BRIEF APRIL 2020 THE CASCADE ANALYSIS TOOL FOR CONTINUUM OF CARE ANALYTICS: AN APPLICATION IN DIABETES CARE IN UKRAINE M any health programs require people to engage repeatedly with the services in order to benefit maximally—whether it is a diabetes control program, mother and child services, tuberculosis treatment, or cancer care. We are using the terms ‘continuum of care’ and ‘care cascade’ to describe the string of services people in need must utilize, starting from a first contact which might be hearing about the service, or getting screened. Ideally, people then progress through the service chain to get the intended health outcome, whether cure, meeting their treatment target, or even survival. Figure 1a shows such a service delivery cascade, giving us a snap-shot of how many people in need of a program get in contact with it, become enrolled, stay retained in the program, and reach the intended outcome of the services received. This cascade view draws our attention to how many people get the intended outcome, and demonstrates that the losses along the cascade quickly make the cascade tumble. It is a multiplier effect leading to low numbers of good outcomes. The outcome is sometimes also called “effective coverage” of a program. It is a product of coverage, quality and efficiency of the full sequence of the services along the cascade. While the usual cascade (figure 1a) gives us the snap-shot, we can ‘deconstruct’ the cascade to show where people actually are on their journey (figure 1b), where each service client is linked to a specific ‘care status’. This is a first step to defining ‘compartments’ in a mathematical model. Figure 1 Service delivery cascade: Overall picture (left) and deconstructed into care status (right) a. Overall picture b. Deconstructed into care status Source: World Bank CASCADE ANALYSIS TOOL FOR CONTINUUM OF CARE ANALYTICS: AN APPLICATION IN DIABETES CARE IN UKRAINE To capture the journey of health service clients best, we need to understand their movements in and out of the care continuum. Are they lost because they dropped out, or did they die? Why did they drop out? Do some of the clients who drop out of care re-enter care? If so, do they come back spontaneously or through being sensitized, recalled, or incentivized by the program? These are important questions and unless the movements of the clients are taken into account, it is challenging to understand what actions the program should take to improve the continuity of care, mitigate bottlenecks and facilitate clients’ access. In order to support cascade analytics, a team of program implementers, software developers and data scientists developed the web-based Cascade Analysis Tool (CAT). The intention was to provide service planners with a tool they can use to analyze and improve technical and allocative efficiencies along the service continuum. THE CASCADE ANALYSIS TOOL The CAT is a mathematical model with a generic, flexible framework which The tool makes practical means different types of service delivery cascades can be analyzed, from use of the increasing various health and development programs where repeat client interactions quantities of data on the are required for the outcome to happen. The tool makes practical use of the costs, coverage, and increasing quantities of data on the costs, coverage, and impact of health and impact of health and development interventions. By integrating various data types, it adds value to development current analytics, and provides additional decision support. interventions. It is designed to address key questions relevant to decision-makers around: ◼ Allocative efficiency (identifying the optimal allocation of funding among available interventions in order to get as many people as possible reach the intended outcome e.g., treatment success) ◼ Technical efficiency (identifying ways to reduce labour or capital input requirements without negatively affecting outcomes), and ◼ Cost-effectiveness (estimating the costs per person reaching an outcome such as treatment success). The CAT is an open-access web-based app (ui.cascade.tools), described in detail elsewhere.1 For ease of use, several cascade frameworks have been pre-designed in the CAT software for diabetes, hypertension, HIV, tuberculosis and cervical cancer (figure 2). Methodologically, the CAT is based on a compartmental model structure which builds on the cascade concept (figure 1). People move between ‘care status’ compartments with certain probabilities, and the CAT therefore provides methods for setting these transition rates between compartments. There is an inbuilt optimization function to calculate what intervention efforts are 1 Kedziora DJ, Abeysuriya R, Kerr CC, Chadderdon GL, Harbuz VȘ, Metzger S, Wilson DP & Stuart RM (2019). The Cascade Analysis Tool: software to analyze & optimize care cascades. Gates Open Research, 3:1488. CASCADE ANALYSIS TOOL FOR CONTINUUM OF CARE ANALYTICS: AN APPLICATION IN DIABETES CARE IN UKRAINE required to reach specific programmatic objectives (e.g., the maximum possible number of clients with treatment success). Practically, CAT users either use a ready-to-use cascade framework or design their own. They can then enter data regarding the interventions, costs and effectiveness of the set of interventions. Based on these inputs, the user can conduct a variety of analyzes under the ‘status quo’ and by assuming specific changes: The ‘what-if’ scenarios look at the