergency Agreement and the presentation of Colombia’s first commitments response systems, and promoting community resilience, can in the Nationally Determined Contributions (NDCs) in 2015. In significantly enhance health outcomes and reduce the burden of 2020, the country presented its NDC update. In the update, it climate-related health impacts. commits to more ambitious measures to reduce emissions; and Moreover, a review by Vardoulakis et al. (2017) highlights that in the health sector, the update explicitly includes two objectives local action can effectively address the localized health effects in terms of (i) adaptation in prevention of climate-sensitive of climate change, including heat-related illnesses, vector- diseases and (ii) adaptation actions by healthcare providers for borne diseases, and air pollution. The review emphasizes the possible events associated with climate variability and change. need for tailored local strategies that account for geographical, The strategies defined in the NDC commitments have become a socioeconomic, and demographic factors to protect vulnerable cornerstone of the fight against climate change in Colombia—so populations effectively. much so that they were raised to legal status with the issuance of Law 2169 of 2022, which promotes the country’s low-carbon Evidence on the effectiveness is relatively scarce. In particular, development by establishing goals and minimum measures in hard evidence in the form of randomized trials is not commonly terms of carbon neutrality and climate resilience. available for the majority of interventions, evaluating specifically how they address the impact of climate change. Such evidence A previous law (Law 1931 of 2018) defines the Comprehensive sometimes may not be ethical to produce and sometimes it may Territorial Climate Change Management Plans (PIGCCT) as the not be feasible (Smith et al. 2023). instruments through which local governments evaluate, prioritize, and define measures and actions for adaptation and mitigation of greenhouse gas emissions. As a result, PIGCCTs are a key vehicle to realize the NDC commitments by taking action at the local level. To date, 24 of the 32 departments in Colombia have already IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 124 formulated PIGCCTs (Minambiente 2022). In health, the governing Costs of Intervention body has articulated in these efforts and the Ministry of Health To illustrate the magnitude of the intervention costs, the costing has developed guidelines for the formulation of locally-tailored of the specific health targets set forth in the NDC were taken as a PIGCCTs specific for the health sector, in terms of both adaptation starting point. Costing exercises to support the development and (Minsalud 2021b; Minsalud 2021c) and mitigation (Minsalud 2021a). implementation of a health surveillance system that integrates Thus, Colombia has a clear framework for actions against climate climatic data and meteorological models were reviewed. The change in general, and particularly in the health sector. Additionally, costing exercise also supports the development of an early warning it is noteworthy that this framework generally responds to the main system for climatic events, as well as knowledge management and recommendations of the literature in terms of creating national the production of guides and action plans. Figure 43 illustrates strategies and plans that are developed and implemented locally the costs of implementing these actions over a 10-year horizon, so that they are tailored to local needs and capacities. with an estimated value of COP 39 billion in net present value (Ricardo Energy‑Ecoversa 2020). In this sense, the country seems to be on the right track. However, Figure 43. Climate-Aware Integrated Health Surveillance as various international studies have pointed out, the pace of Implementation Costs (10-year horizon) reform implementation should accelerate. An illustrative example of this is the PIGCCTS formulation process. Only 15 percent of the published PIGCCTS include a fully developed component of adaptation actions in health. Thus, accelerating the speed at which these specific plans for the health sector are formulated throughout the territory would seem to be a priority need. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 125 To address the second goal of Colombia’s NDC, the costing Discussion exercise included a vulnerability assessment to identify the The results from this analysis show that implementation costs healthcare providers subject to mitigable and non-mitigable risks, are dwarfed by the costs associated with the consequences of and the design and implementation of adaptation measures. Figure climate change on people’s health and the health system. For 5 shows the estimated costs for implementation over 10 years; example, while the economic burden attributable to sub-optimal the total cost in net present value amounts to COP 184 billion temperature ranges between COP 0.6–3.3 trillion over a 10-year (Ricardo Energy‑Ecoversa 2021). period, enhancing climate-sensitive disease prevention efforts Figure 44. Health Care Providers’ Adaptation Implementation through strengthening integrated, conscious early warning and Costs (10-year horizon) surveillance systems of the weather, would cost around COP 43 billion. Although the evidence on the effectiveness of these types of interventions in addressing the consequences of climate change is still accumulating, the potential benefits are large enough to suggest that it is likely to be a good investment. Colombia has set a clear path with the NDC goals specific for the health sector and the PIGCCTs as a key vehicle to assess, prioritize and define measures and actions for adaptation and mitigation of climate change, to be implemented in the local contexts. Yet, it is important to accelerate the pace of its implementation. In particular, the development of a climate-aware integrated surveillance system is one of the measures in line with NDC’s goals that is yet to be a reality. The national government should lead the development and implementation of such a system, to strengthen the current surveillance system that, despite its many strengths, still does not comprehensively and systematically IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION COMPONENT 4 126 include climatic information. Such a system should include early warning systems for climate-relevant hazards, implemented at scale, building on some of the pilot experiences already tried out in Colombia. Recognizing that many of the measures that can ultimately reduce the health effects of climate change must be implemented at the local level and tailored to the local context, the development of fully-fledged PIGCCT for the health sector should be a priority. The national government should lead the strategy to spur the formulations of such plans throughout the country and accompany the subnational governments in charge of the development of the plans through technical assistance and the creation of spaces to share the experiences with other sub-national governments and the civil society in general. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEXES 127 ANNEXES IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 128 ANNEX 1. COMPONENT 1 3. Gray literature published on the internet in the last 30 years, using Google Scholar as a search tool, selecting the most A. Methodological Annex relevant publications and carrying out a secondary search for information through the snowball method. The following section describes each of these procedures. Systematic Literature Review Burden of disease in Latin America • Search engines: PubMed, Embase, EBSCO Host Systematic Review of Epidemiological Studies • Inclusion criteria: Indexed articles published in English, Spanish of Disease Burden by Temperature Variation and Portuguese between 1990 and 2022 in Latin America using Objective: to review the different methodological options for keywords for burden of disease included within health science calculating the burden of disease by temperature. descriptors (DeCS) in the 3 languages https://decs.bvsalud. org/E/homepagee.htm Three procedures were carried out to search for relevant • Search syntax: The search syntax is presented in the following information in the study of this methodology box 1. Studies published in indexed journals that evaluated the burden of disease by chronic diseases, communicable diseases and injuries of external cause in Latin America, through 3 different search engines. 2. Studies published in indexed journals that evaluated the association of risk factors, emphasizing temperature on the burden of disease in Latin America, through 3 different search engines. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 129 Box A1.1. Search syntax disease burden Latin America EBSCO Host (“DALY” OR “Disability” OR “adjust” OR “life” OR “year” OR “YLD” Embase OR “YLL” OR “lost”) AND (“Latinamerica” OR “Colombia” OR (‘disability-adjusted life year’/de OR daly:ab,ti,kw OR dalys:ab,ti,kw “Venezuela” OR “Ecuador” OR “Peru” OR “Bolivia” OR “Paraguay” OR ((disabil* NEAR/4 adjust* NEAR/4 life* NEAR/4 year*):ab,ti,kw) OR “Chile” OR “Argentina” OR “Uruguay” OR “Brazil” OR “Panama” OR yll:ab,ti,kw OR ylls:ab,ti,kw OR ((year* NEXT/2 life* NEXT/1 OR “Costa Rica” OR “Nicaragua” OR “Honduras” OR “Guatemala” lost*):ab,ti,kw) OR yld:ab,ti,kw OR ylds: ab,ti,kw OR ((year* NEAR/3 OR “Salvador” OR “Mexico” OR “Cuba” OR “Haiti” OR “Puerto lived NEAR/3 disabil*):ab,ti,kw)) AND (‘latin america’/exp OR latam Rico” OR “Dominicana”) OR ‘hispanic america/de’ OR iberoamerica OR colombia*:ab,ti,kw OR peru*:ab,ti,kw OR chile*:ab,ti,kw OR argentina*:ab,ti,kw OR PubMed bolivia*:ab,ti,kw OR mexico*:ab,ti,kw OR uruguay*: ab,ti,kw OR (“DALY” OR “DALYs” OR “disability adjusted life year” OR “YLL” brazil*:ab,ti,kw OR venezuela*:ab,ti,kw OR ecuador*:ab,ti,kw OR “YLLs” OR “Year of life lost” OR “YLD” OR “YLDs” OR “years OR paraguay*:ab,ti,kw OR panama*:ab,ti,kw OR ‘costa lived with disability”) AND (“Brazil”[All Fields] OR “Colombia”[All rica*’:ab,ti,kw OR nicaragua*:ab,ti,kw OR honduras*:ab,ti,kw OR Fields] OR “Venezuela”[All Fields] OR “Ecuador”[All Fields] OR guatemala*:ab,ti,kw OR salvador*:ab,ti,kw OR cuba*: ab,ti,kw OR “Peru”[All Fields] OR “Bolivia”[All Fields] OR “Paraguay”[All Fields] haiti*:ab,ti,kw OR dominican*:ab,ti,kw OR ‘puerto rico*’:ab,ti,kw) OR “Chile”[All Fields] OR “Argentina”[All Fields] OR “Uruguay”[All AND [1990-2030]/py Fields] OR “Brazil”[All Fields] OR “Panama”[All Fields] OR “ Costa Rica”[All Fields] OR “Nicaragua”[All Fields] OR “Honduras”[All Fields] OR “Guatemala”[All Fields] OR “Salvador”[All Fields] OR “Mexico”[All Fields] OR “Cuba”[All Fields] OR “Haiti”[All Fields] OR “Puerto Rico”[All Fields] OR “Dominicana”[All Fields]) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 130 Figure A1.1. Flowchart Search Result Burden of Disease Identification of disease burden studies via databases and registries Identified records of: Records deleted before screening: Identification Duplicate records (n =2319) Databases (n = 3) Registrations ineligible according to computer tools (n = 103) 899 PubMed Records deleted for other reasons (n = 5) 1014 Embase 1411 EBSCO Host Total: 2427 Total: 3324 Records reviewed by abstract/abstract Excluded records (n = 897) (n = 701) Screening Articles for review complete Excluded records (n = 196) (n = 90) Articles that meet inclusion criteria Excluded items: (n =106) No methodological information (n = 21) Outcomes outside the object of study (events not associated with temperature) (n = 9) Articles included in the review Systematic review (n = 2) Including 28 Noncommunicable diseases 23 Infectious diseases 23 Trauma and injuries external causes Total: 74 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 131 Data extraction: the data was extracted to an Excel matrix, using Risk factors in Latin America the following categories to synthesize the information • Search engines: PubMed, Embase, EBSCO Host • Inclusion criteria: Indexed articles published in English, Table A1.1. Categories of Analysis of Data Extracted from the Review Spanish, and Portuguese between 1990 and 2022 in Latin America using keywords for risk factors included within Category Description health science descriptors (DeCS) in the 3 languages Overview General aspects study (journal, title, author, year of https://decs.bvsalud.org/E/homepagee.htm publication, objectives, language) Study characteristics Type and group of cause/event, geographical • Search syntax: The search syntax is presented in the distribution, reference period, stratification following box Data adjustment Use of sources of mortality or incidence, data management (adjustment, integration), internal consistency of the study, type of software used DALY (disability- Perspective of YLD (year lived with disability) adjusted life year) estimates (incidence/prevalence), Type of method calculation method calculation of life expectancy-YLL, reported disease model, calculation of disability weight, methods used in the calculation of disability weight (elicitation method, jury panel, severity distribution), comorbidity and adjustment methods, weighting process Injury classification Cause of injury, nature of injury, injury type matrix (only for trauma and and % incident cases by cause and injury. externally caused injuries) Uncertainty Use of uncertainty analysis, sensitivity analysis or scenario analysis IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 132 Box A1.2. Search Syntax Risk Factors Latin America “Honduras” OR “Guatemala” OR “Salvador” OR “Mexico” OR “Cuba” OR “Haiti” OR “Puerto Rico” OR “Dominicana” ) AND [1990-2030] Embase (‘temperature’/de OR ‘weather’/de OR ‘heatwave’/de OR coldspell PubMed OR (attributable AND ‘risk’/de) OR ((attribut* NEAR/3 (risk* ((“temperature”[All Fields] OR “weather”[All Fields] OR OR fraction* OR burden* OR mortalit* OR death*)):ab,ti,kw) “heatwave”[All Fields] OR “coldspell”[All Fields]) AND OR ((comparat* NEAR/3 risk* NEAR/3 assessment*):ab,ti,kw)) (“attributable”[All Fields] OR “risk”[All Fields] OR “mortality”[All AND (‘latin america’/exp OR latam OR ‘hispanic america/de’ Fields] OR “death”[All Fields] OR “fraction”[All Fields] OR OR iberoamerica OR colombia*: ab,ti,kw OR peru*:ab,ti,kw OR “burden”[All Fields]) AND (“Brazil”[All Fields] OR “Colombia”[All chile*:ab,ti,kw OR argentina*:ab,ti,kw OR bolivia*:ab,ti,kw OR Fields] OR “Venezuela”[All Fields] OR “Ecuador”[All Fields] OR mexico*:ab,ti,kw OR uruguay*:ab,ti,kw OR brazil*:ab,ti,kw OR “Peru”[All Fields] OR “Bolivia”[All Fields] OR “Paraguay”[All Fields] venezuela*:ab,ti,kw OR ecuador*:ab,ti,kw OR paraguay*:ab,ti,kw OR “Chile”[All Fields] OR “Argentina”[All Fields] OR “Uruguay”[All OR panama*:ab, ti,kw OR ‘costa rica*’:ab,ti,kw OR Fields] OR “Brazil”[All Fields] OR “Panama”[All Fields] OR “Costa nicaragua*:ab,ti,kw OR honduras*:ab,ti,kw OR guatemala*:ab,ti,kw Rica”[All Fields] OR “Nicaragua”[All Fields] OR “Honduras”[All OR salvador*:ab,ti,kw OR cuba*:ab,ti,kw OR haiti*:ab,ti,kw OR Fields] OR “Guatemala”[All Fields] OR “Salvador”[All Fields] OR dominican*:ab,ti,kw OR ‘puerto rico*’:ab,ti,kw) AND [1990-2030]/py “Mexico”[All Fields] OR “Cuba”[All Fields] OR “Haiti”[All Fields] OR “Puerto Rico”[All Fields] OR “Dominicana”[All Fields])) AND EBSCO Host (1990:2023[pdat]) (“temperature” OR “weather” OR “heatwave” OR “coldspell” ) OR ( “attributable” OR “risk” OR “fraction” OR “mortality” OR “comparative” OR “assessment” ) AND ( “Latinamerica” OR “Colombia” OR “Venezuela” OR “Ecuador” OR “Peru” OR “Bolivia” OR “Paraguay” OR “Chile” OR “Argentina” OR “Uruguay” OR “Brazil” OR “Panama” OR “Costa Rica” OR “Nicaragua” OR IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 133 Figure A1.2. Flowchart Search Results Risk Factors Identification of risk factor studies via databases and registries Identified records of: Records deleted before screening: Identification Duplicate records (n =9782) Databases (n = 3) Registrations not eligible according to computer tools (n = 2079) 3767 PubMed Records deleted for other reasons (n =367) 5040 Embase 4342 EBSCO Host Total: 12228 Total: 13149 Records reviewed by abstract/abstract Excluded records (n = 921) (n = 742) Screening Articles for review complete Excluded records (n = 196) (n = 137) Articles that meet inclusion criteria Excluded items: (n = 42) Null methodological information (n = 7) Outcomes outside the object of study (n = 5) Systematic review (n = 3) Articles included in the review (n = 27) Including IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 134 Data extraction: the data was extracted to an Excel matrix, using Gray literature burden of disease from suboptimal temperature the following categories to synthesize the information exposure • Inclusion criteria: All types of grey literature published in Table A1.2. Categories of Analysis of Data Extracted from the Spanish and Portuguese in Latin America between 1990 and Review 2022 with keywords included within health science descriptors Category Description (DeCS) and terms of free use https://decs.bvsalud.org/E/ General aspects study (journal, title, author, year of homepagee.htm Overview publication, objectives, language) Study Type and group of cause/event, geographical distribution, • Search strategy: the search was performed in Google Scholar, characteristics reference period, stratification using the search syntax shown in Box A1.3 Use of sources of mortality or incidence, data management Data adjustment (adjustment, integration), internal consistency of the study, type of software used Perspective of YLD estimates (incidence/prevalence), Type of method calculation of life expectancy-YLL, disease DALY model, calculation of disability weight, methods used in the calculation calculation of disability weight (elicitation method, jury panel, method severity distribution), comorbidity and adjustment methods, weighting process Clarity of risk factor definition, sources of exposure, risk- outcome combinations, definition of exposure-response Risk function, data source exposure-response curve, definition benchmarking of minimum exposure risk threshold (MRREL), method of calculating attributable risk, use of stratified analysis Use of uncertainty analysis, sensitivity analysis or scenario Uncertainty analysis IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 135 Box A1.3. Search Syntax Gray Literature Subsequently, the most relevant documents were selected and a search was performed using the snowball procedure to complete Spanish the search process. “temperature|climate|heatwaves|extreme cold”|”attributable Results: 75,400 documents were obtained in Spanish and 64,900 risk|fraction|burden|mortality”|”Comparative Risk in Portuguese. After a review of the documents, 139,801 documents Assessment”|Latin America|Colombia| Peru|Chile| were discarded and 499 were reviewed. After an initial screening, Argentina|Brazil |Bolivia | Ecuador|Uruguay| Paraguay| seven documents were selected, which through the snowball Venezuela |Panama|” Costa Rica”|Nicaragua| Honduras procedure allowed to identify four more documents for a total of 11. |Guatemala |Salvador| Cuba | Haiti| Dominican|” Puerto Rico” Data extraction: data were extracted to an Excel matrix and Portuguese classified according to the following categories (Table A1.3): “temperature | climate | heatwave | extreme tempofrio” |” Riscoatribuivel |Fraçao|Cargo |Mortality| ”mediçaocomparativaderisco”| latinamerica | colombia | peru |chile | argentina | brazil| bolivia|ecuador|uruguay|paraguay|venezuela| panama|” Costa Rica”|Nicaragua| Honduras | Guatemala| Salvador|Cuba |Haiti|Dominican |” Puerto Rico” IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 136 Table A1.3. Categories of Analysis of Data Extracted from the Among the most used sources of environmental information for Review temperature are records of the monitoring networks (studies n = 9), followed by satellite temperature records such as ERA5 (studies Category Description n = 7) and not defined (studies n = 2). Some covariates used, part Overview General aspects study (journal, title, author, year of of humidity, atmospheric pressure, and precipitation were some publication, objectives, language) indices such as the Caldas Lang, the Koppen Geiger index, and the Study • Document Type: Thesis, Report, White Paper, Book characteristics Chapter, Article environmental probability index. • Article type: review, research, mixed, white paper With regard to health sources, the most used to assess mortality • Risk factor studied were vital statistics (in most countries) and surveys or verbal • Outcome studied autopsies in Haiti and in some very isolated places in Latin • Burden of disease study (Yes/No) America. For the calculation of the years of life lost (YLLs), the • Environmental data sources predominant method was the use of the standard GBD life tables. • Health Data Source • Study design: Analysis type As a result of the literature review, it was obtained that: Mapping of information sources: define the most suitable sources after the bibliographic review and the piloting of the quality and completeness of these sources. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 137 Literature Review of Economic Studies of were chosen through reading the full text, in order to confirm that Temperature Variation they met the specific eligibility criteria (inclusion and exclusion). A narrative review of the literature was conducted to identify Data extraction and synthesis of evidence parameters and methodological approaches that guide the calculation of the economic burden of premature mortality The characteristics of the selected studies were summarized associated with changes in temperature. For this, PuMed-Medline narratively from what was reported in the original publications. The was used as a search engine, with the terms “economic burden”, whole process was in charge of two researchers, comparing the “climate change”, “temperature”. We reviewed articles published results included in the evaluation report with the results presented in indexed journals published in English, Spanish and Portuguese, in the original publications. between 1995 and 2023. We included studies with estimates of economic parameters on temperature change, studies of economic Article review burden of disease that calculated direct or indirect costs, and We identified 368 references from the PubMed-Medline search. studies that detailed methodological approaches to estimation We excluded 344 references after reading titles and abstracts, of economic burden of disease, carried out in any country in the because they did not correspond to the established criteria of world. Apart from original research, literature reviews were also population, outcomes of interest, or type of publication. After considered. Conference abstracts and letters to the editor were analyzing the remaining 24 full-text references, 10 articles were excluded. excluded. Reference screening and study selection References were screened by two reviewers independently, analyzing titles and abstracts against predefined selection criteria. When there was any doubt about compliance with the established eligibility criteria, the full document was read to determine whether it provided useful information for the analysis developed in the present study. From the group of preselected references, studies IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 138 Figure A1.3. Steps Review Economic Literature Figure A1.4. PRISMA from the Literature Review of Studies of Economic Burden Due to Temperature Changes Identification 368 artículos Eliminados en Revision 24 revisión de abstracts: 344 Source: IETS 2022 Selection 10 Eliminados: 10 The main findings of the selected articles are described in the following table: IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 139 Table A1.4. Characteristics of the Articles Included in the Review of Studies of Economic Burden of Temperature Variation in the World Studio Health Author (year) Country Period Type of cost Main results design Outcome The study included 15,992,310 occupational • Costs associated with maintaining injuries. Overall, 2.72% [95% confidence production (including overtime interval (CI): 2.44–2.97] of all lesions payments and replacement and training were attributed to non-optimal ambient costs), temperatures, with moderate heat Martinez- • Lost earnings (total income lost when a representing the highest fraction. This Descriptive Occupational Solanas et al. Spain 1994–2013 worker suffers an injury and is unable to finding corresponds to an estimated 0.67 study diseases (2018) return to work), million (95% CI: 0.60–0.73) person-days • Associated health costs with treatment of work lost each year in Spain due to and rehabilitation costs, and temperature, or an annual average of 42 • Pain costs and suffering (level of days per 1,000 workers. The estimated disability). annual economic burden is trillions of euros, or 0.03% of Spain’s GDP (EUR 2,015). Between 2015 and 2019, the economic impact of certain health effects of heatwaves amounts to EUR 25.5 billion, • Direct costs: cost-of-illness mainly in mortality (EUR 23,200 million), • Indirect costs: production losses with days of less restricted activity (EUR 2,300 average daily wage million) and morbidity (EUR 0,031 million). Adélaïde. et al. Descriptive Heat stress • Intangible costs: willingness-to-pay French 2015–2019 The total economic valuation of excess (2022) study illness (WTP) mortality was estimated at €23 billion using • Costs of mortality: value of a statistical VSL and EUR 8.3 billion using the VoLY life approach. Under the VSL Approach, the • (VSL) and value of a life year (VoLY) estimated economic impact of mortality during heatwaves ranged from EUR 68 per capita in 2017 to EUR 170 per capita in 2015. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 140 Studio Health Author (year) Country Period Type of cost Main results design Outcome Studies showed that exposure to extreme heat was causing significant economic loss and burden on health systems. Women, USA, All causes of the elderly, low-income families, and ethnic Wondmagegn et Australia, Systematic heat-related minorities had the largest share of Health 2017 Direct, indirect, and economic costs al. (2019) Germany, review illness and care costs in a variety of health service Spain death utilization. Although some studies have estimated the costs of medical care by heat, none of them quantified the temperature- cost relationship in health. Economic losses of RMB 156.1 billion (95% Chen et al. Meta- Heat stress Costs of mortality: value of a statistical life CI: RMB 92.28–211.40 billion), accounting for China 2013–2019 (2022) analysis illness (VSL) 1.81% (95% CI: 1.14–2.45%) of Wuhan’s annual GDP over the seven-year period. A 1°C increase in monthly Tmax was associated with a 0.34 and 0.02 increase in rural and urban heat stress illness (HSI) Ecological Heat stress hospitalization per 100,000 population, Jagai et al. (2017) USA, Illinois 1987–2014 Total and average hospital charge per person study illness respectively. The total hospital charge for HSI cases was COP 167.7 million with a median charge of COP 20,500 per person per year. An estimated total of 169,881 health care visits were observed. The cost of excess Knowlton hospitalization, emergency department USA, All causes of et al. 2000–2009 Case Study Cost overrun visits, and outpatient visits in the 2006 heat California disease (2011) wave was estimated at COP 28.435 million; COP 14.110 million, and COP 136.380 million, respectively. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 141 Studio Health Author (year) Country Period Type of cost Main results design Outcome An estimate of 100 excess annual hospital admissions during the base year with Lin associated costs of COP 0.64 million. et al. USA, New Ecological Respiratory Projected heat-attributable hospitalization 1991–2004 Cost overrun (2012) York State study disease costs for the periods 2046–2065 and 2080–2099 range from COP 5.5 to COP 7.5 million and from COP 26 million to COP 76 million, respectively. A total of approximately 6,200 heat-related Merrill hospitalizations were observed in 2005 at et al. Descriptive Heat-related USA 2005 By heat-related hospital stay a cost of COP 6,200 per hospitalization. (2008) study illness The poor and rural residents were the most vulnerable groups. The number of medical consultations for hyperthermia was 10,007 with a mortality Noe et al. Descriptive Heat-related USA 2004–2005 Total cost rate of 0.06 per 100,000. The average (2012) study illness hospital stay was two days and the total estimated total cost was COP 36 million. A higher mean cost of hospitalization associated with high ambient temperature was evidenced among ethnic minorities: Schmeltz Asians/Pacific Islanders COP 1,208 (COP Descriptive Heat-related et al. USA 2001–2010 Average cost per hospitalization 793–COP 1,624) followed by Blacks COP 319 study illness (2016) (COP 197–COP 440). Women and seniors share the highest cost, accounting for COP 5922 (COP 5,858–COP 5,985) and COP 1,586 (COP 1,466–COP 1,707), respectively. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 142 Studio Health Author (year) Country Period Type of cost Main results design Outcome During the reference year, older people (≥65) had an increased risk of 1.09 (95% CI: 1.06–1.13) of visiting the emergency department (ED) on hot days (≥35 °C). The number of excess visits was projected to be Toloo 98 to 336 (2030) and 229 to 2,300 (2060) Australia, Ecological All causes of et al. 2000–2012 Cost overrun for younger groups and 42 to 127 (2030) Brisbane study disease (2015) and 145 to 1188 (2060) for older people. Based on 2012–13 prices, the additional cost attributable to heat was anticipated to range from COP 59,232 to COP 195,693 in 2030 and from COP 162,587 to COP 1,496,221 in 2060. The initial hospitalization cost (1971–2000) Hübler was COP 98 million. The total projected Ecological All causes of et al. Germany 1971–2000 Total annual cost annual cost of hospitalization for the period study disease (2008) 2071 to 2100 was estimated at about COP 592 million. The risk of mortality increased by 1.28 (95% CI: 1.08–1.57) above a temperature Roldán Spain, Ecological All causes of threshold of 38°C. A total of 107 (95% CI: et al. 2002–2006 Total and overall annual cost Zaragoza study death 42–173) deaths and an associated cost of (2015) COP 509,978 (95% CI: COP 200,178–COP 824,544) were observed. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 143 Methodology of Descriptive Analysis of cerebrovascular vento-stroke (CVA), hypertensive heart disease, Temperature and Demographic Structure cardiomyopathy/myocarditis, chronic obstructive pulmonary disease, diabetes mellitus, chronic kidney disease, traffic We used the temperature estimates of ERA5 for Colombia, a accidents, other transportation accidents, drowning, exposure to satellite database of the ECMWF (European Centre for Medium mechanical forces, contact with animals, exposure to forces of Range Weather Forecasts) with a spatial resolution of 0.25º x 0.25º nature (natural disaster), suicide, interpersonal violence, and other and an hourly time scale, which has temporo-spatial uncertainty unintentional injuries. parameters (European Centre for Medium-Range Weather Forecasts n.d.). These values were interpolated on a daily and For the registered mortality that presented missing data in the annual basis to calculate the zonal temperature by departments variables of department of residence (1.2 percent), age and sex (<0.3 of Colombia. Measurements of central tendency and variability of percent), and an imputation of data was made with the machine temperatures were obtained and the vector of average temperature learning methodology with non-parametric models of random change in each department during the decade was calculated. forest classification with the missForest package (Stekhoven and Buhlmann 2012) of the programming language R version 4.2.3 For the validation of the temperature data, a sensitivity analysis (Rstudio Team 2020). Regarding mortality projections, given that was carried out with the departmental data of fixed monitoring the information provided by DANE is in specific rates for simple stations of 10 cities in Colombia belonging to the network of ages, with the population projections of the same entity for hydro-meteorological stations of the Institute of Hydrology, each year and simple age, the number of deaths was calculated, Meteorology and Environmental Affairs of Colombia (IDEAM). multiplying the specific rate by the projected population for each This sensitivity analysis was performed using two statistical tests: year, department, and age. Subsequently, deaths were grouped by the intra-class correlation coefficient (cci) and the Bland Altman five-year period and by sex. graphs. For mortality data by department of residence, vital statistics of the National Administrative Department of Statistics Population data by department were also obtained from the of Colombia (DANE) were used, with the ICD-10 (ICD refers to the projections of the 2018 DANE Population and Housing Census International Classification of Diseases) codes of the following 17 (National Administrative Department of Statistics (DANE) 2022). events: lower respiratory infections, coronary ischemic disease, IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 144 Estimation of Disease Burden Attributable exposure response curves previously calculated by the GBD for to Suboptimal Temperatures suboptimal temperature. It builds on GBD’s approach to the other approaches reviewed Each of the days was classified as an effect of heat or cold without dismissing the criticisms made in this regard by other according to whether the average daily temperature for the pixel research groups in environmental epidemiology. Some critics exceeded the TMREL of the specific climate zone. suggest underestimation of the effects because the analysis does not take into account: (i) temperature delays, (ii) seasonality, and From the daily temperature of each of the pixels of 0.25 degrees (iii) mortality displacement (Vicedo-Cabrera et al. 2022). However, (30 km) they were assigned an RR according to the climatic zone the authors mention that, although this may be a limitation, of each pixel (average annual temperature) using the response the study has the strength of examining the relationship of exposure curves. Subsequently, the Population Attributable temperature with different causes of death, including injuries from Fraction (PAF) was estimated for each pixel and day of the time external causes, something that had not previously been evaluated series following Burkhart’s methodology (Burkart et al. 2021). in other studies (Burkart et al. 2022). The relative risks (RR) and their uncertainty intervals were estimated by means of a derivation of the exposure-response curves provided by the GBD, which used a Bayesian regression model that allows for the reproduction the patterns of the nonlinear Where c is the cause of death, z is the climatic zone (mean annual curves. For the calculation of the burden of disease attributable temperature), t is the average daily temperature for a specific pixel, to suboptimal temperature, the comparative risk assessment and day d. Population-weighted means were then calculated for approach was used. In this way, the Theoretical Thresholds of each day in department l. Minimum Exposure to the Risk Factor (UTEMFR or TMREL) specific for Colombia were set, which are the counterfactual level of exposure associated with the lower burden of disease of the events included in the analysis, which were extrapolated from the IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 145 Subsequently, the summary exposure values (SEVs) were were abroad were not considered within the estimate. To avoid established as a measure of the weighted prevalence of risk, being overestimating the loss, a mid-period correction was made: that 0 percent the situation in which there is no risk and 100 percent the is, in any age range it is assumed that death occurred on average situation where the entire population is exposed to the maximum in the middle of the period, except in people over 80, for whom the level of the risk factor (RRmax). assumption is assumed that they lose 10 years, given the wider range than in the other groups. The calculation of YLL with the mid-cycle correction is explained as follows: Where t is the average daily temperature, tminzly and tmaxzly are the minimum and maximum daily temperatures observed in Where: department l, zone z, and year y. Subsequently, the values of SEV by department, cause of death, and year were obtained as the dj = deaths average of the specific SEVs of zone weighted by population. evrj = difference between the age at which death occurred and life Finally, the number of daily deaths was computed as follows: expectancy for each age group N l d c = PAFcld * Muertescld Cm = mid-period correction (to avoid overestimating the loss, assuming that in any age range death occurred in the middle of the Where l is the department, d is the specific day, c is the cause of period). death. This analysis plan was discussed and validated with a panel of Finally, the years of life potentially lost due to premature death experts from the National Health Observatory of the National (YLL) were obtained from life expectancy using as a reference Institute of Health of Colombia, and with the Global Burden of the standardized life tables of DANE by department, grouped by Disease by Temperature group of the University of Washington- five-year groups. People whose permanent places of residence Seattle. