Back

Waterlogging exposure and mental well-being: a cross-sectional study in rural southwest Bangladesh

Clech, L.; Bonnet, E.; Rezoan, D.; Kabir, M. M.; Shenk, M.; Ridde, V.

2026-08-06 epidemiology
10.64898/2026.08.04.26359716 medRxiv
Show abstract

Background Waterlogging, a form of chronic, stagnant flooding, is increasing in the Ganges-Brahmaputra delta in Bangladesh due to the compounding effects of land-use change, including the expansion of brackish shrimp farming, poor water management, and changing rainfall regimes. Its effects are negatively impacting livelihoods and health; its association with mental wellbeing is less known. Methods We hypothesised that 1-recent waterlogging, 2-social disadvantage (women, older individuals, the poorest, the least educated, those with chronic illness, and religious minorities) would be associated with lower wellbeing, and 3-chronic illness would modify the association between waterlogging and mental wellbeing. 1260 respondents from 595 households in Tala upazila, southwest Bangladesh, were interviewed about their mental wellbeing, chronic illness, and exposure to waterlogging in the 12 months prior to data collection, in August and September 2022. Associations between WHO-5 wellbeing scores and waterlogging and covariates were assessed using multi-level linear mixed-effects models with household random effects and cluster fixed effects. Results Our results confirm our hypotheses: wellbeing was lower among disadvantaged groups and chronic health vulnerability modifies the association between waterlogging and wellbeing: waterlogging exposure was associated with 18.31-point lower WHO-5 scores among individuals with chronic illness (95% CI -26.45 to -10.17), an association markedly attenuated among those without chronic illness (interaction {beta}=14.21, 95% CI 5.48 to 22.95, p=0.001). Conclusion These results suggest that chronic illness may increase vulnerability to the mental health burden associated with waterlogging. As waterlogging is increasing, policies addressing both its environmental drivers and the needs of vulnerable populations should be considered.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.