Extreme Heat Public Health Preparedness Planning and Response Activities in the Most Populous Jurisdictions in the United States: Survey Results
Bibliographic record
Abstract
Background and Aim: Extreme heat causes more deaths each year than any other type of extreme weather in the U.S. Urban populations can experience heightened heat risk as a result of hazard amplification from the built environment and other factors. Climate change is increasing extreme heat exposure, prompting the need for adaptation measures at all levels of governance. Our study aimed to assess the level and type of heat preparedness and response activities occurring in U.S. jurisdictions with populations of 200,000. Method: We developed and administered an online survey to public health and emergency management agencies in 99 jurisdictions representing municipalities and counties in the US with populations of at least 200,000 people. Survey questions explored heat adaptation activity implementation, including presence or absence of a written heat action plan (HAP), timing of HAP development and updates, critical HAP components, and facilitators and barriers surrounding heat activities. Results: 38.4% (38) jurisdictions responded to the survey. Of those, 60.5% reported having a formal HAP. There was considerable variability in the prevalence of various prevention activities. Jurisdictions with HAPs reported higher engagement in several heat-related activities, including communications about extreme heat, surveillance of heat-related health conditions, and providing climate-controlled shelter for populations experiencing homelessness, compared to jurisdictions without HAPs. The most prevalent communication and outreach modalities were social media, news alert, and internet, which may be less likely to reach populations identified as the most vulnerable to extreme heat (low income, elderly, and people experiencing homelessness). Conclusions: Jurisdictions with formal HAPs broadly reported more engagement in heat adaptation activities. Results indicate reliance on lower-cost outreach strategies that may not adequately reach some at-risk populations. Health departments and emergency agencies should consider establishing HAPs as temperatures rise. Keywords: Extreme heat, climate change, adaptation, public health preparedness
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".