Case-crossover design: Air pollution and health outcomes
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to investigate variants of case-crossover design for assessing correlations between counts of health events over time and exposure to ambient air pollution. For illustrative purposes, daily emergency department (ED) visits for depression among females were considered. MATERIALS AND METHOD: Ambient nitrogen dioxide (NO(2)) was used as a principal ambient air pollutant. In addition, sulphur dioxide (SO(2)) and carbon monoxide (CO) were considered. Different configurations of the control periods (every 1, 2, …, 10 days) and different forms (linear, natural splines) of meteorological factors in the applied conditional logistic regression models were used. The sequence of overlapping age intervals was defined ([0, 19], [1, 20], and so on) and each age group was analyzed separately. The results for the defined age sequences allow identifying age ranges in which the effects are strongest. RESULTS: Consequently, for example, different age ranges for patients for which ED visits for depression were correlated with NO(2) and SO(2) were identified. This age-interval difference explains the very often observed phenomenon whereby two air pollutants used in one model may not show correlations with health outcomes. In this situation they affect separate age groups. The results also show dependency on number-defined control periods for the applied case-crossover technique. The opposite statistical conclusions may be generated by using different control schemas. CONCLUSIONS: The results support the hypothesis that ED visits for depressive disorder may be correlated with exposure to ambient air pollution.
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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 | 0.053 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".