Measuring Substance-Related Disorders Using Canadian Administrative Health Databanks: Interprovincial Comparisons of Recorded Diagnostic Rates, Incidence Proportions and Mortality Rate Ratios: Mesurer les troubles liés aux substances à l’aide des banques de données de santé administratives canadiennes: comparaisons interprovinciales des taux diagnostiques enregistrés, proportions de l’incidence et rapports des taux de mortalité
Bibliographic record
Abstract
ContextAssessing temporal changes in the recorded diagnostic rates, incidence proportions, and health outcomes of substance-related disorders (SRD) can inform public health policymakers in reducing harms associated with alcohol and other drugs.ObjectiveTo report the annual and cumulative recorded diagnostic rates and incidence proportions of SRD, as well as mortality rate ratios (MRRs) by cause of death among this group in Canada, according to their province of residence.MethodsAnalyses were performed on linked administrative health databases (AHD; physician claims, hospitalizations, and vital statistics) in five Canadian provinces (Alberta, Manitoba, Ontario, Québec, and Nova Scotia). Canadians 12 years and older and registered for their provincial healthcare coverage were included. The International Classification of Diseases (ICD-9 or ICD-10 codes) was used for case identification of SRD from April 2001 to March 2018.ResultsDuring the study period, the annual recorded SRD diagnostic rates increased in Alberta (2001–2002: 8.0‰; 2017–2018: 12.8‰), Ontario (2001–2002: 11.5‰; 2017–2018: 14.4‰), and Nova Scotia (2001–2002: 6.4‰; 2017–2018: 12.7‰), but remained stable in Manitoba (2001–2002: 5.5‰; 2017–2018: 5.4‰) and Québec (2001–2002 and 2017–2018: 7.5‰). Cumulative recorded SRD diagnostic rates increased steadily for all provinces. Recorded incidence proportions increased significantly in Alberta (2001–2002: 4.5‰; 2017–2018: 5.0‰) and Nova Scotia (2001–2002: 3.3‰; 2017–2018: 3.8‰), but significantly decreased in Ontario (2001–2002: 6.2‰; 2017–2018: 4.7‰), Québec (2001–2002: 4.1‰; 2017–2018: 3.2‰) and Manitoba (2001–2002: 2.7‰; 2017–2018: 2.0‰). For almost all causes of death, a higher MRR was found among individuals with recorded SRD than in the general population. The causes of death in 2015–2016 with the highest MRR for SRD individuals were SRD, suicide, and non-suicide trauma in Alberta, Ontario, Manitoba, and Québec.DiscussionLinked AHD covering almost the entire population can be useful to monitor the medical service trends of SRD and, therefore, guide health services planning in Canadian provinces.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".