ASSOCIATION BETWEEN COUNTY TEMPERATURE AND LIMITATIONS IN ADLS IN A REPRESENTATIVE SAMPLE OF 1.7 MILLION OLDER ADULTS
Bibliographic record
Abstract
Abstract Few studies have explored whether there is an association between area temperature and limitations in Activities of Daily Living (ADLs) such as serious difficulty with bathing and dressing. The objectives of this study were: (1) To examine the relationship between average county temperature during the coldest month of the year (i.e., January or February) (< 27°F, 27-31.9°F; 32-36.9°F; 37-41.9°F; ≥42°F) and the prevalence and adjusted odds of limitations in ADLs; and (2) To examine the relationship between the area’s hottest month’s average temperature (< 70°F, 70-74.9°F, 75-79.9°F, ≥80°F) and the prevalence and adjusted odds of limitations in ADLs. The sample included 1.7 million adults aged 65+ who responded to the nationally representative American Community Survey (2012-2017). After adjustments for age, sex, education, household income, race and time zone, there was a robust dose-response relationship indicating those living in areas with a higher average temperature during the coldest month had higher odds of limitations in ADLs. For example, in comparison to older adults living in regions where the coldest month’s average temperature was < 27°F, those living in areas where the temperature was slightly above freezing (32-36.9°F) had 22% higher adjusted odds of ADL limitations (OR=1.22; 95%CI=1.19, 1.25) and those living in regions where the temperature averaged ≥42°F had 33% higher odds (OR=1.33: 95%CI-1.30, 1.36). There was no significant relationship between the regional hottest month’s average temperature and ADL limitations. Future research is needed to explore why the absence of sub-freezing temperatures is associated with elevated odds of ADLs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".