Association Between In-Hospital Applications for Long-Term Care Services and Hospital Length of Stay Among Older Adults: Ecological Cross-Sectional Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».