Care setting and 30-day hospital readmissions among older adults: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Despite the fact that many older adults receive home or long-term care services, the effect of these care settings on hospital readmission is often overlooked. Efforts to reduce hospital readmissions, including capacity planning and targeting of interventions, require clear data on the frequency of and risk factors for readmission among different populations of older adults. METHODS: We identified all adults older than 65 years discharged from an unplanned medical hospital stay in Ontario between April 2008 and December 2015. We defined 2 preadmission care settings (community, long-term care) and 3 discharge care settings (community, home care, long-term care) and used multinomial regression to estimate associations with 30-day readmission (and death as a competing risk). RESULTS: We identified 701 527 individuals (mean age 78.4 yr), of whom 414 302 (59.1%) started in and returned to the community. Overall, 88 305 in dividuals (12.6%) were re admitted within 30 days, but this proportion varied by care setting combination. Relative to individuals returning to the community, those discharged to the community with home care (adjusted odds ratio [OR] 1.43, 95% confidence interval [CI] 1.39-1.46) and those returning to long-term care (adjusted OR 1.35, 95% CI 1.27-1.43) had a greater risk of readmission, whereas those newly admitted to long-term care had a lower risk of readmission (adjusted OR 0.68, 95% CI 0.63-0.72). INTERPRETATION: In Ontario, about 40% of older people were discharged from hospital to either home care or long-term care. These discharge settings, as well as whether an individual was admitted to hospital from long-term care, have important implications for understanding 30-day readmission rates. System planning and efforts to reduce readmission among older adults should take into account care settings at both admission and discharge.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".