Statistical modeling of complex health outcomes and air pollution data: Application of air quality health indexing for asthma risk assessment
Bibliographic record
Abstract

 Background: Statistical models for complex health outcome data analyses with; zero inflation, autocorrelation, confounding, seasonality and delayed exposures, play an important role in accurately assessing air pollution risk, especially in public health warning using national air quality health indices (NAQHI). NAQHI assessment generalizes model estimates across all geographies and seasons and neglects area and season specific variations. The aim is to develop complex statistical models, specific to the complex data structures and to demonstrate effectiveness of the model estimates in public health massage delivery.
 Methods: A time series model was fitted to asthma hospital admissions and ambient air pollution data from two sites, Halifax, an urban, traffic and industry polluted site and Sydney, a rural waste disposal polluted site, in the province of Nova Scotia, Canada. I fitted zero inflated, auto regressive, Poisson and Negative Binomial models with lagged effects for sparse asthma admissions and air pollution data and compared the model risk estimates with that of the NAQHI.
 Results: NAQHI used three pollutants, Nitrogen Dioxide, Ozone and particulate matter. I found Carbon monoxide in the urban site and lead in the waste disposal site as prominent pollutants and there were seasonal differences. The findings demonstrated severe under-assessment of asthma relative risk by NAQHI, when prominent pollutants are neglected and also when auto correlation and zero inflation are ignored. 
 Conclusion: This study demonstrated the importance of complex statistical model use and the consequences of lack of such modelling that accounted for data structures in public health risk assessments.
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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 | 0.028 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".