Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries

Seiglie, Jacqueline A.; Marcus, Maja-Emilia; Ebert, Cara; Prodromidis, Nikolaos; Geldsetzer, Pascal; Theilmann, Michaela; Agoudavi, Kokou; Andall‐Brereton, Glennis; Aryal, Krishna Kumar; Bicaba, Brice; Bovet, Pascal; Brian, Garry; Dorobanţu, Maria; Gathecha, Gladwell; Gurung, Mongal Singh; Guwatudde, David; Msaidié, Mohamed; Houéhanou, Corine; Houinato, Dismand; Jorgensen, Jutta M Adelin; Kagaruki, Gibson B.; Karki, Kshitij; Labadarios, Demetre; Martins, João S; Mayige, Mary; Wong-McClure, Roy; Mwangi, Joseph Kibachio; Mwalim, Omar; Норов, Болормаа; Quesnel-Crooks, Sarah; Silver, Bahendeka K; Sturua, Lela; Tsabedze, Lindiwe; Wesseh, Chea Sanford; Stokes, Andrew; Atun, Rifat; Davies, Justine; Vollmer, Sebastián; Bärnighausen, Till; Jaacks, Lindsay M.; Meigs, James B.; Wexler, Deborah J.; Manne‐Goehler, Jennifer

doi:10.2337/dc19-1782

Cited by 101 publications

(73 citation statements)

References 34 publications

(37 reference statements)

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“…Most included studies were conducted in upper-middle-income countries, and few studies were carried out in rural areas or low-or lower-middle-income countries. Mirroring global patterns [67], this research disparity is discordant with epidemiologic evidence showing a substantial diabetes burden in low-income countries and in rural areas of LMICs [68,69]. The EPOC domains of delivery arrangements and implementation strategies were most commonly involved in the included interventions.…”

Section: Discussionmentioning

confidence: 82%

Health system interventions for adults with type 2 diabetes in low- and middle-income countries: A systematic review and meta-analysis

et al. 2020

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Background Effective health system interventions may help address the disproportionate burden of diabetes in low- and middle-income countries (LMICs). We assessed the impact of health system interventions to improve outcomes for adults with type 2 diabetes in LMICs. Methods and findings We searched Ovid MEDLINE, Cochrane Library, EMBASE, African Index Medicus, LILACS, and Global Index Medicus from inception of each database through February 24, 2020. We included randomized controlled trials (RCTs) of health system interventions targeting adults with type 2 diabetes in LMICs. Eligible studies reported at least 1 of the following outcomes: glycemic change, mortality, quality of life, or cost-effectiveness. We conducted a meta-analysis for the glycemic outcome of hemoglobin A1c (HbA1c). GRADE and Cochrane Effective Practice and Organisation of Care methods were used to assess risk of bias for the glycemic outcome and to prepare a summary of findings table. Of the 12,921 references identified in searches, we included 39 studies in the narrative review of which 19 were cluster RCTs and 20 were individual RCTs. The greatest number of studies were conducted in the East Asia and Pacific region (n = 20) followed by South Asia (n = 7). There were 21,080 total participants enrolled across included studies and 10,060 total participants in the meta-analysis of HbA1c when accounting for the design effect of cluster RCTs. Non-glycemic outcomes of mortality, health-related quality of life, and cost-effectiveness had sparse data availability that precluded quantitative pooling. In the meta-analysis of HbA1c from 35 of the included studies, the mean difference was −0.46% (95% CI −0.60% to −0.31%, I2 87.8%, p < 0.001) overall, −0.37% (95% CI −0.64% to −0.10%, I2 60.0%, n = 7, p = 0.020) in multicomponent clinic-based interventions, −0.87% (−1.20% to −0.53%, I2 91.0%, n = 13, p < 0.001) in pharmacist task-sharing studies, and −0.27% (−0.50% to −0.04%, I2 64.1%, n = 7, p = 0.010) in trials of diabetes education or support alone. Other types of interventions had few included studies. Eight studies were at low risk of bias for the summary assessment of glycemic control, 15 studies were at unclear risk, and 16 studies were at high risk. The certainty of evidence for glycemic control by subgroup was moderate for multicomponent clinic-based interventions but was low or very low for other intervention types. Limitations include the lack of consensus definitions for health system interventions, differences in the quality of underlying studies, and sparse data availability for non-glycemic outcomes. Conclusions In this meta-analysis, we found that health system interventions for type 2 diabetes may be effective in improving glycemic control in LMICs, but few studies are available from rural areas or low- or lower-middle-income countries. Multicomponent clinic-based interventions had the strongest evidence for glycemic benefit among intervention types. Further research is needed to assess non-glycemic outcomes and to study implementation in rural and low-income settings.

