Modelling risk factor information for linked census data: The case of smoking.
Bibliographic record
Abstract
BACKGROUND: Statistics Canada has initiated a series of data linkages of Census of Population long form and health outcome data. These linked data lack risk factor information. This study assesses the feasibility of using statistical modelling techniques to assign smoking status to census respondents. DATA AND METHODS: The 2000/2001 Canadian Community Health Survey (CCHS) was used to develop age-/sex-specific predictive models to model smoking status based on variables available on the 1991 Census. The 2002/2003 CCHS was used to validate the modelled variable. Data from the 2002/2003 CCHS linked to data from the Hospital Morbidity Database (2001/2002 to 2004/2005) were used to evaluate the use of modelled versus self-reported smoking status on smoking-related hospitalizations. RESULTS: For the current daily smoker models, income, education, marital status, dwelling ownership and region of birth were significant predictors. For the never smoker models, marital status, dwelling ownership, Aboriginal identity and region of birth were significant predictors. Modelled current daily smoker status was associated with increased odds of smoking-related hospitalization, compared with being a never smoker, even when adjusting for covariates. INTERPRETATION: This study demonstrates the feasibility of using statistical modelling techniques to assign smoking status to census data, provided socio-economic and identity information is available.
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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 | 0.040 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".