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 146 From this analysis, mortality, and YLL rates attributable to Economic Burden Attributable to suboptimal temperature were obtained by cause of death, Suboptimal Temperatures department, sex, age group, and year, with their respective 95 percent confidence intervals taking into account a Poisson Conceptual Framework probability distribution. The economic burden of any disease is measured in three cost Since only mortality was used, the years of life potentially lost due domains: direct costs, indirect costs, and psychosocial costs, to premature death (YPLL) were the same as the YLLs. We used also called intangible costs (Alvis-Zakzuk et al. 2022; Pisu et al. only the term YLL, and not YPLL for clarity. 2010). Description studies and cost analysis basically estimate direct and indirect costs. The former are divided into direct medical costs and direct non-medical costs. Direct medical costs assess the use of health care resources due to the disease, in outpatients or hospitalized, analyzing items such as hospital stay, medications, and consultations, among other items that generate costs (Alvis-Zakzuk et al. 2022; Pisu et al. 2010). On the other hand, direct non-medical costs support the disease care process, but are not “directly” related to it. Among these types of costs are out-of-pocket expenses triggered by the disease and borne by the patient or their family members (Alvis-Zakzuk et al. 2022; Pisu et al. 2010). Indirect costs are associated with productivity losses due to illness or premature death; from the economic point of view, it can be assumed that employment is a resource of great value for the individual and society, so that illness causes a loss of working time, temporary or permanent (Alvis- Zakzuk et al. 2022; Pisu et al. 2010). Psychosocial costs refer to the IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 147 loss of quality of life linked to having an illness; these types of costs Rational and Analysis are difficult to quantify and the methods for estimating them are Current studies on the monetization of health risks caused by not clearly standardized (Alvis-Zakzuk et al. 2022; Pisu et al. 2010). environmental problems consist of the estimation of indirect costs due to morbidity and mortality or the valuation of the intangible costs of disease. Figure A1.5. Cost Taxonomy for Estimating the Economic Burden of Disease As for the calculation of indirect costs, these can be estimated by means of two main methods: C arga económica de enfermedad 1. The human capital method, which estimates the loss of productivity due to morbidity and mortality taking into Costos directos Costos indirectos Costos intangibles account the valuation of the reduction of working hours (level of production) as an effect of the disease (future earnings potentially lost) (Drummond 1992); and Costos directos Costos directos no Mortalidad Morbilidad médicos médicos prematura 2. The friction costs method: This approximation values the time invested by companies in the search and training (called friction time) of a worker who performs the activities of the sick employee, whenever such replacement is necessary (cost of replacing the absent worker) (Koopmanschap et al. 1995), the indirect cost of the disease would then be the multiplication of the frequency and duration of the friction period by the market value of the production (Ripari et al. 2012). On the other hand, intangible costs have been estimated mainly by means of the declared preference method, which estimates IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 148 preferences based on the individual’s willingness-to-pay under were employed, the assignment of a value to domestic services for different scenarios, through contingent valuation or overall analysis those who cannot perform these services due to illness, and the (Ripari et al. 2012). application of labor force participation rates (Rice et al. 1985). This study based the calculation of indirect costs on the modified Finally, the indirect costs of premature mortality (ICPM) were human capital method, which then refers to the capital embodied calculated as follows: in workers, considering GDP per capita as a statistical contribution of the year of life to society, which differs from the traditional ICPM=Annual minimun wage or GDPpc * PYPLL human capital approach, which considers the contribution of the The PYPLL was valued taking into account the productive time labor force to the socio-economy from the perspective of society period of people in Colombia, ranging from 15 to 57 years for as a whole (Chen et al. 2022). women and up to 62 for men. The age of onset was defined as that The human capital approach estimates the loss due to premature age from which it can be defined as economically active population mortality from YLL onwards. This method measures the number (EAP) that also had an employment rate reported in the register of of years between the event of death and the years the individual employment evolution in the DANE. would have lived taking into account the specific life expectancy. In The calculation of the PYPLL and the cost thereof shall be addition, YLL can be converted into potential years of working life estimated taking into account the following formula: lost or productive years of potentially lost life (PYPLL), calculated from the difference between the age of death of the individual and the age at which he or she would cease to be part of the labor force (retirement age). These years are adjusted according to the unemployment rate of the labor market being valued with the Where: average gross market wage or average per capita productivity. PYPLL = productive years of life potentially lost by age group In this sense, the calculations of indirect mortality costs involve the application of average earnings to lost work years for people who Ep = pension age (according to sex of the individual) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 149 MS = age of death (adjusted for half a period) 1. Annual minimum wage (AMW), which was calculated taking as reference the legal minimum wage in force in Colombia for 2021 iEl = Working age of onset of the age group of death that will estimated at COP 12,179,760 (Ministry of Labor 2021). depend on the age of death of the individual (MS) and the start of participation in the labour market in this way: 2. Average productivity of the country evaluated as the gross domestic product per capita (GDPpc) of 2021, valued at COP If ≤ 15 (minimum age for entry into the labour market) then 22,952,795 (Banco de la República – Colombia 2021). iEl = 15, These two scenarios of analysis allowed to obtain a range where Si em>15 entonces iEl = iEgm the valuation of the loss of productivity due to premature deaths due to suboptimal temperatures would move. Additionally, costs iEgm = Age of onset of the mid-cycle adjusted age group of were adjusted to an average annual growth rate (g) and a discount death (added for age groups 2.5) rate (r) of 5 percent according to the following formula: m = number of deaths observed in each age group i In the age groups with initial age below the age of initiation of participation in the labor market, the age of onset for loss of productivity was attributed to the working age of initiation (15 years) and its loss was calculated until the corresponding pension age. The estimates were discriminated by age groups, departments, In the valuation of the economic burden associated with and causes. The capital district was included as a departmental suboptimal temperatures, the average wage or productivity was unit because of the relative weight of its population. Thus, costs estimated in two ways, as follows: were reported with and without discount for each economic valuation scenario. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 150 Methodology of Descriptive Analysis of computed with population projections to obtain the number Temperature and Demographic Structure of deaths attributable to heat or cold, and then the respective attributable YLL. The temperature projections used by the CCKP (Climate Change Knowledge Portal) of the World Bank were used, which To calculate the health co-benefits from greenhouse gas has temperature data with annual averages by department mitigation, the different disease burden indicators between climate according to five different scenarios of greenhouse gas emissions change scenarios were compared, especially between SSP1–1.9 contemplated in the Shared Socioeconomic Trajectories (SSP) and SSP5-8.5, as well as the differences between 2020 and 2050. prepared by the IPCC (Table 2). The change of these temperatures Additionally, the trends in rates attributable to heat and cold was estimated for 2050 and 2100, according to these different between 2020 and 2050 were analyzed, as well as the magnitudes emission scenarios. Additionally, DANE post-COVID population of the rates in the different scenarios. projections by department with data to 2050, by age groups and sex, were used. Different types of models were tested for mortality rates attributable to heat and cold by department and year, finally defining the following linear model: Where TM is the annual attributable mortality rate for heat or cold, is the aintersect, the coefficient, bt the average annual temperature, l the department, and the year. These models were used to project mortality rates with annual temperatures from different climate change scenarios from 2020 to 2050, later adding to have national rates. They were then IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 151 B. Results Attachments Description of Temperatures and Demographic Structure in Colombia Table A1.5. Descriptive Statistics Temperature by Department in Colombia 2010–2019 Department Pixel*day (n) Minimum value Maximum value Average (SD) Mediana (IQR) Amazon 519,436 19.39 31.87 25.31 (±1.02) 25.29 (1.33) Antioquia 307,272 14.37 37.49 23.34 (±3.73) 23.84 (6.82) Arauca 113,398 10.43 33.79 25.46 (±3.53) 26.24 (2.36) San Andrés, Providencia and Santa 3,652 24.12 28.91 25.85 (±0.69) 26.88 (0.95) Catalina Archipelago Atlantic 18,290 23.88 35.57 27.99 (±1.02) 27.95 (1.39) Bogotá D.C. 10,974 7.24 23.08 13.08 (±2.52) 13.46 (4.44) Bolívar 135,346 19.87 36.38 27.16 (±2.12) 27.40 (2.75) Boyacá 117,056 7.08 38.62 15.39 (±4.55) 14.00 (4.46) Caldas 36,580 12.65 37.10 19.98 (±4.47) 18.50 (6.35) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 152 Department Pixel*day (n) Minimum value Maximum value Average (SD) Mediana (IQR) Caquetá 446,276 14.66 34.23 24.56 (±1.98) 24.81 (1.79) Casanare 219,480 15.13 34.87 26.08 (±2.31) 26.29 (2.19) Cauca 149,978 9.27 29.34 18.99 (±4.06) 18.95 (5.84) Cesar 120,714 12.65 38.39 26.02 (±3.23) 26.27 (3.90) Chocó 237,770 16.89 32.07 25.77 (±1.98) 26.15 (1.75) Córdoba 120,714 19.59 33.90 27.36 (1.71) 27.48 (1.78) Cundinamarca 102,424 8.47 36.20 18.26 (±4.94) 17.56 (8.03) Guainía 325,562 20.41 32.02 25.35 (±1.03) 25.29 (1.33) Guaviare 263,376 20.23 34.07 24.86 (±1.12) 24.81 (1.39) Huila 87,792 8.76 33.19 19.32 (±3.67) 19.21 (4.19) La Guajira 106,082 13.96 34.78 26.52 (±2.75) 27.01 (3.18) Magdalena 113,398 10.66 38.10 26.29 (±4.21) 27.52 (4.19) Meta 384,090 8.02 35.36 24.81 (±2.91) 25.31 (2.11) Nariño 142,662 7.69 28.60 21.06 (±4.96) 22.26 (8.56) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 153 Department Pixel*day (n) Minimum value Maximum value Average (SD) Mediana (IQR) Norte de Santander 106,082 8.67 35.51 21.81 (±4.75) 22.56 (6.80) Putumayo 117,056 10.26 31.93 24.02 (±3.29) 24.92 (2.09) Quindío 7,316 12.84 23.42 16.87 (±1.48) 16.79 (2.49) Risaralda 21,948 12.41 24.83 18.88 (±2.15) 19.47 (1.56) Santander 139,004 10.04 37.90 22.19 (±4.98) 22.12 (8.93) Sugar 43,896 24.40 34.66 28.18 (±1.26) 28.03 (1.63) Tolima 117,056 11.26 35.35 20.89 (±4.83) 21.56 (8.95) Valle del Cauca 95,108 10.93 28.51 21.08 (±3.05) 20.99 (3.77) Vaupés 256,060 20.01 32.08 24.94 (±0.99) 24.92 (1.28) Vichada 479,198 20.75 33.99 26.15 (±1.44) 26.00 (1.76) Source: European Centre for Medium-Range Weather Forecasts n.d. Note: ICR = XXX; n = XX; SD = XX IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 154 Figure A1.6. Caldas-Lang Colombia Index (IDEAM) Figure A1.7. Monthly Temperature Behavior by Regions in Colombia Source: IDEAM 2014. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 155 Table A1.6. Linear Regression Temperature Evolution by Year and Department, Colombia 2010–2019 Temperature Temperature Beta Beta Department r2 IC 95% variation ºC/ Department r2 IC 95% variation ºC/ coefficient coefficient decade decade Amazon 0.0002 -0.004 (-0.014;0.006) -0.04 ºC La Guajira 0.0033 0.021* (0.009;0.033) +0.21ºC Antioquia 0.0809 0.088* (0.079;0.098) +0.88ºC Magdalena 0.0848 0.112* (0.100;0.124) +1.12ºC Arauca 0.0008 0.012 (-0.001;0.026) +0.12ºC Meta 0.0016 0.017* (0.003;0.031) +0.17ºC San Andrés, P. Nariño 0.1026 0.064* (0.058;0.071) +0.64ºC 0.0150 0.029* (0.022;0.037) +0.29ºC and ST. Norte de 0.0047 0.023* (0.012;0.035) +0.23ºC Atlantic 0.0452 0.065* (0.055;0.075) +0.65ºC Santander Bogotá D.C. 0.0034 0.013* (0.006;0.021) +0.13ºC Putumayo 0.0013 0.013* (0.001;0.026) +0.13ºC Bolívar 0.0663 0.105* (0.092–0.118) +1.05ºC Quindío 0.0367 0.052* (0.043;0.061) +0.52ºC Boyacá 0.0038 0.013* (0.006;0.021) +0.13ºC Risaralda 0.0406 0.047* (0.039;0.054) +0.47ºC Caldas 0.0860 0.088* (0.079;0.097) +0.88ºC Santander 0.0855 0.092* (0.082;0.102) +0.92ºC Caquetá 0.0003 0.006 (-0.005;0.019) +0.06ºC Sugar 0.0498 0.089* (0.076;0.101) +0.89ºC Casanare 0.0072 0.040* (0.025;0.056) +0.40ºC Tolima 0.0966 0.102* (0.092;0.113) +1.02ºC Cauca 0.0798 0.058* (0.051;0.064) +0.58ºC Valle del 0.0734 0.061* (0.054;0.068) +0.61ºC Cesar 0.0455 0.090* (0.077;0.104) +0.9ºC Cauca Chocó 0.0599 0.069* (0.060;0.078) +0.69ºC Vaupés 0.0006 0.007 (-0.002;0.018) +0.07ºC Córdoba 0.0645 0.088* (0.077;0.098) +0.88ºC Vichada 0.0056 0.030* (0.017;0.044) +0.30ºC Cundinamarca 0.0298 0.040* (0.033;0.048) +0.40ºC Guainía 0.0021 0.015* (0.004;0.025) +0.15ºC *Statistically significant values Guaviare 0.0019 0.015* (0.004;0.027) +0.15ºC Huila 0.0302 0.048* (0.039;0.057) +0.48ºC IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 156 A Temperature Sensitivity Analysis with Fixed Only in six of the 10 selected monitoring stations, the Monitoring Data representativeness of the daily temperature data is less than 75 percent (Medellín, Cali, Barranquilla, Cartagena, Quibdó, and The analysis included stations that brought together different Leticia). Likewise, the table presents the extreme values identified environmental characteristics of Colombia: four stations in the by the interquartile range method (IQR method). Andean region (Bogotá, Soacha, Medellín and Bucaramanga), two in the Pacific region (Quibdó and Cali), two in the Caribbean region Table A1.8. Data Representativeness Monitoring Stations 10 Cities (Barranquilla and Cartagena) and two in the Orinoco-Amazonas Colombia 2010–2019 region (Villavicencio and Leticia). Lower Higher Table A1.7. Overview of Monitoring Stations 10 Cities in Colombia City Representativity extreme extreme values values (Q1–1,5*IQR ) (Q3+1,5*IQR) Geographical coordinates Altitude Station