Analysing risk factors for diabetes in Bangladesh: a Bayesian hierarchical approach using a nationwide...

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

We analysed 13,835 adults in the 2022 Bangladesh Demographic and Health Survey. Diabetes was defined as fasting plasma glucose [Formula: see text] 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.

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 [Formula: see text] 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 ([Formula: see text] 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 [Formula: see text] 600 points versus the unweighted GLMM and survey-weighted GLM; and residual spatial autocorrelation was negligible (Moran's I = 0.06, p = 0.14).

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.