58168 d i s c u s s i o n pa p e r n u m B e r 11 decemBer 2010 d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 1 d e v e l o p m e n t a n d c l i m a t e c h a n g e Costs of Adapting to Climate Change for Human Health in Developing Countries d i s c u s s i o n pa p e r n u m B e r 11 decemBer 2010 d e v e l o p m e n t a n d c l i m a t e c h a n g e Costs of Adapting to Climate Change for Human Health in Developing Countries Kiran pandey* * Note: This is one of several papers commissioned by the World Bank as part of the Economics of Adaptation to Climate Change study. The results reported in the paper are preliminary and subject to revision. They have benefited from helpful comments from Armin Fidler, Gordon Hughes, Sergio Margulis, Urvashi Narain, Tamer Rabie, and participants at two workshops in Washington on the EACC study. 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Note: All dollars are U.S. dollars unless otherwise indicated. taBle oF contents abstract vii secton 1. introduction 1 section 2. methodology and data 3 Baseline Health­Incidence and Burden of Disease 3 Relative Risks from Climate Change 7 Potential Health Impacts of Climate Change 10 Adaptation Costs for Diarrheal Disease and Malaria 10 section 3. sensitivity analysis 13 section 4. discussion 15 Bibliography 16 annex. list of countries by World Bank income group and region 19 Box 1 Fuzzy distribution model for malaria suitability 9 TaBles 1 comparison of methods and data in this study and unFccc 2007 4 2 revisions to current Baseline incidence and Burden of disease 5 3 Baseline incidence and Burden of disease projections, 2010­50 6 4 Baseline incidence and Burden of disease projections, by region, 2010­50 6 5 relative risk: incidence of diarrheal disease from climate change, by region, for 2010­50 9 6 relative risk: Burden of disease (dalYs) of malaria from climate change, by region, 2010­50 9 iv t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e 7 diarrheal disease due to climate change: additional incidence and Burden, by region, 2010­50 10 8 malaria due to climate change: additional incidence and Burden, by region, 2010­50 11 9 average annual adaptation cost to prevent and treat malaria and diarrhea, by year, 2010­50 (billion 2005 dollars) 12 10 average annual adaptation cost to prevent and treat malaria and diarrhea, by region, 2010­50 (billion 2005 dollars) 12 11 sensitivity of average annual adaptation cost for human health, selected years (billion 2005 dollars) 14 d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s vii aBstract This paper is one component of a global study on the and diarrhea and updates to the exposure-response Economics of Adaptation to Climate Change (EACC) functions used to compute the relative risk for malaria. in developing countries; the focus in this paper is human health. The main human health impacts of Average annual adaptation costs in the health sector for climate change are increased incidence of vector-borne diarrhea and malaria prevention and treatment are disease (malaria), water-borne disease (diarrhea), cardio- around $2 billion over the 40-year period 2010­50. respiratory diseases, heat- and cold-related deaths, inju- These estimates are lower than prior estimates of $4­12 ries and deaths from extreme weather events (flooding), billion in 2030. The estimated adaptation costs in 2010 and a greater prevalence of malnutrition. Adaptation lie between $3 billion and $5 billion and decline over measures comprise all actions taken to reduce, prevent, time in absolute terms to less than half that amount by or treat these additional cases of disease or death, 2050. Although the declines are consistent across including actions outside the health sector such as regions, the rate of decline is faster in South Asia and disaster reduction programs, food and water security East Asia and Pacific than in other regions. As a result, measures, and the provision of infrastructure services. by 2050 more than 80 percent of the health sector adap- For tractability and to reduce duplication with other tation costs for malaria and diarrheal diseases are components of the EACC study, the scope of this paper incurred by countries in Sub-Saharan Africa. is limited to conventional public health adaptation activities, with a focus on malaria and diarrhea. Estimating the adaptation costs in the health sector is challenging not only because of the large existing uncer- Adaptation costs are computed for these two diseases in tainties about how the climate will evolve over the each country for each of 16 demographic groups. Costs coming century but also because of the complex and depend on the baseline incidence of disease without often poorly understood chains through which health climate change, the additional risk that climate change impacts are mediated. Climate change is difficult to poses, and the unit cost of preventing and treating addi- predict with accuracy in any projection model that has tional cases of the disease. Earlier estimates of the to contend with uncertainty about potential collective global cost of adaptation followed a similar approach actions to mitigate greenhouse gases as well as unknown but held the baseline incidence of disease (the number factors in climate science itself. The health outcomes of people affected) fixed at current levels. This study that are linked to climate change also depend on a host incorporates a future baseline burden of disease based of other factors as well, some of which are likely not on World Health Organization projections through currently anticipated, such as the emergence of new 2030 and extensions through 2050 using the same diseases, and others that are difficult to predict, such as methods. These projections imply significant reductions the development of vaccines to address existing and new in both the incidence and the incidence rates of ailments. Among the sources of uncertainty that are communicable diseases such as malaria and diarrhea. amenable for quantitative analysis, the baseline health This study also incorporates updates and revisions to status of a country is the single largest determinant of the unit cost of prevention and treatment for malaria the likely impacts of climate change and the cost of adapting to it. d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 1 IntroductIon increase cases of diarrheal diseases; increase cardio- respiratory diseases where ozone exposure concentra- This paper is one component of a global study on the tions rise; increase the number of people at risk of cost of adapting to climate change in developing coun- dengue fever; increase the geographic range and length tries over the period from 2010 to 2050; the focus of of the transmission season of malaria in some regions this paper is human health. The potential impacts of and decrease the range in others; and bring some bene- climate change on human health have been documented fits to health, including fewer deaths due to exposure to extensively in the literature. Most of the health the cold. outcomes related to climate change already occur today as a result of other risk factors. The need to attribute There is a great deal of uncertainty about the magni- these health outcomes to different risk factors, including tude of these potential impacts. Since health records, for climate change, creates uncertainty about the magnitude the most part, do not indicate climate change as the of the potential impacts. Adaptation to climate change cause for a particular health outcome, most impact esti- entails the prevention of the adverse health outcomes mates are based on models, acting primarily as a multi- (mortality and morbidity) that specifically result from plier to existing health risk factors. The potential climate change. Adaptation actions that affect health impacts depend on three factors: the exposure to the outcomes are often either implemented to address climatic-risk factor, the exposure-response function, and multiple goals (access to water supply and sanitation) or the baseline frequency of the health outcome (incidence taken outside of the health sector (reduce malnourish- of disease, cause of injury, or premature death). The ment through increased agricultural production). As expected changes in the exposure to climatic factors are such, estimates of adaptation cost necessarily depend on combined with the exposure-response function to deter- the sectoral boundaries that are set, which in this study mine the proportion of a specific health outcome that is have been defined in relation to other components of attributable to climate. The baseline frequency of the the Economics of Adaptation to Climate Change health outcome is used to convert these proportions to (EACC) study to avoid duplication. absolute impacts. The uncertainty in each of these three factors contributes to the uncertainty in the estimated The report on health from Working Group II in the potential impacts of climate change. Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concluded that First, there is a great deal of uncertainty about the climate change has begun to negatively affect human precise evolution of climate, which depends partly on health and that projected changes in climate are likely global efforts to reduce the emission of greenhouse to increase the risks of climate-sensitive health gases (GHGs) and on the way different General outcomes (Confalonieri et al. 2007). Specifically, climate Circulation Models (GCMs) translate emission patterns change is expected to increase malnutrition and conse- into climate outcomes. Second, the use of exposure- quent health disorders, including child growth and response relationships between climatic factors and development; increase injuries, illnesses, and deaths due health outcomes that are estimated in one location and to heat waves, floods, droughts, storms, and fires; time period to estimate the health outcomes in a 2 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e different location or period can be biased and imprecise EACC study. After a rigorous assessment of the if the estimated relationships have not been appropri- evidence, the World Health Organization (WHO) ately controlled for differences in non-climate risk Global Burden of Diseases (GBD) study estimated the factors. Finally, economic development has improved global health impacts of climate change from malaria, the health conditions across the world and is likely to diarrheal diseases, malnutrition, coastal flooding and continue doing so, changing the baseline burden of inland flooding (McMichael 2004). Other health disease. The extent to which the historical patterns impacts were not included largely because of the lack of observed in developed countries repeat themselves in models to quantify these impacts globally. The GBD developing countries depends on many factors, includ- used two summary measures of population health: ing the efforts of developing countries to improve disability-adjusted life years lost (DALYs) and mortal- health outcomes commensurate with their level of ity. DALYs provide a better measure of population development, the extent to which major global initia- health impacts as they include both the mortality and tives such as the rollback malaria program succeed, the morbidity impacts. About half of the deaths and disease emergence of new technologies and vaccines, and the burden attributed to climate change in the WHO study emergence of new diseases like HIV, SARS, H5N1, or were due to malnutrition, with the remainder about H1N1. equally split between malaria and diarrheal diseases.1 Estimating the cost of adapting for human health is The complementary EACC study on the cost of agri- challenging also because adaptation actions that affect culture adaptation examines the impacts of climate health outcomes are often either implemented to change on food production and availability and its address multiple goals or taken outside of the health implications for the number of malnourished children sector. Policies and measures to prevent potential health (Nelson et al. 2010). The goal of adaptation in that impacts are often implemented to reduce the burden of study is to increase food production to the point where all preventable diseases and not just those related to the number of malnourished children declines to the climate change. Improvements in water and sanitation same levels that would have existed without climate services are often undertaken not only to reduce the change. So in that case, all of the adaptation to reduce incidence of all water-borne disease, including diarrhea, malnourishment takes place in the agriculture sector. but also to meet a broader set of goals such as the Similarly, the complementary EACC study on the Millennium Development Goals. The number of chil- economics of adaptation to extreme weather estimates dren who are stunted due to malnourishment depends the aggregate cost of adaptation related to floods and on food production and availability, which can often be droughts, a portion of which will likely be incurred in increased by expanding irrigation. The need to treat the health sector (Blankespoor et al. 2010). malnourished children (through the promotion of breastfeeding or nutritional programs) and hence the The secretariat of the U.N. Framework Convention on health sector cost of treatment depends on the extent of Climate Change (UNFCCC) estimated the human adaptation that is undertaken in the agriculture sector. health costs of adapting to climate change based on the Similarly, the effectiveness of early warning and disaster risk assessments in the WHO GBD study (UNFCCC preparedness systems determines the number of injuries 2007, Ebi 2008, Ebi 2007). While there have been and lives lost during extreme weather events such as numerous studies on the economic impacts of the floods or storm surges; hence it directly affects the health effects of climate change, the Ebi study was the demand for health care services and costs. first attempt to measure the global cost of adapting to climate change and was the point of departure for this The scope of this paper is narrowly defined to an esti- mation of the costs for preventing additional cases of malaria and diarrheal diseases resulting from climate 1 The study attributed 166,000 additional deaths globally in 2000 to an increase in the average global temperature and associated changes in change for two reasons: amenability to quantitatively climate of 0.20 Celsius between 1990 and 2000. Further, climate change estimate impacts and adaptation costs at the global level alone (holding other risk factors constant) would result in an increase in deaths from malnutrition, malaria, and diarrheal diseases globally of 10, and complementarily with other components of the 5, and 3 percent, respectively, by 2030 under the unmitigated emis- sions scenario (McMichael et al. 2004). d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 3 study. It provides estimates of the cost of preventing or determine the relative risk of these diseases from treating additional cases of diarrheal disease, malaria, climate change, compute the additional cases of each and malnutrition that are attributable to climate change disease, and estimate the total adaptation cost using the in 2030. The adaptation costs for each cause are esti- per unit cost of treatment. The adaptation cost esti- mated in four steps. It starts with estimates of the base- mates differ for a number of reasons, as summarized in line annual incidence of each disease for 2030, which Table 1. are assumed to remain unchanged from the incidence of each disease in 2002, the latest year for which estimates First, for consistency with the rest of the EACC study, were available. It attributes a fraction of the 2030 base- this study uses a common set of assumptions for climate line incidence to climate change using the relative risk projections (based on the NCAR and CSIRO GCMs), of each disease under different climate change scenarios population, and per capita gross domestic product estimated in the WHO GBD Study (McMichael et al. (GDP) to determine the adaptation costs for 10-year 2004). Next, the total treatment cost for each disease is intervals between 2010 and 2050. Second, this study computed by multiplying the additional cases due to uses updated burden of disease projections for 2010­30 climate change by the average cost of interventions for by cause, demographic group, and country that were these diseases available from the Disease Control recently made available by WHO (WHO 2008a, Priorities in Developing Countries (DCP2) project.2 Mathers 2009). These projections are based on updated The total adaptation costs for malaria and diarrheal methods (Mathers and Loncar 2006) and data, which diseases in 2030 were estimated at $4­12 billion if include baseline mortality, incidence, and DALYs at the emissions reductions result in stabilization at 750 parts country level for 16 (age/sex) demographic groups. per million CO2 equivalent by 2210.3 Third, the study extends the WHO baseline projections to 2050 for diarrheal disease and malaria using the The remainder of the paper has three sections. The first methods outlined in Mathers and Loncar (2006). These section describes the methodology and data used to projections indicate that the incidence rates and burden determine adaptation costs, highlighting any differences of diseases for these two causes will decline in the in methods and updates in data with respect to Ebi future and will result in associated declines in the cost (2008). It has four parts: baseline health, relative risk, of adaptation as well. Third, the exposure response potential health impacts, and adaptation costs. The function for malaria is the one used to generate the analysis does not assess the relative merits of specific World Malaria Report 2008 (WHO 2008b). Finally, the interventions or policies to adapt to climate change, and adaptation costs are estimated based on updated