Use of Generalized Linear Mixed Models to Examine the Association between Air Pollution and Health Outcomes
Bibliographic record
Abstract
BACKGROUND: Time-series and case-crossover are two techniques that are widely used for assessing the short-term impact of ambient air pollution exposure on health. MATERIALS AND METHODS: The generalized linear mixed model (GLMM) methodology is proposed here to study the association between ambient air pollution and health outcomes. Poisson random-effects models are applied to analyze the clustered counts, where the groups of days, determined by the triplet <day of week, month, year>, form the clusters. The proposed technique uses a nested structure for the clusters and allows random-effects for hierarchical factors. A random intercept in the models adjusts for different levels of counts among the clusters. A fixed slope represents a common response to the exposure. RESULTS AND CONCLUSIONS: The obtained results are consistent with those generated by a classical approach (for example the case-crossover technique). The GLMM technique is a valid alternative methodology for studying air health effects.
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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".