City Bogotá 32% (1,199/3,652) 1 2 (latitude, longitude) (masl) Botanical garden Bogotá (4.66933333 –74.10266667) 2.552 Medellin 99% (3,640/3,652) 0 0 Olaya Herrera Medellin Cali 85% (3,123/3,652) 1 28 (6.22000000 –75.59000000) 1.490 Airport Solitude/ 77% (2,839/3,652) 45 3 Marco Fidel Cali Barranquilla (3.45450000 –76.49972222) 975 Suarez Air Base Cartagena 95% (3,463/3,652) 7 0 Solitude/ Soacha 60% (2,205/3,652) 13 25 Ernesto Cortissoz (10.91777778 –74.77972222 14 Barranquilla Quibdo 99% (3,603/3,652) 2 11 Rafael Núñez Cartagena (10.44725 –75.51602778) 2 Leticia 84% (3,071/3,652) 62 3 Airport San Jorge Granja Soacha (4.50575 –74.18927778 2.900 Bucaramanga 42% (1,552/3,652) 13 21 El Carano Quibdo (5.69055556 –76.64377778) 75 Villavicencio 21% (764/3,652) 0 7 Leticia Leticia (-4.22252778 –69.94272222) 120 Palonegro Airport Bucaramanga (7.12147222 –73.18452778) 1.189 The average temperature during the period analyzed is relatively Vanguard Villavicencio (4.16191944 –73.61757778) 422 consistent with the geographical characteristics of each city, being IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 157 lower in the city of higher altitude (Soacha) and higher in the lower- The following figures show the time series according to the altitude city (Barranquilla). It should be noted that the average four characteristic regions of the country (Figure A1.8). For the temperature in Cali is somewhat low, which is atypical in relation to Andean region, the city of Medellín is the only one that has a cities at the same altitude and may be due to a greater impact on wide representation of the series during the period analyzed. The the temperature of the El Niño Southern Oscillation phenomenon, other three cities have important time jumps and do not allow to due to its location near the Pacific coast, but this is an event that determine the trends of the series (Figure A1.8). needs to be evaluated in more detail. The following table presents the summary statistics for each station. Figure A1.8. Average Temperature by Colombian Regions, 2010– 2019 Table A1.9. Measures of Central Tendency and Variability Monthly Average Temperature Monitoring Stations 10 Cities in Colombia 2010–2019 Minimum Maximum City Average (SD) Mediana (IQR) value value Bogotá 11.53 18.5 15.33 (±1.06) 15.4 (1.46) Medellin 18.05 28.22 23.3 (±1.88) 23.3 (3.01) Cali 17.4 34.8 22.5 (±1.56) 22.5 (2.1) Solitude/ 23.4 34.4 29.7 (±1.56) 29.8 (2.03) Barranquilla Cartagena 24.6 31.35 28.3 (±1.09) 28.4 (1.65) Soacha 9.7 17.65 13.3 (±1.10) 13.3 (1.4) Quibdo 22.5 31 26.6 (±1.25) 26.6 (1.75) Leticia 16.5 31.2 26.08 (±1.50) 26.2 (1.85) Bucaramanga 12.9 27.1 20.4 (±1.27) 20.5 (1.5) Villavicencio 21.3 30.8 25.7 (±1.74) 25.8 (2.25) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 158 In the Pacific region, there are two cities that have adequate the lowest temperatures during that period (June–August) and in representation. In analyzing the series, several outliers were Bogotá, D.C. and Bucaramanga there are no marked differences observed in Cali throughout the period (Figure A1.8). throughout the year, although this may also be due to the low representativeness of the series. In the Pacific region, there are no For the Caribbean region, Cartagena has greater significant differences throughout the year, while in the Orinoco- representativeness with respect to Barranquilla, although both Amazon region, Leticia has the lowest temperatures during the have sufficient daily data for subsequent studies (>70 percent). year in the months of June, July, and August, and a high number of Barranquilla has no data for 2019. extreme values during those months (Figure A1.9). With respect to the Orinoco-Amazonas region, Villavicencio has the least data of all the cities analyzed, unlike Leticia, which has a relatively constant series throughout the analyzed period (Figure A1.8). Likewise, it shows several low temperature peaks during the analyzed period that seem to coincide with the winter of the southern cone of the continent and sometimes with the phenomenon known as the “frosts of Brazil” which affects the climate of the southern Colombian Amazon. By representing monthly average temperature trends, patterns are identified at the regional level. In the case of the Caribbean region, the temperature is lower in the first quarter of the year, as observed in Barranquilla and Cartagena. In the Andean region, there are some trends associated with the effect of the Intertropical Confluence Zone. In Medellín there is a lower temperature in the last quarter of the year and higher temperatures for the period from June to August. On the other hand, Soacha has IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 159 Figure A1.9. Average Monthly Temperature by Colombian Cities, 2010–2019 For the analysis of annual temperature trends, stations with at least 60 percent of the data and at least 90 percent of the years represented were taken into account. An increase was observed in almost all cities during the last 10 years, except in Leticia, where the temperature dropped by an average of 1.6ºC. Likewise, Soacha had a more pronounced increase of 1.3ºC, followed by Barranquilla (+1ºC) and Cali (+0.6ºC). The results of the analysis are presented in Table A1.10. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 160 Table A1.10. Linear Regression Temperature Evolution by Year Colombia 2010–2019 Variation Beta City Obs (n) r2 IC9s5% T (ºC)/ Coefficient decade Medellin 3640 0,0026 0,0033 (0,012; 0,054)* +0.03ºC Cali 3123 0,0119 0,0601 (0,040; 0,079)* +0.6ºC Barranquilla 2839 0,0217 0,1004 (0,075; 0,125)* +1.0ºC Cartagena 3463 0,0082 0,0339 (0,021; 0,046)* +0.33ºC Soacha 2205 0,1126 0,1351 (0,119; 0,151)* +1.3ºC Quibdo 3603 0,0006 0,0106 (-0,003; 0,025) ON Leticia 3071 0,0888 -0,1624 (-0,180; -0,143)* -1.6ºC * Statistically significant values The above results are indicative, since complete series are not required and the time series includes exclusively the years 2010 to 2019. Figure A1.10 shows the trends. Figure A1.10. Temperature Trends by Cities of Colombia for 2010–2019 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 161 Analysis of agreement between satellite and monitoring station temperatures In relation to the evaluation of the agreement between satellite temperatures and urban temperatures of the monitoring networks, initially cities that had a representativeness greater than 60 percent (n = 7) were included. These were matched with the departments to which they belong and the intraclass correlation coefficient (cci) was calculated; the only ones that had some degree of agreement were the Atlántico-Barranquilla pairs (cci=0.86; CI95%: 0.47–0.99), Antioquia-Medellín (cci=0.55; CI95%: 0.53–0.58), Amazonas-Leticia (cci=0.21; CI95%:0.17–0.24) and The main implication of this finding is the low reproducibility of the (ICC=0.11; CI95%:0.08–0.15). Figure A1.11 shows these correlations temperatures of the urban monitoring networks to be extrapolated using Bland-Altman graphs. to the entire department. Only the temperature of the urban station of Barranquilla could be extrapolated to the temperature Figure A1.11. Bland-Altman Graphs of Temperature by Department and Cities of the Atlantic, although with high uncertainty. As a consequence of the low reproducibility, the use of satellite temperatures was recommended for the calculation of the burden of disease attributable to suboptimal temperatures in the departments of Colombia. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 162 Annex to the description of the demographic structure Table A1.11. Description of Mortality by the 17 Causes Covered by the Study Variable Category N = 884,628 Variable Category N = 884,628 Variable Category N = 884,628 2010 82,795 (9.36%) Norte De Department Vichada 632 (0.07%) 28,484 (3.22%) 2011 79,920 (9.03%) Santander Amazon 517 (0.06%) 2012 82,004 (9.27%) Nariño 28,352 (3.2%) Vaupés 322 (0.04%) 2013 83,669 (9.46%) Boyacá 26,570 (3%) Guainía 296 (0.03%) 2014 85,836 (9.7%) Córdoba 25,679 (2.9%) Ischemic heart Year 348,530 (39.4%) Bolívar 25,553 (2.89%) disease 2015 89,931 (10.17%) Caldas 24,915 (2.82%) Homicide 138,266 (15.63%) 2016 91,128 (10.3%) EPOC 126,978 (14.35%) 2017 92,908 (10.5%) Department Cauca 23,563 (2.66%) ACV 69,491 (7.86%) 2018 97,821 (11.06%) Risaralda 23,120 (2.61%) ERC 52,925 (5.98%) 2019 98,616 (11.15%) Huila 22,466 (2.54%) Traffic accidents 52,372 (5.92%) F 336,637 (38.05%) Magdalena 19,991 (2.26%) Cause of Hypertensive Sex 33,753 (3.82%) M 547,991 (61.95%) Meta 19,781 (2.24%) death heart disease Quindío 15,843 (1.79%) Suicide 23,632 (2.67%) <5 5,164 (0.58%) Cesar 15,481 (1.75%) DM 15,432 (1.74%) 5–14 6,638 (0.75%) Drowning 8,214 (0.93%) Age 15–49 210,512 (23.8%) Sugar 14,454 (1.3%) Mechanical Caquetá 7,399 (0.84%) 3,526 (0.4%) 50–69 179,673 (20.31%) injuries 70+ 482,641 (54.56%) La Guajira 6,797 (0.77%) IVRI 3,139 (0.35%) Antioquia 131,618 (14.88%) Putumayo 5,322 (0.6%) Unintentional 2,857 (0.32%) Bogotá, D. C. 116,664 (13.19%) Arauca 5,034 (0.57%) Disasters 2,178 (0.25%) Casanare 4,943 (0.56%) Related animals 1,247 (0.14%) Department Valle Del Cauca 111,383 (12.59%) Chocó 4,725 (0.53%) Cardiomyopathy Cundinamarca 51,390 (5.81%) 1,226 (0.14%) myocarditis Atlantic 40,655 (4.6%) Foreigner 2,083 (0.24%) Transport San Andres 1,287 (0.15%) 862 (0.1%) Santander 39,691 (4.49%) relationship Tolima 38,426 (4.34%) Guaviare 1.192 (0.13%) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 163 Figure A1.12. Departmental Concentration of the Colombian Population 2010–2050 Source: DANE post-Covid population projections 2022. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 164 Burden of Disease by Suboptimal Temperatures Department Total YLL (n) Outcome 1 Outcome 2 Outcome 3 AVPP (n) AVPP (n) AVPP (n) In relation to the abolition frequency of YLL associated with Archipiélago suboptimal temperature by department, the predominant causes Road traffic Diabetes San Andres Homicide accident mellitus in the departments with greater exposure to heat are homicide, Providencia y Sta Catalina 112.69 61.13 35.83 4.42 traffic accidents, and drowning. COPD and stroke are mainly seen in the departments most exposed to cold. Ischemic heart Road traffic Homicide IHD accident disease occurs from double exposure, but has a greater impact Atlántico 9843.30 4,849.98 2,017.96 1,094.91 on departments exposed to cold. Some departments have events IHD COPD Stroke that do not follow the regional/national pattern. In that regard, it Bogotá DC 20,173.93 9,039,35 4,394.34 2,719.45 is worth highlighting the impact of suicide in Vaupés and diabetes Road traffic mellitus in the archipelago of San Andrés, Providencia, and Santa Bolívar 8,107,54 Homicide IHD accident Catalina. The following table presents the relevant data (Table A1.12). 3,897. 90 1,419.12 1074.73 IHD COPD Stroke Table A1.12. Main Suboptimal Temperature Events, Exposure by Boyacá 4,892.53 1,812.41 1,229.96 577.31 Department IHD COPD Homicide Caldas 4,570.78 2,264.47 779.52 382.50 Outcome 1 Outcome 2 Outcome 3 Department Total YLL (n) Homicide IHD Drowning YLL (n) YLL (n) YLL (n) Caquetá 1,489.30 Homicide Suicide Drowning 440.41 331.29 232.97 Amazonas 77.24 22.33 21.86 14.57 Road traffic Homicide IHD Casanare 1,454.99 accident IHD Homicide COPD 294.94 231.25 Antioquia 453.84 25,562.88 9,056.64 5,165.78 3,853.99 IHD COPD Homicide Road traffic Cauca 3,939.95 Homicide IHD 1,581.46 499.27 436.83 Arauca accident 1,567.58 680.35 181.42 Road traffic 331.70 Homicide IHD Cesar 5,166.23 accident 1,656.39 1,030.81 1,230.99 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 165 Outcome 1 Outcome 2 Outcome 3 Outcome 1 Outcome 2 Outcome 3 Department Total YLL (n) Department Total YLL (n) AVPP (n) AVPP (n) AVPP (n) AVPP (n) AVPP (n) AVPP (n) Homicide Drowning IHD Homicide IHD Drowning Chocó 888.74 Putumayo 1,050.56 543.27 89.25 72.52 329.71 174.30 148.29 Road traffic IHD COPD Stroke Homicide IHD Quindío 2,805.62 Córdoba 7,695.41 accident 3,722.56 1,299.18 1,412.30 580.64 242.22 1,228.20 IHD COPD Homicide IHD COPD Stroke Risaralda 3,882.69 Cundinamarca 9,131.69 1,882.39 672.99 305.67 4,343.24 2,003.92 926.66 IHD COPD Homicide Drowning Homicide IHD Santander 7,474.42 Guainía 44.26 3,070.49 783.33 770.35 15.30 6.64 5.83 Road traffic Homicide Drowning IHD Homicide IHD Sucre 4,431.72 accident Guaviare 269.04 1,514.45 1,097.19 106.17 70.80 30.98 838.65 IHD Homicide Drowning IHD COPD Homicide Huila 3,971.59 Tolima 6,998.75 1,510.95 486.31 430.60 3,302.55 796,06 772,26 Road traffic IHD Homicide COPD Homicide IHD Valle 15,329.05 La Guajira 3,391.90 accident 1,910.95 371.32 6,150.11 3,178.91 1,487.26 553.29 Drowning Suicide Homicide Road traffic Vaupés 75.16 Homicide IHD 35.16 20.62 5.48 Magdalena 4,181.07 accident 1,896.14 760.33 Road traffic 707.10 Homicide IHD Vichada 168.37 accident Road traffic 59.58 19.92 IHD Homicide 47.31 Meta 3,694.04 accident 1,002.78 790.47 495.69 IHD COPD Stroke Nariño 3,855.70 1,262.86 681.90 470.89 Road traffic Norte de Homicide IHD 6,570.85 accident Santander 2,389.20 1,565.85 718.45 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 166 Table A1.13. Burden of Disease Attributable to Heat, by Department Colombia 2010–2019 Attributable Attributable Attributable YLL per Attributable YLL per PAF % Rate per YLL PAF % Rate per YLL Department deaths 100.000 Department deaths 100.000 (CI 95%) 1,000,000 (n) (CI 95%) 1,000,000 (n) (n) n (CI 95%) (n) n (CI 95%) n (CI 95%) n (CI 95%) 2.91 9.85 13.25 36.64 Amazonas 0.41 (0.33–0.49) 2 72.48 Cesar 0.96 (0.93–0.98) 149 4,108.46 (2.20–3.74) (8.52–11.32) (11.70–14.94) (34.04–39.40) 2,82 8,41 3.93 15.03 Antioquia 0.12 (0.12–0.13) 172 5070,46 Chocó 0.39 (0.36–0.41) 20 749.77 (2.11–3.63) (7.17–9.77) (3.12–4.92) (13.39–16.84) 0.023 16.47 55.40 16.69 44.18 Arauca 41 1,370.42 Córdoba 1.12 (1.09–1.16) 288 7,607.20 (0.021–0.024) (14.72–18.32) (52.18–58.76) (14.95–18.59) (41.33–47.21) Archipiélago 0.013 0.21 0.59 Cundinamarca 6 149.74 San Andres 5.42 18.40 (0.013–0.014) (0.05–0.51) (0.31–1.04) 0.24 (0.20–0.27) 3 111.13 Providencia y (4.47–6.57) (16.56–20.37) 2.03 8.51 Sta Catalina Guainía 0.31 (0.21–0.41) 1 37.55 (1.47–2.78) (7.27–9.87) 16.09 40.70 6.55 26.09 Atlántico 1.03 (1.00–1.05) 388 9,732.86 Guaviare 0.42 (0.35–0.49) 5 212.72 (14.38–17.95) (37.95–43.59) (5.47–7.77) (23.90–28.43) Bogotá DC 0 0 0 0 0 2.30 7.65 Huila 0.12 (0.12–0.13) 24 808.75 14.64 39.48 (1.68–3.06) (6.48–8.96) Bolívar 1.16 (1.13–1.19) 295 7,926.31 (13.02–16.42) (36.79–42,35) 12.75 40.57 La Guajira 1.44 (1.39–1.49) 102 3,211.30 3.03 5.55 (11.23–14.41) (37.85–43.49) Boyacá 0.13 (0.13–0.14) 36 663.02 (2.33–3.91) (4.56–6.68) 11.86 31.47 Magdalena 0.79 (0.77–0.81) 151 4,003.60 2.82 6.60 (10.38–13.45) (29.08–34.05) Caldas 0.12 (0.11–0.12) 28 643.68 (2.15–3.69) (5.52–7.82) 4.90 15.63 Meta 0.26 (0.25–0.27) 48 1,530.59 4.40 15.59 (3.97–5.97) (13.96–17.48) Caquetá 0.23 (0.21–0.24) 18 626.56 (3.52–5.42) (13.91–17.43) 0.047 0.81 2.60 Nariño 13 414.33 7.13 24.93 (0.046–0.049) (0.45–1.29) (1.94–3.40) Casanare 0.56 (0.53–0.59) 28 994.67 (6.02–8.42) (22.80–27.23) Norte de 9.36 28.15 0.50 (0.48–0.51) 132 3,966.58 0.006 0.09 0.32 Santander (8.05–10.79) (25.87–30.57) Cauca 1 46.42 (0.005–0.006) (0.01–0.36) (0.14–0.72) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 167 Attributable Attributable Attributable YLL per Attributable YLL per PAF % Rate per YLL PAF % Rate per YLL Department deaths 100.000 Department deaths 100.000 (CI 95%) 1,000,000 (n) (CI 95%) 1,000,000 (n) (n) n (CI 95%) (n) n (CI 95%) n (CI 95%) n (CI 95%) 3.94 15.38 16.09 40.70 Putumayo 0.24 (0.22–0.26) 13 501.70 Atlántico 1.03 (1.00–1.05) 388 9,732.86 (3.12–4.92) (13.72–17.21) (14.38–17.95) (37.95–43.59) Quindío 0 0 0 0 0 Bogotá DC 0 0 0 0 0 0.0003 0.005 0.02 14.64 39.48 Risaralda 1 1.95 Bolívar 1.16 (1.13–1.19) 295 7,926.31 (-0.0001;0.0008) (0–0.18) (0–0.18) (13.02–16.42) (36.79–42.35) 3.86 8.90 3.03 5.55 Santander 0.21 (0.21–0.22) 82 1,873.45 Boyacá 0.13 (0.13–0.14) 36 663.02 (3.03–4.81) (7.64–10.30) (2.33–3.91) (4.56–6.68) 21.17 49.80 2.82 6.60 Sucre 1.31 (1.27–1.36) 186 4,374.51 Caldas 0.12 (0.11–0.12) 28 643.68 (19.18–23.26) (46.75–52.99) (2.15–3.69) (5.52–7.82) 5.08 12.71 4.40 15.59 Tolima 0.21 (0.20–0.22) 67 1,675.31 Caquetá 0.23 (0.21–0.24) 18 626.56 (4.15–6.19) (11.18–14.36) (3.52–5.42) (13.91–17.43) 0.023 0.59 1.99 7.13 24.93 Valle 26 849.20 Casanare 0.56 (0.53–0.59) 28 994.67 (0.023–0.024) (0.31–1.04) (1.42–2.72) (6.02–8.42) (22.80–27.23) 4.27 17.00 0.006 0.09 0.32 Vaupés 0.46 (0.31–0.60) 2 63.13 Cauca 1 46.42 (3.43–5.31) (15.24–18.90) (0.005–0.006) (0.01–0.36) (0.14–0.72) 5.23 15.52 13.25 36.64 Vichada 0.85 (0.72–0.98) 5 158,67 Cesar 0.96 (0.93–0.98) 149 4,108.46 (4.29–6.35) (13.86–17.37) (11.70–14.94) (34.04–39.40) 2.91 9.85 3,93 15,03 Amazonas 0.41 (0.33–0.49) 2 72.48 Chocó 0,39 (0,36–0,41) 20 749,77 (2.20–3.74) (8.52–11.32) (3,12–4,92) (13.39–16.84) 2.82 8.41 16.69 44.18 Antioquia 0.12 (0.12–0.13) 172 5,070.46 Córdoba 1.12 (1.09–1.16) 288 7,607,20 (2.11–3.63) (7.17–9.77) (14.95–18.59) (41.33–47.21) 0.023 16.47 55.40 0.013 0.21 0.59 Arauca 41 1,370.42 Cundinamarca 6 149.74 (0.021–0.024) (14.72–18.32) (52.18–58.76) (0.013–0.014) (0.05–0.51) (0.31–1.04) Archipiélago 2.03 8.51 Guainía 0.31 (0.21–0.41) 1 37.55 San Andres 5.42 18.40 (1.47–2.78) (7.27–9.87) 0.24 (0.20–0.27) 3 111.13 Providencia y (4.47–6.57) (16.56–20.37) 6,55 26,09 Sta Catalina Guaviare 0,42 (0,35–0,49) 5 212,72 (5,47–7,77) (23,90–28,43) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 168 Attributable Attributable Attributable YLL per Attributable YLL per PAF % Rate per YLL PAF % Rate per YLL Department deaths 100.000 Department deaths 100.000 (CI 95%) 1,000,000 (n) (CI 95%) 1,000,000 (n) (n) n (CI 95%) (n) n (CI 95%) n (CI 95%) n (CI 95%) 