Multilevel Risk Factors for Hospital Readmission Among Patients With Opioid Use Disorder in Selected US States: Role of Socioeconomic Characteristics of Patients and Their Community

• Main Author: Jayasree Basu (Author)
• Format: Book
• Published: SAGE Publishing, 2020-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2333-3928; 10.1177/2333392820904240
• Keywords: Medicine (General); R5-920; Public aspects of medicine; RA1-1270
• Source: Health Services Research & Managerial Epidemiology, Vol 7 (2020)

Research Objective: Using a multilevel framework, the study examines the association of socioeconomic characteristics of the individual and the community with all-cause 30-day readmission risks for patients hospitalized with a principal diagnosis of opioid use disorder (OUD). Study Design: The study uses hospital discharge data of adult (18+) patients in 5 US states for 2014 from the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality, linked to community and hospital characteristics using data from Health Resources and Services Administration and American Hospital Association, respectively. A multilevel logistic regression model is applied on data pooled over 5 states adjusting for patient, hospital, and community characteristics. Principal Findings: Higher primary care access, as measured by density of primary care providers, is associated with reduced readmission risks among patients with OUD. Medicare is associated with the highest readmission risk (odds ratio [OR] = 2.0, P < .01) compared to private coverage, while Medicaid coverage is also associated with elevated risk (OR = 1.71, P < .01). Being self-pay or covered by other payers carried a similar risk to private coverage. Urban patients had higher readmission rates than rural patients. Conclusions: Patients' risk of readmission following hospitalization for OUD varies according to availability of primary care providers, expected payer, and geographic location. Understanding which patients are most at risk may allow policy makers to design interventions to prevent readmissions and improve patient outcomes. Future studies may wish to focus on understanding when a decreased readmission rate represents better patient outcomes and when it represents difficulty accessing health care.