Pascal Geldsetzer1, Jennifer Manne-Goehler2, Maja-Emilia Marcus3, Cara Ebert4, Zhaxybay Zhumadilov5, Chea S Wesseh6, Lindiwe Tsabedze7, Adil Supiyev8, Lela Sturua9, Silver K Bahendeka10, Abla M Sibai11, Sarah Quesnel-Crooks12, Bolormaa Norov13, Kibachio J Mwangi14, Omar Mwalim15, Roy Wong-McClure16, Mary T Mayige17, Joao S Martins18, Nuno Lunet19, Demetre Labadarios20, Khem B Karki21, Gibson B Kagaruki17, Jutta M A Jorgensen22, Nahla C Hwalla23, Dismand Houinato24, Corine Houehanou24, Mohamed Msaidié25, David Guwatudde26, Mongal S Gurung27, Gladwell Gathecha28, Maria Dorobantu29, Albertino Damasceno30, Pascal Bovet31, Brice W Bicaba32, Krishna K Aryal33, Glennis Andall-Brereton12, Kokou Agoudavi34, Andrew Stokes35, Justine I Davies36, Till Bärnighausen37, Rifat Atun38, Sebastian Vollmer3, Lindsay M Jaacks39. 1. Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA. 2. Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 3. Department of Economics and Centre for Modern Indian Studies, University of Göttingen, Göttingen, Germany. 4. Department of Economics and Centre for Modern Indian Studies, University of Göttingen, Göttingen, Germany; RWI-Leibniz Institute for Economic Research, Berlin, Germany. 5. National Laboratory Astana, University Medical Center, Nazarbayev University, Astana, Kazakhstan. 6. Ministry of Health, Monrovia, Liberia. 7. Ministry of Health, Mbabane, eSwatini. 8. Laboratory of Epidemiology and Public Health, Center for Life Sciences, Nazarbayev University, Astana, Kazakhstan. 9. Non-Communicable Diseases Department, National Center for Disease Control and Public Health, Tbilisi, Georgia. 10. St Francis Hospital Nsambya, Kampala, Uganda. 11. Epidemiology and Population Health Department, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon. 12. Non-Communicable Diseases, Caribbean Public Health Agency, Port of Spain, Trinidad and Tobago. 13. National Center for Public Health, Ulaanbaatar, Mongolia. 14. Division of Non-Communicable Diseases, Ministry of Health, Nairobi, Kenya; The Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland. 15. Ministry of Health, Zanzibar City, Tanzania. 16. Office of Epidemiology and Surveillance, Costa Rican Social Security Fund, San José, Costa Rica. 17. National Institute for Medical Research, Dar es Salaam, Tanzania. 18. Faculty of Medicine and Health Sciences, National University of East Timor, Dili, Timor-Leste. 19. Department of Public and Forensic Health Sciences and Medical Education, Faculty of Medicine, University of Porto, Porto, Portugal. 20. Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa. 21. Department of Community Medicine and Public Health, Institute of Medicine, Tribhuvan University, Kathmandu, Nepal. 22. D-Tree International, Boston, MA, USA. 23. Faculty of Agricultural and Food Sciences, American University of Beirut, American University of Beirut, Beirut, Lebanon. 24. Laboratory of Epidemiology of Chronic and Neurological Diseases, Faculty of Health Sciences, University of Abomey-Calavi, Cotonou, Benin. 25. Ministry of Health, Solidarity, Social Cohesion and Gender, Government of Comoros, Moroni, Comoros. 26. Department of Epidemiology and Biostatistics, School of Public Health, Makerere University, Kampala, Uganda. 27. Health Research and Epidemiology Unit, Ministry of Health, Thimphu, Bhutan. 28. Division of Non-Communicable Diseases, Ministry of Health, Nairobi, Kenya. 29. Department of Cardiology, Emergency Hospital of Bucharest, Bucharest, Romania. 30. Department of Public and Forensic Health Sciences and Medical Education, Faculty of Medicine, University of Porto, Porto, Portugal; EPIUnit, Institute of Public Health, University of Porto, Porto, Portugal; Faculty of Medicine, Eduardo Mondlane University, Maputo, Mozambique. 31. Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland; Ministry of Health, Victoria, Seychelles. 32. Institut Africain de Santé Publique, Ouagadougou, Burkina Faso. 33. Department for International Development/Nepal Health Sector Programme 3/Monitoring Evaluation and Operational Research, Abt Associates, Kathmandu, Nepal. 34. Ministry of Health, Lome, Togo. 35. Department of Global Health, Boston University School of Public Health, Boston, MA, USA. 36. MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, University of Witwatersrand, Johannesburg, South Africa; Institute of Applied Health Research, University of Birmingham, Birmingham, UK. 37. Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA; Africa Health Research Institute, Somkhele, South Africa; Heidelberg Institute of Global Health, Faculty of Medicine, University of Heidelberg, Heidelberg, Germany. Electronic address: till.baernighausen@uni-heidelberg.de. 38. Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Global Health and Social Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 39. Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA; Public Health Foundation of India, New Delhi, Delhi, India.
