The impact of COVID-19 hospitalizations on nursing home admissions: a regional insight into long-term care and public health
Bibliographic record
Abstract
Background: To obtain the rate of admission to nursing homes (NHs) and to evaluate clinical characteristics and mortality rates of patients admitted to NHs after hospitalizations for COVID-19, compared to non-COVID-19 acutely hospitalized patients. Methods: We analyzed administrative data from Lombardy, a Northen Italian region, in individuals aged ≥50 years who were hospitalized and discharged alive in 2018 for acute conditions or, between February 2020 and June 2022, for COVID-19. Outcomes included NH institutionalization rates within 180 post-discharge day and mortality following NH admission. Kaplan-Meier curves and Cox proportional hazard models adjusted for age, sex, and comorbidities were used to assess the risks. Results: Among 133,216 COVID-19 hospitalizations in 2020-2022 and 239,099 acute hospitalizations in 2018, institutionalization rates within 180 post-discharge days were similar (3.7% for both cohorts). However, COVID-19 patients had higher adjusted risks of institutionalization (HR 1.70; 95% CI 1.63-1.78) and mortality within 6 months after NH admission (HR 2.08; 95% CI 1.90-2.27). Differences were more pronounced when considering patients hospitalized during the first COVID-19 pandemic wave. Conclusion: COVID-19 hospitalization significantly increases the risks of admission to NHs and early mortality after institutionalization in older individuals compared to hospitalizations due to other acute conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".