The impact of assisted living facilities on hospitalization, length of stay, and mortality rates among the elderly: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: With 21.3% of the global population aging, the demand for assisted living facilities (ALFs) for individuals with complex medical conditions has surged. However, residing in ALFs may be associated with higher hospital admission rates, longer hospital stays, and increased mortality compared to living at home. The exact relationship between ALFs and these adverse health outcomes remains unclear. OBJECTIVES: To determine the correlation between ALFs—including nursing homes (NH), home care (HC), and residential care (RC)—and hospitalization rates, length of hospital stay, and mortality compared to community-dwelling individuals. METHODS: A literature search was conducted across five databases, focusing on risk ratios for hospitalization and mortality, as well as mean changes in hospital duration. This study compared interventions involving NH, HC, and RC with community dwelling. Quality appraisal was performed using the Newcastle-Ottawa Scale (NOS), and a forest plot was generated using a random-effects model with 95% confidence intervals (CI). RESULTS: Community-dwelling individuals had a 1.21 times higher likelihood of hospitalization compared to those in ALFs (RR 1.21, 95% CI: 0.97–1.51, I²=100%, p=0.10). Subgroup analysis showed that individuals receiving HC and NH had lower hospitalization rates than those in community settings, while RC residents had a higher risk. Additionally, ALF residents experienced longer hospital stays compared to the control group [MD: -1.21 (95% CI: -3.06 to 0.65, I²=99%, p=0.20)]. Mortality rates were 2.83 times higher among community dwellers than ALF residents (RR 2.83, 95% CI: 1.43–5.61, I²=100%, p=0.003). Subgroup analysis also indicated lower mortality risks among individuals receiving RC, NH, and HC compared to those in community settings. CONCLUSION: ALFs are associated with an increased risk of hospitalization and mortality, as well as a shorter length of hospital stay.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".