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Background: There is concern in the international community regarding the influence of climate change on weather variables and seasonality that, in part, determine the rates of malaria. This study examined the role of sociodemographic variables in modifying the association between temperature and malaria in Kanungu District (Southwest Uganda)., Methods: Hospital admissions data from Bwindi Community Hospital were combined with meteorological satellite data from 2011 to 2014. Descriptive statistics were used to describe the distribution of malaria admissions by age, sex, and ethnicity (i.e. Bakiga and Indigenous Batwa). To examine how sociodemographic variables modified the association between temperature and malaria admissions, this study used negative binomial regression stratified by age, sex, and ethnicity, and negative binomial regression models that examined interactions between temperature and age, sex, and ethnicity., Results: Malaria admission incidence was 1.99 times greater among Batwa than Bakiga in hot temperature quartiles compared to cooler temperature quartiles, and that 6-12 year old children had a higher magnitude of association of malaria admissions with temperature compared to the reference category of 0-5 years old (IRR = 2.07 (1.40, 3.07))., Discussion: Results indicate that socio-demographic variables may modify the association between temperature and malaria. In some cases, such as age, the weather-malaria association in sub-populations with the highest incidence of malaria in standard models differed from those most sensitive to temperature as found in these stratified models., Conclusion: The effect modification approach used herein can be used to improve understanding of how changes in weather resulting from climate change might shift social gradients in health., (© 2022. The Author(s).)

Background: Season and weather are associated with many health outcomes, which can influence hospital admission rates. We examined associations between hospital admissions (all diagnoses) and local meteorological parameters in Southwestern Uganda, with the aim of supporting hospital planning and preparedness in the context of climate change. Methods : Hospital admissions data and meteorological data were collected from Bwindi Community Hospital and a satellite database of weather conditions, respectively (2011 to 2014). Descriptive statistics were used to describe admission patterns. A mixed-effects Poisson regression model was fitted to investigate associations between hospital admissions and season, precipitation, and temperature. Results: Admission counts were highest for acute respiratory infections, malaria, and acute gastrointestinal illness, which are climate-sensitive diseases. Hospital admissions were 1.16 (95% CI: 1.04, 1.31; p = 0.008) times higher during extreme high temperatures (i.e., >95th percentile) on the day of admission. Hospital admissions association with season depended on year; admissions were higher in the dry season than the rainy season every year, except for 2014. Discussion : Effective adaptation strategy characteristics include being low-cost and quick and practical to implement at local scales. Herein, we illustrate how analyzing hospital data alongside meteorological parameters may inform climate-health planning in low-resource contexts.