Creating an 11-year longitudinal substance use harm cohort from linked health and census data to analyse social drivers of health
Bibliographic record
Abstract
Introduction: Research on substance use harm in Canada has been hampered by an absence of linked data to analyse and report on the social drivers of substance use harm. Objectives: This study aims to address this gap by providing a fully annotated Stata do-file that links sociodemographic data to 11 years of hospitalisation and death outcomes. This do-file will greatly facilitate the creation of provincial and national substance use cohorts using line-level data available through Statistics Canada's Research Data Centres (RDC) program. Methods: We used Canadian Census Health and Environment Cohorts (CanCHEC) 2006 to create a cohort of Saskatchewanians followed from 2006 to 2016. We linked sociodemographic information of the 2006 Census (long-form) respondents to their hospitalisation data captured in the Discharge Abstract Database (DAD) (2006 to 2016) and their mortality records in the Canadian Vital Statistics Death Database (CVSD) (2006 to 2016). We developed an algorithm to identify Saskatchewanians who experienced a substance use harm event. We validated the cohort by comparing our descriptive findings with those from other Canadian studies on substance use. Results: We used CanCHEC, a national data resource, whereas most previous studies have used provincial data resources. Despite this difference in constructing the cohorts, our results showed trends consistent with previous studies, including an overrepresentation of individuals with lower socioeconomic status among the people who experienced substance use harm (PESUH). Similar to other Canadian studies, our results indicate an increasing rate of substance use harm from 2006 to 2016. Conclusion: This study provides a Stata do-file that compiles a validated substance use cohort using CanCHEC, enabling comprehensive substance use research by linking sociodemographic data with health outcomes. The do-file is likely to save researchers hundreds of hours and accelerate research on the drivers of substance use harms in Canada.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".