Bibliographic record
Abstract
BACKGROUND: Addressing vulnerability to heat-related mortality is a necessary step in the development of policies dictated by heat action plans. We aimed to provide a systematic assessment of the epidemiologic evidence regarding vulnerability to heat-related mortality. METHODS: Studies assessing the association between high ambient temperature or heat waves and mortality among different subgroups and published between January 1980 and August 2014 were selected. Estimates of association for all the included subgroups were extracted. We assessed the presence of heterogeneous effects between subgroups conducting Cochran Q tests. We conducted random effect meta-analyses of ratios of relative risks (RRR) for high ambient temperature studies. We performed random effects meta-regression analyses to investigate factors associated with the magnitude of the RRR. RESULTS: Sixty-one studies were included. Using the Cochran Q test, we consistently found evidence of vulnerability for the elderly ages >85 years. We found a pooled RRR of 0.99 (95% confidence interval [CI] = 0.97, 1.01) for male sex, 1.02 (95% CI = 1.01, 1.03) for age >65 years, 1.04 (95% CI = 1.02, 1.07) for ages >75 years, 1.03 (95% CI = 1.01, 1.05) for low individual socioeconomic status (SES), and 1.01 (95% CI = 0.99, 1.02) for low ecologic SES. CONCLUSIONS: We found strongest evidence of heat-related vulnerability for the elderly ages >65 and >75 years and low SES groups (at the individual level). Studies are needed to clarify if other subgroups (e.g., children, people living alone) are also vulnerable to heat to inform public health programs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.131 | 0.044 |
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".