Use of GIS to Direct Public Health Interventions for Heat-Related Illness
Bibliographic record
Abstract
ISEE-326 Objective: The aim of this study was to demonstrate how geographic information systems (GIS) can be used to identify urban areas with a high burden of heat-related illness (HRI) and subsequently direct public health interventions to mitigate associated morbidity and mortality. Material and Methods: This study used 911 ambulance dispatch data to assess the geospatial distribution of HRI in Toronto, Canada from 2002 to 2005 to (i) assess any heterogeneity in burden between neighborhoods and (ii) to detect clusters or “hot spots” of HRI calls where public health interventions might best be targeted. The proportion of HRI calls to all emergency calls was mapped for each of the 4 study summers using the latitude and longitude values provided for each call using the software, MapInfo. Results: There is clear geospatial heterogeneity in the burden of HRI in Toronto. Areas with high rates of HRI include those located along the waterfront, particularly areas centered around summer outdoor recreational activities. Other areas within the downtown core experience high rates of HRI 911 calls. Although the reasons for proportionately higher rates are unclear, possible explanations may include poorer housing type and ventilation, particular local heat islands, and social factors like homelessness and low socioeconomic status. Each of these will be further explored in our research program. Conclusions: GIS is a useful tool to describe the geospatial distribution of HRI in a major urban centre. By identifying hot spots that experience the highest burden of HRI it is possible to advise public health stakeholders as to where to best target interventions such as distributing water bottles, guiding community agencies, and opening cooling stations to care for at-risk populations.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".