expo- nothing in this paper should be construed as advocating sure-response functions and the unit cost of treatment specific adaptation measures. The second section of these diseases. analyzes the sensitivity of the results to various assump- tions and the final section of the paper discusses the results and the findings of the study. B a s e l i n e he a lt h --i n c i d e n c e a nd B urde n o F di s e a s e The baseline incidence and burden of disease are Methodology and data important determinants of the absolute costs of climate change for human health as they are used to convert Adaption costs for malaria and diarrheal disease are relative risks to absolute impacts. For historical periods, determined using the same four steps used in Ebi the baseline data for specific causes at the global level (2008): establish the baseline incidence of these diseases, are determined based on a combination of actual health records, where available, and model-based estimates.4 2 See http://www.dcp2.org. 3 The study also estimates the cost of preventing malnutrition under this 4 WHO published initial estimates of the global incidence and burden of scenario of $0.1 billion to0.2 billion. In addition, the estimates for pre- disease for specific causes disaggregated at the regional level as part of venting or treating additional cases under two other climate scenarios the first Global Burden of Disease study for 1990, together with projec- range from $3 billion to $18 billion. tions for 2000, 2010 and 2030 (Murray and Lopez 1996). WHO has sub- 4 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e table 1. coMparIson of Methods and data In thIs study and unfccc 2007 UNFCCC 2007, Ebi 2008, Ebi 2007 This study time frame 2030 only every five years between 2010 and 2050 population based on un projections* source of socioeco- per capita gdp based on emF14 gdp based on average of integrated models* nomic projections data (1995) (*as in other components of eacc study) use of socioeconomic modify exposure response function modify exposure response function for diarrhea projections for diarrhea project baseline incidence 2030­50 2010 and 2030: updated Who projections of incidence (Who 2008a, mathers 2009) same as incidence for 2002 (Who Baseline incidence 2030­50: projection using Who projection model (mathers and lon- 2004) car 2006) based on eacc socioeconomic projection data intermediate years ­ linearly interpolated climate scenario hadcm2 ncar and csiro a2 scenarios diarrhea: based on Who gBd (mcmichael 2004) exposure response Based on Who gBd (mcmichael malaria: based on craig et al. 1999 function 2004) (sensitivity Who gBd: mcmichael 2004) adaptation options and cost-effective treatment options updated cost-effective treatment options based on dcp2 unit costs based on dcp2 The future baseline for communicable diseases such as WHO has also updated projections of the future base- malaria and diarrheal diseases can be quite different line burden of disease for 133 causes through 2030, than the historical baselines, particularly for low-income starting from the 2004 baseline (Mathers and Loncar countries with rapidly rising per capita incomes and 2006). These projections are based on new model esti- human capital levels (Murray and Lopez 1996, Mathers mates using 2,605 country-years of health registration and Loncar 2006). data from 106 countries spanning the 1950­2002 period. The models predict mortality rates for a country The adaptation cost estimates in the UNFCCC study by cause and demographic group (age, sex) based on were based on the baseline incidence of disease for 2002 changes in per capita incomes and the level of human at the regional level published by WHO. Since then, capital and time as a proxy for the availability of more WHO has published estimates of mortality and burden effective technologies. These projections, like earlier of disease for 2004 at the country level for 16 different ones (Murray and Lopez 1996), assume that future demographic groups (WHO 2008a). The new estimates mortality trends in poor countries will have a relation- indicate a marked increase in the deaths and DALYs ship to economic and social development similar to for diarrhea, but large declines for malaria (see Table 2). those that have occurred in higher-income countries. While the incidence of diarrheal disease has risen with increases in population, the burden in terms of deaths The baseline health outcome projections through 2050 and DALYs has increased by nearly 20 percent. In used in this study are determined from the WHO contrast, both the incidence and the burden of malaria projections in two stages: determine the incidence, have decreased by over 30 percent. These changes reflect burden of disease, and mortality rates for each disease both statistical revisions to the data based on improve- for 16 demographic groups and then multiply by the ments in data collection and estimation techniques as corresponding population projections as used in the well as real changes in health outcomes as countries EACC study to determine the total incidence or burden strive to meet the Millennium Development Goals. of disease. The starting point for this study is the WHO's country-level projections of mortality and DALY rates for each demographic group for the "base- sequently published updated estimates for historical periods for 2000, line scenarios" that were available for 2010 through 2002, and 2004 (WHO 2004, WHO 2006). These estimates have under- gone several significant revisions based on improved data collection 2030. Since projections of incidence rates were not and estimation techniques. d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 5 table 2. revIsIons to current baselIne IncIdence and burden of dIsease 2002* 2004* Percent change population (million) 6,122 6,437 3 diarrheal disease incidence (million) 4,513 4,608 2 deaths (thousand) 1,798 2,162 20 dalYs (thousand) 61,966 71,058 17 incidence rate (per thousand) 725 718 ­1 mortality rate (per thousand) 0.289 0.336 16 dalYs (per thousand) 10 11 13 malaria incidence (thousand) 408 241 ­41 deaths (thousand) 1,272 828 ­35 dalYs (thousand) 46,486 33,976 ­27 incidence rate (per thousand) 66 37 ­43 mortality rate (per thousand) 0.204 0.129 ­37 dalYs (per thousand) 7 5 ­29 Notes: * 2002 estimates used in unFccc study; 2004 estimates used in this study. available, they were determined by scaling the demo- per capita income.5 The predicted changes in the graphic-group-specific incidence rates for malaria and mortality rates were also applied proportionately to the diarrhea in each country for 2004 in proportion to the 2030 incidence rates and DALY rates to determine the projected changes in the DALY rates between 2004 and corresponding rates for the 2030­50 period. 2030 reported in the WHO projections (Mathers 2009). The absolute incidence of disease, the number of deaths, and the DALYs lost for each of 16 demographic groups WHO projections were not available beyond 2030; in a country are determined in the second stage by hence, the incidence and burden of disease for each multiplying the applicable rate with the corresponding country were estimated starting from the 2030 projec- population. These estimates provide the counterfactual tions. Changes in the mortality rates between 2030 and against which the impacts of climate change are 2050 were estimated based on cause and demographic- measured in this study (see Tables 3 and 4). They indi- group-specific regression results reported in Mathers cate that in 2010 Sub-Saharan Africa accounts for a and Loncar (2006). These results indicate that mortality fifth of the incidence and about half of the DALYs rates for communicable diseases such as malaria and from diarrheal disease and for over 90 percent of the diarrheal diseases in a country will decline for all age worldwide malaria cases and burden. Projected increases groups (except for those over 70) as per capita incomes and the level of human capital rise in the country. 5 The pessimistic scenario of the WHO projections through 2030 Human capital is more difficult to estimate and project; removes improvements in health outcomes attributed to technological to be conservative, mortality rates for 2030­50 are esti- improvements for all low-income countries. On the other hand, the baseline scenario removes it for low-income countries in Africa while mated based only on the direct effects from changes in reducing it to 25 percent of the average for all other low-income coun- tries. Similarly, improvements in health outcomes due to human capital are reduced for all low-income countries to 50 percent in the WHO baseline scenario and to 25 percent in the pessimistic scenario. (See Annex for a list of countries by World Bank region and income group.) 6 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e table 3. baselIne IncIdence and burden of dIsease projectIons, 2010­50 2004 2010 2020 2030 2040 2050 population (million) 6,437 6,871 7,628 8,276 8,799 9,145 diarrheal disease incidence (million) 4,608 3,409 2,385 1,774 1,469 1,245 deaths (thousand) 2,162 1,537 1,010 713 726 782 dalYs (thousand) 71,058 50,688 32,127 21,038 18,091 16,012 incidence rate (per thous.) 