Does mental illness history affect primary care chronic disease management in older adults? A population-based propensity score-matched study
Bibliographic record
Abstract
BACKGROUND: Older adults living with physical chronic conditions and comorbid mental illness have more complex care needs, and may experience side effects of treatment for mental illness that can exacerbate physical conditions. There is a need to examine variation in health service use and chronic disease management in the context of treatment for mental illness. OBJECTIVE: We compared evidence-informed management of diabetes, heart failure and chronic obstructive pulmonary disease (COPD) amongst older adults based on history of mental illness treatment. DESIGN/SETTING: We conducted a population-based propensity score-matched study in British Columbia, Canada, using health administrative data from 1 April 2020 to 31 March 2023. SUBJECTS: Older adults (aged ≥65) registered for provincial health insurance and diagnosed with diabetes, heart failure and/or COPD. METHODS: Within each chronic disease subgroup, propensity scores (matching for age, sex, rurality and neighbourhood income quintile) paired individuals 1:1 based on mental illness history. Differences in health service utilisation and chronic disease management outcomes were assessed from P-values. RESULTS: Older adults with mental illness history had more primary care contacts, virtual visits and contacts with their usual primary care provider and specialists. However, they also had fewer labs/testing and a lower likelihood of being dispensed drugs for their chronic condition than those without mental illness history. CONCLUSION: Despite more frequent contact with primary care, older adults with mental illness may face barriers to receiving comparable chronic disease management. These findings underscore the need for more integrated, multidisciplinary care models that address both mental and physical health needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".