effect of specific policy or program changes (e.g., an additional screening intervention). The CAT’s ‘optimize the cascade’ function calculates the ideal coverage and funding levels for the cascade services that would allow the program to meet the set objectives. Figure 2 A screen shot of the user-friendly web application of the CAT Source: ui.cascade.tools. APPLICATION OF THE CASCADE ANALYSIS TOOL IN UKRAINE The Ukraine Ministry of Health, Public Health Centers and development partners have been promoting and using the cascade approach to create ‘snap-shots’ for various noncommunicable disease (NCD) programs. While these cascades provided an excellent summary of care stages attained by clients, they did not provide an analysis of the service implementation leading to these attainments. Poltava Region in central Ukraine had a particular interest in going further in the analysis of their type-2 diabetes care cascade which showed two major breakpoints: diagnosis and glucose control.2 At that time, the national health reform brought changes to the diabetes program, most importantly a new cost-sharing for oral diabetes medication. Poltava Region was also a 2 World Bank. 2019. Type-2 Diabetes Care in Ukraine: Breakpoints and Implications for Action. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31157 License: CC BY 3.0 IGO CASCADE ANALYSIS TOOL FOR CONTINUUM OF CARE ANALYTICS: AN APPLICATION IN DIABETES CARE IN UKRAINE beneficiary of the World Bank Health Sector Support Project and was getting assistance from the World Health Organisation (WHO) and Swiss Agency for Development Cooperation, all aiming to reform NCD care towards better outcomes. The descriptive cascade analysis had provided the analysis team with a good understanding of the diabetes program in Poltava The descriptive cascade Region and the various prevention and care/treatment interventions. This analysis had provided the helped the team in defining the framework in the CAT model. The framework analysis team with a split the diabetic population into two types reflecting morbidity levels: Diabetics good understanding of who were ‘uncomplicated’ cases without major vascular damage, and the diabetes program in diabetics who already had vascular damage due to the harmful effects of Poltava Region and the prolonged high blood glucose (left and right pathways in figure 3). In the various prevention and Poltava CAT model, diabetic individuals move between compartments of care care/treatment states due to receiving specific services (vertical arrows). Also, uncomplicated interventions. cases can progress to vascular damage in the absence of effective treatment (horizontal arrows). Uncomplicated diabetes is treated with oral medicine or non-pharmacological, life-style interventions while cases with significant vascular damage are generally prescribed insulin. Figure 3 Schematic indicating the pathways through care for type-2 diabetes cases in Poltava region, Ukraine Source: World Bank, Developed for internal reporting by Robyn Stuart. Notes: IEC = information, education and communication; PHC = primary health care; T2DM = type-2 diabetes model. The objective of the CAT application was to estimate the costs of providing diabetes care in Poltava Region, and the most cost-effective ways to address the main breakpoints in the care continuum - diagnosis and glucose control. Results from Ukraine's annual Health Index Survey suggested that only one-third of Poltava adults sought out-patient care when they were sick, indicating that more CASCADE ANALYSIS TOOL FOR CONTINUUM OF CARE ANALYTICS: AN APPLICATION IN DIABETES CARE IN UKRAINE outreach services may be required to find people in need of care (intervention 2, figure 3). Qualitative research pointed to the fact that diabetes treatment costs may prevent people from both diagnosis and treatment maintenance, hence the need for co-payment schemes (interventions 9+10). To populate the model, we collected data on the unit costs of diabetes interventions and estimated program expenditure. Our rapid intervention costing included consumables, salaries, overheads and value-added tax. We also made estimates on the coverage of each intervention. We then applied the CAT to estimate the optimal combination of facility-based and outreach screening and investigate how additional funding could best be allocated to improve glucose control. KEY RESULTS Of the approximately 65,000 individuals with type-2 diabetes, 68% had been diagnosed and registered, 62% had been linked to diabetes care, 58% were monitored, 42% put on medication, and 16% had evidence of glucose control. We estimated that monitoring costs were higher for those not achieving glucose control (table 1). Table 1 Estimated unit costs of screening, diagnosis, treatment prescription, and enhanced adherence counseling interventions and estimated 2016 coverage levels and spend (USD) Cascade Unit cost (USD) Screening tests 1. Facility-based blood glucose test 0.66 2. Outreach/community-based blood glucose test 0.66 Diagnosis (confirmatory test) 3. Oral glucose tolerance test 3.80 Treatment prescription protocols 4. IEC through residential school/courses 5.76 5. IEC through PHC clinic staff 1.60 6. Support for self-monitoring (diary, glucometer) 20.90 7. Initial clinical exam - simple 12.44 8. Initial clinical exam - complex 12.44 Medication supply 9. Annual co-payments for oral medication 34.30 10. Annual co-payments for insulin 356.56 