718 495 312 214 167 136 mortality rate (per thous.) 0.336 0.223 0.132 0.086 0.083 0.085 dalYs (per thousand) 11 7.4 4.2 2.5 2.1 1.7 malaria incidence (million) 240 179 118 78 73 68 deaths (thousand) 828 771 500 323 286 252 dalYs (thousand) 33,976 29,705 19,151 12,290 10,729 9,317 incidence rate (per thous.) 37 26 15 9 8 7 mortality rate (per thous.) 0.129 0.112 0.065 0.039 0.033 0.028 dalYs (per thousand) 5 4.3 2.5 1.5 1.2 1.0 table 4. baselIne IncIdence and burden of dIsease projectIons, by regIon, 2010­50 Diarrheal Disease Malaria 2010 2030 2050 2010 2030 2050 Incidence (million) east asia and pacific 1,049 633 422 8 3 2 europe and central asia 93 47 30 0 0 0 latin america and caribbean 332 173 120 2 1 1 middle east and north africa 162 87 64 3 3 2 south asia 836 344 203 10 4 3 sub-saharan africa 724 337 285 157 68 61 high-income countries 212 153 119 0 0 0 total 3,409 1,774 1,245 179 78 68 DalYs (thousand) east asia and pacific 4,825 2,076 1,277 1,206 414 270 europe and central asia 868 300 164 128 121 74 latin america and caribbean 1,591 650 455 138 55 40 middle east and north africa 1,900 957 762 260 129 99 south asia 15,663 5,009 2,937 2,337 783 483 sub-saharan africa 25,461 11,779 10,200 25,594 10,764 8,330 high-income countries 379 268 217 42 25 21 total 50,688 21,038 16,012 29,705 12,290 9,317 d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 7 in incomes between 2010 and 2050 enable developing EACC study and are more fully described in Strzepek countries to invest in improving their population health and Schlosser (2010). These models were chosen from outcomes. As a result, the incidence, mortality, and the larger set of 26 General Circulation Models used burden of disease for malaria and diarrheal disease are for IPCC's Fourth Assessment Report for the purposes expected to decline significantly in all regions, with the of the EACC study. These have relatively similar largest declines occurring in East Asia and South Asia. changes in the global moisture index, but they differ By 2050, the disease burdens from these two causes are significantly in their patterns of climate change at expected to be primarily located in Sub-Saharan Africa. regional and country level.6 Monthly temperature and precipitation projections from these models were aver- aged over the 20-year periods 2000­20, 2020­40, and relative ris K s From cl imat e 2040­60 and compared with the base period 1900­ c hange 2002. The changes were downscaled to a resolution of 0.5 degree grid squares used in the CRU historical data The relative risks of malaria and diarrheal disease from and added to them to obtain a consistent set of temper- climate change are determined in relation to the histori- ature and precipitation estimates for these two models cal risks for three time periods centered around the for the three future time periods centered around 2010, years 2010, 2030, and 2050. They are determined from 2030, and 2050. climate projections from the NCAR CCSM-3 and CSIRO-3 (abbreviated to NCAR and CSIRO) General Exposure Response Function for Diarrheal Disease Circulation Models that are used in all components of the EACC study. The climate projections are converted The ER function for diarrheal diseases used in the to relative risks based on exposure response (ER) func- WHO GBD study (McMichael et al. 2004) is also used tions available in the literature. For diarrheal disease, the in this study to estimate the relative risk from climate ER functions are the same as used in the WHO GBD change. The comprehensive review of the literature study (McMichael et al. 2004). For malaria, the ER completed as part of the GBD study indicated that few function used in this study is based on a suitability studies had quantitatively examined the relationship index for malaria developed by Craig et al. (1999), used between the incidence of diarrheal disease and climate. most recently in the World Malaria Report 2008 (WHO The available literature indicated that a 1o Celsius rise 2008b). The ER function in the WHO GBD study was in temperature was associated with an increase in the based on a suitability index for malaria developed by incidence of diarrheal disease of 8 percent in Peru and 3 Tanser et al. (2003), which is analyzed as one of the percent in Fiji (Checkley 2002, Singh 2001). The aver- sensitivity analysis cases. age of these two, 5 percent, is used in the GBD study and is applied to countries with per capita incomes of Climate Scenarios less than $6,000. Increases in temperature are assumed not to have any effect on the incidence of diarrheal The Climate Research Unit (CRU) at the University of disease in countries with higher incomes. A more recent East Anglia has compiled a series of historic weather study from Bangladesh that found increases in the inci- data for land areas of the globe at a resolution of 0.5 dence of non-cholera diarrhea of 5.6 percent for every degree grid squares. The climate data at the grid square 1o Celsius rise in temperature provides additional level for this period define the baseline risks. Summary support to the selected ER function (Hashizume 2007). statistics have been computed for each grid cell for The GBD review did not find clear evidence for esti- monthly average, maximum, and minimum tempera- mating the effect of precipitation on the incidence of tures (in degrees Celsius) and precipitation (in millime- diarrheal disease. ters) for the period 1901­2002. Climate change is characterized in this study based on temperature and precipitation projections from the NCAR and CSIRO models commonly shared with the 6 Compare to Hadley. 8 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e The relative risks for the incidence of diarrheal disease geographical distribution of malaria to higher latitudes for 2010, 2030, and 2050 by World Bank region are and extend the duration of the transmission season. The shown in Table 5 for the NCAR and CSIRO climate relative risk of malaria for each country is computed as change scenarios.7 The relative risks for intermediate the ratio of the population at risk between the future years are linearly interpolated. They reflect the offset- period and the baseline period; as such, it solely focuses ting effects of higher risks with rising temperatures and on the expansion or contraction in the geographic loca- declining risks with improvements in health services tions suitable for malaria transmission. This is similar to and access to sanitation that often accompany rising per the way relative risks were determined in the WHO capita incomes. GBD study, which used a different suitability index based on Tanser et al. (2003) for the MARA/ARMA Climate change is expected to increase the risks of diar- project.8 (The implications of switching between the rheal disease worldwide by around 3 percent in 2030 two suitability indices are discussed in the sensitivity and 2 percent in 2050. A large part of this decline is analysis section of this paper.) due to improvements in environmental and health services that countries put in place as per capita incomes The relative risks for the total burden of disease for rise. The lone exception to this declining trend is malaria resulting from climate change for 2010, 2030, Sub-Saharan Africa, where the relative risk rises by and 2050 are summarized for the NCAR and CSIRO about 6 percent by 2030 and 8­9 percent by 2050. This scenarios in Table 6. The relative risks for intermediate represents a more than twofold increase in the climate- years are linearly interpolated. Climate change is related risk between 2010 and 2050. These results are expected to increase the risks of malaria in a number of consistent across the two climate projections, since the regions. The increases are larger for all regions and over temperature projections in these two GCMs are similar. time under the wetter scenario (NCAR). The relative risks for both NCAR and CSIRO scenarios are larger Exposure Response Function for Malaria than the relative risks reported in the WHO GBD study (which used projections from the HADCM2 The ER function for malaria used in this study is based model). Part of this increase reflects differences in the on a suitability index for malaria transmission developed baseline climate used as a counterfactual (average for by Craig et al. (1999) for the MARA/ARMA project. 