Sociocultural and Demographic Factors Predict Readmissions for General Surgery Patients.
Journal
World journal of surgery
ISSN: 1432-2323
Titre abrégé: World J Surg
Pays: United States
ID NLM: 7704052
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
accepted:
18
08
2023
medline:
5
12
2023
pubmed:
30
9
2023
entrez:
29
9
2023
Statut:
ppublish
Résumé
Readmission is a poor outcome for both patients and healthcare systems. The association of certain sociocultural and demographic characteristics with likelihood of readmission is uncertain in general surgical patients. A multi-centre retrospective cohort study of consecutive unique individuals who survived to discharge during general surgical admissions was conducted. Sociocultural and demographic variables were evaluated alongside clinical parameters (considered both as raw values and their proportion of change in the 1-2 days prior to admission) for their association with 7 and 30 days readmission using logistic regression. There were 12,701 individuals included, with 304 (2.4%) individuals readmitted within 7 days, and 921 (7.3%) readmitted within 30 days. When incorporating absolute values of clinical parameters in the model, age was the only variable significantly associated with 7-day readmission, and primary language and presence of religion were the only variables significantly associated with 30-day readmission. When incorporating change in clinical parameters between the 1-2 days prior to discharge, primary language and religion were predictive of 30-day readmission. When controlling for changes in clinical parameters, only higher comorbidity burden (represented by higher Charlson comorbidity index score) was associated with increased likelihood of 30-day readmission. Sociocultural and demographic patient factors such as primary language, presence of religion, age, and comorbidity burden predict the likelihood of 7 and 30-day hospital readmission after general surgery. These findings support early implementation a postoperative care model that integrates all biopsychosocial domains across multiple disciplines of healthcare.
Identifiants
pubmed: 37775572
doi: 10.1007/s00268-023-07177-0
pii: 10.1007/s00268-023-07177-0
pmc: PMC10694098
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3124-3130Informations de copyright
© 2023. Crown.
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