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Estimated Effect of Increased Diagnosis, Treatment, and Control of Diabetes and Its Associated Cardiovascular Risk Factors Among Low-Income and Middle-Income Countries: A Microsimulation Model Publisher Pubmed



Basu S1, 2, 4, 5, 6 ; Flood D7, 8, 9 ; Geldsetzer P10, 11 ; Theilmann M11 ; Marcus ME12 ; Ebert C13 ; Mayige M14 ; Wongmcclure R15 ; Farzadfar F16 ; Saeedi Moghaddam S17 ; Agoudavi K19 ; Norov B20 ; Houehanou C21 ; Andallbrereton G22 Show All Authors
Authors
  1. Basu S1, 2, 4, 5, 6
  2. Flood D7, 8, 9
  3. Geldsetzer P10, 11
  4. Theilmann M11
  5. Marcus ME12
  6. Ebert C13
  7. Mayige M14
  8. Wongmcclure R15
  9. Farzadfar F16
  10. Saeedi Moghaddam S17
  11. Agoudavi K19
  12. Norov B20
  13. Houehanou C21
  14. Andallbrereton G22
  15. Gurung M23
  16. Brian G24
  17. Bovet P25
  18. Martins J26
  19. Atun R3
  20. Barnighausen T3, 11, 27
  21. Vollmer S11
  22. Mannegoehler J28, 29
  23. Davies J30, 31, 32
Show Affiliations
Authors Affiliations
  1. 1. Center for Primary Care, Harvard Medical School, Boston, MA, United States
  2. 2. Ariadne Labs, Harvard T H Chan School of Public Health, Brigham and Women's Hospital, Boston, MA, United States
  3. 3. Department of Global Health and Population, Harvard T H Chan School of Public Health, Brigham and Women's Hospital, Boston, MA, United States
  4. 4. School of Public Health, Imperial College, London, United Kingdom
  5. 5. Research and Population Health, Collective Health, San Francisco, CA, United States
  6. 6. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
  7. 7. Division of Hospital Medicine, Department of Internal Medicine, National Clinician Scholars Program, University of Michigan, Ann Arbor, MI, United States
  8. 8. Center for Indigenous Health Research, Wuqu' Kawoq, Tecpan, Guatemala
  9. 9. Research Center for the Prevention of Chronic Diseases, Institute of Nutrition of Central America and Panama, Guatemala City, Guatemala
  10. 10. Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, United States
  11. 11. Heidelberg Institute of Global Health, Heidelberg University and University Hospital, Heidelberg, Germany
  12. 12. Department of Economics and Center for Modern Indian Studies, University of Goettingen, Goettingen, Germany
  13. 13. Rheinisch-Westfalisches Institut–Leibniz Institute for Economic Research, Essen, Germany
  14. 14. Epidemiology Department, National Institute for Medical Research, Dar es Salaam, Tanzania
  15. 15. Office of Epidemiology and Surveillance, Costa Rican Social Security Fund, San Jose, Costa Rica
  16. 16. Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
  17. 17. Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
  18. 18. Tehran University of Medical Sciences, Tehran, Iran
  19. 19. Togo Ministry of Health, Lome, Togo
  20. 20. National Center for Public Health, Ulaanbaatar, Mongolia
  21. 21. National Training School for Senior Technicians in Public Health and Epidemiological Surveillance (ENATSE), University of Parakou, Parakou, Benin
  22. 22. Non-Communicable Diseases, Caribbean Public Health Agency, Port of Spain, Trinidad and Tobago
  23. 23. Health Research and Epidemiology Unit, Ministry of Health, Thimphu, Bhutan
  24. 24. The Fred Hollows Foundation, Sydney, NSW, Australia
  25. 25. Ministry of Health, Victoria, Seychelles
  26. 26. Rector of the Univesidade Nacional Timor Lorosae, Dili, Timor-Leste
  27. 27. Africa Health Research Institute, Somkhele, South Africa
  28. 28. Division of Infectious Diseases, Brigham and Women's Hospital, Boston, MA, United States
  29. 29. Medical Practice Evaluation Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
  30. 30. Institute for Applied Health Research, University of Birmingham, Birmingham, United Kingdom
  31. 31. Centre for Global Surgery, Department of Global Health, Stellenbosch University, Cape Town, South Africa
  32. 32. Medical Research Council–Wits University Rural Public Health and Health Transitions Research Unit, Faculty of Health Sciences, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa

Source: The Lancet Global Health Published:2021


Abstract

Background: Given the increasing prevalence of diabetes in low-income and middle-income countries (LMICs), we aimed to estimate the health and cost implications of achieving different targets for diagnosis, treatment, and control of diabetes and its associated cardiovascular risk factors among LMICs. Methods: We constructed a microsimulation model to estimate disability-adjusted life-years (DALYs) lost and health-care costs of diagnosis, treatment, and control of blood pressure, dyslipidaemia, and glycaemia among people with diabetes in LMICs. We used individual participant data—specifically from the subset of people who were defined as having any type of diabetes by WHO standards—from nationally representative, cross-sectional surveys (2006–18) spanning 15 world regions to estimate the baseline 10-year risk of atherosclerotic cardiovascular disease (defined as fatal and non-fatal myocardial infarction and stroke), heart failure (ejection fraction of <40%, with New York Heart Association class III or IV functional limitations), end-stage renal disease (defined as an estimated glomerular filtration rate <15 mL/min per 1·73 m2 or needing dialysis or transplant), retinopathy with severe vision loss (<20/200 visual acuity as measured by the Snellen chart), and neuropathy with pressure sensation loss (assessed by the Semmes-Weinstein 5·07/10 g monofilament exam). We then used data from meta-analyses of randomised controlled trials to estimate the reduction in risk and the WHO OneHealth tool to estimate costs in reaching either 60% or 80% of diagnosis, treatment initiation, and control targets for blood pressure, dyslipidaemia, and glycaemia recommended by WHO guidelines. Costs were updated to 2020 International Dollars, and both costs and DALYs were computed over a 10-year policy planning time horizon at a 3% annual discount rate. Findings: We obtained data from 23 678 people with diabetes from 67 countries. The median estimated 10-year risk was 10·0% (IQR 4·0–18·0) for cardiovascular events, 7·8% (5·1–11·8) for neuropathy with pressure sensation loss, 7·2% (5·6–9·4) for end-stage renal disease, 6·0% (4·2–8·6) for retinopathy with severe vision loss, and 2·6% (1·2–5·3) for congestive heart failure. A target of 80% diagnosis, 80% treatment, and 80% control would be expected to reduce DALYs lost from diabetes complications from a median population-weighted loss to 1097 DALYs per 1000 population over 10 years (IQR 1051–1155), relative to a baseline of 1161 DALYs, primarily from reduced cardiovascular events (down from a median of 143 to 117 DALYs per 1000 population) due to blood pressure and statin treatment, with comparatively little effect from glycaemic control. The target of 80% diagnosis, 80% treatment, and 80% control would be expected to produce an overall incremental cost-effectiveness ratio of US$1362 per DALY averted (IQR 1304–1409), with the majority of decreased costs from reduced cardiovascular event management, counterbalanced by increased costs for blood pressure and statin treatment, producing an overall incremental cost-effectiveness ratio of $1362 per DALY averted (IQR 1304–1409). Interpretation: Reducing complications from diabetes in LMICs is likely to require a focus on scaling up blood pressure and statin medication treatment initiation and blood pressure medication titration rather than focusing on increasing screening to increase diabetes diagnosis, or a glycaemic treatment and control among people with diabetes. Funding: None. © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
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