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Hypertension care cascades and reducing inequities in cardiovascular disease in low- and middle-income countries

Nature Medicine volume 30, pages 414–423 (2024)Cite this article

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Abstract

Improving hypertension control in low- and middle-income countries has uncertain implications across socioeconomic groups. In this study, we simulated improvements in the hypertension care cascade and evaluated the distributional benefits across wealth quintiles in 44 low- and middle-income countries using individual-level data from nationally representative, cross-sectional surveys. We raised diagnosis (diagnosis scenario) and treatment (treatment scenario) levels for all wealth quintiles to match the best-performing country quintile and estimated the change in 10-year cardiovascular disease (CVD) risk of individuals initiated on treatment. We observed greater health benefits among bottom wealth quintiles in middle-income countries and in countries with larger baseline disparities in hypertension management. Lower-middle-income countries would see the greatest absolute benefits among the bottom quintiles under the treatment scenario (29.1 CVD cases averted per 1,000 people living with hypertension in the bottom quintile (Q1) versus 17.2 in the top quintile (Q5)), and the proportion of total CVD cases averted would be largest among the lowest quintiles in upper-middle-income countries under both diagnosis (32.0% of averted cases in Q1 versus 11.9% in Q5) and treatment (29.7% of averted cases in Q1 versus 14.0% in Q5) scenarios. Targeted improvements in hypertension diagnosis and treatment could substantially reduce socioeconomic-based inequalities in CVD burden in low- and middle-income countries.

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Fig. 1: Hypertension care cascades by wealth quintile across country-level income groups.
Fig. 2: Absolute difference in mean CVD risk comparing bottom and top wealth quintiles by country and scenario.
Fig. 3: CVD cases averted compared to baseline across wealth quintiles, by scenario and country-level income group.
Fig. 4: Percent of total CVD cases averted compared to baseline across wealth quintiles, by scenario and country-level income group.

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Data availability

Many surveys contained in the HPACC dataset are publicly available. The two most common repository sources are the WHO data repository (https://extranet.who.int/ncdsmicrodata/index.php/home) and the DHS website (https://dhsprogram.com/data/). Several additional surveys have been obtained through formal requests of survey teams whose data are not publicly available. The pooled, harmonized, de-identified participant-level HPACC dataset and accompanying data dictionary have been created through a partnership among Harvard University, the University of Göttingen and Heidelberg University, in collaboration with all country-level survey teams. Access can be requested by contacting the HPACC team. More information about HPACC, including contact information for the collaboration and processes for requesting data, can be found at https://www.hpaccproject.org/.

Code availability

Replication code is available on GitHub (https://github.com/dorittalia/CVD-Equity-HPACC.git).

References

  1. Roth, G. A. et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J. Am. Coll. Cardiol. 76, 2982–3021 (2020).

    PubMed  PubMed Central  Google Scholar 

  2. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19.1 million participants. Lancet 389, 37–55 (2017).

    PubMed Central  Google Scholar 

  3. Sudharsanan, N. & Geldsetzer, P. Impact of coming demographic changes on the number of adults in need of care for hypertension in Brazil, China, India, Indonesia, Mexico, and South Africa. Hypertension 73, 770–776 (2019).

    CAS  PubMed  Google Scholar 

  4. Prabhakaran, D. et al. Cardiovascular, respiratory, and related disorders: key messages from Disease Control Priorities, 3rd edition. Lancet 391, 1224–1236 (2018).

    PubMed  Google Scholar 

  5. Samb, B. et al. Prevention and management of chronic disease: a litmus test for health-systems strengthening in low-income and middle-income countries. Lancet 376, 1785–1797 (2010).

    PubMed  Google Scholar 

  6. Beaglehole, R. et al. Improving the prevention and management of chronic disease in low-income and middle-income countries: a priority for primary health care. Lancet 372, 940–949 (2008).

    PubMed  Google Scholar 

  7. Kruk, M. E., Nigenda, G. & Knaul, F. M. Redesigning primary care to tackle the global epidemic of noncommunicable disease. Am. J. Public Health 105, 431–437 (2015).

    PubMed  PubMed Central  Google Scholar 

  8. Chow, C. K. et al. Availability and affordability of medicines and cardiovascular outcomes in 21 high-income, middle-income and low-income countries. BMJ Glob. Health 5, e002640 (2020).

