Association Between Area Temperature and Severe Vision Impairment in a Nationally Representative Sample of Older Americans
Bibliographic record
Abstract
Purpose Several small studies have associated exposure to elevated average temperature with specific vision problems. However, no large-scale studies have examined the relationship between vision impairment and average area temperature in the general population. We conducted a cross-sectional analysis of a large nationally representative sample of older adults to further explore this relationship.Methods Secondary analysis of the American Community Survey (ACS). The survey was conducted through mail, telephone and in-person interviews. Data from six consecutive years of the cross-sectional survey were analysed (2012–2017). The subsample analysed included community-dwelling and institutionalized older adults aged 65 and older in the coterminous US who lived in the same state in which they were born (n = 1,707,333). The question on severe vision impairment was “Is this person blind or does he/she have serious difficulty seeing even when wearing glasses?”. Average annual temperature data from the National Oceanic and Atmospheric Administration was combined into a 100-year average and mapped to corresponding US Census Bureau’s public use microdata areas from the ACS.Results Higher average temperature is consistently associated with increased odds of severe vision impairment across all cohorts (i.e. age, sex, race, income, and educational attainment cohorts) with the exception of Hispanic older adults. Compared to those who lived in counties with average temperature of < 50 °F (< 10 °C) , the odds of severe vision impairment were 44% higher in counties with average temperature of 60 °F (15.5 °C) or above (OR 1.44; 95% CI 1.42–1.46).Conclusion If the association is found to be causal, the predicted rise in global temperatures could impact the number of older Americans affected by severe vision impairment and the associated health and economic burden.
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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 | 0.002 |
| 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".