Social outcomes associated with alcohol-related diagnoses: a population-based analysis using linked administrative data
Bibliographic record
Abstract
ABSTRACT ObjectiveThe objective of this population health research is to identify the social burden associated with having an alcohol related diagnosis. MethodsWe used linkable population-based administrative data files held in the Population Health Research Data Repository to conduct our research, fiscal years 1990/91 to 2014/15. Data came from several domains including health, social services, justice, and the Canadian Census. We used ICD-9/10-CA codes from the hospital abstract database, medical claims data, and prescription drug data to identify individuals with an alcohol-related diagnosis. Individuals’ socioeconomic status was determined using neighbourhood-level income data from the Canadian Census. We matched, 3:1, diagnosed cases to individuals in our repository using on age, sex, income, and community area. We linked cases and matches to administrative data from justice and social services to identify social outcomes associated with having an alcohol-related diagnosis. Outcomes included receipt of income assistance, residence in publically funded social housing, having a child apprehended by child and family protective services, having a charge for driving under the influence recorded in the justice data, and having a charge for domestic violence recorded in the justice data. We modelled rates using generalized estimating equations from 5 years before date of diagnosis to a maximum of 20 years after date of diagnosis. Models tested for significant differences in rates between cases and matches both before and after diagnosis; as well, we tested for time trends in rates both before and after diagnosis. ResultsWe identified 52,991 individuals with an alcohol related diagnosis between 1990/91 and 2014/15: 34,145 males and 18,846 females. 80.3% of cases had a mental-health related alcohol diagnosis. Diagnoses followed a socioeconomic gradient with the greatest number of cases coming from low-income neighbourhoods. Cases had a significant spike in rates from one year before to one year after diagnosis date, compared with matches, across all indicators. When we followed individuals for 20 years after diagnosis, we found a significantly elevated rate of social service use and involvement with the justice system, across all outcomes, for all years. ConclusionReceiving an alcohol-related diagnosis is associated with subsequent increased use of social services and contacts with the justice system. Upstream efforts to reduce alcohol-related diagnoses may result in reduced use of social services and justice contacts.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".