USE OF REMOTE SENSING ESTIMATES OF NO2 AND PM2.5 EXPOSURE IN A STUDY OF HOSPITAL AND EMERGENCY ROOM VISITS IN ONTARIO, CANADA
Bibliographic record
Abstract
Background and Aims: Remote sensing (RS) offers an alternative to ground based air pollution monitoring that provides broad, cost-effective coverage. However, the use of RS estimates in epidemiological studies has not been thoroughly studied, especially for acute health effects. We examine the use of RS to estimate exposure in a case-crossover study of acute myocardial infarction (MI) and asthma hospital visits in Ontario, Canada. Methods: Daily estimates of NO2 and PM2.5 levels were developed by combining retrievals from the OMI, MODIS and MISR instruments with daily profiles from a chemical transport model for the summer of 2005 (June-September). For comparison, ground-based measurements were obtained from Environment Canada’s National Air Pollution Surveillance (NAPS) network. Hospitalization and emergency room visit data, including individual level data on main cause of admission/visit (ICD-10 code), age, sex, and three-digit postal code of home residence, were obtained from databases maintained by the Canadian Institutes for Health Research. Individual exposure was estimated by linkage of residential postal code centroid to the RS estimate (at approximately 10 km resolution) and the nearest NAPS station within 40km. We will use a case-crossover analysis with full-stratum bidirectional control selection as well as a sensitivity analysis with two-week bidirectional control selection to examine the acute health effects of air pollution exposure as estimated by both RS and ground-based methods. Results: There were 7,562 hospitalizations for MI and 26,962 emergency room visits for asthma in Ontario in the summer of 2005. Preliminary analyses show RS estimates were available for PM2.5 on 36% of days and for NO2 on 24% of days over this time period. RS estimates of PM2.5 were highly correlated with daily-average ground-based NAPS measurements (r=0.73), while daily NO2 measurements were moderately correlated (r=0.48). Conclusions: RS estimates of PM2.5 and NO2 are well correlated with ground-based measurements and offer a promising alternative for estimating exposure in a study of acute health effects.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".