HEALTH IMPACTS OF THE BUILT ENVIRONMENT: PHYSICAL INACTIVITY, EXPOSURE TO AIR POLLUTION, AND ISCHEMIC HEART DISEASE
Bibliographic record
Abstract
Background: Physical inactivity and exposure to air pollution are important risks globally. Urban planning and the built environment may influence exposures to these risk factors in different ways and thus differentially impact the health of urban populations. Aims: To investigate the built environment’s influence on air pollution exposure and physical inactivity and subsequent health impacts. Methods: We use a regional travel survey to estimate within-urban variability in physical inactivity and air pollution exposure (PM2.5, NOx, O3) for ~30,000 individuals in Los Angeles. We then estimate the resulting risk for ischemic heart disease (IHD) using literature-derived dose-response values for the general public and for a sensitive subpopulation. We compare estimated IHD mortality risks between neighborhoods based on “walkability” scores, and conduct sensitivity analyses to explore robustness of our results. Results: The proportion of non-sedentary individuals is ~2× larger in high-vs. low-walkability neighborhoods (24.9% vs. 12.5%); however, since a small share of the total population is physically active, between-neighborhood variability in estimated IHD mortality attributable to physical inactivity is modest (7 fewer IHD deaths per 100,000 in low-vs. high-walkability neighborhoods). Since spatial patterns differ for air pollutants, risks from air pollution exposure are similar between neighborhoods (9 fewer [13 more] IHD deaths per 100,000 for PM2.5 [O3] in low-vs. high-walkability neighborhoods). This suggests health benefits from increased physical activity may be realized in high-walkability neighborhoods only because of tradeoffs in air pollution exposure. Comparisons to data from other cities suggest our core conclusions are not unique to Los Angeles. Conclusions: Accounting for exposure to air pollution is a critical aspect of planning for more clean and health-promoting cities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".