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Rotavirus Infections · Journal article
Global Health Action · July 30, 2026
Early or partial results. Treat as a signal, not a conclusion.
This time series analysis of 4,337 hospitalised rotavirus cases in Bangladeshi children <5 years found that higher maximum temperatures were associated with increased hospitalisation risk in some divisions (peak RR 5.9 at 95th percentile t-max), but regional heterogeneity was substantial. Scenario-based projections for 2040 are presented as exploratory extrapolations rather than forecasts, and authors emphasise cautious interpretation beyond observed temperature ranges.
Time series analysis with distributed-lag non-linear modelling. Children aged <5 years hospitalised with rotavirus at seven surveillance hospitals in Bangladesh; climate data from the Bangladesh Meteorological Department. Intervention: Temperature exposure (analysed as continuous variable; maximum temperature t-max). n = 4,337. Bangladesh; seven surveillance hospitals.
4,337 hospitalised rotavirus cases reported during August 2013–June 2017 in children <5 years 1,781 (41%) cases attributable to temperatures above reference value (25.4°C) countrywide Peak hospitalisation risk at 95th percentile of maximum temperature (RR: 5.9; 95% CI: 3.6–9.7)
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These findings suggest temperature is associated with rotavirus hospitalisation risk in parts of Bangladesh, warranting context-specific climate-sensitive public health interventions. However, the marked regional variation and cautionary tone around projections indicate that locally tailored epidemiological assessment is needed before implementing broad prevention strategies.
Time series analysis with distributed-lag modelling identifies temperature–rotavirus associations in Bangladesh with regional heterogeneity, but lacks a comparator arm and relies on observational data with acknowledged uncertainty in out-of-range projections.
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These findings suggest temperature is associated with rotavirus hospitalisation risk in parts of Bangladesh, warranting context-specific climate-sensitive public health interventions. However, the marked regional variation and cautionary tone around projections indicate that locally tailored epidemiological assessment is needed before implementing broad prevention strategies.
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Background. Limited evidence exists concerning the association between climate factors and hospitalised rotavirus cases in Bangladesh.Objectives. To assess the association between temperature and hospitalised rotavirus cases among children aged < 5 years in Bangladesh.Methods. We obtained weekly hospital admissions data for rotavirus from seven surveillance hospitals and climate data from the Bangladesh Meteorological Department for August 2013-June 2017. We used a maximum temperature (t-max)-based distributed-lag non-linear model (DLNM) to estimate the association between temperature and hospitalised rotavirus cases, adjusting for seasonality using natural splines. We estimated relative risk (RR), attributable number, and attributable fraction (AF), and scenario-based exploratory projections for 2040 under different Representative Concentration Pathways (RCPs).Results. During the study period, 4,337 hospitalised rotavirus cases were reported among children <5 years. Countrywide, 1,781 (41%) cases were attributable to temperatures above the reference value (25.4°C), reflecting regional heterogeneity. Hospitalisation risk peaked at the 95th percentile of t-max (RR: 5.9; 95% CI: 3.6-9.7). Temperature-hospitalisation associations differed across divisions, with inverse or minimal associations observed in Dhaka, Rangpur, and Sylhet. Scenario-based exploratory projections suggested the highest risk at 0-week lag, with risk increasing again at week-4 lag above 41.7°C.Conclusions. Associations between t-max and rotavirus hospitalisation varied across Bangladesh, with higher temperatures associated with increased hospitalisation risk in some divisions but inverse or minimal associations in others, highlighting the importance of context-specific climate-sensitive interventions. Scenario-based projections beyond the observed temperature range should be interpreted cautiously, as these estimates represent exploratory extrapolations rather than forecasts of disease burden.
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