Indoor overheating: A review of vulnerabilities, causes, and strategies to prevent adverse human health outcomes during extreme heat events
Bibliographic record
Abstract
The likelihood of exposure to overheated indoor environments is increasing as climate change is exacerbating the frequency and severity of hot weather and extreme heat events (EHE). Consequently, vulnerable populations will face serious health risks from indoor overheating. While the relationship between EHE and human health has been assessed in relation to outdoor temperature, indoor temperature patterns can vary markedly from those measured outside. This is because the built environment and building characteristics can act as an important modifier of indoor temperatures. In this narrative review, we examine the physiological and behavioral determinants that influence a person's susceptibility to indoor overheating. Further, we explore how the built environment, neighborhood-level factors, and building characteristics can impact exposure to excess heat and we overview how strategies to mitigate building overheating can help reduce heat-related mortality in heat-vulnerable occupants. Finally, we discuss the effectiveness of commonly recommended personal cooling strategies that aim to mitigate dangerous increases in physiological strain during exposure to high indoor temperatures during hot weather or an EHE. As global temperatures continue to rise, the need for a research agenda specifically directed at reducing the likelihood and impact of indoor overheating on human health is paramount. This includes conducting EHE simulation studies to support the development of consensus-based heat mitigation solutions and public health messaging that provides equitable protection to heat-vulnerable people exposed to high indoor temperatures.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".