1991­2002 for this study versus average for 1961­2000 The index has most recently been used in the World for the GBD study).9 Malaria Report 2008 to estimate the historical incidence of malaria in areas with limited health records (WHO 2008b, Korenromp 2005). The suitability index for an area is constructed from the temperature and precipita- tion characteristics of that area (see Box 1). In this study, the suitability index has been applied to each 0.5 degree grid square under the baseline condi- 8 Suitability for malaria transmission in any month is determined by four criteria: a) three-month moving average temperature exceeds 19.5o tions and for the three time periods centered around Celsius plus the standard deviation of mean monthly temperatures; b) 2010, 2030, and 2050 based on climate projections from minimum yearly temperature exceeds 5o Celsius; c) three-month mov- ing average precipitation exceeds 60 millimeters; and d) three-month the NCAR and CSIRO GCMs. The population living moving average of precipitation exceeds 80 millimeters for at least one in transmission-suitable areas is potentially at risk of month. An area that meets all four criteria is considered suitable for malaria transmission in that month. In addition, an area whose suitabili- malaria. Climate change is expected to affect the ty is interrupted for a month but has met all of the criteria in the pre- ceding and succeeding months is assumed to be suitable for transmission during the interrupted month. 9 Relative risks were also computed for these two climate projections 7 The country-level relative risks have been weighted by the baseline using the exposure response function defined by Tanser et al. (2003) incidence in the region to obtain the relative risk of incidence in the and used in the WHO GBD study. The relative risks for all countries are region. The incidence rate for diarrheal disease declines faster than the lower for all regions compared with the relative risks reported in Table mortality rates as a result, so regional relative risks using the number of 6. For the NCAR scenario, the relative risks worldwide are 1.052, 1.070, deaths or overall burden of disease as weights would result in higher and 1.087 for 2010, 2030, and 2050 respectively. The corresponding regional relative risk for deaths or burden of disease. numbers for the CSIRO scenario are 1.044, 1059, and 1.080. d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 9 table 5. relatIve rIsk: IncIdence of dIarrheal dIsease froM clIMate change, by regIon, for 2010­50 NCAR Scenario CSIRO Scenario 2010 2030 2050 2010 2030 2050 east asia and pacific 1.048 1.006 1.002 1.030 1.005 1.002 europe and central asia 1.025 1.028 1.000 1.014 1.018 1.000 latin america and caribbean 1.005 1.003 1.003 1.003 1.003 1.002 middle east and north africa 1.038 1.026 1.012 1.022 1.016 1.009 south asia 1.046 1.060 1.014 1.035 1.064 1.014 sub-saharan africa 1.041 1.063 1.092 1.027 1.057 1.079 high-income countries 1.000 1.000 1.000 1.00 1.000 1.000 all countries 1.037 1.028 1.024 1.026 1.026 1.020 box 1. fuzzy dIstrIbutIon Model for MalarIa suItabIlIty no distinct boundaries separate malarious from non-malarious areas. climatic factors act as spatial gradients shifting the distribu- tion of these areas. the suitability of an area for malaria transmission can be categorized based on the climatic conditions neces- sary during the transmission life cycle based on fuzzy logic. transmission is unlikely below 18° celsius, likely between 22° and 32° celsius, and unlikely above 40° celsius. similarly, transmission is unlikely in areas with less than 0 millimeters of monthly precipita- tion while it is likely in areas exceeding 80 millimeters. the mean daily minimum winter temperature of less than 4° celsius makes transmission unlikely, while minimum temperatures exceeding 6° celsius makes an area suitable for transmission. each area is assigned a fuzzy value (between 0 and 1) for each month and each criterion. areas where transmission is likely are assigned a value of 1, while unlikely areas are assigned values of 0. the fuzzy values for intermediate areas are determined using a simple sigmoidal function between the limit points for the criteria. the suitability of an area for transmission in a given month is determined as the minimum fuzzy value across the three criteria. the suitability index for each area is based on the highest fuzzy value span- ning any five-month period, constituting a transmission season. populations living in areas with suitability index larger than 0.5 were considered at risk. table 6. relatIve rIsk: burden of dIsease (dalys) of MalarIa froM clIMate change, by regIon, 2010­50 NCAR Scenario CSIRO Scenario 2010 2030 2050 2010 2030 2050 east asia and pacific 1.010 1.010 1.014 1.008 1.009 1.011 europe and central asia 1.000 1.000 1.000 1.000 1.000 1.000 latin america and caribbean 1.056 1.056 1.064 1.053 1.050 1.048 middle east and north africa 1.000 1.00 1.000 1.000 1.000 1.000 south asia 1.000 1.050 1.043 1.000 1.000 1.000 sub-saharan africa 1.073 1.096 1.138 1.044 1.072 1.089 high-income countries 1.000 1.001 1.001 1.001 1.001 1.001 all countries 1.064 1.088 1.125 1.038 1.063 1.079 10 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e poten tia l he a lth impa cts o F a d a p tat i o n co s t s F o r c limate change di a rrhe a l di s e a s e a nd ma l ar i a Estimates of the additional incidence and DALYs for The potential cost of interventions for malaria and diar- diarrheal disease and malaria for 2010­50 for the two rheal disease are based on currently deployed cost-effec- climate scenarios are summarized in Tables 7 and 8. tive interventions. The treatment options selected are the same as in Ebi (2008) and were based on the The additional incidence and DALYs attributed to Disease Control Priorities in Developing Countries climate change for both causes are projected to decline project (http://www.dcp2.org). For diarrhea, this significantly largely reflecting trends in the baseline includes breastfeeding promotion and immunizations incidence and DALYs for these causes under both against rotavirus, cholera, and measles for children climate scenarios. Sub-Saharan Africa, with the highest under five, at an average cost per child of $15.03, plus baseline burden and the highest relative risks for both improvements in sanitation at an average cost of $53 causes under both scenarios, accounts for an increasing per incidence (in 2001 dollars). Both of these costs were share of the worldwide burden from these causes. converted to 2005 dollars for consistency with the rest of the EACC study and applied to the additional inci- dence of diarrheal disease. table 7. dIarrheal dIsease due to clIMate change: addItIonal IncIdence and burden, by regIon, 2010­50 NCAR Scenario CSIRO Scenario 2010 2030 2050 2010 2030 2050 Incidence (thousand) east asia and Pacific 47.9 3.7 0.8 30.6 3.0 0.6 europe and Central asia 2.2 1.3 0.0 1.3 0.9 0.0 latin america and Caribbean 1.7 0.6 0.4 1.1 0.6 0.3 Middle east and North africa 6.0 2.2 0.8 3.5 1.4 0.5 south asia 36.4 19.5 2.9 28.8 20.7 2.8 sub-saharan africa 28.2 19.8 24.0 18.9 18.4 20.9 High-income countries 0.0 0.0 0.0 0.0 0.0 0.0 Total 122.7 47.2 28.8 84.2 44.9 25.1 DalYs (thousand) east asia and Pacific 203 32 15 133 28 13 europe and Central asia 46 19 0 26 13 0 latin america and Caribbean 16 5 4 10 5 3 Middle east and North africa 90 52 18 52 34 12 south asia 732 312 88 554 307 71 sub-saharan africa 1030 714 863 694 670 752 High-income countries 0 0 0 0 0 0 Total 2,117 1,133 987 1,469 1,056 851 d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 11 table 8. MalarIa due to clIMate change: addItIonal IncIdence and burden, by regIon, 2010­50 NCAR Scenario CSIRO Scenario 2010 2030 2050 2010 2030 2050 Incidence (thousand) east asia and Pacific 127 48 52 114 49 42 europe and Central asia 0 0 0 0 0 0 latin america and Caribbean 134 67 64 138 62 54 Middle east and North africa 0 0 0 0 0 0 south asia 1 174 116 1 0 0 sub-saharan africa 11,990 6,301 7,905 6,895 5,222 5,800 High-income countries 0 0 0 0 0 0 Total 12,251 6,591 8,136 7,148 5,333 5,897 DalYs (thousand) east asia and Pacific 12 4 4 9 4 3 europe and Central asia 0 0 0 0 0 0 latin america and Caribbean 7 3 2 7 3 2 Middle east and North africa 0 0 0 0 0 0 south asia 0 38 20 0 0 0 sub-saharan africa 1,751 949 1,007 1075 721 680 High-income countries 0 0 0 0 0 0 Total 1,771 993 1,033 1,092 728 685 For malaria, the authors of the cost effectiveness analy- regions, ITN plus ACT plus IRS combined with inter- sis recently updated their analysis, which has decreased mittent presumptive treatment in pregnancy (IpTp) at the unit costs of these interventions.10 Incremental costs $48/DALY in AfrD and $148/DALY in AfrE. All of per DALY averted are separately available for WHO's these costs were converted to 2005 dollars and applied AfrD and AfrE regions for the following treatment to the projected additional DALYs for countries in the options:11 (a) for AfrD, insecticide-treated bednets AfrD and AfrE regions. For all other regions, the aver- (ITN) at $29/DALY; (b) for AfrE, insecticide-treated age cost per DALY averted in the AfrD and AfrE bednets plus case management with artemisinin-based regions were used. combination therapy (ACT) at $57/DALY plus indoor residual spraying (IRS) at $60/DALY; and (c) for both The average annual adaptation costs in the health sector for preventing and treating diarrheal disease and malaria attributed to climate change in 2010 ranges between $3 10 