A Longitudinal Study on the Impact of Indoor Temperature on Heat-Related Symptoms in Older Adults Living in Non–Air-Conditioned Households
Bibliographic record
Abstract
Background: Both chronic and acute heat result in a substantial health burden globally, causing particular concern for at-risk populations, such as older adults. Outdoor temperatures are often assessed as the exposure and are used for heat warning systems despite individuals spending most of their time indoors. Many studies use ecological designs, with death or hospitalizations rates. Individual-level outcomes that are directly related to heat-symptoms should also be considered to refine prevention efforts. Objectives: In this longitudinal study, we assessed the association between indoor temperature and proximal symptoms in individuals ≥60 years of age living in non–air-conditioned households in Montérégie, Quebec, during the 2017–2018 summer months. Methods: We gathered continuously measured indoor temperature and humidity from HOBO sensors and repeated health-related questionnaires about health-related symptoms administered across three periods of increasing outdoor temperatures, where the reference measurement (T1) occurred during a cool period with a target temperature of 18–22°C and two measurements (T2 and T3) occurred during warmer periods with target temperatures of 28–30°C and 30–33°C, respectively. We used generalized estimating equations with Poisson regression models and estimated risk ratios (RRs) between temperature, humidity, and each heat-related symptom. Results: Participants (n=277) had an average age (mean±standard deviation) of 72.8±7.02y. Higher indoor temperatures were associated with increased risk of dry mouth (T3 RR=2.5; 95% CI: 1.8, 3.5), fatigue (RR=2.3; 95% CI: 1.8, 3.0), thirst (RR=3.4; 95% CI: 2.5, 4.5), less frequent urination (RR=3.7; 95% CI: 1.8, 7.3), and trouble sleeping (RR=2.2; 95% CI: 1.6, 3.2) compared with T1. We identified a nonlinear relationship with indoor temperatures across most symptoms of interest. Discussion: This study identified that increasing indoor temperatures were associated with various health symptoms. By considering the prevalence of these early stage outcomes and indoor temperature exposures, adaptation strategies may be improved to minimize the burden of heat among vulnerable communities. https://doi.org/10.1289/EHP10291
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".