Brief Communication Use of Administrative Data for the Surveillance of Mental Disorders in 5 Provinces
Bibliographic record
Abstract
Objective: To evaluate the usefulness of administrative data for the surveillance of mental illness in Canada using databases in the following 5 provinces: British Columbia, Ontario, Quebec, Nova Scotia, and Alberta. Method: We used a population-based record-linkage analysis with data from physician billings, hospital discharge abstracts, and community-based clinics. The following diagnostic codes from the International Classification of Diseases, Ninth Edition, were used to define cases: 290 to 319, inclusive. Results: The prevalence of treated psychiatric disorder was similar in Nova Scotia, British Columbia, Alberta, and Ontario at about 15%. The prevalence for Quebec was slightly lower at 12%. Findings from the provinces showed remarkable consistency across age and sex, despite variations in data coding. Women tended to show a higher prevalence overall of treated mental disorders than men. Prevalence increased steadily to middle age, declining in the 50s and 60s, and then increasing again after age 70 years. Conclusions: Provincial and territorial administrative data can provide a useful, reliable, and economical source of information for the surveillance of treated mental disorders. Such a surveillance system can provide longitudinal data at little cost to support health service provision and planning. Can J Psychiatry. 2009;54(8):571–575. Clinical Implications The prevalence of treated mental disorders across Canada is fairly consistent (15%), although Quebec has a slightly lower rate of 12%. Women have a higher prevalence. Provincial and territorial administrative data from hospital morbidity and physician billings are useful for the surveillance of treated mental disorders. These data complement clinical records and community surveys. Such a surveillance system can provide longitudinal data at little cost for health service provision and planning. Using postal codes and census data, it is possible to study differences within jurisdictions based on location (for example, rurality) or socioeconomic status.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".