2.30 7,65 5.23 15.52 Huila 0.12 (0.12–0.13) 24 808,75 Vichada 0.85 (0.72–0.98) 5 158.67 (1.68–3.06) (6,48–8,96) (4.29–6.35) (13.86–17.37) 12.75 40.57 La Guajira 1.44 (1.39–1.49) 102 3,211.30 (11.23–14.41) (37.85–43.49) 11.86 31.47 Magdalena 0.79 (0.77–0.81) 151 4,003.60 (10.38–13.45) (29.08–34.05) 4.90 15.63 Meta 0.26 (0.25–0.27) 48 1,530.59 (3.97–5.97) (13.96–17.48) 0.047 0.81 2.s60 Nariño 13 414.33 (0.046–0.049) (0.45–1.29) (1.94–3.40) Norte de 9.36 28.15 0.50 (0.48–0.51) 132 3,966.58 Santander (8.05–10.79) (25.87–30.57) 3.94 15.38 Putumayo 0.24 (0.22–0.26) 13 501.70 (3.12–4.92) (13.72–17.21) Quindío 0 0 0 0 0 0.0003 0.005 0.02 Risaralda 1 1.95 (-0.0001;0.0008) (0–0.18) (0–0.18) 3.86 8.90 Santander 0.21 (0.21–0.22) 82 1,873.45 (3.03–4.81) (7.64–10.30) 21.17 49.80 Sucre 1.31 (1.27–1.36) 186 4,374.51 (19.18–23.26) (46.75–52.99) 5.08 12.71 Tolima 0.21 (0.20–0.22) 67 1,675.31 (4.15–6.19) (11.18–14.36) 0.023 0.59 1.99 Valle 26 849.20 (0.023–0.024) (0.31–1.04) (1.42–2.72) 4.27 17.00 Vaupés 0.46 (0.31–0.60) 2 63.13 (3.43–5.31) (15.24–18.90) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 169 Table A1.14. Burden of Disease Attributable to Cold, by Department Colombia 2010–2019 Attributable Attributable Attributable YLL per Attributable YLL per PAF % Rate per YLL PAF % Rate per YLL Department deaths 100.000 Department deaths 100.000 (CI 95%) 1,000,000 (n) (CI 95%) 1,000,000 (n) (n) n (CI 95%) (n) n (CI 95%) n (CI 95%) n (CI 95%) 0.43 0.65 12.17 21.58 Amazonas 0.06 (0.02–0.09) 1 4.76 Caquetá 0.66 (0.64–0.69) 49 862.74 (0.20–0.85) (0.57–1.48) (10.71–13.83) (19.61–23.73) 20.96 33.75 5.93 11.58 Antioquia 0.87 (0.86–0.89) 1278 20,492.42 Casanare 0.47 (0.45–0.49) 24 460.31 (18.99–23.05) (31.25–36.39) (4.92–7.12) (10.15–13.19) 4.75 8.08 16.46 26.36 Arauca 0.23 (0.22–0.25) 12 197.15 Cauca 0.99 (0.97–1.00) 233 3,893.52 (3.84–5.80) (6.90–9.44) (14.72–18.32) (24.14–28.6) Archipiélago 5.36 9.47 Cesar 0.36 (0.35–0.37) 60 1,057.77 San Andres (4.38–6.46) (8.19–10.95) 0.008 0.16 0.25 Providencia 1 1.56 1.15 2.77 (0.0009–0.015) (0.03–0.43) (0.08–0.58) Chocó 0.12 (0.11–0.13) 6 138.96 and Sta (0.72–1.72) (2.11–3.63) Catalina 0.016 0.31 0.51 0.011 0.33 0.46 Córdoba 5 88.20 Atlántico 8 110.43 (0.013–0.018) (0.11–0.65) (0.23–0.91) (0.009–0.014) (0.14–0.72) (0.20–0.85) 25.44 35.72 19.34 27.80 Cundinamarca 1.07 (1.05–1.08) 641 8,981.94 Bogotá DC 1.00 (0.98–1.01) 1,407 20,173.93 (23.28–27.76) (33.12–38.41) (17.46–21.37) (25.53–30.20) 0.93 1.50 0.036 0.56 0.90 Guainía 0.14 (0.08–0.19) 1 6.70 Bolívar 11 181.22 (0.57–1.48) (1.01–2.14) (0.0033–0.039) (0.27–0.98) (0.53–1.42) 4.18 7.01 Guaviare 0.29 (0.25–0.33) 3 56.31 26.28 35.50 (3.35–5.19) (5.88–8.26) Boyacá 1.07 (1.06–1.09) 313 4,229.51 (24.10–28.64) (32.93–38.21) 20.13 29.98 Huila 0.87 (0.85–0.88) 213 3,162.84 (18.23–22.21) (27.64–32.49) 27.89 39.94 Caldas 0.98 (0.96–1.00) 275 3,927.09 (25.63–30.31) (37.22–42.81) 1.16 2.27 La Guajira 0.13 (0.12–0.14) 9 180.60 (0.72–1.72) (1.68–3.06) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 170 Attributable Attributable YLL per PAF % Rate per YLL Department deaths 100.000 (CI 95%) 1,000,000 (n) (n) n (CI 95%) n (CI 95%) 0.046 0.84 1.38 Magdalena 11 177.47 (0.043–0.049) (0.49–1.36) (0.93–2.02) 12.94 22.01 Meta 0.59 (0.58–0.61) 127 2,163.44 (11.42–14.62) (19.99–24.15) 14.32 21.47 Nariño 0.78 (0.77–0.80) 230 3,441.36 (12.69–16.05) (19.46–23.57) Norte de 11.96 18.31 0.53 (0.52–0.59) 170 2,604.27 Santander (10.48–13.56) (16.47–20.27) 8.52 16.70 Putumayo 0.57 (0.54–0.60) 28 548.85 (7.31–9.93) (14.95–18.59) 37.45 53.60 Quindío 1.15 (1.12–1.17) 196 2,805.62 (34.81–40.23) (50.43–56.90) 27.70 42.28 Risaralda 0.99 (0.97–1.01) 255 3,880.73 (25.44–30.10) (39.49–45.24) 17.38 26.77 Santander 0.81 (0.79–0.82) 364 5,600.96 (15.61–19.32) (24.53–29.11) 0.023 0.48 0.66 Sucre 4 57.21 (0.018–0.027) (0.23–0.91) (0.34–1.11) 28.39 40.34 Tolima 0.82 (0.81–0.83) 375 5,323.44 (26.11–30.83) (37.61–43.23) 19.00 33.33 Valle 0.76 (0.75–0.77) 830 14,479.85 (17.13–21.00) (30.86–35.97) 1.58 3.21 Vaupés 0.19 (0.13–0.25) 1 12.03 (1.09–2.25) (2.46–4.08) 0.67 0.96 Vichada 0.10 (0.06–0.14) 1 9.69 (0.38–1.17) (0.57–1.48) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 171 Summary Exposure Values (SEV) Figure A1.13. Evolution of Summary Exposure Values (SEV) in Colombia 2010–2019 Summary exposure values (SEVs) are used to capture the prevalence of risk-weighted exposure. This indicator is interpreted as the percentage of the population that is exposed to a maximum risk and goes from a range of 0–100 percent, where 0 percent implies that there is no exposure to the risk factor and 100 percent is when the entire population is completely exposed to this same factor. The average value of the SEV for all causes in Colombia is 65.4 percent (CI95: 63.9–57.0 percent), with the years 2010 and 2016 being where the highest values were presented. The following figure shows the evolution of SEVs during the period 2010–2019. The five main causes of SVS in Colombia are ischemic heart disease (97 percent), stroke-cerebrovascular disease (97 percent), chronic obstructive pulmonary disease (COPD) (92 percent), homicides (89 percent) and hypertensive heart disease (88 percent). The complete distribution of SEVs is shown in the following figure. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 172 Figure A1.14. Average SEV by Cause of Death Table A1.15. Summary Exposure Values Antioquia Exhibition and Coffee Axis 2010–2019 Cause of death Antioquia Caldas Quindío Risaralda Traffic accident 49% 36% 12% 98% Stroke (cerebrovascular 100% 100% 100% 100% disease) Drowning 96% 95% 100% 100% Hypertensive heart disease 99% 100% 100% 100% Ischemic heart disease 100% 100% 100% 100% Disasters 0.05% 0.02% 0% 0% Diabetes mellitus 98% 100% 100% 100% EPOC 99% 100% 100% 100% Chronic kidney disease 98% 100% 100% 100% Homicide 99% 67% 94% 91% Lower respiratory infection 99% 100% 100% 100% Mechanical injuries 11% 10% 0.3% 0.3% With regard to the distribution by regions, in the region of Antioquia Cardiomyopathy 95% 92% 100% 67% and the Coffee Axis, the department of Antioquia has the highest Unintentional injuries 34% 13% 21% 17% percentage of SEV (66 percent) followed by Risaralda (65 percent). Other transportation- 3% 1% 0% 0.01% The predominant causes are ischemic heart disease (99.9 percent), related injuries stroke (99.8 percent), and COPD (99.7 percent). Contact with animals 0.4% 0.06% 0% 0% Suicide 39% 19% 0.8% 32% The department with the highest percentage of SEV in the Total SEV 66% 61% 61% 65% Central Andean Zone is Tolima (64 percent), followed by Huila (63 percent) and Cundinamarca (57 percent). The causes with a higher proportion of SEVs are ischemic heart disease (99.6 percent), stroke (99.1 percent) and drowning (98.8 percent) (Table A1.15). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 173 Table A1.16. Summary Exposure Values Central Andean Zone In the Eastern Zone, Norte de Santander has the highest Exhibition 2010–2019 proportion of SEVS (74 percent), where the main causes of SVS are ischemic heart disease (99.8 percent), cerebrovascular disease Cause of death Bogotá Boyacá Cundinamarca Huila Tolima (99.7 percent), and COPD (98.6 percent) (Table A1.17). Traffic accident 0.1% 11% 14% 68% 60% ACV 100% 100% 100% 98% 98% Table A1.17. Summary Exposure Values Exhibition Eastern Zone Drowning 100% 99% 100% 100% 96% 2010–2019 Hypertensive 100% 100% 99% 85% 88% heart disease Cause of death Norte de Santander Santander Ischemic heart Traffic accident 91% 56% 100% 100% 100% 99% 99% disease Stroke (cerebrovascular disease) 100% 100% Disasters 0% 0.002% 0% 0.003% 0.02% Drowning 97% 96% Diabetes mellitus 100% 100% 99% 83% 86% Hypertensive heart disease 96% 99% EPOC 100% 100% 99% 91% 92% Ischemic heart disease 100% 100% Chronic kidney 100% 100% 99% 81% 84% disease Disasters 0.07% 0% Homicide 46% 29% 40% 95% 93% Diabetes mellitus 96% 99% Lower respiratory EPOC 98% 99% 100% 100% 100% 88% 90% infection Chronic kidney disease 95% 99% Mechanical 0.3% 4% 1.6% 8% 17% injuries Homicide 93% 94% Cardiomyopathy 100% 97% 95% 71% 73% Lower respiratory infection 98% 99% Unintentional 12% 6% 8% 49% 47% Mechanical injuries 54% 14% injuries Cardiomyopathy 83% 71% Other transportation- 0% 3% 0.5% 5% 7% Unintentional injuries 58% 24% related injuries Other transportation-related injuries 7% 5% Contact with Contact with animals 0.4% 0.06% 0% 0.02% 0.14% 0.5% 1% animals Suicide 89% 53% Suicide 0.17% 9% 11% 54% 58% Total SEV 74% 65% Total SEV 56% 56% 57% 63% 64% IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 174 Chocó is the department in the Western Zone with the highest In the Caribbean region, the departments of Magdalena (74 proportion of SEV (72 percent), followed by Valle (34 percent). percent), Bolívar (73 percent), and Sucre (73 percent) have the Stroke is the cause that has the greatest impact (98.6 percent), highest percentage of SEV. Among the causes, those with the followed by drowning (98.3 percent) and ischemic heart disease highest proportion are homicides (99.9 percent), followed by traffic (97.9 percent) (Table A1.18). accidents (99.6 percent) and stroke (99.4 percent) (Table A1.19). Table A1.18. Summary Exposure Values Western Zone Exhibition 2010–2019 Cause of death Cauca Chocó Nariño Valley Traffic accident 44% 100% 33% 97% Stroke (cerebrovascular disease) 99% 98% 98% 99% Drowning 100% 97% 98% 99% Hypertensive heart disease 99% 96% 97% 97% Ischemic heart disease 100% 98% 96% 98% Disasters 0.01% 0% 0% 0% Diabetes mellitus 98% 96% 96% 97% EPOC 99% 97% 97% 97% Chronic kidney disease 98% 96% 87% 96% Homicide 82% 100% 46% 97% Lower respiratory infection 99% 97% 97% 98% Mechanical injuries 0.2% 32% 0.08% 0.2% Cardiomyopathy 96% 89% 90% 43% Unintentional injuries 22% 31% 6% 30% Other transportation-related injuries 0.1% 1% 0.03% 0.4% Contact with animals 0.09% 0.2% 0% 0.001% Suicide 28% 98% 28% 83% Total SEV 62% 72% 57% 67% IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 175 Table A1.19. Summary Exposure Values Exhibition Caribbean Region 2010–2019 Cause of death Atlantic Bolívar Cesar Córdoba Guajira Magdalena Sugar San Andres Traffic accident 100% 100% 98% 100% 100% 100% 100% 100% Stroke (cerebrovascular disease) 100% 100% 98% 100% 98% 100% 100% 100% Drowning 16% 25% 84% 22% 47% 59% 21% 49% Hypertensive heart disease 100% 100% 86% 99% 94% 100% 100% 92% Ischemic heart disease 100% 100% 100% 100% 98% 100% 100% 88% Disasters 0% 0% 0% 0% 0.01% 0% 0% 0% Diabetes mellitus 100% 99% 82% 99% 93% 100% 100% 100% EPOC 100% 100% 92% 99% 95% 100% 100% 93% Chronic kidney disease 100% 99% 75% 99% 91% 100% 100% 81% Homicide 100% 100% 100% 100% 100% 100% 100% 100% Lower respiratory infection 100% 100% 90% 99% 95% 100% 100% 100% Mechanical injuries 97% 96% 44% 96% 75% 78% 98% 4% Cardiomyopathy 100% 98% 54% 98% 82% 98% 100% 100% Unintentional injuries 1.4% 8% 54% 5% 26% 10% 4% 0% Other transportation-related injuries 1.8% 14% 27% 7% 8% 10% 17% 0% Contact with animals 0% 0.03% 0.7% 0.01% 0.1% 0.004% 0% 0% Suicide 100% 100% 98% 100% 100% 100% 100% 89% Total SEV 71% 73% 70% 72% 71% 74% 73% 64% In the Orinoquia, Vichada is the department with the highest proportion of SEV (77 percent), followed by Arauca (74 percent) and Casanare (72 percent). The causes of death with the highest percentage in this region are drowning (99.4 percent), homicides (99.1 percent) and traffic accidents (98.9 percent) (Table A1.20). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 176 Table A1.20. Summary Exposure Values Orinoquia Exhibition 2010–2019 Cause of death Meta Arauca Casanare Guainía Guaviare Vichada Traffic accident 98% 96% 99% 100% 100% 100% Stroke (cerebrovascular disease) 98% 97% 99% 84% 88% 96% Drowning 100% 99% 100% 100% 100% 98% Hypertensive heart disease 90% 83% 76% 60% 35% 89% Ischemic heart disease 98% 99% 99% 83% 93% 96% Disasters 0.003% 0% 0% 0% 0.01% 0% Diabetes mellitus 89% 82% 77% 59% 32% 89% EPOC 93% 89% 85% 68% 56% 91% Chronic kidney disease 82% 79% 71% 54% 22% 88% Homicide 98% 98% 99% 100% 100% 100% Lower respiratory nfection 93% 87% 86% 68% 51% 92% Mechanical injuries 30% 65% 54% 17% 25% 74% Cardiomyopathy 54% 76% 64% 52% 15% 87% Unintentional injuries 70% 80% 85% 67% 93% 90% Other transportation-related injuries 23% 31% 30% 6% 19% 21% Contact with animals 3% 1% 6% 0.8% 4% 0.7% Suicide 82% 93% 99% 100% 100% 100% Total SEV 71% 74% 72% 60% 55% 77% In the Amazon, the department with the greatest impact on SEVs is Caquetá (69 percent), followed by Amazonas (61.4 percent). Drowning (100 percent), homicides (97 percent), and traffic accidents (93 percent) are the causes of the highest proportion of SEV in this region (Table A1.21). IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 177 Table A1.21. Summary Exposure Values Amazon Exhibition 2010–2019 Cause of death Caquetá Putumayo Amazon Vaupés Traffic accident 95% 78% 100% 100% Stroke (cerebrovascular disease) 98% 95% 87% 80% Drowning 100% 100% 100% 100% Hypertensive heart disease 78% 73% 66% 29% Ischemic heart disease 99% 99% 85% 89% Disasters 0.02% 0% 0% 0.009% Diabetes mellitus 78% 69% 67% 21% EPOC 86% 82% 73% 53% Chronic kidney disease 73% 61% 62% 13% Homicide 100% 89% 100% 100% Lower respiratory infection 85% 79% 73% 41% Mechanical injuries 24% 9% 8% 4% Cardiomyopathy 59% 45% 61% 5% Unintentional injuries 79% 71% 61% 52% Other transportation-related injuries 18% 9% 2% 5% Contact with animals 4% 2% 0.3% 1% Suicide 93% 78% 100% 100% Total SEV 69% 61.1% 61.4% 46% IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 178 Economic Burden PYPLL Economic burden* Table A1.22. Productive Years of Life Potentially Lost and Total Cause of death Total Attributable Economic Burden of the 17 Causes Studied and Attributable to Total Attributable Floor Roof Floor Roof Non-Optimal Temperatures in Colombia, 2001–2019 Ischemic heart COP COP COP COP 480,269 5,789 disease 5,855,993 16,410,362 70,585 200,936 PYPLL Economic burden* COP COP COP Traffic accidents 1,153,960 8,172 COP 99,541 14,056,115 95,189,802 592,761 Cause of death Total Attributable COP COP COP Drowning 255,877 5,651 COP 68,827 Total Attributable Floor Roof Floor Roof 3,116,638 24,444,695 642,750 COP COP COP COP COP COP Disasters 54,451 - COP 0 COP 0 Homicide 4,009,688 25,166 663,253 3,790,120 48,838,770 290,078,534 306,522 1,554,702 COP COP COP COP COP COP Related animals 18,324 1 COP 17 COP 144 Total 7,293,919 52,887 223,217 1,183,982 88,853,010 522,642,053 644,308 3,518,995 Transport COP COP *Values in millions of Colombian pesos 17,925 2 COP 20 COP 281 relationship 218,338 1,314,535 Unintentional 72,879 58 COP COP COP 704 COP 3,903 Table A1.23. PYPLL and Economic Burden Attributable to Heat and 887,697 4,921,892 Cold by Colombian Departments, 2010–2019, without Discount Mechanical COP 18,938 190 COP 655,107 COP 2,320 COP 6,442 injuries 230,982 Economic burden Hypertensive COP COP PYPLL 83,931 100 COP 1,221 COP 6,535 heart disease 1,022,366 6,488,029 Department Cold Heat COP COP COP DM 32,316 426 COP 5,198 Cold Heat AMW GDPpc AMW GDPpc 393,958 1,620,653 20,214 Cardiomyopathy COP COP COP COP COP COP COP 49,676 670 COP 8,179 Antioquia 4,844 2,785 myocarditis 606,098 1,728,039 22,583 59,024A 323,780 33,919 209,557 COP COP COP COP COP EPOC 21,744 268 COP 3,260 Atlántico 14 4,431 COP 171 COP 516 264,854 2,210,132 24,148 53,972 248,708 COP COP COP COP COP ERC 128,455 1,097 COP 13,366 Bogotá. D.c. 2,533 - COP 0 COP 0 1,565,509 6,949,491 59,770 30,874 211,940 COP COP COP COP COP COP IVRI 77,951 993 COP 12,098 Bolívar 36 3,770 COP 440 949,473 8,328,488 111,345 1,684 45,919 204,278 COP COP COP Suicide 226,682 2,494 COP 30,400 COP COP COP COP 2,762,752 11,939,556 128,361 Boyacá 555 203 6,760 37,976 2,478 15,642 COP COP COP COP ACV 590,855 1,810 7,196,996 45,388,635 22,050 144,120 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 179 Economic burden Economic burden PYPLL PYPLL Department Cold Heat Department Cold Heat Cold Heat AMW GDPpc AMW PIBpc Cold Heat AMW GDPpc AMW PIBpc COP COP COP COP COP COP Caldas 530 268 Risaralda 741 1 COP 12 COP 36 6,460 14,190 3,268 8,631 9,034 24,935 COP COP COP COP COP COP COP COP Caquetá 261 392 Santander 1,076 766 3,176 8,195 4,774 12,751 13,112 86,228 9,334 67,610 COP COP COP COP COP Cauca 950 27 COP 332 Sucre 7 1,803 COP 84 COP 139 11,576 44,569 1,392 21,967 56,360 COP COP COP COP COP COP COP COP Cesar 293 2,077 Tolima 790 757 3,572 16,775 25,305 126,452 9,628 24,281 9,219 26,097 COP COP COP COP COP COP Córdoba 17 3,509 COP 210 COP 249 Valle Del Cauca 4,196 522 42,740 63,418 51,119 250,523 6,361 33,721 COP COP COP COP COP COP Cundinamarca 1,130 68 COP 826 Arauca 49 814 COP 592 13,777 73,756 5,379 1,028 9,917 17,086 COP COP COP COP COP Chocó 52 466 COP 635 COP 847 Casanare 136 541 COP 1,661 5,677 7,624 9,236 6,591 37,851 COP COP COP COP COP COP COP COP Huila 664 473 Putumayo 179 308 8,087 18,603 5,762 14,408 2,185 15,282 3,749 27,790 COP COP COP Archipiélago De San La Guajira 64 1,907 COP 784 COP 1,687 23,228 51,250 Andrés. Providencia Y 0 69 COP 2 COP 5 COP 842 5,626 Santa Catalina COP COP Magdalena 41 1,953 COP 505 COP 1,021 COP 23,788 53,256 Amazonas 1 43 COP 9 COP 26 COP 522 1,226 COP COP COP COP Meta 549 862 Guainía 2 22 COP 21 COP 30 COP 274 COP 530 6,695 340,666 10,503 588,896 COP COP COP COP COP Guaviare 12 127 COP 150 COP 175 Nariño 587 226 COP 7,153 1,547 2,296 13,800 2,753 5,954 COP COP COP COP COP Vaupés 6 45 COP 69 COP 180 COP 551 Norte De Santander 549 2,225 1,646 6,685 14,943 27,106 68,932 COP COP COP Vichada 2 95 COP 20 COP 16 COP 1,154 Quindío 464 - COP 0 COP 0 1,247 5,648 16,067 Note: AMW = annual minimum wage; GDPpc = gross domestic product per capita IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 180 Table A1.24. Economic Burden Attributable to Heat and Cold by Huila COP 2,354.9 COP 