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Davies JI, Reddiar SK, Hirschhorn LR, Ebert C, Marcus ME, Seiglie JA, Zhumadilov Z, Supiyev A, Sturua L, Silver BK, Sibai AM, Quesnel-Crooks S, Norov B, Mwangi JK, Omar OM, Wong-McClure R, Mayige MT, Martins JS, Lunet N, Labadarios D, Karki KB, Kagaruki GB, Jorgensen JMA, Hwalla NC, Houinato D, Houehanou C, Guwatudde D, Gurung MS, Bovet P, Bicaba BW, Aryal KK, Msaidié M, Andall-Brereton G, Brian G, Stokes A, Vollmer S, Bärnighausen T, Atun R, Geldsetzer P, Manne-Goehler J, Jaacks LM. Association between country preparedness indicators and quality clinical care for cardiovascular disease risk factors in 44 lower- and middle-income countries: A multicountry analysis of survey data. PLoS Med 2020; 17:e1003268. [PMID: 33170842 PMCID: PMC7654799 DOI: 10.1371/journal.pmed.1003268] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 09/18/2020] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Cardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs) are regularly collected by ministries of health and global health agencies. We aimed to assess whether these indicators are associated with patient receipt of quality clinical care. METHODS AND FINDINGS We did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases ('NCD readiness indicators' from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08-3.21], p = 0.03) and availability of funding with being controlled (OR, 2.26 [95% CI 1.09-4.69], p = 0.03). Hospital beds (OR, 1.14 [95% CI 1.02-1.27], p = 0.02), nurses/midwives (OR, 1.24 [95% CI 1.06-1.44], p = 0.006), and physicians (OR, 1.21 [95% CI 1.11-1.32], p < 0.001) per 1,000 people were positively associated with being diagnosed and, similarly, with being treated; and the number of physicians was additionally associated with being controlled (OR, 1.12 [95% CI 1.01-1.23], p = 0.03). For diabetes, no positive associations were seen between NCD readiness indicators and outcomes. There was no association between country development, health service finance, or health service performance and readiness indicators and any outcome, apart from GDP (OR, 1.70 [95% CI 1.12-2.59], p = 0.01), HDI (OR, 1.21 [95% CI 1.01-1.44], p = 0.04), and number of physicians per 1,000 people (OR, 1.28 [95% CI 1.09-1.51], p = 0.003), which were associated with being diagnosed. Six countries had data on cascades of care and nationwide-level data on facility preparedness. Of the 27 associations tested between facility preparedness indicators and outcomes, the only association that was significant was having metformin available, which was positively associated with treatment (OR, 1.35 [95% CI 1.01-1.81], p = 0.04). The main limitation was use of blood pressure measurement on a single occasion to diagnose hypertension and a single blood glucose measurement to diagnose diabetes. CONCLUSION In this study, we observed that indicators of country preparedness to deal with CVDRFs are poor proxies for quality clinical care received by patients for hypertension and diabetes. The major implication is that assessments of countries' preparedness to manage CVDRFs should not rely on proxies; rather, it should involve direct assessment of quality clinical care.
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Affiliation(s)
- Justine I. Davies
- Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
- MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, University of Witwatersrand, Johannesburg, South Africa
- King’s Centre for Global Health, King’s College London, United Kingdom
- Centre for Global Surgery, Department of Global Health, Stellenbosch University, Stellenbosch, South Africa
| | - Sumithra Krishnamurthy Reddiar
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Lisa R. Hirschhorn
- Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Cara Ebert
- RWI Leibniz Institute for Economic Research, Berlin Office, Berlin, Germany
| | - Maja-Emilia Marcus
- Department of Economics and Centre for Modern Indian Studies, University of Goettingen, Göttingen, Germany
| | - Jacqueline A. Seiglie
- Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Zhaxybay Zhumadilov
- National Laboratory Astana, University Medical Center, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Adil Supiyev
- Laboratory of Epidemiology and Public Health, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Lela Sturua
- Non-Communicable Disease Department, National Center for Disease Control and Public Health, Tbilisi, Georgia
| | | | - Abla M. Sibai
- Department of Epidemiology & Population Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
| | | | - Bolormaa Norov
- National Center for Public Health, Ulaanbaatar, Mongolia
| | - Joseph K. Mwangi
- Division of Non-Communicable Diseases, Kenya Ministry of Health, Nairobi, Kenya
| | | | - Roy Wong-McClure
- Epidemiology Office and Surveillance, Caja Costarricense de Seguro Social, San Jose, Costa Rica
| | - Mary T. Mayige
- National Institute for Medical Research, Dar es Salaam, Tanzania
| | - Joao S. Martins
- Postgraduate Program Office, Universidade Nacional Timor Lorosae, Dili, Timor-Leste
| | - Nuno Lunet
- Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Demetre Labadarios
- Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Khem B. Karki
