Association Between In-Hospital Applications for Long-Term Care Services and Hospital Length of Stay Among Older Adults: Ecological Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Delayed discharge among older patients presents a major challenge for the efficiency of health service delivery. Prolonged hospitalizations limit bed turnover, increase costs, and reduce the availability of hospital resources. In Japan, older adults must undergo a formal care needs certification process to access public long-term care (LTC) services. Initiating this process during hospitalization is considered ideal for ensuring continuity of care. However, the relationship between the timing of LTC certification applications and hospital length of stay (LOS) remains unclear. OBJECTIVE: This study examined the association between the timing of LTC certification applications-specifically those submitted during hospitalization-and average LOS among older inpatients across Japanese prefectures. METHODS: We conducted an ecological cross-sectional analysis using data from all 47 prefectures in Japan for fiscal year 2020. The exposure variable was the proportion of LTC certification applications submitted during hospitalization among all new LTC applications in each prefecture. Exposure data were sourced from the Long-Term Care Database Open Data (Kaigo DB Open Data). The outcome was average LOS among individuals aged ≥65 years at the prefectural level from the 2020 Patient Survey. Linear regression models were used to evaluate the association between the exposure and outcome variables adjusting for relevant covariates. Prefecture-level covariates included proportion of residents living alone, with cognitive decline, or with higher dependency; the proportion requiring dialysis or a respirator before application; the number of health care providers per 100 beds; and the number of nursing and care home beds per 1000 LTC recipients. Sensitivity analyses were conducted using alternative LOS data sources (eg, 2018 and 2020 Hospital Report and 2017 Patient Survey). RESULTS: The median proportion of in-hospital LTC certification applications was 30.5% (IQR 24.5%-36.1%). The median LOS for older adults was 40 (IQR 37-45.5; range 30-82) days. Prefectures with a higher proportion of in-hospital applications had substantially longer average LOSs. In univariate analysis, the association was statistically significant (β=0.04; P=.003), indicating that a 1% increase in in-hospital applications was associated with an approximately 2-day increase in average LOS. This association remained statistically significant after adjustment for all covariates in multivariate models (β=0.06; P=.04). Findings were consistent across sensitivity analyses. CONCLUSIONS: Although initiating LTC certification during hospitalization is essential for supporting timely discharge, our findings indicate a positive association with extended hospital stays. This may reflect systemic delays in the certification process. Even with ideal discharge planning, such delays could extend hospitalization and lead to suboptimal allocation of health care resources. As this study was ecological in design, the findings should be interpreted cautiously. Further individual-level data research is warranted to clarify the mechanisms and inform strategies for improving transitional care efficiency in aging populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".