    PubMed  PubMed Central  Google Scholar 

  9. Attaei, M. W. et al. Availability and affordability of blood pressure-lowering medicines and the effect on blood pressure control in high-income, middle-income, and low-income countries: an analysis of the PURE study data. Lancet Public Health 2, e411–e419 (2017).

    PubMed  Google Scholar 

  10. Geldsetzer, P. et al. The state of hypertension care in 44 low-income and middle-income countries: a cross-sectional study of nationally representative individual-level data from 1.1 million adults. Lancet 394, 652–662 (2019).

    PubMed  Google Scholar 

  11. Manne-Goehler, J. et al. Health system performance for people with diabetes in 28 low- and middle-income countries: a cross-sectional study of nationally representative surveys. PLoS Med. 16, e1002751 (2019).

    PubMed  PubMed Central  Google Scholar 

  12. Marcus, M. E. et al. Unmet need for hypercholesterolemia care in 35 low- and middle-income countries: a cross-sectional study of nationally representative surveys. PLoS Med. 18, e1003841 (2021).

    PubMed  PubMed Central  Google Scholar 

  13. Flood, D. et al. The state of diabetes treatment coverage in 55 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 680 102 adults. Lancet Healthy Longev. 2, e340–e351 (2021).

    PubMed  PubMed Central  Google Scholar 

  14. Martinez, R. et al. Trends in premature avertable mortality from non-communicable diseases for 195 countries and territories, 1990–2017: a population-based study. Lancet Glob. Health 8, e511–e523 (2020).

    PubMed  Google Scholar 

  15. Kruk, M. E. et al. Mortality due to low-quality health systems in the universal health coverage era: a systematic analysis of amenable deaths in 137 countries. Lancet 392, 2203–2212 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. Jan, S. et al. Action to address the household economic burden of non-communicable diseases. Lancet 391, 2047–2058 (2018).

    PubMed  Google Scholar 

  17. Hessel, P., Rodríguez-Lesmes, P. & Torres, D. Socio-economic inequalities in high blood pressure and additional risk factors for cardiovascular disease among older individuals in Colombia: results from a nationally representative study. PLoS ONE 15, e0234326 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Peiris, D. et al. Cardiovascular disease risk profile and management practices in 45 low-income and middle-income countries: a cross-sectional study of nationally representative individual-level survey data. PLoS Med. 18, e1003485 (2021).

    PubMed  PubMed Central  Google Scholar 

  19. Malta, D. C. et al. Inequalities in health care and access to health services among adults with self-reported arterial hypertension: Brazilian National Health Survey. Cad. Saude Publica 38, e00125421 (2022).

    Google Scholar 

  20. Joshi, S. & Thapa, B. B. Socioeconomic risk factors of hypertension and blood pressure among persons aged 15–49 in Nepal: a cross-sectional study. BMJ Open 12, e057383 (2022).

    PubMed  PubMed Central  Google Scholar 

  21. Gatimu, S. M. & John, T. W. Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors. Int. J. Equity Health 19, 213 (2020).

    PubMed  PubMed Central  Google Scholar 

  22. Yang, F., Qian, D., Liu, X., Healthy Aging and Development Study Group in Nanjing Medical University & Data Mining Group of Biomedical Big Data in Nanjing Medical University. Socioeconomic disparities in prevalence, awareness, treatment, and control of hypertension over the life course in China. Int. J. Equity Health 16, 100 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. Jung, L. et al. The interaction between district-level development and individual-level socioeconomic gradients of cardiovascular disease risk factors in India: a cross-sectional study of 2.4 million adults. Soc. Sci. Med. 239, 112514 (2019).

    PubMed  Google Scholar 

  24. Haider, M. R. & Gupta, R. D. Inequalities in undiagnosed hypertension among adult population in Bangladesh: evidence from a nationally representative survey. High. Blood Press. Cardiovasc. Prev. 29, 57–64 (2022).

    PubMed  Google Scholar 

  25. Adisasmito, W., Amir, V., Atin, A., Megraini, A. & Kusuma, D. Geographic and socioeconomic disparity in cardiovascular risk factors in Indonesia: analysis of the Basic Health Research 2018. BMC Public Health 20, 1004 (2020).