4,219.6 COP 1,194.8 COP 2,610.6 Colombian Departments, 2010–2019, with Discount La Guajira COP 207.7 COP 355.3 COP 5,206.4 COP 9,980.5 Magdalena COP 166.4 COP 237.6 COP 5,660.7 COP 10,401.9 Economic burden Meta COP 1,960.5 COP 53,734.3 COP 2,322.0 COP 90,885.5 Department Cold Heat Nariño COP 2,157.6 COP 2,934.3 COP 593.8 COP 1,079.6 AMW GDPpc AMW GDPpc Norte de Amazonas COP 2.6 COP 4.0 COP 110.3 COP 207.1 COP 2,174.7 COP 3,426.0 COP 6,171.6 COP 13,165.6 Santander Antioquia COP 17,880.2 COP 67,620.2 COP 7,499.2 COP 37,879.2 Putumayo COP 588.8 COP 2,633.1 COP 818.0 COP 4,644.5 Arauca COP 183.2 COP 306.1 COP 2,098.7 COP 3,547.4 Quindio COP 2,024.7 COP 3,973.1 COP 0.0 COP 0.0 San Andrés, P Risaralda COP 2,963.4 COP 6,038.6 COP 3.0 COP 7.6 COP 1.5 COP 3.5 COP 188.9 COP 1,035.2 y ST. Santander COP 4,097.5 COP 18,637.9 COP 2,245.6 COP 12,960.9 Atlántico COP 69.4 COP 155.4 COP 12,798.4 COP 47,806.2 Sucre COP 35.2 COP 44.3 COP 5,708.5 COP 11,336.1 Bogotá COP 11,733.7 COP 53,559.2 COP 0.0 COP 0.0 Tolima COP 3,296.9 COP 6,018.3 COP 2,186.4 COP 5,159.0 Bolívar COP 145.9 COP 378.5 COP 10,638.5 COP 38,235.8 Valle del Cauca COP 14,080.9 COP 50,799.2 COP 1,329.4 COP 5,916.4 Boyacá COP 2,376.8 COP 8,349.1 COP 668.0 COP 3,096.3 Vaupés COP 16.4 COP 29.7 COP 89.5 COP 237.0 Caldas COP 2,432.6 COP 3,951.1 COP 771.3 COP 1,698.1 Vichada COP 9.7 COP 5.8 COP 288.2 COP 262.1 Caquetá COP 861.4 COP 1,553.1 COP 1,033.8 COP 2,262.3 Casanare COP 461.6 COP 2,058.8 COP 1,511.0 COP 7,570.0 Note: Values in billion Colombian pesos; AMW = annual minimum wage; GDPpc = Cauca COP 3,211.5 COP 8,360.1 COP 69.1 COP 237.9 gross domestic product per capita Cesar COP 1,018.2 COP 3,277.6 COP 5,897.7 COP 23,195.4 Chocó COP 149.6 COP 164.4 COP 1,141.6 COP 1,384.0 Cundinamarca COP 5,072.5 COP 17,164.4 COP 188.4 COP 966.3 Córdoba COP 82.2 COP 79.1 COP 10,344.6 COP 13,324.1 Guainía COP 5.6 COP 7.1 COP 48.1 COP 80.7 Guaviare COP 63.1 COP 60.0 COP 346.6 COP 444.5 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 181 Figure A1.15. Indirect Costs per Capita Attributable to Suboptimal Temperatures in Colombia, 2010–2019: Scenario of Annual Minimum Wage (AMW) and GDP per Capita, without Discount (C) AMW minimum wage – (C) GDPpc minimum suboptimal (A) AMW minimum wage for heat (B) AMW minimum wage for cold suboptimal temperatures temperatures Source: World Bank 2023 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 182 Figure A1.16. Relationship between the Rate of YLL Attributable to Cold and Heat and GDP per Capita by Departments in Colombia, 2010–2019 (A) Heat association (B) Cold association IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 183 Disease Burden and Economic Projections Table A1.25. Average Temperature Projections According to Scenarios by Department SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5 Department Average Temperature ºC 2020–2100 (Minimum Value-Maximum Value) Amazon 27.1 (26.7–27.4) 27.4 (26.6–27.8) 27.8 (26.6–28.8) 28.4 (26.6–30.8) 28.8 (26.6–31.5) Antioquia 23.1 (22.6–23.6) 23.5 (22.8–23.8) 23.9 (22.8–24.7) 24.3 (22.7–26.3) 24.7 (22.7–27.1) Arauca 24.8 (24.4–25.4) 25.3 (24.5–25.9) 25.8 (24.4–26.7) 26.3 (24.5–28.7) 26.9 (24.4–29.7) Atlantic 28.2 (27.8–28.6) 28.5 (27.9–28.9) 28.9 (27.9–29.7) 29.4 (27.8–31.2) 29.8 (27.9–32.1) Bogotá 15.5 (15.1–16.0) 15.9 (15.4–16.4) 16.4 (15.3–17.4) 17.0 (15.2–19.0) 17.5 (15.3–20.2) Bolívar 26.9 (26.5–27.3) 27.3 (26.7–27.6) 27.7 (26.6–28.5) 28.2 (26.6–30.3) 28.6 (26.6–31.0) Boyacá 17.5 (17.0–17.9) 17.9 (17.1–18.4) 18.4 (17.1–18.4) 18.9 (17.1–21.1) 19.4 (17.1–22.2) Caldas 20.6 (20.1–21.0) 21.0 (20.3–21.4) 21.4 (20.3–22.3) 21.8 (20.3–23.8) 22.3 (20.2–24.8) Caquetá 26.2 (25.7–26.5) 26.5 (25.8–27.0) 27.0 (25.7–28.0) 27.5 (25.7–29.6) 28.0 (25.8–30.8) Casanare 26.0 (25.5–26.5) 26.4 (25.6–27.0) 26.9 (25.6–28.0) 27.4 (25.7–29.6) 28.0 (25.6–30.9) Cauca 21.3 (20.8–21.7) 21.6 (21.0–22.0) 22.1 (20.9–22.9) 22.4 (25.6–29.8) 22.9 (20.9–25.2) Cesar 28.2 (27.8–28.6) 28.6 (27.9–29.1) 29.1 (27.9–29.9) 29.6 (27.9–31.8) 30.2 (28–32.8) Chocó 26.5 (25.9–26.8) 26.7 (26.1–27.1) 27.1 (26.1–27.8) 27.4 (26.0–29.0) 27.8 (26–29.7) Córdoba 25.8 (25.4–26.2) 26.2 (25.6–26.5) 26.6 (25.5–27.3) 27.0 (25.6–28.9) 27.4 (25.5–29.7) Cundinamarca 20.6 (20.1–21.0) 20.9 (20.2–21.4) 21.4 (20.2–22.5) 21.9 (20.2–24.1) 22.4 (20.2–25.9) Guainía 27.0 (26.6–27.4) 27.3 (26.5–27.8) 27.8 (26.5–28.7) 28.3 (26.5–30.3) 28.8 (26.4–31.6) Guaviare 26.2 (25.7–26.6) 26.6 (25.8–27.1) 27.1 (25.8–28.1) 27.6 (25.9–29.7) 28.1 (25.8–30.9) Huila 19.2 (18.8–19.6) 19.6 (18.9–20.0) 20.0 (18.8–21.0) 20.5 (18.8–22.4) 20.9 (18.8–23.5) La Guajira 26.3 (25.9–26.6) 26.7 (26.0–27.0) 27.1 (26.0–27.9) 27.5 (25.9–29.5) 27.9 (26.0–30.2) Magdalena 28.1 (27.7–28.5) 28.5 (27.9–28.8) 28.9 (27.8–29.7) 29.4 (27.7–31.5) 29.9 (27.8–32.4) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 184 SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5 Department Average Temperature ºC 2020–2100 (Minimum Value-Maximum Value) Meta 25.5 (25.0–25.9) 25.9 (25.1–26.4) 26.4 (25.1–27.5) 26.9 (25.2–29.2) 27.4 (25.0–30.3) Nariño 21.2 (20.7–21.6) 21.5 (20.9–21.9) 21.9 (20.8–22.7) 22.3 (20.8–24.1) 22.7 (20.8–25) Norte de Santander 23.1 (22.6–23.5) 23.5 (22.7–23.9) 23.9 (22.7–25) 24.4 (22.7–26.7) 25.0 (22.8–27.6) Putumayo 25.2 (24.8–25.6) 25.6 (24.8–26.0) 26.0 (24.8–27.0) 26.5 (24.7–28.6) 27.0 (24.8–29.7) Quindío 18.4 (17.9–18.9) 18.8 (18.1–19.2) 19.2 (18.0–20.1) 19.6 (18.0–21.5) 20.1 (18.8–22.6) Risaralda 22.3 (21.8–22.7) 22.6 (22.0–23.0) 23.0 (21.9–23.8) 23.4 (21.9–25.3) 23.9 (21.9–26.2) Archipelago of San Andrés, 27.3 (27.0–27.6) 27.5 (26.9–27.9) 27.8 (26.9–28.6) 28.1 (26.9–29.5) 28.4 (27–30.2) Providencia and Santa Catalina Santander 22.4 (22.0–22.9) 22.8 (22.1–23.3) 23.3 (22.1–24.2) 23.8 (22.1–26.0) 24.3 (22.1–26.9) Sugar 27.6 (27.2–27.9) 27.9 (27.3–28.3) 28.3 (27.3–29.1) 28.8 (27.2–30.7) 29.2 (27.3–31.5) Tolima 19.4 (18.9–19.9) 19.8 (19.1–20.2) 20.2 (19.1–21.2) 20.7 (19.0–22.7) 21.1 (19.1–23.7) Valley 24.0 (23.5–24.4) 24.4 (23.7–24.7) 24.7 (23.7–25.5) 25.1 (23.6–27.0) 25.5 (23.6–27.8) Vaupés 26.6 (26.2–27.0) 26.9 (26.1–27.4) 27.4 (26.1–28.4) 27.9 (26.2–30.1) 28.4 (26.2–31.1) Vichada 27.6 (27.1–28.1) 28.0 (27.1–28.5) 28.4 (27.1–29.4) 28.9 (27.1–31.1) 29.5 (27.1–32.4) IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 185 Table A1.26. Vector of Change (2050–2100) Expected According to Scenarios by Department SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5 Department Vector of change Period 2020–2050 and Period 2020–2100 2050 2100 2050 2100 2050 2100 2050 2100 2050 2100 Amazon 2050:-0.03ºC 2100:-0.08 ºC 2050:+0.1ºC 2100: +0.5ºC 2050:+0.7ºC 2100:+1.8ºC 2050:+1.4ºC 2100:+3.6ºC 2050:+1.7ºC 2100:+4.5ºC Antioquia 2050:-0.01ºC 2100:-0.04 °C 2050:+0.1ºC 2100:+0.4ºC 2050:+0.6ºC 2100:+1.7ºC 2050:+1.2ºC 2100:+3.1ºC 2050:+1.5ºC 2100:+4ºC Arauca 2050:-0.02ªC 2100:-0.07ºC 2050:+0.2ºC 2100:+0.7ºC 2050:+0.8ºC 2100:+2ºC 2050:+1.5ºC 2100:+3.8ºC 2050:+1.9ºC 2100:+5ºC Atlantic 2050:+0.003ºC 2100:+0.008ºC 2050:+0.1ºC 2100:+0.5ºC 2050:+0.6ºC 2100:+1.6ºC 2050:+1.2ºC 2100:+3ºC 2050:+1.5ºC 2100:+4ºC Bogotá 2050:-0.03ºC 2100: -0.08 ºC 2050:+0.1ºC 2100: +0.5ºC 2050:+0.7ºC 2100: +1.9ºC 2050:+1.4ºC 2100: +3.6ºC 2050:+1.8ºC 2100:+4.8ºC Bolívar 2050:-0.006ºC 2100:-0.01ºC 2050:+0.1ºC 2100:+0.5ºC 2050:+0.6ºC 2100:+1.7ºC 2050:+1.2ºC 2100:+3.3ºC 2050:+1.6ºC 2100:+4.1ºC Boyacá 2050:-0.03ºC 2100:-0.08ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.9ºC 2050:+1.4ºC 2100:+3.6ºC 2050:+1.8ºC 2100:+4.7ºC Caldas 2050:-0.006ºC 2100:-0.01ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.6ºC 2100:+1.8ºC 2050:+1.2ºC 2100:+3.2ºC 2050:+1.6ºC 2100:+4ºC Caquetá 2050:-0.03ºC 2100:-0.08ºC 2050:+0.2ºC 2100:+0.6ºC 2050:0.7ºC 2100:+1.9ºC 2050:+1.3ºC 2100:+3.5ºC 2050:+1.7ºC 2100:+4.6ºC Casanare 2050:-0.02ºC 2100:-0.05ºC 2050:+0.2ºC 2100:+0.7ºC 2050:0.7ºC 2100:+2ºC 2050:+1.4ºC 2100:+3.7ºC 2050:+1.9ºC 2100:+4.9ºC Cauca 2050:-0.009ºC 2100:-0.02ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.6ºC 2100:+1.8ºC 2050:+1.2ºC 2100:+3.1ºC 2050:+1.5ºC 2100:+3.9ºC Cesar 2050:+0.0006ºC 2100:+0.001ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.8ºC 2050:+1.4ºC 2100:+3.5ºC 2050:+1.8ºC 2100:+4.5ºC Chocó 2050:+0.009ºC 2100:+0.02ºC 2050:+0.1ºC 2100:+0.6ºC 2050:+0.6ºC 2100:+1.5ºC 2050:+1ºC 2100:+2.6ºC 2050:+1.4ºC 2100:+3.5ºC Córdoba 2050:-0.02ºC 2100:-0.07ºC 2050:+0.1ºC 2100:+0.5ºC 2050:+0.6ºC 2100:+1.6ºC 2050:+1.2ºC 2100:+3ºC 2050:+1.5ºC 2100:+3.9ºC Cundinamarca 2050:-0.01ºC 2100:-0.04ºC 2050:+0.02ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.8ºC 2050:+1.3ºC 2100:+3.5ºC 2050:+1.7ºC 2100:+4.6ºC Guainía 2050:-0.02ºC 2100:-0.05ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.9ºC 2050:+1.3ºC 2100:+3.4ºC 2050:+1.8ºC 2100:+4.7ºC Guaviare 2050:-0.03ºC 2100:-0.08ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.9ºC 2050:+1.3ºC 2100:+3.4ºC 2050:+1.8ºC 2100:+4.6ºC IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 186 Huila 2050:-0.03ºC 2100:-0.07ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.8ºC 2050:+1.3ºC 2100:+3.4ºC 2050:+1.7ºC 2100:+4.3ºC La Guajira 2050:+0.003ºC 2100:+0.008ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.6ºC 2100:+1.8ºC 2050:+1.2ºC 2100:+3ºC 2050:+1.5ºC 2100:+3.9ºC Magdalena 2050:+0.0006ºC 2100:+0.004ºC 2050:+0.2ºC 2100:+0.5ºC 2050:+0.6ºC 2100:+1.7ºC 2050:+1.3ºC 2100:+3.3ºC 2050:+1.7ºC 2100:+4.2ºC Meta 2050:-0.03ºC 2100:-0.07ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+2ºC 2050:+1.4ºC 2100:+3.6ºC 2050:+1.8ºC 2100:+4.7ºC Nariño 2050:-0.0006ºC 2100:-0.001ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.6ºC 2100:+1.7ºC 2050:+1.1ºC 2100:+3ºC 2050:+1.5ºC 2100:+3.8ºC Norte de 2050:-0.009ºC 2100:-0.02ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.8ºC 2050:+1.4ºC 2100:+3.6ºC 2050:+1.8ºC 2100:+4.6ºC Santander Putumayo 2050:-0.01ºC 2100:-0.03ºC 2050:+0.02ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.9ºC 2050:+1.4ºC 2100:+3.5ºC 2050:+1.7ºC 2100:+4.4ºC Quindío 2050: -0.009ºC 2100:-0.02ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.6ºC 2100:+1.7ºC 2050:+1.2ºC 2100:+3.2ºC 2050:+1.6ºC 2100:+4.16ºC Risaralda 2050:+0.001ºC 2100:+0.003ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.6ºC 2100:+1.7ºC 2050:+1.1ºC 2100:+3ºC 2050:+1.5ºC 2100:+3.9ºC Archipelago of San Andrés, Providencia 2050:+0.003ºC 2100: +0.008ºC 2050:+0.2ºC 2100:+0.5ºC 2050:+0.5ºC 2100:+1.4ºC 2050:+0.9ºC 2100:+2.3ºC 2050:+1.2ºC 2100:+3ºC and Santa Catalina Santander 2050:-0.03ºC 2100:-0.08ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.8ºC 2050:+1.4ºC 2100:+3.5ºC 2050:+1.7ºC 2100:+4.5ºC Sugar 2050:-0.01ºC 2100:-0.03ºC 2050:+0.2ºC 2100:+0.5ºC 2050:+0.6ºC 2100:+1.6ºC 2050:+1.2ºC 2100:+3.1ºC 2050:+1.5ºC 2100:+4ºC Tolima 2050:-0.01ºC 2100:-0.04 °C 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.8ºC 2050:+1.3ºC 2100:+3.3ºC 2050:+1.7ºC 2100:+4.2ºC Valley 2050:-0.006ºC 2100:-0.01ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.6ºC 2100:+1.7ºC 2050:+1.1ºC 2100:+2.9ºC 2050:+1.4ªC 2100:+3.7ºC Vaupés 2050:-0.03ºC 2100:-0.08ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.8ºC 2050:+1.3ºC 2100:+3.4ºC 2050:+1.8ºC 2100:+4.6ºC Vichada 2050:-0.01ºC 2100:-0.04ºC 2050:+0.2ºC 2100:+0.6ºC 2050:+0.7ºC 2100:+1.9ºC 2050:+1.4ºC 2100:+3.6ºC 2050:+1.9ºC 2100:+4.9ºC IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 187 Table A1.27. Economic Burden Projections Attributable to Heat and Year Scenario Cold - AMW Heat - AMW Cold - GDPpc Heat – GDPpc Cold by Colombian Departments, 2020-2050, without Discount 2024 SSP_370 COP 1,116 COP 854 COP 1,856 COP 1,270 2024 SSP_245 COP 1,127 COP 1,017 COP 1,872 COP 1,517 Year Scenario Cold - AMW Heat - AMW Cold - GDPpc Heat – GDPpc 2024 SSP_126 COP 1,128 COP 1,039 COP 1,874 COP 1,550 2020 SSP_585 COP 1,111 COP 883 COP 1,854 COP 1,313 2024 SSP_119 COP 1,118 COP 869 COP 1,857 COP 1,278 2020 SSP_370 COP 1,110 COP 859 COP 1,852 COP 1,276 2025 SSP_585 COP 1,123 COP 970 COP 1,863 COP 1,442 2020 SSP_245 COP 1,110 COP 868 COP 1,854 COP 1,296 2025 SSP_370 COP 1,120 COP 929 COP 1,860 COP 1,391 2020 SSP_126 COP 1,115 COP 942 COP 1,861 COP 1,405 2025 SSP_245 COP 1,120 COP 930 COP 1,859 COP 1,370 2020 SSP_119 COP 1,116 COP 956 COP 1,863 COP 1,440 2025 SSP_126 COP 1,122 COP 957 COP 1,862 COP 1,421 2021 SSP_585 COP 1,118 COP 924 COP 1,864 COP 1,378 2025 SSP_119 COP 1,120 COP 929 COP 1,859 COP 1,365 2021 SSP_370 COP 1,113 COP 857 COP 1,857 COP 1,275 2026 SSP_585 COP 1,118 COP 933 COP 1,853 COP 1,373 2021 SSP_245 COP 1,118 COP 922 COP 1,863 COP 1,363 2026 SSP_370 COP 1,119 COP 951 COP 1,855 COP 1,405 2021 SSP_126 COP 1,119 COP 944 COP 1,866 COP 1,411 2026 SSP_245 COP 1,123 COP 1,008 COP 1,860 COP 1,492 2021 SSP_119 COP 1,113 COP 852 COP 1,856 COP 1,261 2026 SSP_126 COP 1,120 COP 961 COP 1,856 COP 1,428 2022 SSP_585 COP 1,121 COP 940 COP 1,867 COP 1,393 2026 SSP_119 COP 1,117 COP 910 COP 1,852 COP 1,354 2022 SSP_370 COP 1,119 COP 904 COP 1,863 COP 1,346 2027 SSP_585 COP 1,119 COP 997 COP 1,852 COP 1,479 2022 SSP_245 COP 1,123 COP 973 COP 1,870 COP 1,455 2027 SSP_370 COP 1,121 COP 1,034 COP 1,856 COP 1,533 2022 SSP_126 COP 1,120 COP 925 COP 1,865 COP 1,373 2027 SSP_245 COP 1,117 COP 959 COP 1,849 COP 1,417 2022 SSP_119 COP 1,117 COP 879 COP 1,861 COP 1,303 2027 SSP_126 COP 1,115 COP 926 COP 1,846 COP 1,375 2023 SSP_585 COP 1,122 COP 943 COP 1,867 COP 1,400 2027 SSP_119 COP 1,108 COP 827 COP 1,835 COP 1,207 2023 SSP_370 COP 1,121 COP 922 COP 1,865 COP 1,371 2028 SSP_585 COP 1,119 COP 1,056 COP 1,850 COP 1,568 2023 SSP_245 COP 1,125 COP 980 COP 1,871 COP 1,464 2028 SSP_370 COP 1,115 COP 984 COP 1,843 COP 1,458 2023 SSP_126 COP 1,123 COP 946 COP 1,867 COP 1,406 2028 SSP_245 COP 1,113 COP 944 COP 1,840 COP 1,396 2023 SSP_119 COP 1,123 COP 958 COP 1,869 COP 1,433 2028 SSP_126 COP 1,114 COP 970 COP 1,842 COP 1,437 2024 SSP_585 COP 1,125 COP 981 COP 1,869 COP 1,460 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 188 Year Scenario Cold - AMW Heat - AMW Cold - GDPpc Heat – GDPpc Year Scenario Cold - AMW Heat - AMW Cold - GDPpc Heat – GDPpc 2028 SSP_119 COP 1,110 COP 908 COP 1,836 COP 1,343 2033 SSP_245 COP 1,088 COP 936 COP 1,789 COP 1,389 2029 SSP_585 COP 1,113 COP 1,019 COP 1,838 COP 1,508 2033 SSP_126 COP 1,090 COP 971 COP 1,791 COP 1,426 2029 SSP_370 COP 1,106 COP 901 COP 1,826 COP 1,327 2033 SSP_119 COP 1,087 COP 929 COP 1,787 COP 1,368 2029 SSP_245 COP 1,114 COP 1,032 COP 1,839 COP 1,528 2034 SSP_585 COP 1,086 COP 1,012 COP 1,783 COP 1,489 2029 SSP_126 COP 1,111 COP 977 COP 1,834 COP 1,445 2034 SSP_370 COP 1,081 COP 923 COP 1,775 COP 1,355 2029 SSP_119 COP 1,104 COP 863 COP 1,823 COP 1,276 2034 SSP_245 COP 1,079 COP 896 COP 1,772 COP 1,314 2030 SSP_585 COP 1,111 COP 1,052 COP 1,831 COP 1,554 2034 SSP_126 COP 1,082 COP 950 COP 1,777 COP 1,392 2030 SSP_370 COP 1,098 COP 842 COP 1,811 COP 1,232 2034 SSP_119 COP 1,077 COP 864 COP 1,769 COP 1,258 2030 SSP_245 COP 1,102 COP 905 COP 1,818 COP 1,328 2035 SSP_585 COP 1,083 COP 1,074 COP 1,776 COP 1,581 2030 SSP_126 COP 1,104 COP 941 COP 1,821 COP 1,388 2035 SSP_370 COP 1,073 COP 903 COP 1,760 COP 1,321 2030 SSP_119 COP 1,106 COP 960 COP 1,823 COP 1,419 2035 SSP_245 COP 1,075 COP 937 COP 1,764 COP 1,378 2031 SSP_585 COP 1,107 COP 1,066 COP 1,822 COP 1,570 2035 SSP_126 COP 1,072 COP 889 COP 1,759 COP 1,305 2031 SSP_370 COP 1,103 COP 999 COP 1,816 COP 1,464 2035 SSP_119 COP 1,070 COP 860 COP 1,756 COP 1,261 2031 SSP_245 COP 1,102 COP 987 COP 1,816 COP 1,463 2036 SSP_585 COP 1,078 COP 1,083 COP 1,764 COP 1,592 2031 SSP_126 COP 1,102 COP 983 COP 1,816 COP 1,457 2036 SSP_370 COP 1,074 COP 1,017 COP 1,759 COP 1,505 2031 SSP_119 COP 1,099 COP 928 COP 1,810 COP 1,374 2036 SSP_245 COP 1,065 COP 878 COP 1,746 COP 1,285 2032 SSP_585 COP 1,097 COP 985 COP 1,804 COP 1,452 2036 SSP_126 COP 1,073 COP 1,000 COP 1,757 COP 1,470 2032 SSP_370 COP 1,101 COP 1,064 COP 1,812 COP 1,580 2036 SSP_119 COP 1,060 COP 796 COP 1,738 COP 1,176 2032 SSP_245 COP 1,098 COP 1,005 COP 1,806 COP 1,481 2037 SSP_585 COP 1,071 COP 1,076 COP 1,752 COP 1,586 2032 SSP_126 COP 1,094 COP 941 COP 1,800 COP 1,384 2037 SSP_370 COP 1,068 COP 1,017 COP 1,747 COP 1,493 2032 SSP_119 COP 1,075 COP 659 COP 1,771 COP 953 2037 SSP_245 COP 1,067 COP 1,011 COP 1,746 COP 1,483 2033 SSP_585 COP 1,090 COP 973 COP 1,791 COP 1,432 2037 SSP_126 COP 1,064 COP 960 COP 1,742 COP 1,411 2033 SSP_370 COP 1,088 COP 945 COP 1,788 COP 1,385 2037 SSP_119 COP 1,056 COP 831 COP 1,729 COP 1,207 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 189 Year Scenario Cold - AMW Heat - AMW Cold - GDPpc Heat – GDPpc Year Scenario Cold - AMW Heat - AMW Cold - GDPpc Heat – GDPpc 2038 SSP_585 COP 1,065 COP 1,079 COP 1,740 COP 1,591 2042 SSP_126 COP 1,028 COP 931 COP 1,675 COP 1,363 2038 SSP_370 COP 1,059 COP 992 COP 1,732 COP 1,449 2042 SSP_119 COP 1,018 COP 776 COP 1,660 COP 1,123 2038 SSP_245 COP 1,060 COP 1,002 COP 1,733 COP 1,468 2043 SSP_585 COP 1,035 COP 1,159 COP 1,682 COP 1,703 2038 SSP_126 COP 1,055 COP 925 COP 1,726 COP 1,346 2043 SSP_370 COP 1,027 COP 1,033 COP 1,671 COP 1,514 2038 SSP_119 COP 1,054 COP 896 COP 1,724 COP 1,316 2043 SSP_245 COP 1,021 COP 933 COP 1,662 COP 1,364 2039 SSP_585 COP 1,058 COP 1,081 COP 1,727 COP 1,589 2043 SSP_126 COP 1,016 COP 843 COP 1,654 COP 1,228 2039 SSP_370 COP 1,051 COP 966 COP 1,717 COP 1,427 2043 SSP_119 COP 1,005 COP 681 COP 1,638 COP 988 2039 SSP_245 COP 1,052 COP 978 COP 1,718 COP 1,441 2044 SSP_585 COP 1,025 COP 1,124 COP 1,666 COP 1,653 2039 SSP_126 COP 1,046 COP 887 COP 1,709 COP 1,287 2044 SSP_370 COP 1,016 COP 958 COP 1,651 COP 1,403 2039 SSP_119 COP 1,038 COP 758 COP 1,696 COP 1,085 2044 SSP_245 COP 1,015 COP 957 COP 1,651 COP 1,402 2040 SSP_585 COP 1,056 COP 1,169 COP 1,722 COP 1,727 2044 SSP_126 COP 1,009 COP 840 COP 1,640 COP 1,221 2040 SSP_370 COP 1,042 COP 936 COP 1,701 COP 1,370 2044 SSP_119 COP 1,006 COP 798 COP 1,637 COP 1,162 2040 SSP_245 COP 1,041 COP 919 COP 1,699 COP 1,340 2045 SSP_585 COP 1,018 COP 1,120 COP 1,652 COP 1,641 2040 SSP_126 COP 1,036 COP 837 COP 1,692 COP 1,222 2045 SSP_370 COP 1,007 COP 939 COP 1,636 COP 1,364 2040 SSP_119 COP 1,033 COP 794 COP 1,687 COP 1,151 2045 SSP_245 COP 1,005 COP 897 COP 1,632 COP 1,305 2041 SSP_585 COP 1,043 COP 1,072 COP 1,701 COP 1,587 2045 SSP_126 COP 1,000 COP 817 COP 1,625 COP 1,183 2041 SSP_370 COP 1,036 COP 954 COP 1,689 COP 1,388 2045 SSP_119 COP 1,002 COP 856 COP 1,629 COP 1,254 2041 SSP_245 COP 1,035 COP 934 COP 1,688 COP 1,366 2046 SSP_585 COP 1,009 COP 1,097 COP 1,638 COP 1,624 2041 SSP_126 COP 1,031 COP 871 COP 1,682 COP 1,268 2046 SSP_370 COP 1,006 COP 1,035 COP 1,632 COP 1,511 2041 SSP_119 COP 1,030 COP 854 COP 1,681 COP 1,246 2046 SSP_245 COP 1,002 COP 965 COP 1,626 COP 1,419 2042 SSP_585 COP 1,039 COP 1,118 COP 1,692 COP 1,650 2046 SSP_126 COP 1,000 COP 940 COP 1,623 COP 1,370 2042 SSP_370 COP 1,036 COP 1,060 COP 1,687 COP 1,563 2046 SSP_119 COP 991 COP 782 COP 1,609 COP 1,137 2042 SSP_245 COP 1,031 COP 974 COP 1,679 COP 1,424 2047 SSP_585 COP 1,006 COP 1,159 COP 1,630 COP 1,705 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 190 Year Scenario Cold - AMW Heat - AMW Cold - GDPpc Heat – GDPpc C. Limitations and Methodological Considerations 2047 SSP_370 COP 999 COP 1,045 COP 1,620 COP 1,527 2047 SSP_245 COP 992 COP 931 COP 1,610 COP 1,355 2047 SSP_126 COP 986 COP 829 COP 1,601 COP 1,208 • Temperatures vary greatly, particularly in departments 2047 SSP_119 COP 982 COP 761 COP 1,595 COP 1,104 located in the Andean zone, and the aggregation by 2048 SSP_585 COP 1,001 COP 1,189 COP 1,620 COP 1,745 departments could mask the differing temperature values 2048 SSP_370 COP 988 COP 979 COP 1,601 COP 1,429 of subregions and municipalities, both in departments with 2048 SSP_245 COP 991 COP 1,023 COP 1,605 COP 1,488 high temperature averages and in those with low averages. 