Health risk of air pollution on people living with major chronic diseases: a Canadian population-based study
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to use health administrative and environmental data to quantify the effects of ambient air pollution on health service use among those with chronic diseases. We hypothesised that health service use would be higher among those with more exposure to air pollution as measured by the Air Quality Health Index (AQHI). SETTING: Health administrative data was used to quantify health service use at the primary (physician office visits) and secondary (emergency department visits, hospitalisations) level of care in Ontario, Canada. PARTICIPANTS: We included individuals who resided in Ontario, Canada, from 2003 to 2010, who were ever diagnosed with one of 11 major chronic diseases. OUTCOME MEASURES: Rate ratios (RR) from Poisson regression models were used to estimate the short-term impact of incremental unit increases in AQHI, nitrogen dioxide (NO2; 10 ppb), fine particulate matter (PM2.5; 10 µg/m(3)) and ozone (O3; 10 ppb) on health services use among individuals with each disease. We adjusted for age, sex, day of the week, temperature, season, year, socioeconomic status and region of residence. RESULTS: Increases in outpatient visits ranged from 1% to 5% for every unit increase in the 10-point AQHI scale, corresponding to an increase of about 15,000 outpatient visits on a day with poor versus good air quality. The greatest increases in outpatient visits were for individuals with non-lung cancers (AQHI:RR=1.05; NO2:RR=1.14; p<0.0001) and COPD (AQHI:RR=1.05; NO2:RR=1.12; p<0.0001) and in hospitalisations, for individuals with diabetes (AQHI:RR=1.04; NO2:RR=1.07; p<0.0001) and COPD (AQHI:RR=1.03; NO2:RR=1.09; p<1.001). The impact remained 2 days after peak AQHI levels. CONCLUSIONS: Among individuals with chronic diseases, health service use increased with higher levels of exposure to air pollution, as measured by the AQHI. Future research would do well to measure the utility of targeted air quality advisories based on the AQHI to reduce associated health service use.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".