Adverse health effects of climate change and air pollution in people with disabilities: a systematic review
Bibliographic record
Abstract
Global warming and air pollution adversely affect the health of the entire human population, particularly older adults, people with disabilities (PWDs), and children. In this systematic review, we investigated the adverse health effects of climate change and air pollution in PWDs. We conducted a comprehensive literature search of the PubMed database using the terms "disab*," "air pollution," and "climate change" on July 4, 2023, and August 8, 2023 and searched the Web of Science (WOS) database on December 28, 2023. We identified 425 and 1,169 studies on climate change cited in PubMed and WOS, respectively, as well as 333 studies on air pollution in PubMed and 495 studies on air pollution in WOS. The studies were classified by type of exposure, and full-text screening was conducted to confirm that the population, intervention or exposure, comparator, outcome statement, and inclusion and exclusion criteria were met. The Newcastle-Ottawa Scale was used to assess the quality of the included cohort and case-control studies and for data analysis. In extreme temperatures, PWDs experienced higher rates of injury, heat-related illness, functional impairment, heart disease, mental disorders, and mortality than people who were non-disabled (ND). Exposure to air pollution resulted in higher rates of obesity, cardiovascular disease, poststroke neurological and functional disability, and mortality in PWDs than in people who were ND. Therefore, because PWDs were more affected by climate change and air pollution than people who were ND, sensitive policies and preparedness measures should be developed for PWDs.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".