    PubMed  PubMed Central  Google Scholar 

  26. Sommer, I. et al. Socioeconomic inequalities in non-communicable diseases and their risk factors: an overview of systematic reviews. BMC Public Health 15, 914 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. Adjaye-Gbewonyo, K., Kawachi, I., Subramanian, S. V. & Avendano, M. Income inequality and cardiovascular disease risk factors in a highly unequal country: a fixed-effects analysis from South Africa. Int. J. Equity Health 17, 31 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. Marmot, M. & Allen, J. J. Social determinants of health equity. Am. J. Public Health 104, S517–S519 (2014).

    PubMed  PubMed Central  Google Scholar 

  29. Wagstaff, A. Poverty and health sector inequalities. Bull. World Health Organ. 80, 97–105 (2002).

    PubMed  PubMed Central  Google Scholar 

  30. Kreiner, C. T., Nielsen, T. H. & Serena, B. L. Role of income mobility for the measurement of inequality in life expectancy. Proc. Natl Acad. Sci. USA 115, 11754–11759 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zimmerman, F. J. & Anderson, N. W. Trends in health equity in the United States by race/ethnicity, sex, and income, 1993–2017. JAMA Netw. Open 2, e196386 (2019).

    PubMed  PubMed Central  Google Scholar 

  32. Niessen, L. W. et al. Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the Sustainable Development agenda. Lancet 391, 2036–2046 (2018).

    PubMed  Google Scholar 

  33. Allen, L. et al. Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review. Lancet Glob. Health 5, e277–e289 (2017).

    PubMed  PubMed Central  Google Scholar 

  34. Ochmann, S. et al. Diagnostic testing for hypertension, diabetes, and hypercholesterolaemia in low-income and middle-income countries: a cross-sectional study of data for 994 185 individuals from 57 nationally representative surveys. Lancet Glob. Health 11, e1363–e1371 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Mohanty, S. K. et al. Awareness, treatment, and control of hypertension in adults aged 45 years and over and their spouses in India: a nationally representative cross-sectional study. PLoS Med. 18, e1003740 (2021).

    PubMed  PubMed Central  Google Scholar 

  36. Ahmed, S., Tariqujjaman, M., Rahman, M. A., Hasan, M. Z. & Hasan, M. M. Inequalities in the prevalence of undiagnosed hypertension among Bangladeshi adults: evidence from a nationwide survey. Int. J. Equity Health 18, 33 (2019).

    PubMed  PubMed Central  Google Scholar 

  37. Antignac, M. et al. Socioeconomic status and hypertension control in Sub-Saharan Africa: the multination EIGHT study (Evaluation of Hypertension in Sub-Saharan Africa). Hypertension 71, 577–584 (2018).

    CAS  PubMed  Google Scholar 

  38. Murphy, A. et al. Inequalities in the use of secondary prevention of cardiovascular disease by socioeconomic status: evidence from the PURE observational study. Lancet Glob. Health 6, e292–e301 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. Basu, S. & Millett, C. Social epidemiology of hypertension in middle-income countries: determinants of prevalence, diagnosis, treatment, and control in the WHO SAGE study. Hypertension 62, 18–26 (2013).

    CAS  PubMed  Google Scholar 

  40. Jaacks, L. M. et al. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol. 7, 231–240 (2019).

    PubMed  PubMed Central  Google Scholar 

  41. Popkin, B. M. The nutrition transition: an overview of world patterns of change. Nutr. Rev. 62, S140–S143 (2004).

    PubMed  Google Scholar 

  42. NCD Risk Factor Collaboration (NCD-RisC).Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature 569, 260–264 (2019).

    ADS  PubMed Central  Google Scholar 

  43. Wetzel, S. et al. Changing socioeconomic and geographic gradients in cardiovascular disease risk factors among Indians aged 15–49 years – evidence from nationally representative household surveys. Lancet Reg. Health Southeast Asia. 12, 100188 (2023).

    PubMed  PubMed Central  Google Scholar 

  44. World Health Organization. HEARTS: technical package for cardiovascular disease management in primary health care: risk-based CVD management. https://www.who.int/publications/i/item/9789240001367 (World Health Organization, 2020).