Morrel et al. 2005. billion (CSIRO scenario) and $5 billion (NCAR 11 Countries in WHO AfrD region are Algeria, Angola, Benin, Burkina Faso, scenario) (see Table 9). These costs are expected to Cameroon, Cape Verde, Chad, Comoros, Equatorial Guinea, Gabon, decline as basic health services improve with develop- Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Madagascar, Mali, Mauritania, Mauritius, Niger, Nigeria, Saõ Tomé and Príncipe, Senegal, ment, making residents less susceptible to these diseases. Seychelles, Sierra Leone, and Togo. Overall, the average cost of adaptation for diarrheal Countries in WHO AfrE region are Botswana, Burundi, Central African Republic, Congo, Côte d'Ivoire, Democratic Republic of the Congo, disease and malaria is around $2 billion a year over the Eritrea, Ethiopia, Kenya, Lesotho, Malawi, Mozambique, Namibia, 40-year period 2010­50. Almost all of the costs are Rwanda, South Africa, Swaziland, Uganda, United Republic of Tanzania, Zambia, and Zimbabwe. related to diarrheal disease, with malaria accounting for 12 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e table 9. average annual adaptatIon cost to prevent and treat MalarIa and dIarrhea, by year, 2010­50 (bIllIon 2005 dollars) 2010 2020 2030 2040 2050 NCaR scenario diarrheal disease 4.6 2.4 1.8 1.2 1.1 malaria 0.2 0.1 0.1 0.1 0.1 total 4.8 2.5 1.9 1.3 1.2 CsIRo scenario diarrheal disease 3.2 2.0 1.7 1.1 0.9 malaria 0.1 0.1 0.1 0.1 0.1 total 3.3 2.1 1.8 1.2 1.0 table 10: average annual adaptatIon cost to prevent and treat MalarIa and dIarrhea, by regIon, 2010­50 (bIllIon 2005 dollars) 2010 2020 2030 2040 2050 NCaR scenario east asia and Pacific 1.8 0.3 0.1 0.1 0.0 europe and Central asia 0.1 0.1 0.0 0.0 0.0 latin america and Caribbean 0.0 0.0 0.0 0.0 0.0 Middle east and North africa 0.2 0.1 0.1 0.1 0.0 south asia 1.4 1.0 0.7 0.2 0.1 sub-saharan africa 1.2 1.0 0.8 0.9 1.0 Total 4.8 2.5 1.9 1.3 1.2 CsIRo scenario east asia and Pacific 1.2 0.2 0.1 0.1 0.2 europe and Central asia 0.0 0.0 0.0 0.0 0.0 latin america and Caribbean 0.0 0.0 0.0 0.0 0.0 Middle east and North africa 0.1 0.1 0.1 0.0 0.0 south asia 1.1 0.9 0.8 0.2 0.1 sub-saharan africa 0.8 0.8 0.8 0.8 0.9 Total 3.3 2.1 1.8 1.2 1.0 less than 10 percent. By 2050, most of the additional in the projected baseline burden of disease is the cases (hence costs) attributed to climate change are primary reason for the lower costs estimated in this expected to occur in Sub-Saharan Africa (see Table 10). study. In addition, the costs for malaria are lower due to These estimates are lower than prior estimates of $4­12 the 30­40 percent downward revision of the current billion in 2030, which was about equally split between diarrheal disease and malaria (Ebi 2008).12 Reductions 12 The costs for diarrheal disease ranged between $2.0 billion and $6.8 the S750 scenario. The costs for malnutrition were between $0.1 billion billion and for malaria between $1.9 billion and $5.6 billion in 2030 for and $0.2 billion. d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 13 baseline incidence, mortality, and DALYs between 2002 Two additional cases were run for diarrheal disease, first and 2004 and to revisions in the unit cost treatment.13 to estimate costs based on DALYS and second based on an alternate method of extrapolating the incidence of disease based on projections of mortality instead of DALYS. Keusch (2006) provides estimates of the sensItIvIty analysIs median costs per DALY averted for cost-effective treat- ments of diarrheal disease of $1,032 for low- and There is a great deal of uncertainty around the esti- middle-income counties. Separate estimates of costs per mated potential health impacts and the associated costs. DALY averted are also available by World Bank region, The sources of these uncertainties include the baseline ranging from $132/DALY averted in East Asia and the health, the climate scenarios, the exposure response Pacific to $2,564 in the Middle East and North Africa. functions, and the unit cost of effective intervention Applying these regional costs to the additional DALYs methods. A number of analyses were completed to esti- from diarrheal disease lowers the costs in the early years mate sensitivity of the potential impacts and adaptation compared with the base case, but it makes little differ- costs for diarrheal disease and malaria to the different ence by 2050. The final sensitivity for diarrheal disease assumptions, methods, and data (see Table 11). The is based on changing the method by which the baseline analyses indicate that the overall trends and regional incidence projections are done from one based on distributions of the impacts and costs are robust to trends in DALY rates to based on trends in mortality these changes. Hence, only the aggregate adaptation rates. Costs are smaller compared with the base case, costs are summarized for each cause over time. The top but not by much. half of Table 11 provides cost estimates for diarrheal disease followed by malaria in the bottom half. The top In the case of malaria, a final sensitivity was run to row in each block shows the results for the base case, examine the effect of changing the exposure response which is reported in detail in the previous section of function to that used in the GBD study. Impact esti- this paper. Results are reported for the NCAR and mates and adaptation costs are lower with the ER func- CSIRO climate scenarios for 2010, 2030, and 2050. In tion used in the GBD study by 10­20 percent under the each case the costs are consistently higher in the wetter CSIRO scenario and around 40 percent under the NCAR scenario compared with the drier CSIRO wetter NCAR scenario. scenario. Besides the above sensitivity analyses, the estimated The first two sensitivity analyses relate to the uncer- costs were also compared with the total cost of eradicat- tainty around the cost of treating additional cases of ing malaria worldwide over the next few decades as diarrhea or averting additional DALYs from malaria. estimated by the Roll Back Malaria Partnership The cost differences from the base case are large in (RBMP 2008). Efforts were under way to scale up 2010 for both climate scenarios but rapidly narrow by efforts in all malaria-endemic countries starting in 2009 2050 as a result of the declining baseline. The effect of and 2010 with the intent of eradicating malaria globally the declining baseline is examined in the third sensitiv- over the next few decades. If these goals are indeed met, ity analysis for each cause. For all years, the baseline reduced baseline incidence of malaria would also have incidence of diarrheal disease and the baseline DALYS the effect of reducing the climate-change-attributed for malaria are the same as in 2004, which implies a burden of malaria cases. Implementing the RBM reduction in the baseline per capita incidence and program is expected to cost about $5.2 billion annually burden when the population is rising. This analysis through 2020, $3.3 billion annually in the 2020s, and comes closest to the approach used in Ebi (2008). By $1.5 billion by the 2030s. Assuming that about 5 far, adaptation costs are highest for this case compared percent of the current burden of malaria is due to with all other cases. climate change, this implies a share of around $250 million in adaptation cost, which is of the same order of magnitude as the estimated costs for malaria. If the 13 Revisions to the unit cost alone have the effect of reducing adaptation share of the malaria burden attributable to climate costs by seven to eight times. 14 table 11. sensItIvIty of average annual adaptatIon cost for huMan health, selected years (bIllIon 2005 dollars) NCAR Scenario CSIRO Scenario Health Exposure Cause Baseline endpoint response Unit costs 2010 2030 2050 2010 2030 2050 Diarrheal Disease mathers with extension incidence gBd average 4.6 1.8 1.1 3.2 1.7 0.9 1. mathers with extension incidence gBd low 2.0 0.8 0.5 1.4 0.7 0.4 2. mathers with extension incidence gBd High 7.2 2.8 1.7 4.9 2.6 1.5 3. Constant at 2004 levels incidence gBd average 6.3 5.4 3.8 4.3 5.2 3.3 4. Mathers with extension based on incidence gBd average 3.9 1.4 0.9 2.7 1.3 0.8 deaths 5. mathers with extension DalYs gBd Keuschetal 2.0 1.2 1.1 1.4 1.1 0.9 Malaria mathers with extension dalYs craig et al average 0.2 0.1 0.1 0.1 0.1 0.1 1. mathers with extension dalYs craig et al low 0.1 0.1 0.1 0.1 0.0 0.0 2. mathers with extension dalYs craig et al High 0.2 0.1 0.1 0.2 0.1 0.1 3. Constant at 2004 levels dalYs craig et al average 0.2 0.3 0.4 0.1 0.2 0.3 4. mathers with extension dalYs Tanser et al average 0.1 0.1 0.1 0.1 0.1 0.1 Baseline: mathers with extension (health endpoint dalYs) ­ 2010­30 ( Who projections of dalYs per 1000 people by age, sex within a country); 2030­50 (adjusts 2030 Who projections of dalYs per 1000 people for changes in income) mathers with extension (health endpoint incidence) ­ 2010­30 (adjusts 2004 incidence rates in proportion to changes in dalYs (or deaths) per 1000 people by age/sex/country); 