Treatment maintenance 11. Enhanced adherence counseling at PHC clinic 0.62 12. Enhanced adherence counseling at Feldsher post 0.55 13. Annual patient monitoring costs Non-pharma, glucose controlled 24.48 Non-pharma, not achieving glucose target 28.28 Oral, glucose controlled 25.61 Oral, not achieving glucose target 52.85 Insulin, glucose controlled 48.97 Insulin, not achieving glucose target 78.85 Source: World Bank. Notes: IEC = information, education and communication; PHC = primary health care. CASCADE ANALYSIS TOOL FOR CONTINUUM OF CARE ANALYTICS: AN APPLICATION IN DIABETES CARE IN UKRAINE We found that outreach screening campaigns could play a significant role in improving outcomes: depending on how well-targeted and scalable such campaigns are, we estimated that 10–46% of all screening could be conducted via outreach, at a cost per diagnosed case of USD 7.12–9.63. Investments in initiatives to improve treatment adherence (medication co-payment, enhanced adherence counseling) are likely to reduce barriers along the care Investments in initiatives to continuum and can lead to savings in care costs. For instance, if the improve treatment adherence share of patients achieving sustained glucose control was increased by (medication co-payment, just 1 percentage point, the Poltava diabetes program could see a enhanced adherence reduction in annual patient monitoring costs of around USD 10,000 (0.5% counseling) are likely to of patient monitoring spend). These additional funds would be sufficient reduce barriers along the care to increase coverage of enhanced adherence counselling by six-fold, continuum and can lead to enabling almost half of patients to have access to these services. savings in care costs. CONCLUSIONS The Poltava Department of Health appreciated this innovative analytical work on their diabetes cascade and what it means for their decision-making on improving case finding, assessment of ‘what works’ in treatment maintenance, and budgeting for an effective diabetes response in their region. Key staff were trained in CAT applications and associated activities like generation of unit costs and patient file review for evaluating care outcomes. The Poltava diabetes model is parametrized and can be updated and used for scenario and optimization analyzes. The modelling study demonstrated that investments to improve case detection and treatment adherence are the most efficient interventions for improved diabetes control in Poltava region. Quantitative tools which capture service delivery and outcomes like the CAT provide decision support and evaluation for targeting investment into services which close the gaps in implementation. The full findings of this analysis are available here: Stuart, R.M., Khan, O., Abeysuriya, R. et al. Diabetes care cascade in Ukraine: an analysis of breakpoints and opportunities for improved diabetes outcomes. BMC Health Serv Res 20, 409 (2020). https://doi.org/10.1186/s12913-020-05261-y. Investments to improve case detection and treatment adherence are the most efficient interventions for improved diabetes control in Poltava region. CASCADE ANALYSIS TOOL FOR CONTINUUM OF CARE ANALYTICS: AN APPLICATION IN DIABETES CARE IN UKRAINE ACKNOWLEDGEMENTS Department of Health, Poltava, Ukraine Viktor Lysak, Tetyana Kryvchun, Elvira Kaidashova, Volodymyr Mykhailets, Nina Durdykulyieva, Alla Bredikhina. Department of Mathematical Sciences, University of Copenhagen, Denmark Robyn Margaret Stuart Monash University, Melbourne, Australia Romesh Abeysuriya. The World Bank Olga Khan, Olena Doroshenko, David Wilson, Feng Zhao The brief was written by Nicole Fraser-Hurt and Zara Shubber Funding for this work was provided by the Bill and Melinda Gates Foundation and a Swiss Development Cooperation Trust Fund administered by the World Bank. © International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington DC 20433 Internet: www.worldbank.org; Telephone: 202 473 1000 This work is a product of the staff of The World Bank with external contributions. Note that The World Bank does not necessarily own each component of the content included in this work. The World Bank therefore does not warrant that the use of the content contained in the work will not infringe on the rights of third parties. The risk of claims resulting from such infringement rests solely with you. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of The World Bank or other partner institutions or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved. Rights and Permissions This work is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) unported license https://creativecommons.org/licenses/by/4.0/. Under the Creative Commons Attribution license, you are free to copy, distribute and adapt this work, including for commercial purposes, under the following conditions: Attribution – Please cite the work as follows: The Cascade Analysis Tool for Continuum of Care Analytics: An Application in Diabetes Care In Ukraine. Washington DC. 2020. World Bank. License: Creative Commons Attribution CC by 4.0 Translations – If you create a translation of this work, please add the following disclaimer along with the attribution: This translation was not created by The World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in its translation. All queries on rights and licenses should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington DC, 20433, USA; fax: 202-522-2625; email: pubrights@worldbank.org.