A New Longitudinal Design for Identifying Subgroups of The Population who are Susceptible to the Short-term Effects of Ambient Air Pollution
Bibliographic record
Abstract
A longitudinal design is proposed to investigate the short-term effects of air pollution on health status. The study design exploits a new statistical methodology developed by Dewanji and Moolgavkar (2000, 2002) that makes use of a Poisson counting process for the incidence of events in a longitudinal cohort study. The methodology allows for modeling of subject-specific baseline hazards of multiple events and time-dependent ecological (i.e., daily levels of air pollution and weather conditions) and individual covariates (e.g., comorbid conditions, indices of declines in health status). The study will determine the association between short-term changes in air pollution and incidence of hospitalization, emergency department visits, "intermediate health conditions" (e.g., changes in health status reflected by changes in filled prescriptions), and mortality. Data from the universal Quebec medicare system are used, including data for residents of Montreal age 65 yr and above. The cohorts include individuals who have at baseline airways disease, chronic coronary artery disease, congestive heart failure, hypertension, cerebrovascular disease, atherosclerosis, dysrhythmias, and diabetes, and these will be followed for the incidence of the already mentioned adverse outcomes, adjusting for time-dependent individual and ecological covariates. This type of study will provide findings that can be used to confirm or refute results of time series analyses. Using a different methodology that includes subject-specific information will greatly increase our understanding as to whether short-term exposures to ambient air pollution cause serious changes in health status among subgroups of the population. These results will also help in understanding mechanisms by identifying groups that are susceptible to the effects of 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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".