The Early Burden of Disability in Individuals With Mood and Other Common Mental Disorders in Ontario, Canada
Bibliographic record
Abstract
Importance: Large population-based data on the trajectory to disability after the first diagnosis of a mood disorder are lacking. Objective: To assess the time between an incident mood disorder diagnosis and the receipt of disability services during a follow-up period of as long as 20 years. Design, Setting, and Participants: This cohort study used health administrative and social service data from ICES for 1 902 792 adults aged 18 to 59 years living in Ontario, Canada. A narrow cohort of individuals who had a new diagnosis of a mood disorder between October 1, 1997, and March 31, 2007, matched by sex and age to individuals with no history of mood disorder, included 278 296 participants. A broader cohort of individuals who had a new diagnosis of other common mental disorders during the same period, matched by sex and age to individuals with no history of mental disorder diagnosis, included 1 624 496 individuals. All individuals were followed up to a maximum end date of March 31, 2017. Data analysis was conducted from November 2017 to June 2018. Exposure: Incident diagnosis of mood or common mental disorder. Main Outcomes and Measures: Disability outcomes were as follows: (1) entry into the Ontario Disability Support Program (ODSP), signifying long-term inability to work because of a disability, and (2) admission into a long-term care (LTC) residence, signifying the inability to live in independent housing. Cox proportional hazards models were used. Results: In the full cohort of 1 902 792 individuals, 278 296 participants (14.6%) were included in the mood disorder cohort (mean [SD] age, 37.5 [11.9] years; 157 386 [56.6%] women), and 1 624 496 participants (85.4%) were included in the common mental disorder cohort (mean [SD], 36.5 [11.8] years; 932 545 [57.4%] women). The incidence of ODSP initiation was greater among individuals with mood disorders than those without (51.5 per 10 000 person-years vs 25.5 per 10 000 person-years; adjusted hazard ratio [aHR], 2.03; 95% CI, 1.95-2.11) and for those with common mental disorders (45.0 per 10 000 person-years vs 27.6 per 10 000 person-years; aHR, 1.57; 95% CI, 1.55-1.60). The aHR for admission to LTC was also higher among individuals with mood disorders compared with those without (aHR, 2.20; 95% CI, 1.80-2.69) and those with common mental disorders compared with those without (aHR, 1.21; 95% CI, 1.14-1.29). Individuals with bipolar disorders had greater ODSP rates than individuals with major depressive disorders (crude rate ratio: 4.31 [95% CI, 3.56-5.17] vs 1.82 [95% CI, 1.36-2.43]). Conclusions and Relevance: This cohort study found that mood disorders were associated with elevated and early rates of disability services. Effective early intervention strategies targeting functional impairment in this population are encouraged.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".