A case-crossover investigation of associations between extreme heat and pediatric health
Bibliographic record
Abstract
BACKGROUND: Globally, climates are changing causing more frequent and severe extreme heat events (EHEs). In Canada, annual EHE frequency is anticipated to double in just the next 3 decades. A large body of literature links EHEs to multiple health endpoints, including heatstroke and exacerbating medical conditions. However, there remains a paucity of knowledge concerning the specific health outcomes associated with heat in children. Compared to adults, children have higher surface area to mass ratios, lower sweating capacity, higher temperature at which sweating begins, lower cardiac output, and lower blood volume. They are also believed to be more vulnerable to EHEs due to external factors including activity patterns and dependence on caregivers. METHODS: This space-time stratified case-crossover analysis of Ontario’s 2005-2015 emergency healthcare data applied conditional quasi-Poisson regression to assess associations between FSA-level EHE exposure with primary causes of pediatric emergency hospital admissions and emergency department (ED) visits. RESULTS: Positive associations were found both for pediatric hospital admissions and ED visits for primary causes of asthma; general heat-related illness, heatstroke; and lower respiratory infections. General injuries and transportation related injuries were negatively associated with both pediatric hospital admissions and ED visits. EHEs increased risk of pediatric hospital admissions for causes of general respiratory illnesses by 26% (CI:14%-40%), asthma by 29% (CI:16%-44%); general infectious and parasitic diseases by 36% (CI:24%-50%), lower respiratory infections by 50% (CI:36%-67%), and enteritis by 19% (CI:7%-32%). EHEs also increased risk of ED visits for asthma by 18% (CI:7%-29%) and lower respiratory infections by 10% (CI:0%-21%). All-cause hospital admissions and ED visits were not associated with EHEs. However, in stratified analyses all-cause hospital admissions were positively associated with EHEs for children 13-18 and males, and all-cause ED visits were negatively associated with EHEs among children 5-12. CONCLUSION: EHEs elevate risk of pediatric emergency healthcare utilization for respiratory illnesses, asthma; infectious and parasitic diseases, lower respiratory infections, and enteritis in Ontario. It is imperative that policies and programs be tailored to reflect the specific heat related vulnerabilities of children to respiratory and infections illnesses in face of a rapidly warming climate.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".