2030­50 (adjusts 2030 projections of incidence rates for changes in income) constant at 2004 levels ­ incidence or dalYs held constant at 2004 levels; implies declining baseline rates with rising population exposure response function: diarrhea: all cases are based on the er function used in Who gBd; rr determined for grid 0.5 degree malaria: suitability index based on craig et al. (1999) or tanser et al. (2003). Who gBd uses tanser et al. (2003). rr determined at the country level based on popula- tion weighted areal extent of suitability. heath endpoint: choice based on availability of cost data, diarrhea (incidence or dalYs); malaria (dalYs only). unit cost: diarrhea low (breast feeding promotion and immunizations, $15.09 / child) based on Keusch (2006) high (improved water supply and sanitation, $53 /child) based on Keusch (2006) average ($34 /child, average of low and high) Keusch (2006) based on regional costs per dalY (range $132­2,564 per dalY) http://www.dcp2.org/pubs/dcp/19/table/19.2 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e malaria low ($29/dalY in afrd for itn, $58.50/daly in afre for itn+act+irs, average of afrd and afre elsewhere) high ($48/dalY in afrd, $148/daly in afre, average of afrd and afre elsewhere, for act+itn+irs+iptp) average (average of low and high) based on morrel et al. 2005. d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 15 change rises to 7 percent by 2030, the adaptation cost The analysis suggests that climate change will account for malaria would be around $100 million, consistent for a small share of the total cases of the weather-sensi- with the estimates reported in the study. tive health outcomes in most countries. A dispropor- tionate share of the added burden falls on the poor, particularly in Sub-Saharan Africa, which faces the largest increase in risk and has the least capacity to cope dIscussIon with these risks. More than any other source of uncer- tainty, the baseline health status of a country is the Estimating the adaptation costs in the health sector is single largest determinant of the likely impacts of challenging because of the large existing uncertainties climate change and the cost of adapting to it. Economic about how the climate will evolve over the coming development has historically led to significant improve- century but also because of the complex and often ments in the health status of countries. Independently, poorly understood chains through which health impacts although not all countries will meet their Millennium are mediated. Climate change is difficult to predict with Development Goals, most countries are making rapid accuracy in any projection model that has to contend progress toward meeting them. These goals include with uncertainty about potential collective actions to reductions in child mortality and the mortality burdens mitigate greenhouse gases as well as unknown factors in from climate-sensitive diseases such as malaria. The climate science itself. The health outcomes that are success of such programs will also be an effective mech- linked to climate change also depend on a host of other anism for adapting to the added risks that emanate factors as well, some of which are likely not currently from climate change. anticipated, such as the emergence of new diseases, and others that are difficult to predict, such as the develop- The analysis explored a number of other sources of ment of vaccines to address existing and new ailments. uncertainty, including specific climate projections and the cost of alternative interventions. Two specific This paper attempts to examine known factors in the climate scenarios--globally the driest and the wettest human health­climate change nexus. It builds on prior ones--resulted in potential health impacts and cost esti- studies by WHO on the health impacts of climate mates that were relatively narrow in range, suggesting change and by UNFCCC on the cost to prevent and that uncertainty about the specific differences between treat these impacts. The analysis systematically exam- climate projections may not be as important. However, ines the various sources of uncertainty to identify the this result may just be an artifact of the poor knowledge most important factors affecting the cost of adapting to of exposure-response relationships. Projections from climate change in developing countries. different climate models have generally agreed on warming trends for most places but have significant Climate change does not create a novel type of environ- differences about precipitation, especially at a local scale. mental exposure. It is expected to alter regional weather The lack of knowledge relating precipitation and health patterns, which in turn will result in increased frequency effects means that the effects of this uncertainty in and/or intensity of extreme events as well as increases in climate projections could not be separately tested. average temperature and changes in precipitation levels. Changes in the cost of intervention vary across loca- Some of these factors affect health directly (such as tions and may also partially depend on the appropriate- heatwaves) but often they affect health indirectly ness of selected measures in a local context. Often through altered transmission pathways for infectious alternative measures are taken together to increase the (vector-, rodent-, water-, and food-borne) diseases or effectiveness of the different interventions, as is the case decreased productivity of land or ecosystems (resulting for malaria. Variations in unit costs are, however, not in malnutrition or disrupted livelihoods). Climate sufficient to result in orders of magnitude differences in change will also modulate the exposure-outcome rela- the global cost of adaptation. tionship of these complex chains. Modulating influences related to susceptibility and concurring factors will also Building adaptive capacity for health will require a likely have large effects, depending on local contexts. cross-disciplinary dialogue between health practitioners, 16 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e decision makers, the public, and the climate change Bosello, F., R. Roson, and R. S. J. Tol. 2006. "Economy- science community. As a component of the overall need wide Estimates of the Implications of Climate for adaptation to climate change, adaptive strategies in Change: Human Health." Ecological Economics 58: health has to be based on actions that governments, 579­91. institutions, and the public can take to adjust to impacts, moderate their damage, or cope with their Checkley, W., L. D. Epstein, R. H. Gilman, D. consequences. Most relevant actions for adaptive capac- Figueroa, R. I. Cama, J. A. Patz, R. E. Black. 2000. ity will be those enabling commonly accepted good "Effects of El Niño and Ambient Temperature on public health and development practices, beyond Hospital Admissions for Diarrhoeal Diseases in climate change considerations. The creation and main- Peruvian Children." Lancet 355: 442­50. tenance of basic public health infrastructure in terms of training, surveillance, immunization, vector control, and Confalonieri, U., B. Menne, R. Akhtar, K. L. Ebi, M. emergency preparedness and response will both provide Hauengue, R. S. Kovats, B. Revich, and A. development benefits and increase resilience to health Woodward. 2007. "Human Health." In Climate impacts of climate change. As the situation changes, Change: Impacts, Adaptation and Vulnerability. novel actions and strategies may need to be developed, Contribution of Working Group II to the Fourth new technologies invented, and the relationships Assessment Report of the Intergovernmental Panel between natural and man-made systems and human on Climate Change, ed. M. L. Parry, O. F. Canziani, health may need to be better analyzed. J. P. Palutikof, P. J. van der Linden, and C. E. Hanson, 391­431. Cambridge, UK: Cambridge Finally, the cost estimates for the health sector reported University Press. in this paper are an underestimate of the total health sector cost determined in the EACC study. To avoid Craig, M. H., R. W. Snow, and D. LeSueur. 1999. "A double counting, these estimates are reported in the Climate-Based Distribution Model of Malaria following complementary papers: the additional cost of Transmission in Sub-Saharan Africa." Parasitology climate-proofing hospitals, clinics, and other health Today 15: 105­11. sector infrastructure of $200­400 million per year (Hughes et al. 2010); the cost of adapting to extreme Ebi, K. 2007. Health Impacts of Climate Change. Report weather (floods and droughts) of $6­7 billion per year, to the UNFCCC Financial and Technical Support some of which occur in the health sector (Blankespoor Division, United Nations Framework Convention on et al. 2010), and reducing additional cases of malnutri- Climate Change. Bonn, Germany. tion in agriculture (Nelson et al. 2010). The health sector adaptation cost reported here would be higher if ------. 