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Health services research. 2024 Jul 7. doi: 10.1111/1475-6773.14353 Q13.22025

Using social risks to predict unplanned hospital readmission and emergency care among hospitalized Veterans

利用社会风险预测退伍军人的非计划住院和急诊护理 翻译改进

Portia Y Cornell  1  2, Cassandra L Hua  3, Zachary M Buchalksi  1, Gina R Chmelka  4  5, Alicia J Cohen  6  7, Marguerite M Daus  8, Christopher W Halladay  1, Alita Harmon  4  9, Jennifer W Silva  4, James L Rudolph  1  6  7

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作者单位

  • 1 Center of Innovation for Long Term Services and Supports, Providence VA Medical Center, Providence, Rhode Island, USA.
  • 2 Centre for the Digital Transformation of Health, University of Melbourne, Melbourne, Victoria, Australia.
  • 3 Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts, Lowell, Massachusetts, USA.
  • 4 National Social Work Program, Care Management and Social Work, Patient Care Services, Department of Veterans Affairs, Washington, DC, USA.
  • 5 Tomah VA Medical Center, Tomah, Wisconsin, USA.
  • 6 Department of Health Services, Policy and Practice, Brown University, Providence, Rhode Island, USA.
  • 7 Department of Family Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
  • 8 Rocky Mountain Regional Medical Center, Aurora, Colorado, USA.
  • 9 Gulf Coast Veterans Health Care System, Biloxi, Mississippi, USA.
  • DOI: 10.1111/1475-6773.14353 PMID: 38972911

    摘要 Ai翻译

    Objectives: (1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index.

    Data sources and setting: We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program.

    Study design: We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans' health-related social risk.

    Data extraction: We included 156,690 Veterans' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022.

    Principal findings: The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA.

    Conclusions: Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.

    Keywords: Veterans; area deprivation index; emergency department use; food insecurity; hospital readmission; housing insecurity; social determinants of health; social isolation; social risk factors.

    Keywords:social risks; hospital readmission; emergency care; hospitalized veterans

    Copyright © Health services research. 中文内容为AI机器翻译,仅供参考!

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    期刊名:Health services research

    缩写:HEALTH SERV RES

    ISSN:0017-9124

    e-ISSN:1475-6773

    IF/分区:3.2/Q1

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    Using social risks to predict unplanned hospital readmission and emergency care among hospitalized Veterans