Linking the Canadian Community Health Survey and the Canadian Mortality Database: An enhanced data source for the study of mortality.
Bibliographic record
Abstract
BACKGROUND: This study summarizes the linkage of the Canadian Community Health Survey (CCHS) and the Canadian Mortality Database (CMDB), which was performed to examine relationships between social determinants, health behaviours and mortality in the household population. DATA AND METHODS: The 2000/2001-to-2011 Canadian Community Health Surveys were linked to the 2000-to-2011 CMDB using probabilistic methods based on common identifiers (names, date of birth, postal code and sex) for eligible respondents (85%; n = 614,774). Mortality records from January 1, 2000 through December 31, 2011 for people aged 12 or older were eligible for linkage (n = 2.774 million). The linkage was enhanced with information from the Historical Tax Summary File. Quality assessment consisted of internal and external validation. Cox survival analysis (age-adjusted) was conducted to estimate hazard ratios (HRs) associated with selected health behaviours. RESULTS: Overall, 5.3% of eligible CCHS respondents linked to a mortality record; false positive and false negative rates were 0.04% and 2.43%, respectively. Linkage rates were higher among males (5.8%) and people aged 75 or older (20.2%), reflecting known mortality risks. Survival analyses confirmed elevated mortality risk associated with heavy (HR 2.36, CI 1.84, 2.89) and light smoking (HR 1.91, CI 1.52, 2.33), compared with not smoking; underweight and obesity, compared with normal and overweight; low fruit and vegetable consumption; and lack of physical activity. INTERPRETATION: Linking health behaviour information from the CCHS to mortality data from the CMDB allows for a greater understanding of modifiable determinants of mortality.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.033 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".