  45. Haakenstad, A., Coates, M., Marx, A., Bukhman, G. & Verguet, S. Disaggregating catastrophic health expenditure by disease area: cross-country estimates based on the World Health Surveys. BMC Med. 17, 36 (2019).

    PubMed  PubMed Central  Google Scholar 

  46. Haakenstad, A., Coates, M., Bukhman, G., McConnell, M. & Verguet, S. Comparative health systems analysis of differences in the catastrophic health expenditure associated with non-communicable vs communicable diseases among adults in six countries. Health Policy Plan. 37, 1107–1115 (2022).

    PubMed  PubMed Central  Google Scholar 

  47. Pesec, M. et al. Primary health care that works: the Costa Rican experience. Health Aff. (Millwood) 36, 531–538 (2017).

  48. Seidman, G. & Atun, R. Does task shifting yield cost savings and improve efficiency for health systems? A systematic review of evidence from low-income and middle-income countries. Hum. Resour. Health 15, 29 (2017).

    PubMed  PubMed Central  Google Scholar 

  49. Joshi, R. et al. Task shifting for non-communicable disease management in low and middle income countries—a systematic review. PLoS ONE 9, e103754 (2014).

    ADS  PubMed  PubMed Central  Google Scholar 

  50. Husain, M. J. et al. Cost of primary care approaches for hypertension management and risk-based cardiovascular disease prevention in Bangladesh: a HEARTS costing tool application. BMJ Open 12, e061467 (2022).

    PubMed  PubMed Central  Google Scholar 

  51. Ueda, P. et al. Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys. Lancet Diabetes Endocrinol. 5, 196–213 (2017).

    PubMed  PubMed Central  Google Scholar 

  52. Nazarzadeh, M. et al. Blood pressure-lowering treatment for prevention of major cardiovascular diseases in people with and without type 2 diabetes: an individual participant-level data meta-analysis. Lancet Diabetes Endocrinol. 10, 645–654 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Hajifathalian, K. et al. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys. Lancet Diabetes Endocrinol. 3, 339–355 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. WHO CVD Risk Chart Working Group. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob. Health 7, e1332–e1345 (2019).

    Google Scholar 

  55. Mauer, N. et al. Longitudinal evidence on treatment discontinuation, adherence, and loss of hypertension control in four middle-income countries. Sci. Transl. Med. 14, eabi9522 (2022).

    PubMed  Google Scholar 

  56. Rubin, D. B. Inference and missing data. Biometrika 63, 581–592 (1976).

    MathSciNet  Google Scholar 

  57. Prioritarianism in Practice (eds. Adler, M. D. & Norheim, O. F.) (Cambridge Univ. Press, 2022).

  58. World Bank. World Bank Country and Lending Groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

  59. Manne-Goehler, J. et al. Data resource profile: the Global Health and Population Project on Access to Care for Cardiometabolic Diseases (HPACC). Int. J. Epidemiol. 51, e337–e349(2022).

    PubMed  Google Scholar 

  60. World Health Organization. Package of Essential Noncommunicable (PEN) Disease Interventions for Primary Health Care in Low-resource Settings. https://www.who.int/publications/i/item/9789240009226 (World Health Organization, 2020).

  61. Culyer, A. J. & Wagstaff, A. Equity and equality in health and health care. J. Health Econ. 12, 431–457 (1993).

    CAS  PubMed  Google Scholar 

  62. Law, M. R., Morris, J. K. & Wald, N. J. Use of blood pressure lowering drugs in the prevention of cardiovascular disease: meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies. BMJ 338, b1665 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Institute for Health Metrics and Evaluation. Global Burden of Disease Study 2019 (GBD 2019) Data Resources. https://ghdx.healthdata.org/gbd-2019

Download references

Acknowledgements

D.T.S. and S.V. acknowledge funding from the Trond Mohn Foundation and NORAD through Bergen Center for Ethics and Priority Setting (project no. 813596). L.C.C.B. is partly supported by the Brazilian National Research Agency (CNPq grant 307329/2022-4). The study sponsor had no role in the collection, analysis, interpretation of data, writing of the report or decision to submit the manuscript for publication. Earlier versions of this manuscript were presented during seminars at Harvard University as well as during the 2022 Global Symposium for Health Systems Research in Bogotá, Colombia. At these occasions and others, we received valuable comments from participants, including A. Pandya, G. Danaei, C. Boyer and D. Cutler. Statistical support was provided by N. Greifer at the Institute for Quantitative Social Science at Harvard University. We thank three reviewers for valuable and constructive comments.

