Effective Community-Based Interventions for the Prevention and Management of Heat-Related Illnesses: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Extreme temperatures have negative consequences on the environment, ecosystem, and human health. With recent increases in global temperatures, there has been a rise in the burden of heat-related illnesses, with a disproportionate impact on low- and middle-income countries. Effective population-level interventions are critical to a successful public health response. OBJECTIVE: This scoping review aims to summarize the evidence on the effectiveness of population-level heat-related interventions and serve as a potential guide to the implementation of these interventions. METHODS: Studies that evaluated the effectiveness of community-based interventions to mitigate or reduce the impact of extreme heat on heat-related mortality and morbidity were sought by searching four electronic databases. Studies published in the English language and those that had quantifiable, measurable mortality, morbidity or knowledge score outcomes were included. RESULTS: The initial electronic search yielded 2324 articles, and 17 studies were included. Fourteen studies were based in high-income countries (HICs) (Europe, US, Canada) and discussed multiple versions of (1) heat action plans, which included but were not limited to establishing a heat monitoring system, informative campaigns, the mobilization of health care professionals, volunteers, social workers and trained caregivers in the surveillance and management of individuals with known vulnerabilities, or stand-alone (2) education and awareness campaigns. Multi-pronged heat action plans were highly effective in reducing heat-related mortality and morbidity, especially among vulnerable populations such as the elderly and those with chronic conditions. CONCLUSIONS: The heat action plans covered in these studies have shown promising results in reducing heat-related mortality and morbidity and have included instituting early warning systems, building local capacity to identify, prevent or treat and manage heat-related illnesses, and disseminating information. Nevertheless, they need to be cost-effective, easy to maintain, ideally should not rely on a mass effort from people and should be specifically structured to meet the local needs and resources of the community.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".