Spatiotemporal clusters of acute respiratory infections associated with socioeconomic, meteorological, and air pollution factors in South Punjab, Pakistan
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".