- Institute of Medicine, Tribuvan, University Kathmandu, Nepal
| | | | | | - Nahla C. Hwalla
- Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut, Lebanon
| | - Dismand Houinato
- Laboratory of Epidemiology of Chronic and Neurological Diseases, Faculty of Health Sciences, University of Abomey–Calavi, Cotonou, Benin
| | - Corine Houehanou
- Laboratory of Epidemiology of Chronic and Neurological Diseases, Faculty of Health Sciences, University of Abomey–Calavi, Cotonou, Benin
| | - David Guwatudde
- Department of Epidemiology and Biostatistics, School of Public Health, Makerere University, Kampala, Uganda
| | - Mongal S. Gurung
- Health Research and Epidemiology Unit, Ministry of Health, Thimphu, Bhutan
| | - Pascal Bovet
- University Center of Primary Care and Health Services (Unisanté), Lausanne, Switzerland
- Ministry of Health, Victoria, Republic of Seychelles
| | - Brice W. Bicaba
- Institut Africain de Santé publique (IASP), Ouagadougou, Burkina Faso
| | - Krishna K. Aryal
- Monitoring Evaluation and Operational Research Project, Abt Associates, Kathmandu, Nepal
| | - Mohamed Msaidié
- Ministry of Health, Solidarity, Social Cohesion and Gender, Government of the Union of Comoros, Moroni, Union of Comoros
| | | | - Garry Brian
- The Fred Hollows Foundation New Zealand, Auckland, New Zealand
| | - Andrew Stokes
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Sebastian Vollmer
- Department of Economics and Centre for Modern Indian Studies, University of Goettingen, Göttingen, Germany
| | - Till Bärnighausen
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
- Africa Health Research Institute (AHRI), Somkhele and Durban, South Africa
| | - Rifat Atun
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America
| | - Pascal Geldsetzer
- Division of Primary Care and Population Health, Department of Medicine, Stanford University, Palo Alto, California, United States of America
| | - Jennifer Manne-Goehler
- Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Lindsay M. Jaacks
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Public Health Foundation of India, New Delhi, Delhi, India
- Global Academy of Agriculture and Food Security, The University of Edinburgh, Midlothian, United Kingdom
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Abstract
BACKGROUND Cardiovascular diseases are among the most common causes of hospital admissions and deaths in Zanzibar. This study assessed prevalence of, and antecedent factors and care access for the two common cardiovascular risk factors, hypertension and diabetes, to support health system improvements. METHODS Data was from a population based nationally representative survey. Prevalence of hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg or a self-reported diagnosis of hypertension; diabetes was defined as a fasting blood glucose ≥6.1 mmol/L or a self-reported diagnosis of diabetes. Care-cascades for hypertension and diabetes were created with four stages: being tested, diagnosed, treated, and achieving control. Multivariable logistic regression models were constructed to evaluate individual-level factors - including symptoms of mental illness - associated with having hypertension or diabetes, and with progressing through the hypertension care cascade. Whether people at overt increased risk of hypertension or diabetes (defined as > 50 years old, BMI > 30 kg/m2, or currently smoking) were more likely to be tested was assessed using chi squared. RESULTS Prevalence of hypertension was 33.5% (CI 30.6-36.5). Older age (OR 7.7, CI 4.93-12.02), some education (OR 0.6, CI 0.44-0.89), obesity (OR 3.1, CI 2.12-4.44), and raised fasting blood glucose (OR 2.4, CI 2.38) were significantly independently associated with hypertension. Only 10.9% (CI 8.6-13.8) of the entire hypertensive population achieved blood pressure control, associated factors were being female (OR 4.8, CI 2.33-9.88), formally employed (OR 3.0, CI 1.26-7.17), and overweight (OR 2.5, CI 1.29-4.76). The prevalence of diabetes was 4.4% (CI 3.4-5.5), and associated with old age (OR 14.1, CI 6.05-32.65) and almost significantly with obesity (OR 2.1, CI 1.00-4.37). Only 11.9% (CI 6.6-20.6) of the diabetic population had achieved control. Individuals at overt increased risk were more likely to have been tested for hypertension (chi2 19.4) or diabetes (chi2 33.2) compared to the rest of the population. Symptoms of mental illness were not associated with prevalence of disease or progress through the cascade. CONCLUSION High prevalence of hypertension and suboptimal management along the care cascades indicates a large unmet need for hypertension and diabetes care in Zanzibar.
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Affiliation(s)
- Jutta M Adelin Jorgensen
- Mnazi Mmoja Referral Hospital, Kaunda Rd, Vuga, Po Box 3793, Zanzibar, Tanzania.
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
| | | | - Omar Mwalim Omar
- Head of NCD unit, Zanzibar Ministry of Health, Zanzibar, Tanzania
| | - Justine I Davies
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, University of Witwatersrand, Johannesburg, South Africa
- King's Centre for Global Health, King's College, London, UK
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