Impact of heat on respiratory hospitalizations among older adults living in 120 large US urban areas
Bibliographic record
Abstract
ABSTRACT Objectives A nationwide study of the impact of high temperature on respiratory disease hospitalizations among older adults (65+) living in large urban centers. Methods Daily rates of short-stay, inpatient respiratory hospitalizations were examined with respect to variations in ZIP-code-level daily mean temperature in the 120 largest US cities between 2000-2017. For each city, we estimated cumulative associations (lag-days 0-6) between warm-season temperatures (June-September) and cause-specific respiratory hospitalizations using time-stratified conditional quasi-Poisson regression with distributed lag non-linear models. We estimated nationwide associations using meta-regression and updated city-specific associations via best linear unbiased prediction. With stratified models, we explored effect modification by age, sex, and race (Black/white). Results were reported as percent change in hospitalizations at high temperatures (95th percentile) compared to median temperatures for each outcome, demographic-group, and metropolitan area. Excess hospitalization rates were estimated for days above median temperatures. Results At high temperatures, we observed increases in the percent of all-cause respiratory hospitalizations [1.2 (0.4, 2.0)], primarily driven by an increase in respiratory tract infections [1.8 (0.6, 3.0)], and chronic respiratory diseases/respiratory failure [1.2 (0.0, 2.4)]. East North Central, New England, Mid-Atlantic, and Pacific cities accounted for 98.5% of the excess burden. By demographic group, we observed disproportionate burdens of heat-related respiratory hospitalizations among the oldest beneficiaries (85+ years), and among Black beneficiaries living in South Atlantic cities. Conclusion This study found robust impacts of high temperature on respiratory failure and chronic inflammatory and fibrotic diseases among older adults. The geographic variation suggests that contextual factors account for disproportionate burdens.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".