Background Bangladesh’s diabetes burden is accelerating alongside rapid urbanisation and population ageing. Contemporary, population-representative risk estimates are needed to guide prevention. Methods We analysed 13,835 adults in the 2022 Bangladesh Demographic and Health Survey. Diabetes was defined as fasting plasma glucose <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23762_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:\ge\:\)</EquationSource> </InlineEquation> 7 mmol/L or current use of diabetes medication. Associations with demographic, socioeconomic and clinical covariates were modelled using a Bayesian survey-weighted logistic mixed model with primary sampling unit random intercepts; design-unadjusted GLMM and survey-weighted GLM served as comparators. Posterior medians and 95% credible intervals (CrI) are reported. Results Age, adiposity and hypertension dominated the risk profile. Relative to 18–24 years, the odds of diabetes rose to 5.13 (4.22–6.24) for adults <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23762_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:\ge\:\)</EquationSource> </InlineEquation> 65 years; overweight and obesity increased odds to 1.37 (1.22–1.53) and 1.78 (1.50–2.10), whereas underweight had lower odds at 0.80 (0.69–0.92). Hypertension conferred an odds ratio of 1.52 (1.35–1.70). Rural residence 0.75 (0.65–0.87) and residence in Rajshahi, Mymensingh or Khulna divisions (<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23762_Article_IEq3.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:\approx\:\)</EquationSource> </InlineEquation> 0.55–0.66) were lower, while the richest wealth quintile carried higher risk at 1.77 (1.51–2.10). Model diagnostics were favourable: AUC = 0.79 indicates good discrimination; WAIC = 10,501 improved by <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23762_Article_IEq4.gif" Format="GIF" Height="11" Rendition="HTML" Resolution="72" Type="Linedraw" Width="18" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:>\)</EquationSource> </InlineEquation> 600 points versus the unweighted GLMM and survey-weighted GLM; and residual spatial autocorrelation was negligible (Moran’s I = 0.06, p = 0.14). Conclusions Diabetes in Bangladesh clusters among older adults, overweight/obese individuals, those with hypertension, and the socio-economically affluent, while remaining lower in rural and several northern divisions. Priority actions include age- and BMI-targeted screening, integrated hypertension–diabetes services, and urban lifestyle interventions, particularly in Dhaka and other high-prevalence regions. The Bayesian survey-weighted mixed-model framework yields policy-relevant, nationally generalisable estimates and offers a robust platform for tracking Sustainable Development Goal 3.4 indicators.

Analysing risk factors for diabetes in Bangladesh: a Bayesian hierarchical approach using a nationwide cross-sectional study

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2025年10月1日

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Mashfiqul Huq Chowdhury,
Noor Muhammad Khan,
Md. Mizanoor Rahman

摘要

Background

Bangladesh’s diabetes burden is accelerating alongside rapid urbanisation and population ageing. Contemporary, population-representative risk estimates are needed to guide prevention.

Methods

We analysed 13,835 adults in the 2022 Bangladesh Demographic and Health Survey. Diabetes was defined as fasting plasma glucose \(\:\ge\:\) 7 mmol/L or current use of diabetes medication. Associations with demographic, socioeconomic and clinical covariates were modelled using a Bayesian survey-weighted logistic mixed model with primary sampling unit random intercepts; design-unadjusted GLMM and survey-weighted GLM served as comparators. Posterior medians and 95% credible intervals (CrI) are reported.

Results

Age, adiposity and hypertension dominated the risk profile. Relative to 18–24 years, the odds of diabetes rose to 5.13 (4.22–6.24) for adults \(\:\ge\:\) 65 years; overweight and obesity increased odds to 1.37 (1.22–1.53) and 1.78 (1.50–2.10), whereas underweight had lower odds at 0.80 (0.69–0.92). Hypertension conferred an odds ratio of 1.52 (1.35–1.70). Rural residence 0.75 (0.65–0.87) and residence in Rajshahi, Mymensingh or Khulna divisions ( \(\:\approx\:\) 0.55–0.66) were lower, while the richest wealth quintile carried higher risk at 1.77 (1.51–2.10). Model diagnostics were favourable: AUC = 0.79 indicates good discrimination; WAIC = 10,501 improved by \(\:>\) 600 points versus the unweighted GLMM and survey-weighted GLM; and residual spatial autocorrelation was negligible (Moran’s I = 0.06, p = 0.14).

Conclusions

Diabetes in Bangladesh clusters among older adults, overweight/obese individuals, those with hypertension, and the socio-economically affluent, while remaining lower in rural and several northern divisions. Priority actions include age- and BMI-targeted screening, integrated hypertension–diabetes services, and urban lifestyle interventions, particularly in Dhaka and other high-prevalence regions. The Bayesian survey-weighted mixed-model framework yields policy-relevant, nationally generalisable estimates and offers a robust platform for tracking Sustainable Development Goal 3.4 indicators.