Browsing by Author "Marcus, Maja-Emilia"

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    Analysis of Attained Height and Diabetes Among 554,122 Adults Across 25 Low- and Middle Income Countries
    (2020-10) Teufel, Felix; Geldsetzer, Pascal; Manne-Goehler, Jennifer; Karlsson, Omar; Koncz, Viola; Deckert, Andreas; Theilmann, Michaela; Marcus, Maja-Emilia; Ebert, Cara; A. Seiglie, Jacqueline; Agoudavi, Kokou; Andall-Brereton, Glennis; Gathecha, Gladwell; Gurung, Mongal S; Guwatudde, David; Houehanou, Corine; Hwalla, Nahla; Kagaruki, Gibson B.; Karki, Khem B.; Labadarios, Demetre; Martins, Joao S; Msaidie, Mohamed; Norov, Bolormaa; Sibai, Abla M.; Sturua, Lela; Tsabedze, Lindiwe; Wesseh, Chea S.; Davies, Justine; Atun, Rifat; Vollmer, Sebastian; Subramanian, S.V.; Barnighausen, Till; Jaacks, Lindsay M.; Barnighausen, JTill; Jaacks, Lindsay M.; De Neve, Jan-Walter
    Objective: The prevalence of type 2 diabetes is rising rapidly in low-income and middle-income countries (LMICs), but the factors driving this rapid increase are not well understood. Adult height, in particular shorter height, has been suggested to contribute to the pathophysiology and epidemiology of diabetes and may inform how adverse environmental conditions in early life affect diabetes risk. We therefore systematically analyzed the association of adult height and diabetes across LMICs, where such conditions are prominent. Research design and methods: We pooled individual-level data from nationally representative surveys in LMICs that included anthropometric measurements and diabetes biomarkers. We calculated odds ratios (ORs) for the relationship between attained adult height and diabetes using multilevel mixed-effects logistic regression models. We estimated ORs for the pooled sample, major world regions, and individual countries, in addition to stratifying all analyses by sex. We examined heterogeneity by individual-level characteristics. Results: Our sample included 554,122 individuals across 25 population-based surveys. Average height was 161.7 cm (95% CI 161.2-162.3), and the crude prevalence of diabetes was 7.5% (95% CI 6.9-8.2). We found no relationship between adult height and diabetes across LMICs globally or in most world regions. When stratifying our sample by country and sex, we found an inverse association between adult height and diabetes in 5% of analyses (2 out of 50). Results were robust to alternative model specifications. Conclusions: Adult height is not associated with diabetes across LMICs. Environmental factors in early life reflected in attained adult height likely differ from those predisposing individuals for diabetes.
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    Analysis of Attained Height and Diabetes Among 554,122 Adults Across 25 Low- and Middle Income Countries
    (Diabetes Care, 2020) Teufel, Felix; Geldsetzer, Pascal; Manne-Goehler, Jennifer; Karlsson, Omar; Koncz, Viola; Deckert, Andreas; Theilmann, Michaela; Marcus, Maja-Emilia; Ebert, Cara; Seiglie, Jacqueline A.; Agoudavi, Kokou; Andall-Brereton, Glennis; Gathecha, Gladwell; Gurung, Mongal S.; Guwatudde, David; Houehanou, Corine; Hwalla, Nahla; Kagaruki, Gibson B.; Karki, Khem B.; Labadarios, Demetre; Martins, Joao S.; Msaidie, Mohamed; Norov, Bolormaa; Sibai, Abla M.; Sturua, Lela; Tsabedze, Lindiwe; Wesseh, Chea S.; Davies, Justine; Atun, Rifat; Vollmer, Sebastian; Subramanian, S.V.; Barnighausen, Till; Jaacks, Lindsay M.; Neve, Jan-Walter De
    The prevalence of type 2 diabetes is rising rapidly in low-income and middle-income countries (LMICs), but the factors driving this rapid increase are notwell understood. Adult height, in particular shorter height, has been suggested to contribute to the pathophysiology and epidemiology of diabetes and may inform how adverse environmental conditions in early life affect diabetes risk. We therefore systematically analyzed the association of adult height and diabetes across LMICs, where such conditions are prominent. RESEARCH DESIGN AND METHODS We pooled individual-level data from nationally representative surveys in LMICs that included anthropometric measurements and diabetes biomarkers. We calculated odds ratios (ORs) for the relationship between attained adult height and diabetes using multilevel mixed-effects logistic regression models. We estimated ORs for the pooled sample,major world regions, and individual countries, in addition to stratifying all analyses by sex. We examined heterogeneity by individual-level characteristics. RESULTS Our sample included 554,122 individuals across 25 population-based surveys. Average height was 161.7 cm (95% CI 161.2–162.3), and the crude prevalence of diabetes was 7.5% (95% CI 6.9–8.2). We found no relationship between adult height and diabetes across LMICs globally or in most world regions. When stratifying our sample by country and sex, we found an inverse association between adult height and diabetes in 5% of analyses (2 out of 50). Results were robust to alternative model specifications. CONCLUSIONS Adult height is not associated with diabetes across LMICs. Environmental factors in early life reflected in attained adult height likely differ from those predisposing individuals for diabetes.
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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 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.