Long-term exposure to fine particulate matter and diabetes in Bangladeshi adults: Can clean air targets curb the rising diabetes burden?
Bibliographic record
Abstract
Background Diabetes is a growing public health challenge in Bangladesh, potentially exacerbated by high levels of air pollution. However, no nationally representative epidemiological evidence exists in Bangladesh. We therefore investigated the association between long-term exposure to PM 2.5 and diabetes and estimated the potential health benefits of achieving national and international clean air targets. Methods This retrospective study included 13,965 adults (8284 women and 6965 men) who participated in the nationally representative Bangladesh Demographic and Health Survey (BDHS) 2022. The annual average concentrations of PM 2.5 were derived from high-resolution, calibrated satellite data matched to their residential addresses. Diabetes was identified as fasting plasma glucose ≥7.0 mmol/L and/or self-reported use of glucose-lowering medication. Generalized Estimating Equation models were used to estimate the associations between PM 2.5 and diabetes, and a Generalized Additive Model was employed to characterize the exposure-response relationship. We also evaluated potential Effect Measure Modification across various sociodemographic groups. The diabetes burden attributable to PM 2.5 and the potential health benefits of achieving national and World Health Organization (WHO) clean air targets were estimated using the attributable fraction. Results Each 10 μg/m 3 increase in 3-year average PM 2.5 concentration (with a 1-year lag) was associated with a 10 % higher risk of diabetes (adjusted Risk Ratio: 1.10; 95 % CI: 1.04, 1.17), with a stronger effect observed among individuals with obesity. Achieving national and WHO air quality targets could potentially reduce the population-level prevalence of diabetes by 4.6 % to 7.5 %. Greater benefits were seen among women, older adults, individuals with obesity or hypertension, and urban populations. Conclusion Our results demonstrate that long-term exposure to PM 2.5 was associated with increased prevalence of diabetes in Bangladeshi adults. Achieving clean air targets can substantially reduce the national and regional population-level burden of diabetes, underscoring the significant health benefits of reducing air pollution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".