Mental disorders among Manitoba adults with and without intellectual and developmental disabilities: a population-based study
Bibliographic record
Abstract
Introduction: Approximately 20% of Canadians live with a mental disorder each year. Prior population-based research from other jurisdictions suggested that persons with intellectual and developmental disabilities (IDD) have a higher prevalence of mental disorders compared to the general population. The purpose of the study was to calculate and compare prevalence of mental disorders between Manitoba adults with and without IDD. Methods: A retrospective matched cohort study was conducted. Population-based administrative data was analyzed from the Manitoba Population Research Data Repository housed and maintained by the Manitoba Centre for Health Policy. Five years of administrative data (April 1, 2015, to March 31, 2020) were linked to identify mental disorders. All years of data from multiple datasets available prior to April 1, 2015, were linked to identify the study cohort (i.e., adults with IDD) and the matched comparison group. Adults with and without IDD were matched by age, sex, and region of residence at a 1:3 ratio. Descriptive analyses were conducted to describe the study population. The generalized estimating equation technique, odds ratios, and 95% confidence intervals were used to determine statistically significant differences in the prevalence of mental disorders between the IDD study cohort and the matched comparison group. Results: Adults with IDD had a significantly higher prevalence of any mental disorders (OR = 2.13; 95%CI: 2.06, 2.21), mood and anxiety disorders (OR = 1.87; 95%CI: 1.80, 1.94), psychotic disorders (OR = 7.43; 95%CI: 6.82, 8.09), and substance use disorders (OR = 1.9; 95%CI: 1.78, 2.03) compared to the matched comparison group. Prevalence of mental disorders among persons with IDD varied by age, sex, and region of residence. Conclusion: Consistent with the studies from other jurisdictions, it was found that prevalence of mental disorders was significantly higher among adults with IDD compared to those without IDD. Study findings can help with the development of interventions, policies, and programs, as well as advocacy to provide more accessible mental health supports to promote the mental health of Manitobans with IDD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".