Abstract
BACKGROUND: Evidence from nationally representative studies in low-income and middle-income countries (LMICs) on where in the hypertension care continuum patients are lost to care is sparse. This information, however, is essential for effective targeting of interventions by health services and monitoring progress in improving hypertension care. We aimed to determine the cascade of hypertension care in 44 LMICs-and its variation between countries and population groups-by dividing the progression in the care process, from need of care to successful treatment, into discrete stages and measuring the losses at each stage. METHODS: In this cross-sectional study, we pooled individual-level population-based data from 44 LMICs. We first searched for nationally representative datasets from the WHO Stepwise Approach to Surveillance (STEPS) from 2005 or later. If a STEPS dataset was not available for a LMIC (or we could not gain access to it), we conducted a systematic search for survey datasets; the inclusion criteria in these searches were that the survey was done in 2005 or later, was nationally representative for at least three 10-year age groups older than 15 years, included measured blood pressure data, and contained data on at least two hypertension care cascade steps. Hypertension was defined as a systolic blood pressure of at least 140 mm Hg, diastolic blood pressure of at least 90 mm Hg, or reported use of medication for hypertension. Among those with hypertension, we calculated the proportion of individuals who had ever had their blood pressure measured; had been diagnosed with hypertension; had been treated for hypertension; and had achieved control of their hypertension. We weighted countries proportionally to their population size when determining this hypertension care cascade at the global and regional level. We disaggregated the hypertension care cascade by age, sex, education, household wealth quintile, body-mass index, smoking status, country, and region. We used linear regression to predict, separately for each cascade step, a country's performance based on gross domestic product (GDP) per capita, allowing us to identify countries whose performance fell outside of the 95% prediction interval. FINDINGS: Our pooled dataset included 1 100 507 participants, of whom 192 441 (17·5%) had hypertension. Among those with hypertension, 73·6% of participants (95% CI 72·9-74·3) had ever had their blood pressure measured, 39·2% of participants (38·2-40·3) had been diagnosed with hypertension, 29·9% of participants (28·6-31·3) received treatment, and 10·3% of participants (9·6-11·0) achieved control of their hypertension. Countries in Latin America and the Caribbean generally achieved the best performance relative to their predicted performance based on GDP per capita, whereas countries in sub-Saharan Africa performed worst. Bangladesh, Brazil, Costa Rica, Ecuador, Kyrgyzstan, and Peru performed significantly better on all care cascade steps than predicted based on GDP per capita. Being a woman, older, more educated, wealthier, and not being a current smoker were all positively associated with attaining each of the four steps of the care cascade. INTERPRETATION: Our study provides important evidence for the design and targeting of health policies and service interventions for hypertension in LMICs. We show at what steps and for whom there are gaps in the hypertension care process in each of the 44 countries in our study. We also identified countries in each world region that perform better than expected from their economic development, which can direct policy makers to important policy lessons. Given the high disease burden caused by hypertension in LMICs, nationally representative hypertension care cascades, as constructed in this study, are an important measure of progress towards achieving universal health coverage. FUNDING: Harvard McLennan Family Fund, Alexander von Humboldt Foundation.
BACKGROUND: Evidence from nationally representative studies in low-income and middle-income countries (LMICs) on where in the hypertension care continuum patients are lost to care is sparse. This information, however, is essential for effective targeting of interventions by health services and monitoring progress in improving hypertension care. We aimed to determine the cascade of hypertension care in 44 LMICs-and its variation between countries and population groups-by dividing the progression in the care process, from need of care to successful treatment, into discrete stages and measuring the losses at each stage. METHODS: In this cross-sectional study, we pooled individual-level population-based data from 44 LMICs. We first searched for nationally representative datasets from the WHO Stepwise Approach to Surveillance (STEPS) from 2005 or later. If a STEPS dataset was not available for a LMIC (or we could not gain access to it), we conducted a systematic search for survey datasets; the inclusion criteria in these searches were that the survey was done in 2005 or later, was nationally representative for at least three 10-year age groups older than 15 years, included measured blood pressure data, and contained data on at least two hypertension care cascade steps. Hypertension was defined as a systolic blood pressure of at least 140 mm Hg, diastolic blood pressure of at least 90 mm Hg, or reported use of medication for hypertension. Among those with hypertension, we calculated the proportion of individuals who had ever had their blood pressure measured; had been diagnosed with hypertension; had been treated for hypertension; and had achieved control of their hypertension. We weighted countries proportionally to their population size when determining this hypertension care cascade at the global and regional level. We disaggregated the hypertension care cascade by age, sex, education, household wealth quintile, body-mass index, smoking status, country, and region. We used linear regression to predict, separately for each cascade step, a country's performance based on gross domestic product (GDP) per capita, allowing us to identify countries whose performance fell outside of the 95% prediction interval. FINDINGS: Our pooled dataset included 1 100 507 participants, of whom 192 441 (17·5%) had hypertension. Among those with hypertension, 73·6% of participants (95% CI 72·9-74·3) had ever had their blood pressure measured, 39·2% of participants (38·2-40·3) had been diagnosed with hypertension, 29·9% of participants (28·6-31·3) received treatment, and 10·3% of participants (9·6-11·0) achieved control of their hypertension. Countries in Latin America and the Caribbean generally achieved the best performance relative to their predicted performance based on GDP per capita, whereas countries in sub-Saharan Africa performed worst. Bangladesh, Brazil, Costa Rica, Ecuador, Kyrgyzstan, and Peru performed significantly better on all care cascade steps than predicted based on GDP per capita. Being a woman, older, more educated, wealthier, and not being a current smoker were all positively associated with attaining each of the four steps of the care cascade. INTERPRETATION: Our study provides important evidence for the design and targeting of health policies and service interventions for hypertension in LMICs. We show at what steps and for whom there are gaps in the hypertension care process in each of the 44 countries in our study. We also identified countries in each world region that perform better than expected from their economic development, which can direct policy makers to important policy lessons. Given the high disease burden caused by hypertension in LMICs, nationally representative hypertension care cascades, as constructed in this study, are an important measure of progress towards achieving universal health coverage. FUNDING: Harvard McLennan Family Fund, Alexander von Humboldt Foundation.
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