Impact of Heat on Respiratory Hospitalizations among Older Adults in 120 Large U.S. Urban Areas
Bibliographic record
Abstract
Abstract Rationale Extreme heat exposure is a well-known cause of mortality among older adults. However, the impacts of exposure on respiratory morbidity across U.S. cities and population subgroups are not well understood. Objectives A nationwide study was conducted to determine the impact of high heat on respiratory disease hospitalizations among older adults (≥65 yr of age) living in the 120 largest U.S. cities between 2000 and 2017. Methods Daily rates of inpatient respiratory hospitalizations were examined with respect to variations in ZIP code–level daily mean temperature or heat index. For each city, we estimated cumulative associations (lag days 0–6) between warm-season heat (June to September) and cause-specific respiratory hospitalizations using time-stratified conditional quasi-Poisson regression with distributed lag nonlinear models. We estimated nationwide associations using multivariate 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 or White). Results are reported as percentage change in hospitalizations at high temperatures (95th percentile) compared with median temperatures for each outcome, demographic group, and metropolitan area. Results We identified 3,275,033 respiratory hospitalizations among Medicare beneficiaries across 120 large U.S. cites between 2000 and 2017. Nationwide, 7-day cumulative associations at high temperatures resulted in a 1.2% (95% confidence interval, 0.4–2.0%) increase in hospitalizations for primary diagnoses of all-cause respiratory disease, driven primarily by increases in respiratory tract infections (1.8% [95% confidence interval, 0.6–3.0%]) and chronic respiratory diseases and/or respiratory failure (1.2% [95% confidence interval, 0.0–2.4%]). Stronger associations were observed when exposure was defined using the heat index instead of mean temperature. Across the 120 cities, we observed considerable geographic variation in the relative risk of heat-related respiratory hospitalizations, and we observed disproportionate burdens of heat-related respiratory hospitalizations among the oldest beneficiaries (≥85 yr of age) and among Black beneficiaries living in South Atlantic cities. During the 18-year study period, there were an estimated 11,710 excess respiratory hospitalizations due to heat exposure. Conclusions Results suggest that high temperature and humidity contribute to exacerbation of respiratory tract infections and chronic lung diseases among older adults. Geographic variation in heat-related hospitalization rates suggests that contextual factors largely account for disproportionate burdens, and area-level influences should be further investigated in multicity studies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".