Author information

Authors and Affiliations

  1. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Dorit Talia Stein, Rifat Atun & Stéphane Verguet

  2. Department of Health Policy, Stanford School of Medicine, Stanford University, Stanford, CA, USA

    Marissa B. Reitsma

  3. Division of Primary Care and Population Health, Stanford University, Stanford, CA, USA

    Pascal Geldsetzer

  4. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Pascal Geldsetzer

  5. Noncommunicable Disease Program, Ministry of Health, Lomé, Togo

    Kokou Agoudavi

  6. Bergen Centre for Ethics and Priority Setting in Health, Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway

    Krishna Kumar Aryal

  7. Public Health Promotion and Development Organization, Kathmandu, Nepal

    Krishna Kumar Aryal

  8. MKPGMS-Uganda Martyrs University, Kampala, Uganda

    Silver Bahendeka

  9. St. Francis Hospital, Nsambya, Kampala, Uganda

    Silver Bahendeka

  10. Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Luisa C. C. Brant

  11. Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

    Farshad Farzadfar

  12. Policy and Planning Division, Ministry of Health, Thimphu, Bhutan

    Mongal Singh Gurung

  13. Department of Epidemiology and Biostatistics, School of Public Health, Makerere University, Kampala, Uganda

    David Guwatudde

  14. National School of Public Health, University of Parakou, Parakou, Benin

    Yessito Corine Nadège Houehanou

  15. Department Maternal Child and Public Health, Nursing School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Deborah Carvalho Malta

  16. Faculty of Medicine and Health Sciences, Universidade Nacional Timor Lorosa’e, Díli, Timor-Leste

    João Soares Martins

  17. Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

    Sahar Saeedi Moghaddam

  18. Kiel Institute for the World Economy, Kiel, Germany

    Sahar Saeedi Moghaddam

  19. World Health Organization, Pretoria, South Africa

    Kibachio Joseph Mwangi

  20. Division of Non-Communicable Diseases, Ministry of Health, Nairobi, Kenya

    Kibachio Joseph Mwangi

  21. Nutrition Department, National Center for Public Health, Ulaanbaatar, Mongolia

    Bolormaa Norov

  22. National Center for Disease Control and Public Health, Tbilisi, Georgia

    Lela Sturua

  23. Petre Shotadze Tbilisi Medical Academy, Tbilisi, Georgia

    Lela Sturua

  24. School of Medicine, Nazarbayev University, Astana, Kazakhstan

    Zhaxybay Zhumadilov

  25. Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany

    Till Bärnighausen, Michaela Theilmann & Nikkil Sudharsanan

  26. Harvard Center for Population and Development Studies, Cambridge, MA, USA

    Till Bärnighausen

  27. Africa Health Research Institute, KwaZulu-Natal, South Africa

    Till Bärnighausen

  28. Institute of Applied Health Research, University of Birmingham, Birmingham, UK

    Justine I. Davies

  29. Department of Global Health, Centre for Global Surgery, Stellenbosch University, Cape Town, South Africa

    Justine I. Davies

  30. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    David Flood

  31. Center for Indigenous Health Research, Wuqu’ Kawoq, Tecpán, Guatemala

    David Flood

  32. Brigham and Women’s Hospital, Boston, MA, USA

    Maja E. Marcus

  33. Harvard Medical School, Boston, MA, USA

    Maja E. Marcus

  34. Professorship of Behavioral Science for Disease Prevention and Health Care, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany

    Michaela Theilmann & Nikkil Sudharsanan

  35. Department of Economics & Centre for Modern Indian Studies, University of Göttingen, Göttingen, Germany

    Sebastian Vollmer

  36. Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, MA, USA

    Jennifer Manne-Goehler

  37. Medical Practice Evaluation Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Jennifer Manne-Goehler

  38. Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, USA

    Rifat Atun

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  1. Dorit Talia Stein
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  2. Marissa B. Reitsma
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Contributions

D.T.S. and S.V. conceived the study. D.T.S. and M.B.R. performed data analysis. D.T.S. and S.V. wrote the initial draft of the paper, with input from M.B.R., R.A., P.G., J.M.-G., N.S., J.I.D., D.F., M.T. and T.B. All authors had access to the data. D.T.S. and M.B.R. have accessed and verified the data. All co-authors read and reviewed the final paper and agreed with the decision to submit the paper for publication.

