Estimating Intra‐Urban Inequities in PM <sub>2.5</sub> ‐Attributable Health Impacts: A Case Study for Washington, DC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub‐groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health risks. We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra‐urban heterogeneity in fine particulate matter (PM 2.5 )‐ attributable mortality and morbidity. We estimate PM 2.5 ‐attributable cases of all‐cause mortality, chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically derived health impact functions. Data inputs include satellite‐derived annual mean surface PM 2.5 concentrations; age‐resolved population estimates; and statistical neighborhood‐, zip code‐ and ward‐scale disease counts. We find that PM 2.5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause‐specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM 2.5 ‐attributable health risks are unevenly and inequitably distributed across the District. Higher PM 2.5 ‐attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational attainment. Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub‐groups, and highlights the need for both high‐resolution disease rates and concentration estimates for understanding intra‐urban disparities in air pollution‐related health risks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it