Using administrative health data to estimate prevalence and mortality rates of alcohol and other substance‐related disorders for surveillance purposes
Bibliographic record
Abstract
INTRODUCTION: Administrative health databases (AHD) are critical to guide health service management and can inform the whole spectrum of substance-related disorders (SRD). This study estimates prevalence and mortality rates of SRD in administrative health databases. METHODS: The Quebec Integrated Chronic Disease Surveillance System consists of linked AHD. Analyses were performed on data of all Quebec residents aged 12 and over and eligible for health-care coverage using the International Classification of Diseases (ninth or tenth revision) for case identification. Mortality rate ratios stratified by causes of death were obtained to calculate an excess of mortality. RESULTS: Since 2001-2002, the annual age-adjusted prevalence rate of diagnosed overall SRD remained stable (8.6 per 1000 in 2017-2018). In any given year, the annual prevalence rate was significantly higher in males; adolescents had the lowest rate, while adults 65 years and older the highest. The annual 2017-2018 rate was 2.1 per 1000 for alcohol-induced disorder, 1.9 for other drug-induced disorder, 0.7 for alcohol intoxication and 0.6 for other drug intoxications. Cumulative rate of any diagnosis related to alcohol was 32 per 1000 females and 53 per 1000 males (2001-2018), and 33 per 1000 females and 49 per 1000 males for any diagnosis related to other drugs. There was an excess of all-cause mortality among individuals with SRD compared to the general population. DISCUSSION AND CONCLUSIONS: AHD can complement epidemiological surveys in monitoring SRD jurisdiction-wide. Surveillance of services utilisation and interventions, coupled with health outcomes like mortality, could be useful in guiding health services planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".