2048 SSP_126 COP 981 COP 853 COP 1,590 COP 1,244 This leads to losing some awareness of the temperature 2048 SSP_119 COP 970 COP 684 COP 1,573 COP 979 variability. 2049 SSP_585 COP 1,000 COP 1,297 COP 1,617 COP 1,918 • This analysis used the exposure response curves 2049 SSP_370 COP 989 COP 1,105 COP 1,600 COP 1,634 (temperature – risk of death) of the GBD by climatic zone; 2049 SSP_245 COP 977 COP 904 COP 1,582 COP 1,322 although they are constructed, including data from Colombia, 2049 SSP_126 COP 976 COP 898 COP 1,581 COP 1,310 they are not specific to the country, so the effect may have 2049 SSP_119 COP 958 COP 610 COP 1,554 COP 878 been underestimated or overestimated. 2050 SSP_585 COP 985 COP 1,174 COP 1,593 COP 1,730 • The use of deaths by place of residence (rather than deaths 2050 SSP_370 COP 974 COP 979 COP 1,575 COP 1,417 by place of occurrence) could potentially influence the 2050 SSP_245 COP 969 COP 892 COP 1,568 COP 1,298 observed results. However, this approach was preferred as 2050 SSP_126 COP 970 COP 905 COP 1,569 COP 1,321 it captures exposure more accurately, because migration in 2050 SSP_119 COP 955 COP 669 COP 1,547 COP 968 Colombia results in a large floating population, large numbers *Values in billion Colombian pesos; AMW = annual minimum wage; GDPpc = gross of internally displaced persons, and high immigration. domestic product per capita • A variation of the GBD approach was used with respect to population attributable fractions, which were computed with mortality on a daily basis and not annually. This makes IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 1. Component 1 191 the results less comparable with those of GBD. However, approach measures the “potential” productivity loss, rather this methodology may be more precise because it more than the actual loss experienced by society. This leads to high accurately captures the daily variations in mortality due to estimated values of productivity loss, particularly for chronic daily exposure to temperature. diseases or concerning young populations. • The use of the modified human capital method—which • Another method used in estimates of economic burden of refers to the capital embodied in workers, considering premature mortality is the statistical value of life method. GDP per capita as a statistical contribution of the year of However, existing estimates are mainly for high-income life to society—was justified in the sense that the analysis countries, such as the United States and European countries considered two scenarios of economic burden: (i) assessing (Toloo et al. 2015). premature mortality through the use of the annual minimum • Burden projections in climate scenarios were used to wage and (ii) through the average productivity of a Colombian model, in a linear fashion, annual attributable mortality rates. by GDP per capita. This probably leads to an underestimation of the burden • What value should be used to determine the potential loss because the variability of the daily temperature is partially of a person who dies prematurely due to climate variability? lost. However, it is estimated that the results do capture the In Colombia, the income structure shows that 86 percent trends and a part of the variability, and therefore are useful of Colombians earn a monthly minimum wage. This means for decision making. that it would be plausible to assume that a person who dies • The temperature and mortality projections are an before fulfilling his working life expectancy would continue approximation of the potential effect of the different to earn this salary. On the other hand, a ceiling scenario, interventions to mitigate greenhouse gas emissions, but it such as GDP per capita, enables assessment of the average is important to bear in mind that there is a high uncertainty productivity of labor in the country. in the future consequences estimates due to the complex • A limitation of the modified human capital approach is that nature of the relationship between temperature and health. the values resulting from the loss of productivity may have biases caused by inaccuracies in obtaining representative income patterns for each population group. In addition, this IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 192 ANNEX 2. COMPONENT 2 Population Maps Figure A2.2. Geolocated Population Representation Examples All the maps and tables are produced by authors as part of this work. The information on data sources are included for additionally. (A) (Un)constrain (B) Census block level Boundaries Figure A2.1. Colombia Political Divisions Departments Municipalities Source: WBG staff using data from Geological Service of Colombia IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 193 Hazard Maps Figure A2.3. Examples of Floods and Fault Line as Proxy to Landslides in Colombia Floods 1 in 100 years return period Fault Line as proxy to high risk of landslides IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 194 Population Exposure to Hazards Figure A2.5. Population Exposed to Floods, Department Level Figure A2.4. Colombian Population Exposed to Floods and Fault Lines as Proxy of Landslides Population 30 25 Percent of exposed population 20 15 10 5 0 Risk Floods Landslides Note: Spatial resolution = 30 m (meters) * 30 m. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 195 Table A2.1. Exposed Population to Floods at Department Level Order by Percentage of Exposed Population Department Percentage of Department Percentage of Department name Exposed population Department name Exposed population code exposed population code exposed population 97 Vaupés 71,438 71.82 68 Santander 538,812 20.14 91 Amazonas 72,433 53.01 25 Cundinamarca 756,054 19.35 81 Arauca 241,176 52.98 66 Risaralda 209,250 18.44 27 Chocó 352,937 50.85 73 Tolima 296,877 17.06 95 Guaviare 86,106 46.75 70 Sucre 172,909 16.91 94 Guainía 51,075 43.95 23 Córdoba 413,465 16.31 47 Magdalena 712,430 43.32 44 La Guajira 239,360 16.15 76 Valle del Cauca 2,404,441 42.71 11 Bogotá 1,658,847 16.15 13 Bolívar 1,336,889 40.03 17 Caldas 159,520 14.87 86 Putumayo 213,882 39.19 8 Atlántico 433,689 13.51 50 Meta 376,764 30.38 63 Quindio 52,648 7.51 85 Casanare 266,655 29.2 88 San Andres 2,370 2.19 20 Cesar 362,323 29.18 18 Caquetá 192,218 28.6 99 Vichada 27,112 28.34 15 Boyacá 417,348 28.3 5 Antioquia 2,021,317 25.51 19 Cauca 446,907 24.67 41 Huila 334,501 22.72 54 Norte de Santander 351,457 22.28 52 Nariño 467,848 21.16 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 196 Figure A2.6. Population Exposed to Fault Lines as Proxy of Table A2.2. Exposed Population to Landslides at Department Level, Landslides at Department Level Ordered by Percentage of Exposed Population Department Percentage of Department name Exposed population code exposed population 66 Risaralda 152,899 13.47 73 Tolima 207,919 11.95 54 Norte de Santander 143,245 9.08 19 Cauca 150,655 8.32 68 Santander 171,284 6.4 63 Quindio 43,361 6.19 41 Huila 89,468 6.08 76 Valle del Cauca 313,288 5.56 85 Casanare 40,958 4.48 18 Caquetá 29,510 4.39 52 Nariño 90,980 4.11 27 Chocó 27,756 4 17 Caldas 41,404 3.86 5 Antioquia 298,374 3.77 50 Meta 46,497 3.75 15 Boyacá 53,915 3.66 88 San Andres 3,735 3.45 47 Magdalena 56,581 3.44 20 Cesar 31,984 2.58 99 Vichada 2,270 2.37 25 Cundinamarca 90,983 2.33 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 197 Department Department name Exposed population Percentage of Infrastructure Exposure to Hazards code exposed population Figure A2.7. Exposed Healthcare Facilities to Floods and Fault 13 Bolívar 74,110 2.22 Lines as Proxy of Landslides, Where (A) Depicts the Exposed 23 Córdoba 51,012 2.01 Primary Healthcare Facilities, and (B) and (C) Illustrate the Exposed 86 Putumayo 9,068 1.66 Hospitals 44 La Guajira 23,375 1.58 (A) PHCs 81 Arauca 6,840 1.5 30 70 Sucre 12,297 1.2 25 97 Vaupés 142 0.14 Percent of exposed PHCs 8 Atlántico 2,075 0.06 20 11 Bogotá 2,198 0.02 15 91 Amazonas 0 0 94 Guainía 0 0 10 95 Guaviare 0 0 5 0 (B) Category II hospitals (C) Category III hospitals 30 30 Percent of exposed category III hospitals Percent of exposed category II hospitals 25 25 20 20 15 15 10 10 5 5 0 0 Risk Floods Landslides IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 198 Figure A2.8. Exposed Primary Health Care Facilities to Floods, by Figure A2.9. Hospitals Exposed to Floods by Department Department (A) Category II hospitals (B) Category III hospitals exposed to floods by to exposed floods by department department IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 199 Table A2.3. Healthcare Infrastructure Exposed to Floods at Department Level, Ordered by Percentage of Exposed Population Number of healthcare facilities exposed to floods Percent of healthcare facilities exposed to floods Department code Department name PHC Category II Category III PHC Category II Category III Total 95 Guaviare 14 1 0 50 100 0 51.72 27 Chocó 135 1 0 50.75 100 0 50.94 91 Amazonas 10 8 0 41.67 66.67 0 50 20 Cesar 306 0 0 50.58 0 0 49.92 47 Magdalena 309 7 1 48.13 50 100 48.25 81 Arauca 74 5 0 44.85 45.45 0 44.89 86 Putumayo 53 4 0 44.54 40 0 44.19 94 Guainía 5 1 0 38.46 100 0 42.86 76 Valle del Cauca 813 9 0 42.37 39.13 0 42.28 97 Vaupés 4 17 0 20 41.46 0 34.43 52 Nariño 245 0 1 32.8 0 100 32.71 54 Norte de Santander 166 3 1 32.94 15 100 32.38 5 Antioquia 603 22 4 28.38 24.44 50 28.3 15 Boyacá 141 7 0 26.01 18.92 0 25.52 99 Vichada 3 1 0 23.08 25 0 23.53 19 Cauca 111 0 1 23.37 0 100 23.33 50 Meta 96 0 0 21.15 0 0 21.05 13 Bolívar 165 3 0 16.8 33.33 0 16.94 41 Huila 57 3 0 15.79 60 0 16.26 68 Santander 164 4 0 15.05 44.44 0 15.23 11 Bogotá 438 0 14 15.02 0 10.69 14.81 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 200 Number of healthcare facilities exposed to floods Percent of healthcare facilities exposed to floods Department code Department name PHC Category II Category III PHC Category II Category III Total 17 Caldas 49 4 0 14.29 30.77 0 14.76 85 Casanare 29 0 0 14.36 0 0 14.29 18 Caquetá 15 0 0 14.15 0 0 13.64 44 La Guajira 46 7 0 12.74 22.58 0 13.52 66 Risaralda 47 3 0 12.84 37.5 0 13.33 25 Cundinamarca 96 8 0 12.83 14.29 0 12.94 23 Córdoba 78 0 0 9.29 0 0 9.22 88 San Andres 1 0 1 4.55 0 25 7.69 70 Sucre 40 1 0 7.14 25 0 7.27 8 Atlántico 69 0 0 5.76 0 0 5.74 73 Tolima 28 1 0 5.27 11.11 0 5.35 63 Quindio 6 0 0 2.54 0 0 2.42 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 201 Figure A2.10. Primary Health Care Facilities Exposed to Landslides Figure A2.11. Hospitals Exposed to Landslides by Department by Department (A) Category II hospitals (B) Category III hospitals exposed to landslides by to exposed landslides by department department IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 202 Table A2.4. Healthcare Infrastructure to Exposed Landslides at Department Level, Ordered by Percentage of Exposed Population Number of healthcare facilities exposed to floods Percent of healthcare facilities exposed to floods Department Department name code PHC Category II Category III PHC Category II Category III Total 73 Tolima 59 0 1 11.11 0 50 11.07 66 Risaralda 37 2 0 10.11 25 0 10.4 76 Valle del Cauca 189 3 0 9.85 13.04 0 9.88 54 Norte de Santander 25 4 0 4.96 20 0 5.52 17 Caldas 17 2 0 4.96 15.38 0 5.29 27 Chocó 14 0 0 5.26 0 0 5.24 86 Putumayo 5 0 0 4.2 0 0 3.88 20 Cesar 23 0 0 3.8 0 0 3.75 15 Boyacá 19 1 0 3.51 2.7 0 3.45 25 Cundinamarca 26 1 0 3.48 1.79 0 3.36 47 Magdalena 21 1 0 3.27 7.14 0 3.35 19 Cauca 15 0 0 3.16 0 0 3.12 85 Casanare 6 0 0 2.97 0 0 2.96 52 Nariño 21 0 0 2.81 0 0 2.79 18 Caquetá 2 0 0 1.89 0 0 1.82 68 Santander 20 0 0 1.83 0 0 1.81 41 Huila 6 0 0 1.66 0 0 1.63 63 Quindio 2 1 0 0.85 9.09 0 1.21 81 Arauca 2 0 0 1.21 0 0 1.14 5 Antioquia 20 3 1 0.94 3.33 12.5 1.08 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 203 Number of healthcare facilities exposed to floods Percent of healthcare facilities exposed to floods Department Department name code PHC Category II Category III PHC Category II Category III Total 23 Córdoba 8 0 0 0.95 0 0 0.95 50 Meta 3 0 0 0.66 0 0 0.66 13 Bolívar 6 0 0 0.61 0 0 0.6 70 Sucre 2 0 0 0.36 0 0 0.35 44 La Guajira 1 0 0 0.28 0 0 0.26 8 Atlántico 0 0 0 0 0 0 0 11 Bogotá 0 0 0 0 0 0 0 88 San Andres 0 0 0 0 0 0 0 91 Amazonas 0 0 0 0 0 0 0 94 Guainía 0 0 0 0 0 0 0 95 Guaviare 0 0 0 0 0 0 0 97 Vaupés 0 0 0 0 0 0 0 99 Vichada 0 0 0 0 0 0 0 IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 204 Table A2.5. Information Used for Clustering Departments Exposed to Floods IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 205 Table A2.6. Information Used for Clustering Departments Exposed to Fault Line as Proxy for Landslides IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 206 Facility Prioritizations Table A2.7. Health Facility Prioritization at Department Level, Top Three PHCs for Flooding Hospital/PHC ID Name Category Priority Department Hospital/PHC ID Name Category Priority Department 910010001901 -- 1 1 Amazonas 110011617101 -- 1 2 Bogotá, D.C. 915400001925 -- 1 2 Amazonas 110011552801 -- 1 3 Bogotá, D.C. 917980001927 -- 1 3 Amazonas 135490035501 -- 1 1 Bolívar Empresa Social del E.S.E. Hospital San 135490009501 1 2 Bolívar Estado Hospital Nicolas de Tolentino 53900509001 1 1 Antioquia Antonio Roldan E.S.E. Hospital San Betancur 135490009504 1 3 Bolívar Nicolas de Tolentino Empresa Social del 152380277101 -- 1 1 Boyacá 55850472501 Estado Hospital 1 2 Antioquia Octavio Olivares 151760162001 -- 1 2 Boyacá 58370556846 -- 1 3 Antioquia 150470210507 -- 1 3 Boyacá 810010053901 -- 1 1 Arauca 238070092904 -- 1 1 Córdoba Empresa Social del 230680209605 -- 1 2 Córdoba 810010006101 Estado Jaime Alvarado 1 2 Arauca y Castilla E.S.E. Hospital San 238070010607 1 3 Córdoba Jose de Tierralta E.S.E. Moreno y 817940020634 1 3 Arauca E.S.E. Hospital San Clavijo 177770005401 1 1 Caldas Lorenzo de Supia Archipielago de San 880010027904 -- 1 1 Andres, Providencia 173800051903 -- 1 2 Caldas y Santa Catalina 173800051905 -- 1 3 Caldas 80010322901 -- 1 3 Atlantico 180010755103 -- 1 1 Caquetá 80010445421 -- 1 1 Atlántico 187530747501 -- 1 2 Caquetá 80010423901 -- 1 2 Atlántico 182470748201 -- 1 3 Caquetá 110010101031 -- 1 1 Bogotá, D.C. 853250010001 -- 1 1 Casanare IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 207 Hospital/PHC ID Name Category Priority Department Hospital/PHC ID Name Category Priority Department Red Salud Casanare Empresa Social 853250042213 1 2 Casanare E.S.E. del Estado Red de 950010000302 1 1 Guaviare Servicios de Salud de 852500282501 -- 1 3 Casanare Primer Nivel Empresa Social del 950010013701 -- 1 2 Guaviare 195130719602 Estado Norte 3 - 1 1 Cauca E.S.E. 950010000101 -- 1 3 Guaviare 193000913901 -- 1 2 Cauca 413960129508 -- 1 1 Huila 195730618805 -- 1 3 Cauca 410010217001 -- 1 2 Huila Hospital San Jose E.S.E. San Sebastian 203830045301 1 1 Cesar 413960043213 1 3 Huila E.S.E. de La Plata Huila 200130167803 -- 1 2 Cesar 443780033910 -- 1 1 La Guajira E.S.E. Hospital 443780057101 -- 1 2 La Guajira 200010046440 Eduardo Arredondo 1 3 Cesar Daza 440900033914 -- 1 3 La Guajira 274950007115 -- 1 2 Chocó E.S.E. Hospital Local 475700024401 1 1 Magdalena San Jose 276150111201 -- 1 1 Chocó 471890024205 -- 1 2 Magdalena E.S.E. Hospital 270010002617 Local Ismael Roldan 1 3 Chocó 471890024208 -- 1 3 Magdalena Valencia 500010149203 -- 1 1 Meta 257540380801 -- 1 1 Cundinamarca E.S.E. del Municipio 500010054022 1 2 Meta 252690222101 -- 1 2 Cundinamarca de Villavicencio 252690014925 -- 1 3 Cundinamarca 500010199501 -- 1 3 Meta 85730070701 -- 1 2 Guainía Centro Hospital 528350090509 1 1 Nariño Divino Niño E.S.E. 85730200501 -- 1 3 Guainía 524900066925 -- 1 2 Nariño 943430005702 -- 1 1 Guainía Empresa Social del 520010145712 Estado Pasto Salud 1 3 Nariño E.S.E. