Use of Administrative Data for the Surveillance of Mood and Anxiety Disorders
Bibliographic record
Abstract
OBJECTIVE: There is increasing interest in the use of administrative data for surveillance and research in Australia. The purpose of the present study was to evaluate the usefulness of such data for the surveillance of mood and anxiety disorder using databases from the following Canadian provinces: British Columbia, Ontario, Quebec and Nova Scotia. METHOD: A population-based record-linkage analysis was done using data from physician billings and hospital discharge abstracts, and community-based clinics using a case definition of ICD-9 diagnoses of 296.0-296.9, 311.0, and 300.0-300.9. RESULTS: The prevalence of treated mood and/or anxiety disorder was similar in Nova Scotia, British Columbia, and Ontario at approximately 10%. The prevalence for Quebec was slightly lower at 8%. Findings from the provinces showed consistency across age and sex despite variations in data coding. Women tended to show a higher prevalence overall of mood and anxiety disorder than men. There was considerably more variation, however, when treated anxiety (300.0-300.9) and mood disorders (296.0-296.9, 311.0) were considered separately. Prevalence increased steadily to middle age, declining in the 50s and 60s, and then increased after 70 years of age. CONCLUSIONS: Administrative data can provide a useful, reliable and economical source of information for the surveillance of treated mood and/or anxiety disorder. Due to the lack of specificity, however, in the diagnoses and data capture, it may be difficult to conduct surveillance of mood and anxiety disorders as separate entities. These findings may have implications for the surveillance of mood and anxiety disorders in Australia with the development of a national network for the extraction, linkage and analysis of administrative data.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".