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 $$\:\ge\:$$ 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. 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). 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.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

In rural Bangladesh, financial protection is limited, and care seeking occurs across government and non-government sectors. This study investigated how social and behavioral factors—family monthly income, education, and adherence to prescribed treatment—relate to the usual source of care and patient satisfaction. A cross-sectional survey was conducted among 374 adults in Sirajganj district. Propensity scores were estimated and analyzed using 1:1 nearest-neighbor matching (caliper 0.20) and inverse probability of treatment weighting (IPTW). We used both propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) to reduce confounding and to check the robustness of the estimated associations. Primary outcomes were the usual treatment source and satisfaction with that source; exposures included income, education, and adherence. Adjusted odds ratios (AORs) and 95% confidence intervals (CI) were derived from matched and weighted logistic models. Prespecified interactions and sensitivity analyses assessed robustness. Higher family income strongly predicted use of non-government providers (matching AOR 8.50; weighted AOR 10.11) but showed no significant association with satisfaction. Adherence to prescribed treatment was associated with lower use of non-government care (matching AOR 0.35) and greater satisfaction (matching AOR 3.50), with comparable IPTW estimates. Education above the primary level was not related to provider type but was linked to lower satisfaction (matching AOR 0.30). The positive association between adherence and satisfaction persisted across income and education strata. Sensitivity analyses yielded similar findings. In rural Bangladesh, higher income primarily drives private-sector care use; treatment adherence predicts higher satisfaction and reduced private use; and higher education is associated with lower reported satisfaction. Integrating financial protection with adherence support and experience-of-care improvements may advance equity and patient-reported outcomes toward universal health coverage.