Air pollution exposure among people with limitations in activities of daily living in the United States
Bibliographic record
Abstract
Abstract Structural barriers including limited healthcare access and disability-related health conditions make disabled people differentially susceptible to air pollution-related adverse health outcomes compared to nondisabled people. We used 2020 census-tract level counts of individuals with limitations in activities of daily living (ADLs) to identify a subset of disabled people. We described geographic areas where this population was highly exposed to air pollution in the contiguous U.S., indicating health risk. We assessed census tract-level exposure to PM 2.5 , O 3 , NO 2 (2016–2020), and wildfire PM 2.5 (2016–2023). We mapped high ADL limitation prevalence and high air pollution exposure census tracts. Because environmental injustice means race and poverty strongly predict air pollution exposure, we also assessed exposure among people with ADL limitations by these demographic factors to identify doubly vulnerable subpopulations. High ADL limitation prevalence and PM 2.5 /NO 2 exposure co-occurred in urban areas, California’s Central Valley, Eastern Washington, and parts of the Southeast. Among people with ADL limitations, Asian and Hispanic individuals and those experiencing poverty were more exposed to PM 2.5 , O 3 , and NO 2 . Disability is not fully captured by ADL limitations; future studies should explore other definitions of disability. Future studies should evaluate interventions to reduce air pollution-related morbidity and mortality, especially in regions and subpopulations identified here, where disabled people face high exposure and multiple vulnerabilities.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".