Transfers to acute care hospitals at the end of life: do rural/remote regions differ from urban regions?
Notice bibliographique
Résumé
INTRODUCTION: In population-based studies, transfers into hospitals and hospital deaths are typically considered to be indicators of potentially inappropriate care settings at the end of life. Despite a plethora of research into where people die, few studies have examined whether hospital transfers at the end of life differ in rural versus urban areas. In the present study hospitalizations in the last month before death in one mid-Western Canadian province were examined. The study had three main objectives, to: (1) compare hospitalizations in rural/remote with urban regions; (2) examine the role of healthcare resources in hospitalizations; and (3) explore more specifically whether day-to-day patterns of hospitalization shortly before death differ between rural/remote and urban areas. METHODS: The source of data was administrative healthcare records, with the study including all adults (aged over 19 years; excluding nursing home residents) who died in the province of Manitoba in 2003-2004 (n = 6523). Whether the decedents were hospitalized in the 30 days before death was determined from hospital files. The number of hospital days incurred was counted. Region of residence was defined along regional health authority boundaries, with 7 regions identified as rural/remote and 2 as urban. Healthcare resources were measured in terms of the number of: physicians, hospital beds, nursing home beds, and home care services per 1000 population. Age, sex and trajectory groups, which categorized decedents according to their cause of death, were included in all analyses. RESULTS: Residents of 4 of the 7 rural/remote regions had increased odds of being hospitalized relative to the comparison, the larger urban region (adjusted odds ratios [AOR] ranged from 1.25 to 1.70). Hospital days did not differ across regions. Further analyses showed that having more physicians (AOR = .75) and more hospital beds per 1000 population (AOR = .95) both significantly reduced the odds of being hospitalized. Nursing home beds and home care services were not related to hospitalizations. Growth curve models indicated that daily patterns of hospitalizations generally did not differ across rural/remote versus urban regions. CONCLUSION: The findings suggest that residents of some rural/remote regions were at a disadvantage in terms of access to an appropriate care setting at the end of life. The regional variation in hospitalization can, at least in part, be attributed to the availability of healthcare resources, specifically the number of physicians and hospital beds (per 1000 population). However, the variation that emerged across regions also suggests that conclusions should not be over-generalized to all rural/remote regions; rather, local differences in healthcare resources should be considered when examining healthcare usage at the end of life.
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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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».