Browsing by Author "Agoudavi, Kokou"
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Item 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-WalterObjective: 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.Item 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 DeThe 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.Item 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, TillDespite 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.Item 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, JenniferDiabetes 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.Item Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys(PLoS Med, 2019) Manne-Goehler, Jennifer; Geldsetzer, Pascal; Agoudavi, Kokou; Andall- Brereton, Glennis; Aryal, Krishna K.; Wilfried Bicaba, Brice; Guwatudde, David; Barnighausen, Till W.; Jaacks, Lindsay M.The prevalence of diabetes is increasing rapidly in low- and middle-income countries (LMICs), urgently requiring detailed evidence to guide the response of health systems to this epidemic. In an effort to understand at what step in the diabetes care continuum individuals are lost to care, and how this varies between countries and population groups, this study examined health system performance for diabetes among adults in 28 LMICs using a cascade of care approach. Methods and findings We pooled individual participant data from nationally representative surveys done between 2008 and 2016 in 28 LMICs. Diabetes was defined as fasting plasma glucose � 7.0 mmol/l (126 mg/dl), random plasma glucose � 11.1 mmol/l (200 mg/dl), HbA1c � 6.5%, or reporting to be taking medication for diabetes. Stages of the care cascade were as follows: tested, diagnosed, lifestyle advice and/or medication given (“treated”), and controlled (HbA1c < 8.0% or equivalent). We stratified cascades of care by country, geographic region, World Bank income group, and individual-level characteristics (age, sex, educational attainment, household wealth quintile, and body mass index [BMI]). We then used logistic regression models with country-level fixed effects to evaluate predictors of (1) testing, (2) treatment, and (3) control. The final sample included 847,413 adults in 28 LMICs (8 low income, 9 lower-middle income, 11 upper-middle income). Survey sample size ranged from 824 in Guyana to 750,451 in India. The prevalence of diabetes was 8.8% (95% CI: 8.2%–9.5%), and the prevalence of undiagnosed diabetes was 4.8% (95% CI: 4.5%–5.2%). Health system performance for management of diabetes showed large losses to care at the stage of being tested, and low rates of diabetes control. Total unmet need for diabetes care (defined as the sum of those not tested, tested but undiagnosed, diagnosed but untreated, and treated but with diabetes not controlled) was 77.0% (95% CI: 74.9%–78.9%). Performance along the care cascade was significantly better in upper-middle income countries, but across all World Bank income groups, only half of participants with diabetes who were tested achieved diabetes control. Greater age, educational attainment, and BMI were associated with higher odds of being tested, being treated, and achieving control. The limitations of this study included the use of a single glucose measurement to assess diabetes, differences in the approach to wealth measurement across surveys, and variation in the date of the surveys. Conclusions The study uncovered poor management of diabetes along the care cascade, indicating large unmet need for diabetes care across 28 LMICs. Performance across the care cascade varied by World Bank income group and individual-level characteristics, particularly age, educational attainment, and BMI. This policy-relevant analysis can inform country-specific interventions and offers a baseline by which future progress can be measured.