Comparison of the estimated prevalence of mood and/or anxiety disorders in Canada between self-report and administrative data
Bibliographic record
Abstract
BACKGROUND: To compare trends in the estimated prevalence of mood and/or anxiety disorders identified from two data sources (self-report and administrative). Reviewing, synthesising and interpreting data from these two sources will help identify potential factors that underlie the observed estimates and inform public health action. METHOD: We used self-reported, diagnosed mood and/or anxiety disorder cases from the Canadian Community Health Survey (CCHS) across a 5-year span (from 2003 to 2009) to estimate the prevalence among the Canadian population aged ≥15 years. We also estimated the prevalence of mood and/or anxiety disorders using the Canadian Chronic Disease Surveillance System (CCDSS), which identified cases using ICD-9/-10-CA codes from physician billing claims and hospital discharge records during the same time period. The prevalence rates for mood and/or anxiety disorders were compared across the CCHS and CCDSS by age and sex for all available years of data from 2003 to 2009. Summary rates were age-standardised to the Canadian population as of 1 October 1991. RESULTS: In 2009, the prevalence of mood and/or anxiety disorders was 9.4% using self-reported data v. 11.3% using administrative data. Prevalence rates obtained from administrative data were consistently higher than those from self-report for both men and women. However, due to an increase in the prevalence of self-reported cases, these differences decreased over time (rate ratios for both sexes: 1.6-1.2). Prevalence estimates were consistently higher among females compared with males irrespective of data source. While differences in the prevalence estimates between the two data sources were evident across all age groups, the reduction of these differences was greater among adolescent, young and middle-aged adults compared with those 70 years and older. CONCLUSIONS: The overall narrowing of differences over time reflects a convergence of information regarding the prevalence of mood and/or anxiety disorders trends between self-report and administrative data sources. While the administrative data-based prevalences remained relatively stable, the self-reported prevalences increased over time. These observations may reflect positive societal changes in the perceptions of mental health (declining stigma) and/or increasing mental health literacy. Additional research using non-ecological data is required to further our understanding of the observed findings and trends, including a data linkage exercise permitting a comparison of prevalence estimates and population characteristics from these two data sources both separately and merged.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".