Browsing by Author "Stokes, Andrew"

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    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) Davies, Justine I.; Krishnamurthy Reddiar, Sumithra; Hirschhorn, Lisa R.; Ebert, Cara; Marcus, Maja-Emilia; Seiglie, Jacqueline A.; Zhumadilov, Zhaxybay; Supiyev, Adil; Sturua, Lela; Silver, Bahendeka K.; Sibai, Abla M.; Quesnel-Crooks, Sarah; Norov, Bolormaa; Mwangi, Joseph K.; Mwalim Omar, Omar; Wong-McClure, Roy; Mayige, Mary T.; Martins, Joao S.; Lunet, Nuno; Labadarios, Demetre; Karki, Khem B.; Kagaruki, Gibson B.; Jorgensen, Jutta M. A.; Hwalla, Nahla C.; Houinato, Dismand; Houehanou, Corine; Guwatudde, David; Gurung, Mongal S.; Bovet, Pascal; Bicaba, Brice W.; Aryal, Krishna K.; Msaidie, Mohamed; Andall-Brereton, Glennis; Brian, Garry; Stokes, Andrew; Vollmer, Sebastian; Ba¨rnighausen, Till; Atun, Rifat; Geldsetzer, Pascal; Manne-Goehler, Jennifer; Jaacks, Lindsay M.
    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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    Diabetes diagnosis and care in sub-Saharan Africa: pooled analysis of individual data from 12 countries
    (The lancet Diabetes & endocrinology, 2016) Manne-Goehler, Jennifer; Atun, Rifat; Stokes, Andrew; Goehler, Alexander; Houinato, Dismand; Houehanou, Corine; Hambou, Mohamed Msaidie Salimani; Longo Mbenza, Benjamin; Sobngwi, Eugène; Balde, Naby; Kibachio Mwangi, Joseph; Gathecha, Gladwell; Ngugi, Paul Waweru; Wesseh, C. Stanford; Damasceno, Albertino; Lunet, Nuno; Bovet, Pascal; Labadarios, Demetre; Zuma, Khangelani; Mayige, Mary; Kagaruki, Gibson; Ramaiya, Kaushik; Agoudavi, Kokou; Guwatudde, David; Bahendeka, Silver K.; Mutungi, Gerald; Geldsetzer, Pascal; Levitt, Naomi S.; Geldsetzer, Joshua; Yudkin, John S.; Vollmer, Sebastian; Bärnighausen, Till
    Despite widespread recognition that the burden of diabetes is rapidly growing in many countries in sub-Saharan Africa, nationally representative estimates of unmet need for diabetes diagnosis and care are in short supply for the region. We use national population-based survey data to quantify diabetes prevalence and met and unmet need for diabetes diagnosis and care in 12 countries in sub-Saharan Africa. We further estimate demographic and economic gradients of met need for diabetes diagnosis and care. Methods We did a pooled analysis of individual-level data from nationally representative population-based surveys that met the following inclusion criteria: the data were collected during 2005–15; the data were made available at the individual level; a biomarker for diabetes was available in the dataset; and the dataset included information on use of core health services for diabetes diagnosis and care. We fi rst quantifi ed the population in need of diabetes diagnosis and care by estimating the prevalence of diabetes across the surveys; we also quantifi ed the prevalence of overweight and obesity, as a major risk factor for diabetes and an indicator of need for diabetes screening. Second, we determined the level of met need for diabetes diagnosis, preventive counselling, and treatment in both the diabetic and the overweight and obese population. Finally, we did survey fi xed-eff ects regressions to establish the demographic and economic gradients of met need for diabetes diagnosis, counselling, and treatment. Findings We pooled data from 12 nationally representative population-based surveys in sub-Saharan Africa, representing 38 311 individuals with a biomarker measurement for diabetes. Across the surveys, the median prevalence of diabetes was 5% (range 2–14) and the median prevalence of overweight or obesity was 27% (range 16–68). We estimated seven measures of met need for diabetes-related care across the 12 surveys: (1) percentage of the overweight or obese population who received a blood glucose measurement (median 22% [IQR 11–37]); and percentage of the diabetic population who reported that they (2) had ever received a blood glucose measurement (median 36% [IQR 27–63]); (3) had ever been told that they had diabetes (median 27% [IQR 22–51]); (4) had ever been counselled to lose weight (median 15% [IQR 13–23]); (5) had ever been counselled to exercise (median 15% [IQR 11–30]); (6) were using oral diabetes drugs (median 25% [IQR 18–42]); and (7) were using insulin (median 11% [IQR 6–13]). Compared with those aged 15–39 years, the adjusted odds of met need for diabetes diagnosis (measures 1–3) were 2·22 to 3·53 (40–54 years) and 3·82 to 5·01 (≥55 years) times higher. The adjusted odds of met need for diabetes diagnosis also increased consistently with educational attainment and were between 3·07 and 4·56 higher for the group with 8 years or more of education than for the group with less than 1 year of education. Finally, need for diabetes care was signifi cantly more likely to be met (measures 4–7) in the oldest age and highest educational groups. Interpretation Diabetes has already reached high levels of prevalence in several countries in sub-Saharan Africa. Large proportions of need for diabetes diagnosis and care in the region remain unmet, but the patterns of unmet need vary widely across the countries in our sample. Novel health policies and programmes are urgently needed to increase awareness of diabetes and to expand coverage of preventive counselling, diagnosis, and linkage to diabetes care. Because the probability of met need for diabetes diagnosis and care consistently increases with age and educational attainment, policy makers should pay particular attention to improved access to diabetes services for young adults and people with low educational attainment.