2008. "Adaptation Costs for Climate Change­ any of the agriculture sector adaptation measures fail, Related Cases of Diarrhoeal Disease, Malnutrition, raising levels of malnutrition. Even when these addi- and Malaria in 2030." Globalization and Health 4: 9. tions are included, the reported costs still underestimate the true cost of adapting to climate change because of Hashizume, M., B. Armstrong, S. Hajat, Y.Wagatsuma, the omission of adaptation costs for other health A. S. G. Faruque, T. Hayashi, D. A. Sack. 2007. impacts. "Association between Climate Variability and Hospital Visits for Non-cholera Diarrhoea in Bangladesh: Effects and Vulnerable Groups", International Journal of Epidemiology 36:1030­1037. bIblIography Hay, S. I., C. A. Guerra, P. W. Gething, A. P. Patil, A. J. Blankespoor, B., S. Dasgupta, B. Laplante, and D. Tatem et al. 2009. "A World Malaria Map: Wheeler. 2010. The Economics of Adaptation to Plasmodium falciparum Endemicity in 2007." PLoS Extreme Weather Events in Developing Countries. Medicine 6 (3): e1000048. Washington, DC: World Bank. d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 17 Hughes, G., P. Chinowsky, and K. Strzepek. 2010. The Masahiro, H., B. Armstrong, S. Hajat, Y. Wagatsuma, A. Cost of Adapting to Climate Change in Infrastructure. S. G. Faruque, T. Hayashi, and D. Sack. 2007. Washington, DC: World Bank. "Association between Climate Variability and Hospital Visits for Non-cholera Diarrhoea in IPCC (Intergovernmental Panel on Climate Change). Bangladesh: Effects and Vulnerable Groups." 2000. Special Report on Emissions Scenarios. A Special International Journal of Epidemiology 36: 1030­37. Report of Working Group III of the Intergovernmental Panel on Climate Change, ed. N. Mathers, C. 2009. Personal communications. August. Nakicenovic et al. Cambridge, UK: Cambridge University Press. Mathers, C. D., and D. Loncar. 2006. "Projections of Global Mortality and Burden of Disease from 2002 ------. 2007. "Summary for Policymakers." In Climate to 2030." PLoS Medicine 3 (11): e442. Change 2007: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fourth McMichael, A.J., D. Campbell-Lendrum, S. Kovats, S. Assessment Report of the Intergovernmental Panel W. Edwards, T. Wilson, R. Nicholls, S. Hales, F. on Climate Change, ed. M. L. Parry, O. F. Canziani, Tanser, D. LeSueur, M. Schlesinger, and N. J. P. Palutikof, P. J. van der Linden, and C. E. Andronova. 2004. "Global Climate Change." In Hanson. Cambridge, UK: Cambridge University Comparative Quantification of Health Risks: Global Press. and Regional Burden of Disease due to Selected Major Risk Factors, ed. M. Ezzati, A. Lopez, A. Rodgers, Keusch, G. T., O. Fontaine, A. Bhargava, C. Boschi- and C. Murray, 1543­649. Geneva: World Health Pinto, Z. A. Bhutta, E. Gotuxxo, J. Rivera, J. Chow, Organization. S. A. Shahid-Salles, and R. Laxminarayan. 2006. Diarrhoeal Diseases. Disease Control Priorities in Morrel, C. M., J. A. Lauer, and D. B. Evans. 2005. Developing Countries, 2nd ed. New York: Oxford "Achieving the Millennium Development Goals for University Press. doi:10.1596/978-0-821-36179-5/ Health - Cost Effectiveness Analysis of Strategies to Chpt-19. http://www.dcp2.org. Combat Malaria in Developing Countries." BMJ 331: 1299. Kiszewski, A., B. Johns, A. Schapria, C. Delacollette, V. Crowell, T. Tan-Torres, B. Ameneshewa, A. Murray, C. J. L., and A. D. Lopez. 1996. "Alternative Teklehaimanot, and F. Nafo-Traore. 2007. Visions of the Future: Projecting Mortality and "Estimated Global Resources Needed to Attain Disability, 1990­2020." In The Global Burden of International Malaria Control Goals." Bulletin of the Disease, ed. C. J. L. Murray and A. D. Lopez, 325­ World Health Organization 85: 623­30. 97. Cambridge, MA: Harvard University Press. Korenromp, E. 2005. "Malaria Incidence Estimates at Murray, C. J. L., M. Ezzati, A. D. Lopez, A. Rodgers, Country Level for the Year 2004" (draft, 11 March). and S. Vander Hoorn. 2003. "Comparative Geneva: World Health Organization. Available at Quantification of Health Risks: Conceptual http://www.who.int/malaria/publications/atoz/inci- Framework and Methodological Issues." Population dence_estimations2.pdf. Health Metrics 1: 1. Lopez, A. D., C. D. Mathers, M. Ezzati, D. T. Jamison, Nelson, G. C., M. W. Rosegrant, J. Koo, R. Robertson, C. J. L. Murray. 2006. "Global and Regional Burden T. Sulser, T. Zhu, C. Ringler, S. Msangi, A. Palazzo, of Disease and Risk Factors, 2001: Systematic M. Batka, M. Magalhaes, R. Valmonte-Santos, M. Analysis of Population Health Data." The Lancet. Ewing, and D. Lee. 2010. The Costs of Agricultural 367(9524): 1747-1757. Adaptation to Climate Change. Washington, DC: World Bank. 18 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e Parry, M., N. Arnell, P. Berry, D. Dodman, S. Stenberg, J., B. Johns, R. W. Scherpbier, and T. T. Fankhauser, C. Hope, S. Kovats, R. Nicholls, D. Edeger. 2007. "A Financial Road Map to Scaling Up Satterthwaite, R. Tiffin, and T. Wheeler. 2009. Essential Child Health Interventions in 75 Assessing the Costs of Adaptation to Climate Change: A Countries." WHO Bulletin 5: 305­14. Review of the UNFCCC and Other Recent Estimates. London: International Institute for Environment Strzepek, K., and C. A. Schlosser. 2010. Climate Change and Development and the Grantham Institute for Scenarios and Climate Data. Washington, DC: World Climate Change, Imperial College. Bank. Reiter, P., C. J. Thomas, P. M. Atkinson, S. I. Hay, S. E. Tanser, F. C., B. Sharp, and D. LeSueur. 2003. "Potential Randolph, D. J. Rogers, G. D. Shanks, R. W. Snow, Effect of Climate Change on Malaria Transmission and A. Spielman. 2004. "Global Warming and in Africa." The Lancet 362 (9398): 1792­98. Malaria: A Call for Accuracy." The Lancet Infectious Diseases 4 (6): 323­24. UNFCCC (United Nations Framework Convention on Climate Change). 2007. "Background Paper on Roll Back Malaria Partnership. 2008. "The Global Analysis of Existing and Planned Investment and Malaria Action Plan for a Malaria Free World." Financial Flows Relevant to the Development of Available at http://www.rollbackmalaria.org/gmap/ Effective and Appropriate International Response to gmap.pdf. Climate Change." Bonn. Singh, R. B..K., S. Hales, N. d. Wet, R. Raj, M. WHO (World Health Organization). 2004. World Hearnden, P. Weinstein. 2001. "The Influence of Health Report 2004­Changing History. Geneva. Climate Variation and Change on Diarrheal Disease in the Pacific Islands." Environmental Health ------. 2008a. The Global Burden of Disease: 2004 Perspectives 109(2): 155-159. Update. Geneva. ------. 2008b: World Malaria Report 2008. Geneva. d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 19 annex -- lIst of countrIes by World bank IncoMe group and regIon Region Low Income Lower Middle Income Upper Middle Income east asia cambodia, dpr Korea, lao pdr, mongo- china, Fs of micronesia, Fiji, malaysia, palau Pacific lia, myanmar, papua new guinea, solo- indonesia, Kiribati, marshall mon islands, vietnam islands, philippines, samoa, thai- land, tonga, tuvalu, vanuatu europe and Kyrgyzstan, tajikistan, uzbekistan albania, armenia, azerbaijan, Bulgaria, croatia, hungary, Central asia Belarus, Bosnia and herzegovina, Kazakhstan, latvia, lithuania, georgia, moldova, tFYr macedo- poland, romania, russian Feder- nia, turkmenistan, ukraine ation, serbia, slovakia, turkey latin haiti, honduras Bolivia, colombia, cuba, domini- argentina, Belize, Bolivia, Brazil, america and can republic, ecuador, el salva- chile, costa rica, dominica, gre- Carribean dor, guatemala, guyana, nada, mexico, panama, saint Jamaica, nicaragua, paraguay, Kitts and nevis, saint lucia, saint peru, suriname vincent and the grenadines, uru- guay, venezuela Middle east Yemen algeria, djibouti, egypt, iran lebanon, libyan arab Jamahiriya, North africa (islamic republic of), iraq, Jordan, oman morocco, syrian arab republic, tunisia south asia afghanistan, Bangladesh, india, nepal, Bhutan, maldives, sri lanka pakistan sub saharan Benin, Burkina Faso, Burundi, central afri- angola, cameroon, cape verde, Botswana, equatorial guinea, africa can rep, chad, comoros, côte d'ivoire, congo, lesotho, namibia, swazi- gabon, mauritius, seychelles, dr congo, eritrea, ethiopia, gambia, land south africa ghana, guinea, guinea-Bissau, Kenya, liberia, madagascar, malawi, mali, maurita- nia, mozambique, niger, nigeria, rwanda, são tomé & príncipe, senegal, sierra leone, somalia, sudan, togo, uganda, ur tanzania, Zambia, Zimbabwe High-income countries: andorra, antigua and Barbuda, australia, austria, Bahamas, Bahrain, Barbados, Belgium, Brunei darussalam, canada, cyprus, czech republic, denmark, estonia, Finland, France, germany, greece, iceland, ireland, israel, italy, Japan, Kuwait, luxembourg, malta, monaco, netherlands, new Zealand, norway, portugal, Qatar, republic of Korea, san marino, saudi arabia, singapore, slovenia, spain, sweden, switzerland, trinidad and tobago, united arab emirates, united Kingdom, united states of america. 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