A Spatio-Temporal Modelling of Climate Variability on Respiratory Disease Morbidity in Kampala City, Uganda
This study indicates that climatic variability and air pollution significantly influence the distribution of respiratory diseases in Kampala, with specific seasonal peaks for different conditions.
Where it sits
this study against the rest of the bam-15 corpusSummary and findings
This study assessed the influence of climatic variability and ambient air pollution on respiratory disease morbidity in Kampala City from 2021 to 2024, utilizing monthly aggregated data. A total of 89,253 respiratory illnesses were recorded, with pneumonia accounting for 65%, asthma for 23%, and COPD for 12%. The study found significant spatial clustering and temporal variation in respiratory diseases.
Abstract
<title>Abstract</title> <p> <bold>Background</bold> : Climate change poses serious public health risks, particularly in rapidly growing urban areas. Similarly, the interaction between month and year was not statistically significant (edf = 0.75, p = 0.296), indicating a relatively stable seasonal pattern over the study period together with urbanization, these factors contribute to respiratory diseases such as asthma, chronic obstructive pulmonary disease, and pneumonia. However, limited evidence exists on how these environmental exposures influence the spatial and temporal distribution of respiratory diseases in Kampala <bold>Objectives.</bold> The study aimed to assess the influence of climatic variability and ambient air pollution on the spatio-temporal distribution of respiratory disease morbidity in Kampala City between 2021 and 2024. <bold>Methods:</bold> A retrospective ecological study design was employed, using monthly aggregated data on respiratory disease illnesses obtained from the Ministry of Health. Climate variables were accessed and extracted through the Health Information Program Uganda, and air quality was obtained from Air Qo devices through Kampala Capital City Authority. Negative binomial ST-GAMs with tensor-product smooths were fitted to account for overdispersion, nonlinear environmental effects, and spatial-temporal interactions while adjusting for population size through an offset term. Residual checks and model diagnostics were conducted to assess goodness of fit and verify model assumptions [1]using randomised quantile residuals and smoothing terms <bold>.</bold> <bold>Results:</bold> Between 2021-2024, a total of 89253 respiratory illnesses were recorded, comprising pneumonia (65%), asthma (23%), and COPD (12%). ST-GAM results showed strong spatial clustering (edf ≈ 4, p <0.001) and temporal variation. Spatiotemporal analysis using ST-GAMs showed significant clustering of respiratory diseases across Kampala (edf ≈ 4, p < 0.001), with Central, Makindye, and Kawempe divisions. Temporally, Pneumonia cases peaked between March and May and again from September to November, while asthma and COPD were more prevalent between January and February and from June to August. <bold>Conclusion:</bold> The study shows that PM2.5 is a key modifiable risk factor for respiratory diseases in Kampala. Environmental exposures affect disease patterns differently across disease types, locations, and seasons. </p>