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    Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries
    (Diabetes Care, 2020) Seiglie, Jacqueline A.; Marcus, Maja-Emilia; Ebert, Cara; Prodromidis, Nikolaos; Geldsetzer, Pascal; Theilmann, Michaela; Agoudavi, Kokou; Andall-Brereton, Glennis; Aryal, Krishna K.; Bicaba, Brice Wilfried; Bovet, Pascal; Brian, Garry; Dorobantu, Maria; Gathecha, Gladwell; Singh Gurung, Mongal; Guwatudde, David; Msaidie, Mohamed; Houehanou, Corine; Houinato, Dismand; Jorgensen, Jutta Mari Adelin; Kagaruki, Gibson B.; Karki, Khem B.; Labadarios, Demetre; Martins, Joao S.; Mayige, Mary T.; Wong-McClure, Roy; Kibachio Mwangi, Joseph; Mwalim, Omar; Norov, Bolormaa; Quesnel-Crooks, Sarah; Silver, Bahendeka K.; Sturua, Lela; Tsabedze, Lindiwe; Stanford Wesseh, Chea; Stokes, Andrew; Atun, Rifat; Davies, Justine I.; Vollmer, Sebastian; Barnighausen, Till W.; Jaacks, Lindsay M.; Meigs, James B.; Wexler, Deborah J.; Manne-Goehler, Jennifer
    Diabetes is a rapidly growing health problem in low- and middle-income countries (LMICs), but empirical data on its prevalence and relationship to socioeconomic status are scarce. We estimated diabetes prevalence and the subset with undiagnosed diabetes in 29 LMICs and evaluated the relationship of education, household wealth, and BMI with diabetes risk. RESEARCH DESIGN AND METHODS We pooled individual-level data from 29 nationally representative surveys conducted between 2008 and 2016, totaling 588,574 participants aged ‡25 years. Diabetes prevalence and the subset with undiagnosed diabetes was calculated overall and by country, World Bank income group (WBIG), and geographic region. Multivariable Poisson regression models were used to estimate relative risk (RR). RESULTS Overall, prevalence of diabetes in 29 LMICs was 7.5% (95% CI 7.1–8.0) and of undiagnosed diabetes 4.9% (4.6–5.3). Diabetes prevalence increased with increasing WBIG: countries with low-income economies (LICs) 6.7% (5.5–8.1), lowermiddle-income economies (LMIs) 7.1% (6.6–7.6), and upper-middle-income economies (UMIs) 8.2% (7.5–9.0). Compared with no formal education, greater educational attainment was associated with an increased risk of diabetes across WBIGs, after adjusting for BMI (LICs RR 1.47 [95% CI 1.22–1.78], LMIs 1.14 [1.06– 1.23], and UMIs 1.28 [1.02–1.61]). CONCLUSIONS Among 29 LMICs, diabetes prevalence was substantial and increased with increasing WBIG. In contrast to the association seen in high-income countries, diabetes risk was highest among those with greater educational attainment, independent of BMI. LMICs included in this analysis may be at an advanced stage in the nutrition transition but with no reversal in the socioeconomic gradient of diabetes risk.