Spatiotemporal clusters of acute respiratory infections associated with socioeconomic, meteorological, and air pollution factors in South Punjab, Pakistan
Abstract Background In Pakistan, acute respiratory infections (ARI) continue to be a major public health problem. However, there is still a lack of scholarly work regarding different environmental and socioeconomic influencing factors and how they interact with respiratory infections. Furthermore, w...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-025-21741-4 |
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| Summary: | Abstract Background In Pakistan, acute respiratory infections (ARI) continue to be a major public health problem. However, there is still a lack of scholarly work regarding different environmental and socioeconomic influencing factors and how they interact with respiratory infections. Furthermore, we do not know much about geographic variation in this context. Therefore, our study examines the ecological-level spatial and temporal patterns of acute respiratory infection incidence (ARI) and their geographic relationship with selected socio-economic, meteorological, and air pollution factors in Pakistan. Methods We applied the spatiotemporal scan statistics to examine the purely temporal, spatial, and spatiotemporal clusters of ARI in South Punjab, Pakistan for five years (2016–2020). Generalized Linear Model (GLM) and geographically weighted regression (GWR) were also applied to model the linear and non-linear spatial relationships between selected variables and ARI. Results Our results indicate that in the central and northern regions of Pakistan, two spatial clusters of ARI were present, accounting for 28.5% of the total cases. A spatiotemporal cluster with a relative risk of 1.57 was discovered in the northeastern area. The results obtained from the season-based GLM highlighted the significance of climatic factors (temperature, fog, dust storms) and air pollutants (NO2) in influencing ARI incidence, while socio-economic variables (rural population, literacy) had limited impact. In addition, GWR revealed that the relationships between predictors and ARI incidence varied across locations, emphasizing the importance of considering local settings. Season-based non-stationary GLM revealed a multifaceted interaction among socio-economic, meteorological, and air pollution factors. Conclusions Our study provides evidence about environmental and socio-economic factors significantly associated with ARI incidence. In addition, this study provides the first baseline of ARI cases in Pakistan to plan for intervention and adaptation strategies and may be replicated in other regions of comparable settings worldwide. |
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| ISSN: | 1471-2458 |