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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".