Corresponding author

Correspondence to Stéphane Verguet.

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Competing interests

R.A. reports consulting and speaking engagements for Merck & Co., Novartis and F. Hoffmann-La Roche unrelated to this study or the subject. He also reports grants to his institution from Novo Nordisk, Roche, Novartis and the Union for International Cancer Control for work unrelated to this study. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Amanda Honeycutt, Muhammad Husain and Andre Kengne for their contribution to the peer review of this work. Primary Handling Editor: Jennifer Sargent, in collaboration with the Nature Medicine team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Diagram describing the main steps undertaken in the modeling analysis.

Individual-level data from survey respondents living with hypertension in the WHO STEPwise approach to Surveillance and attendant surveys (2007–2019) were used to estimate baseline 10-year cardiovascular disease risk. Effect sizes from a meta-analysis were then used to estimate the effect of improvements in diagnosis and treatment rates. Targets for improved diagnosis and treatment rates in each scenario were set at the level of the best-performing wealth quintile across all countries. Relative and absolute changes in cardiovascular disease events compared to baseline were summarized by scenario, country, and wealth quintile. CVD = cardiovascular disease.

Extended Data Fig. 2 Performance-based target results.

Results from sensitivity analysis where scenario targets were set separately by terciles (top, mid, bottom) of baseline care cascade performance. A) Cardiovascular disease cases averted per 1,000 people living with hypertension compared to baseline across wealth quintiles, by modelled scenario (either ‘diagnosis’ or ‘treatment’ scenario) and baseline performance tercile. B) Percent of total estimated cardiovascular disease cases averted compared to baseline across wealth quintiles, by modelled scenario (either ‘diagnosis’ or ‘treatment’ scenario) and baseline performance tercile.

Extended Data Fig. 3 Relative increase target results.

Results from sensitivity analysis where scenario targets were set as a 50% relative improvement applied to each country quintile’s baseline cascade performance. A) Cardiovascular disease cases averted per 1,000 people living with hypertension compared to baseline across wealth quintiles, by modelled scenario (either ‘diagnosis’ or ‘treatment’ scenario) and country-level income group. B) Percent of total estimated cardiovascular disease cases averted compared to baseline across wealth quintiles, by modelled scenario (either ‘diagnosis’ or ‘treatment’ scenario) and country-level income group.

Extended Data Fig. 4 Absolute difference in mean cardiovascular disease risk comparing bottom and top wealth quintiles by country and scenario for relative increase targets.

Dots represent the absolute (percentage point) difference in mean 10-year cardiovascular disease (CVD) risk comparing the bottom (Q1) and top (Q5) wealth quintiles, color-coded by scenario (‘baseline’, ‘diagnosis’ or ‘treatment’ scenario) for each country. The vertical gray dotted line at x = 0 represents the point where there is no difference in CVD risk comparing bottom (Q1) and top (Q5) wealth quintiles. Values to the right of the dotted line are where mean CVD risk is higher in the bottom (Q1) quintile and values to the left of the dotted line are where mean CVD risk is higher in the top (Q5) quintile.

Extended Data Fig. 5 Within-country target results.

Results from sensitivity analysis where scenario targets were set to the level of the best-performing wealth quintile within each country. A) Cardiovascular disease cases averted per 1,000 people living with hypertension compared to baseline across wealth quintiles, by modelled scenario (either ‘diagnosis’ or ‘treatment’ scenario) and country-level income group. B) Percent of total estimated cardiovascular disease cases averted compared to baseline across wealth quintiles, by modelled scenario (either ‘diagnosis’ or ‘treatment’ scenario) and country-level income group.

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Stein, D.T., Reitsma, M.B., Geldsetzer, P. et al. Hypertension care cascades and reducing inequities in cardiovascular disease in low- and middle-income countries. Nat Med 30, 414–423 (2024). https://doi.org/10.1038/s41591-023-02769-8

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