Exposure to urban air pollution and emergency department visits for diseases of the ear and mastoid processes
Bibliographic record
Abstract
Exposure to ambient air pollutants may cause adverse health effects. This study sought to assess the association of five major air pollutants concentration levels and number of Emergency Department (ED) visits for ear and mastoid pathology in the large Canadian urban city, Toronto. A time-stratified case-crossover study design was used for the period between April 1, 2004 and December 31, 2015. Daily air pollution data were collected from the National Air Pollution Surveillance Database for carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ground-level ozone (O3) and fine particulate matter (PM2.5). Data were also collected for weather variables. ED visit data were extracted from the National Ambulatory Care Reporting System database. Conditional Poisson models were applied using daily counts of the ED visits. Temperature and relative humidity in the models were represented by natural splines. Independent variables (air pollutants and weather conditions) were lagged by the same number of exposure days, from 0 to 14. The analyses were grouped by strata according to patients’ sex and age, also by seasons. For each studied pollutant, 270 models were derived (15 lags × 18 strata). There were 188,997 ED visits during the 140-month study period. Children under 10 years represented 31.7% of all visits, compared to 53.8% for patients aged 11–60 years, and 14.5% for those above 60 years. Short-term (time lags 4 and 5 days) NO2 exposure was positively associated with number of ED visits for ear and mastoid pathology for children independent of sex. Boys had relative risks (RR) of 1.030 (95% confidence intervals: 1.009–1.053) and 1.033 (1.011–1.055) for ED visits on days 4 and 5 after exposure to NO2 by one interquartile range (IQR = 8.8 ppb), respectively. Girls had RR of 1.046 (1.021–1.071) and 1.039 (1.014–1.064) on days 4 and 5 after exposure to NO2 by the same IQR, respectively. For lag 4, a one unit increase in the calculated air quality health index results in a 1.038 (1.020–1.057), 1.042 (1.017–1.067), and 1.036 (1.014–1.058), for all children, girls, and boys, respectively. In this large urban cohort, number of ED visits for ear and/or mastoid process infections peaked in the acute period following elevated overall air pollution and NO2 specifically, especially among young children.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".