Increasing comorbidity and health services utilization in older adults with prior stroke
Bibliographic record
Abstract
OBJECTIVE: To characterize comorbid chronic conditions, describe health services use, and estimate health care costs among community-dwelling older adults with prior stroke. METHODS: This is a retrospective cohort study using administrative data from Ontario, Canada. We identified all community-dwelling individuals aged 66 and over on April 1, 2008 (baseline), who had experienced a stroke at least 6 months prior. We estimated the prevalence of 14 comorbid conditions at baseline; we captured all physician visits, emergency department visits, hospital admissions, home care contacts, and associated costs over 5 years stratifying by number of comorbid conditions. Where possible, we distinguished between health services use for stroke- and non-stroke-related reasons. RESULTS: A total of 29,673 individuals met our criteria. Only 1% had no comorbid conditions, while 74.9% had 3 or more. The most common conditions were hypertension (89.8%) and arthritis (65.8%); 5 other conditions had a prevalence of 20% or more (ischemic heart disease, diabetes, chronic obstructive pulmonary disease, inflammatory bowel disease, and dementia). Use of all health services doubled with increasing comorbidity and was largely attributed to non-stroke-related reasons. Total and per-patient costs increased with comorbidity. Main cost drivers shifted from physician and home care visits to hospital admissions with greater comorbidity. CONCLUSIONS: Our findings demonstrate the importance of community-based patient-centered care strategies for stroke survivors that address their range of health needs and prevent more costly acute care use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".