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Section: Discussionmentioning

confidence: 82%

Health system interventions for adults with type 2 diabetes in low- and middle-income countries: A systematic review and meta-analysis

et al. 2020

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“…In contrast to findings in HICs, our study found that individuals in the higher per capita expenditure quantile higher educational attainment (a proxy for higher socioeconomic status) were more likely to report multimorbidity. Earlier studies in LMICs have also shown a higher prevalence of NCDs in higher socioeconomic groups [ 45 ] as these countries undergo epidemiological transition [ 46 – 49 ]. This may be partly explained by higher-income groups, who have better access to healthcare services and better health literacy, receiving diagnoses of their conditions to a greater extent than lower-income groups.…”

Section: Discussionmentioning

confidence: 99%

Effect of multimorbidity on utilisation and out-of-pocket expenditure in Indonesia: quantile regression analysis

Anindya

Atun

et al. 2021

BMC Health Serv Res

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Background Multimorbidity (the presence of two or more non-communicable diseases) is a major growing challenge for many low-income and middle-income countries (LMICs). Yet, its effects on health care costs and financial burden for patients have not been adequately studied. This study investigates the effect of multimorbidity across the different percentiles of healthcare utilisation and out-of-pocket expenditure (OOPE). Methods We conducted a secondary data analysis of the 2014/2015 Indonesian Family Life Survey (IFLS-5), which included 13,798 respondents aged ≥40 years. Poisson regression was used to assess the association between sociodemographic characteristics and the total number of non-communicable diseases (NCDs), while multivariate logistic regression and quantile regression analysis was used to estimate the associations between multimorbidity, health service use and OOPE. Results Overall, 20.8% of total participants had two or more NCDs in 2014/2015. The number of NCDs was associated with higher healthcare utilisation (coefficient 0.11, 95% CI 0.07–0.14 for outpatient care and coefficient 0.09 (95% CI 0.02–0.16 for inpatient care) and higher four-weekly OOPE (coefficient 27.0, 95% CI 11.4–42.7). The quantile regression results indicated that the marginal effect of having three or more NCDs on the absolute amount of four-weekly OOPE was smaller for the lower percentiles (at the 25th percentile, coefficient 1.0, 95% CI 0.5–1.5) but more pronounced for the higher percentile of out-of-pocket spending distribution (at the 90th percentile, coefficient 31.0, 95% CI 15.9–46.2). Conclusion Multimorbidity is positively correlated with health service utilisation and OOPE and has a significant effect, especially among those in the upper tail of the utilisation/costs distribution. Health financing strategies are urgently required to meet the needs of patients with multimorbidity, particularly for vulnerable groups that have a higher level of health care utilisation.