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 208 Hospital/PHC ID Name Category Priority Department Hospital/PHC ID Name Category Priority Department E.S.E. Hospital Local E.S.E. Centro de 544050100801 1 1 Norte de Santander 702650099401 1 3 Sucre Municipio Los Patios Salud de Guaranda 540010086112 E E.S.E. Imsalud 1 2 Norte de Santander 734490011101 -- 1 1 Tolima 540010060304 -- 1 3 Norte de Santander Hospital Nuestra 730670081801 Señora de Lourdes 1 2 Tolima E.S.E. Hospital E.S.E. 868650001001 Sagrado Corazon de 1 1 Putumayo Jesus 734490197501 -- 1 3 Tolima E.S.E. Hospital 763640375607 -- 1 1 Valle del Cauca 868650001006 Sagrado Corazon de 1 2 Putumayo Jesus 763641109802 -- 1 2 Valle del Cauca 867600079403 -- 1 3 Putumayo Red de Salud del Oriente Empresa E.S.E. Hospital San 760010395725 1 3 Valle del Cauca 633020043001 1 1 Quindio Social del Estado Vicente de Paul E.S.E Hospital San Roque 970010000601 -- 1 1 Vaupés de Cordoba Quindio 632120050301 1 2 Quindio Empresa Social del 970010001301 -- 1 2 Vaupés Estado 970010000101 -- 1 3 Vaupés 631300040306 -- 1 3 Quindio 996240012201 -- 1 1 Vichada 664000120302 -- 1 1 Risaralda 996240000609 -- 1 2 Vichada 664000186101 -- 1 2 Risaralda 995240000608 -- 1 3 Vichada 664000020811 -- 1 3 Risaralda 683070214401 -- 1 1 Santander 683070212014 -- 1 2 Santander 680810502902 -- 1 3 Santander E.S.E. Hospital Local 707130039008 1 1 Sucre de San Onofre 707710129802 -- 1 2 Sucre IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 209 Table A2.8. Health Facility Prioritization at Department Level, Top Hospital ID Name Category Priority Department Three Hospitals for Flooding Unidad de Servicios de 110013029132 3 3 Bogotá, D.C. Salud Verbenal Hospital ID Name Category Priority Department E.S.E. Hospital La Divina Centro de Salud Tarapaca 134680049204 Misericordia Sede San 2 1 Bolívar 917980001927 - E.S.E Hospital San Rafael 2 1 Amazonas Juan de Dios Mompós de Leticia 134300049205 Santisima Trinidad 2 2 Bolívar Centro de Salud Tarapaca E.S.E. Hospital Nuestra 916690001926 - E.S.E Hospital San Rafael 2 2 Amazonas 132440049301 2 3 Bolívar Se√ëora del Carmen de Leticia Unidad Basica de Atencion Centro de Salud Puerto 155180079803 2 1 Boyaca del Municipio de Pajarito 915360001920 Arica- E.S.E Hospital San 2 3 Amazonas Hospital Regional de Rafael de Leticia 157590079801 Sogamoso Empresa 2 2 Boyaca ESE Hospital Cesar Uribe Social del Estado 51540220101 2 1 Antioquia Piedrahita Puesto de Salud Pueblo ESE Hospital San Rafael 155720080702 2 3 Boyaca 53600433902 2 2 Antioquia Nuevo de Itagui Centro de Salud San Centro de Salud Felix 176140087404 2 1 Caldas 58370228703 2 3 Antioquia Lorenzo Londoño 176530064609 Centro de Salud San Felix 2 2 Caldas 810010007701 Hospital San Vicente ESE 2 1 Arauca Puesto de Salud de Puesto de Salud Bajo San 173800051903 2 3 Caldas 817360006711 2 2 Arauca Guarinocito Joaquin Hospital Universitario San Puesto de Salud Puerto 190010003101 Jose de Popayan Empresa 3 1 Cauca 817360006715 2 3 Arauca Lleras Social del Estado Archipielago Nueva Empresa Social Hospital Local de de San Andres, del Estado Hospital 885640027901 3 1 270010116901 2 1 Chocó Providencia Providencia y Departamental San Santa Catalina Francisco de Asis Unidad de Servicios de Centro de Salud de 110013029640 3 1 Bogotá, D.C. 257450002603 2 1 Cundinamarca Salud San Bernardino Simijaca - (257450002603) Unidad de Servicios de 110013029131 3 2 Bogotá, D.C. Salud Orquideas IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 210 Hospital ID Name Category Priority Department Hospital ID Name Category Priority Department E.S.E. Hospital San Hospital San Juan de Dios Norte de 545180037201 2 2 255130002801 Rafael de Pacho - 2 2 Cundinamarca de Pamplona Santander (255130002801) Centro de Salud Divino Norte de 542230037206 2 3 Puesto de Salud de Pasca Niño de Cucutilla Santander 255350003611 2 3 Cundinamarca - (255350003611) Puesto de Salud Pi√ëu√ëa 865730018505 2 1 Putumayo 975110000155 U.B.P. Puerto Esperanza 2 1 Guainía negro Empresa Social del Estado Centro de Salud Santiago 867490001703 2 2 Putumayo 950010000101 Hospital San Jose del 2 1 Guaviare Rengifo Guaviare Jose María Hernandez Ese Hospital Promoción y 413960040702 Departamental San 2 1 Huila 860010003823 2 3 Putumayo Mantenimiento de La Antonio de Padua Salud Centro Integral de 412980041902 2 2 Huila Empresa Social del Estado Terapias 664000071601 Hospital San Pedro y San 2 1 Risaralda 412980041903 Sede Ambulatoria 2 3 Huila Pablo La Virginia Puesto de Salud Los Centro de Atención 446500028614 2 1 La Guajira Tunales 661700027805 Ambulatorio Santa 2 2 Risaralda 446500028607 Centro de Salud Caracoli 2 2 La Guajira Teresita Empresa Social del Estado 661700027803 Puesto de Salud Frailes 2 3 Risaralda 446500028601 2 3 La Guajira Hospital San Rafael Empresa Social del Estado Empresa Social del Estado 680810079701 Hospital Regional del 2 1 Santander 472450024901 2 1 Magdalena Magdalena Medio Hospital La Candelaria Hospital Universitario Julio Empresa Social del Estado 470010065001 3 2 Magdalena 682760071701 Hospital San Juan de Dios 2 2 Santander Mendez Barreneche de Floridablanca 471890024207 Puesto de Salud Miramar 2 3 Magdalena Hospital Psiquiatrico Hospital Universitario 680810070202 San Camilo Sede 2 3 Santander 520010110201 3 1 Nariño Departamental de Nariño Barrancabermeja E.S.E Hospital Ese Hospital Regional Ii Norte de 707080033101 2 1 Sucre 540010037101 Universitario Erasmo 3 1 Nivel de San Marcos Santander Meoz IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 211 Hospital ID Name Category Priority Department Table A2.9. Health Facility Prioritization at Department Level, Top Hospital San Rafael de El Three PHCs for Landslides 732680079401 Espinal Empresa Social 2 1 Tolima del Estado E.S.E. PHC ID Name Category Priority Department Hospital Isaias Duarte 50010210903 -- 1 1 Antioquia 760010511501 Cancino Empresa Social 2 1 Valle del Cauca del Estado 51010213902 -- 1 2 Antioquia E.S.E. Hospital 51010213903 -- 1 3 Antioquia Departamental Tomas 768340465201 2 2 Valle del Cauca 817940029501 -- 1 1 Arauca Uribe Uribe de Tulua Ese Empresa Social del Estado 817940011701 -- 1 2 Arauca Puesto de Salud De 766220170911 2 3 Valle del Cauca E.S.E. Hospital Local San Cajamarca 136700007601 1 1 Bolívar Pablo 970010000154 U.B.P. Camanaos 2 1 Vaupes E.S.E. Hospital San Juan 138100002707 1 3 Bolívar 970010000150 U.B.P. Virabazu 2 2 Vaupes De Puerto Rico E.S.E. Hospital San 138730074801 -- 1 2 Bolívar 970010000101 2 3 Vaupes Antonio 151760202902 -- 1 1 Boyaca Hospital Sede Santa 996240000609 2 1 Vichada Empresa Social del Estado Rosalia 153670006401 1 2 Boyaca Centro de Salud Jenesano 151760193501 -- 1 3 Boyaca 235550198901 -- 1 1 Córdoba 235550189302 -- 1 2 Córdoba E.S.E. Camu De San 236860045311 1 3 Córdoba Pelayo 170010020402 -- 1 1 Caldas 170010081746 Assbasalud E.S.E. 1 2 Caldas 170010221601 -- 1 3 Caldas 182560200203 E.S.E. Sor Teresa Adele 1 1 Caqueta 182470705501 -- 1 2 Caqueta IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 212 PHC ID Name Category Priority Department PHC ID Name Category Priority Department 852790042217 Red Salud Casanare E.S.E. 1 1 Casanare 446500028613 -- 1 1 La Guajira Empresa Social del Estado Empresa Social del Estado 850010014406 1 2 Casanare Salud Yopal 479800023813 Hospital Local de Zona 1 1 Magdalena Bananera 852300042209 Red Salud Casanare E.S.E. 1 3 Casanare Ese Alejandro Prospero Cxayu`ce Jxut Empresa 470010007129 1 2 Magdalena 198210004001 1 1 Cauca Reverend Social del Estado 470010114801 -- 1 3 Magdalena 190500011413 Ese Sur Occidente 1 2 Cauca 500010133101 -- 1 1 Meta Empresa Social del Estado 191100000505 1 3 Cauca Norte 1 E.S.E. 506890150501 -- 1 2 Meta 202380053102 Hospital San Roque Ese 1 1 Cesar Empresa Social del Estado Hospital Heli Moreno 506890045701 Hospital Local de San 1 3 Meta 205170051801 1 2 Cesar Martin de Los Llanos Blanco E.S.E E.S.E. Hospital Inmaculada Ese Centro de Salud San 526850137801 1 1 Nariño 201750036015 Concepcion de 1 3 Cesar Bernardo Chimihcagua 520010202302 -- 1 2 Nariño 277870040702 -- 1 1 Chocó Ese Centro de Salud San 526850137802 1 3 Nariño Bernardo 273610034201 -- 1 2 Chocó Empresa Social del E.S.E. Hospital San Jose Norte de 277870009701 1 3 Chocó 542450102016 Estado Hospital Regional 1 1 de Tado Santander Noroccidental 252690222101 -- 1 1 Cundinamarca Norte de 544980190101 -- 1 2 252690014925 -- 1 2 Cundinamarca Santander Norte de 252690396801 -- 1 3 Cundinamarca 540010206602 -- 1 3 Santander E.S.E. Hospital San Jose 413590042402 1 1 Huila 867550001704 -- 1 1 Putumayo de Isnos Empresa Social del Estado 867490068501 -- 1 2 Putumayo 413570047401 1 2 Huila Hospital Maria Auxiliadora 867550079405 -- 1 3 Putumayo E.S.E. Carmen Emilia 410010045107 1 3 Huila 630010122302 -- 1 1 Quindio Ospina IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 213 PHC ID Name Category Priority Department Table A2.10. Health Facility Prioritization at Department Level, Top Three Hospitals for Landslides 630010114501 -- 1 2 Quindio 661700227002 -- 1 1 Risaralda Hospital Name Nivel Priority Department 661700027804 -- 1 2 Risaralda E.S.E Hospital San Vicente de 51290214601 2 1 Antioquia 661700075907 -- 1 3 Risaralda Pa√öl de Caldas Ese Hospital San Juan de Dios de 682550239101 -- 1 1 Santander 50040547802 2 2 Antioquia Abriaqui Empresa Social del Estado E.S.E Hospital La Merced de 51010213901 2 3 Antioquia 680010070117 Instituto de Salud de 1 2 Santander Ciudad Bolivar Bucaramanga 156730033202 Centro de Salud de San Mateo 2 1 Boyaca 680010427302 -- 1 3 Santander 176530064609 Centro de Salud San Felix 2 1 Caldas 705080189201 -- 1 1 Sucre 176530064607 Centro de Promocion y Prevencion 2 2 Caldas 700010139601 -- 1 2 Sucre Puesto de Salud Pe√ëa Negra - 251230004313 2 1 Cundinamarca (251230004313) 730010297401 -- 1 1 Tolima 471890024204 Puesto de Salud de Palmor 2 1 Magdalena Hospital Centro E.S.E. de 735550103101 1 2 Tolima Empresa Social del Estado Hospital Norte de Planadas 544980054701 2 1 Emiro Quintero Ca√ëizares Santander 730010255901 -- 1 3 Tolima Norte de 544980054704 Puesto de Salud Otare 2 2 760011261801 -- 1 1 Valle del Cauca Santander Norte de Red de Salud de Ladera 543440054709 Centro de Salud de Hacari 2 3 760010395903 1 2 Valle del Cauca Santander Empresa Social del Estado 631300040307 Centro de Salud Balcones 2 1 Quindio 760010837601 -- 1 3 Valle del Cauca 661700027803 Puesto de Salud Frailes 2 1 Risaralda Centro de Atención Ambulatorio 661700027804 2 2 Risaralda Jap√ìn Hospital Federico Lleras Acosta 730010104701 3 1 Tolima E.S.E. Centro de Rehabilitación En Salud Valle del 760010360902 2 1 Mental - Cresm Cauca E.S.E. Hospital Departamental Valle del 760010360901 2 2 Psiquiátrico Universitario del Valle Cauca Valle del 766220170904 Puesto de Salud Higueroncito 2 3 Cauca IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 214 Artificial Intelligence-Based Integrated block-level population census (Stevens et al. 2015). Figure A2.2a Climate-Sensitive Risk Index: Method (above) illustrates examples of the (un)constrain and block-level geolocated population representation, respectively. Details This section describes the methodology to compute the risk index This approach can also handle different political divisions to for the exposed population and healthcare infrastructure impacted perform a national or subnational exposure analysis. Thus, different by a natural hazard as depicted in Figure 25. administrative levels are supported. For instance, Figure A2.2 (above) shows different administrative levels where the exposure The first part of the methodology takes as input a natural hazard analysis can be performed. under study (c.f. Figure A2.4). The natural hazard is represented as geometric location and attributes information of geographical A spatial intersection can be defined as a point where two or more entities. The geographic entities are represented by points, lines, spatial objects, like lines or polygons, intersect. Intersection points or polygons (areas). Associated with geographical information, the are used for different objectives, such as location identification, spatial entity contains attribute values representing the intensity relationship definition between features, and spatial analysis. For or quantity of the hazard. This element will be used to identify both example, an intersection can be used to determine if a census unit the population and the healthcare infrastructure exposed to the block overlaps a hazard polygon. If a spatial object A intersects all natural hazard. its surfaces with another spatial entity B, it can be inferred that B includes A. Thus, this operation is used to determine whether a The second step looks at the geolocated population and the healthcare facility is contained in a polygon representing a hazard location of the healthcare facilities. Regarding the geolocated for exposure analysis. It is essential to highlight that census units population representation, the method is able to digest top-down or blocks outside administrative boundaries are assigned to the unconstrained population grid (National Service of Meteorology closest administrative region. and Hydrology of Peru 2023; Gaughan et al. 2016; Sorichetta et al. 2015), which is most suitable for historical or change analyses. For To output the exposure analysis for population, our approach some countries, top-down constrained population grid (Sorichetta counts the number of individuals where the census units or blocks 2015; WorldPop 2023) is more convenient and accurate, or centroid intersects with the polygon representing the natural IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 215 hazard. For healthcare facilities, the method counts the number of To characterize a territorial unit, the method uses vectors facilities contained in a polygon modeling a natural hazard filtered composed of different dimensions, such as poverty, hospital by healthcare service complexity, such as primary healthcare and density, number of native populations, percentage of exposed hospitals. population, percentage of exposed primary healthcare infrastructure, percentage of exposed hospitals, accessibility time Once the exposed population and healthcare infrastructure to the healthcare system (Tariverdi et al. 2023), etc. For instance, are identified, it is necessary to integrate these results to the Figure A2.12 depicts the similarity θ of vectors X and I, which population’s sociodemographic characteristics and healthcare represent two different districts. In this example, x is the poverty facilities’ features. index, y is the percentage of exposed population, and z is the percentage of exposed hospitals. Thus, to compute the distance The idea behind the use of these variables, by territorial unit, between them, the Cosine Similarity represented by the following is to represent the vulnerability of the exposed population and equation is used. healthcare infrastructure. Additionally, vulnerability could be composed of different elements such as sensibility or fragility, adaptive capacity, health damage, etc. The proposed tool is flexible in integrating different vulnerability conceptual frameworks. There are two methods to establish the risk level (very high, high, medium, or low) of the territorial units: grouping and risk index. Figure A2.12. Cosine Similarity Between the Ideal Vector I and a The former relies on an unsupervised Machine Learning technique Vector X Representing Two Districts called k-means (Hartigan and Wong 1979), which uses the Cosine similarity (Lahitani, Permanasari and Setiawan 2016) as distance function. Therefore, the k-means algorithm is able to group the territorial units into four different groups (k=4) sharing the same hazard exposure, socioeconomic attributes, and infrastructure features. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 2. Component 2 216 The risk index method establishes a risk index ranking using Table A2.11. Targets of Different Factor Variables cosine similarity. Accordingly, an ideal vector I, capturing country objectives is defined. For example, an ideal vector could be Target value % Female-headed households with no spouse and children under the age represented by 10 percent of poor population, 5 percent of 0.05 of 18 exposed population, and 15 percent of the exposed hospitals. No access to reliable electricity 0.05 Therefore, the ranking measures how far a territorial unit is from No sewerage system at dwelling 0.05 the ideal values. Then, the quarterlies of the ranking are tagged as No access to internet 0.05 very high, high, medium, and low risk level. It is important to note Dwelling with more than two families 0.01 that the example only consider three features, but the groups Prevalence of protein intake deficiency by department 0.05 method can handle high dimensional characterizations depending Chronic malnutrition in children 0 to 4 years old 0.065 on the sector under study and availability of data. Mortality rate due to malnutrition per 100,000 inhabitants females 0.065 Mortality rate due to malnutrition per 100,000 inhabitants males 0.065 Average distance to primary health care (km) 2 Average distance to hospitals (km) 5 Exposed population 0.05 Percentage of exposed Primary healthcare 0.01 Percentage of exposed hospital category II 0.01 Percentage of exposed hospital category III 0.01 Native and indigenous population 0.05 Number of beds per department* 2.2 Number of physicians per department* 3.5 Number of nurses per department* 9 * The consulted document reports statistics for the year 2016. IMPACT OF CLIMATE CHANGE IN HEALTH IN COLOMBIA AND RECOMMENDATIONS FOR MITIGATION AND ADAPTATION ANNEX 3. COMPONENT 3 217 ANNEX 3. COMPONENT 3 Figure A3.1. Results of Performance Measurements at the Departmental Level in Colombia, 2021 Source: Prepared using data from the DNP (2021). 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