Chronic disease management among people with serious mental illness across rural, small urban, and metropolitan settings
Bibliographic record
Abstract
Context: People with serious mental illness (SMI) are at increased risk of complications and earlier death from comorbid chronic diseases, compared with patients without mental illness. Despite similar prevalence of mental illness across rural, small urban, and metropolitan settings, those living in a rural context experience disproportionately worse chronic disease related health outcomes. These disparities may be related to differences in access to recommended chronic disease management. Objective: To describe patterns of chronic disease management across rural, small urban, and metropolitan settings, and explore variation by coinciding treatment for mental illness. Study Design: Observational analysis of linked administrative data Setting or Dataset: Physician billing records (including laboratory tests), prescriptions dispensed, and patient registry data in British Columbia, Canada, accessed via Population Data BC Population Studied: All people ages 20-105 registered for provincial health insurance between April 1, 2022 and March 31, 2023 who were treated for diabetes or hypertension in the two preceding years Instrument: Comparison across metropolitan, small urban, and rural contexts, among people treated for serious mental illness, common mental illness, or not treated for mental illness Outcome Measures: Chronic disease management including primary care visits, billing premiums indicating responsibility for longitudinal care, lab tests, prescriptions, and referrals. Results: The proportion of patients with a premium billed for diabetes or hypertension was highest in small urban areas, but lower amongst patients with SMI across all three geographic settings, with particularly low values in metro. The proportions of people with diabetes or hypertension-related blood testing were generally similar across the three geographic settings, but people treated for SMI and particularly living rurally had lower access overall. The proportions of people with any diabetes or anti-hypertensive drug were similar across settings, but with some variation in patterns for insulin and non-insulin diabetes drugs. Referral to and/or visit with an ophthalmologist was most common in metropolitan settings, with similarly lower values in small urban and rural. Conclusions: This analysis highlights geographic variation in chronic disease management, and pronounced gaps in access among people treated for SMI, particularly in rural settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".