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“…Furthermore, our study shows that education level plays a significant role in modifying diabetes prevalence and heterogeneity over time; most notably, this became apparent for non-Hispanic Whites. The role of education level and ethnicity in modifying the clinical course of diabetes had been previously explored , which could partially explain the differences observed in our study (14). Further pathophysiological, genetic, and socioeconomic determinants in diabetes phenotypes across ethnic groups in the U.S. should be assessed to understand their diabetes subgroup incidence role.…”

Section: Discussionmentioning

confidence: 87%

Prevalence trends of diabetes subgroups in US: A data-driven analysis spanning three decades from NHANES (1988-2018)

Antonio-Villa

Fernández‐Chirino

Vargas‐Vázquez

et al. 2020

Preprint

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AIMSData-driven diabetes subgroups have been proposed as an alternative to address diabetes heterogeneity; changes in trends for these subgroups have not previously been reported. Here, we analyzed trends of diabetes subgroups, stratified by sex, race, education level, and age categories in the U.S.METHODSWe used data from consecutive NHANES cycles spanning the 1988-2018 period. Diabetes subgroups (mild obesity-related [MOD], severe-insulin deficient [SIID], severe-insulin resistant [SIRD], and age-related diabetes [MARD]) were classified using self-normalizing neural networks. SAID was assessed for NHANES-III cycles. Prevalence was estimated using examination sample weights considering 2-year cycles (biannual change [B.C.]) to evaluate trends.RESULTSDiabetes prevalence in the US increased from 7.5% (95%CI 7.1-7.9) in 1988-1989 to 13.9% (95%CI 13.4-14.4) in 2016-2018 (BC 1.09%, 95%CI 0.98-1.31, p<0.001). Non-Hispanic Blacks had the highest prevalence. Overall, MOD, MARD, and SIID had the highest increase during the studied period. Non-Hispanic Blacks had a sharp increase in MARD and SIID, Mexican Americans in SIID, and non-Hispanic Whites in MARD. Males, subjects with primary school/no-education, and adults aged 40-64 years had the highest increase in MOD prevalence. Trends in diabetes subgroups sustained after stratification by body-mass index categories.CONCLUSIONSThe prevalence of diabetes and its data-driven subgroups in the U.S. has increased from 1988-2018. These trends were different across sex, ethnicities, education, and age categories, indicating significant heterogeneity in diabetes within the U.S. Sex-specific factors, population aging, and socioeconomic aspects, together with obesity prevalence increase, could be implicated in the uprising trends of diabetes in the U.S.RESEARCH IN CONTEXTWhat is already known about this subject?Diabetes is a heterogeneous disease with increasing prevalence worldwide.An approach to characterize diabetes heterogeneity uses data-driven subgroups, including mild obesity-related (MOD), severe-insulin deficient (SIID), severe-insulin resistant (SIRD), severe autoimmune (SAID) and age-related diabetes (MARD).Prevalence trends of data-driven diabetes subgroups in the U.S. remain as an area of opportunity to assess diabetes heterogeneity over time in the U.S.What is the key question?What are the diabetes subgroup prevalence trends in adult subjects from the National Health and Nutrition Examination Survey in the U.S. from 1988 to 2018?What are the new findings?Diabetes prevalence in the U.S. has steadily increased from 1988 to 2018, with Non-Hispanic Blacks and Mexican Americans having the highest diabetes prevalence.MOD, MARD, and SIID had the highest increase during the studied period; SIRD and SAID remained unchanged.Males, subjects with primary school/no-education, and adults aged 40-64 years had sharp increases in MOD.How might this impact on clinical practice in the foreseeable future?The increase in specific diabetes subgroups could help to implement tailored preventive measures depending on age and ethnicity. Targeted treatment and public health measures should be informed using clinical characteristics for each subgroup.

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Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries

Cited by 101 publications

References 34 publications

Health system interventions for adults with type 2 diabetes in low- and middle-income countries: A systematic review and meta-analysis

Health system interventions for adults with type 2 diabetes in low- and middle-income countries: A systematic review and meta-analysis

Effect of multimorbidity on utilisation and out-of-pocket expenditure in Indonesia: quantile regression analysis

Prevalence trends of diabetes subgroups in US: A data-driven analysis spanning three decades from NHANES (1988-2018)

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