Mood Disorders in Late Life: A Population-based Analysis of Prevalence, Risk Factors, and Consequences in Community-dwelling Older Adults in Ontario: Troubles de l’humeur en âge avancé : Une analyse dans la population de la prévalence, des facteurs de risque et des conséquences chez des adultes âgés vivant en milieu communautaire en Ontario
Bibliographic record
Abstract
OBJECTIVE: Mental health issues in late life are a growing public health challenge as the population aged 65 and older rapidly increases worldwide. An updated understanding of the causes of mood disorders and their consequences in late life could guide interventions for this underrecognized and undertreated problem. We undertook a population-based analysis to quantify the prevalence of mood disorders in late life in Ontario, Canada, and to identify potential risk factors and consequences. METHOD: Individuals aged 65 or older participating in 4 cycles of a nationally representative survey were included. Self-report of a diagnosed mood disorder was used as the outcome measure. Using linked administrative data, we quantified associations between mood disorder and potential risk factors such as demographic/socioeconomic factors, substance use, and comorbidity. We also determined associations between mood disorders and 5-year outcomes including health service utilization and mortality. RESULTS: The prevalence of mood disorders was 6.1% (4.9% among males, 7.1% among females). Statistically significant associations with mood disorders included younger age, female sex, food insecurity, chronic opioid use, smoking, and morbidity. Individuals with mood disorders had increased odds of all consequences examined, including placement in long-term care (adjusted odds ratio [OR] =2.28; 95% confidence interval [CI], 1.71 to 3.02) and death (adjusted OR = 1.35; 95% CI, 1.13 to 1.63). CONCLUSIONS: Mood disorders in late life were strongly correlated with demographic and social/behavioral factors, health care use, institutionalization, and mortality. Understanding these relationships provides a basis for potential interventions to reduce the occurrence of mood disorders in late life and their consequences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".