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

Hospital admissions among patients with Comorbid Substance Use disorders: a secondary analysis of predictors from the NavSTAR Trial

BACKGROUND

Individuals with substance use disorders (SUDs) frequently use acute hospital services. The Navigation Services to Avoid Rehospitalization (NavSTAR) trial found that a patient navigation intervention for hospitalized patients with comorbid SUDs reduced subsequent inpatient admissions compared to treatment-as-usual (TAU).

METHODS

This secondary analysis extends previous findings from the NavSTAR trial by examining whether selected patient characteristics independently predicted hospital service utilization and moderated the effect of the NavSTAR intervention. Participants were 400 medical/surgical hospital patients with comorbid SUDs. We analyzed 30- and 90-day inpatient readmissions (one or more readmissions) and cumulative incidence of inpatient admissions through 12 months using multivariable logistic and negative binomial regression, respectively.

RESULTS

Consistent with primary findings and controlling for patient factors, NavSTAR participants were less likely than TAU participants to be readmitted within 30 (P = 0.001) and 90 (P = 0.03) days and had fewer total readmissions over 12 months (P = 0.008). Hospitalization in the previous year (P < 0.001) was associated with cumulative readmissions over 12 months, whereas Medicaid insurance (P = 0.03) and index diagnoses of infection (P = 0.001) and injuries, poisonings, or procedural complications (P = 0.004) were associated with fewer readmissions. None of the selected covariates moderated the effect of the NavSTAR intervention.

CONCLUSIONS

Previous findings showed that patient navigation could reduce repeat hospital admissions among patients with comorbid SUDs. Several patient factors were independently associated with readmission. Future research should investigate risk factors for hospital readmission among patients with comorbid SUDs to optimize interventions.

TRIAL REGISTRATION

NIH ClinicalTrials.gov NCT02599818, Registered November 9, 2015 https://classic.

CLINICALTRIALS

gov/ct2/show/NCT02599818 .

A state-level analysis of macro-level factors associated with hospital readmissions

Investigation of the factors that contribute to hospital readmissions has focused largely on individual level factors. We extend the knowledge base by exploring macrolevel factors that may contribute to readmissions. We point to environmental, behavioral, and socioeconomic factors that are emerging as correlates to readmissions. Data were taken from publicly available reports provided by multiple agencies. Partial Least Squares-Structural Equation Modeling was used to test the association between economic stability and environmental factors on opioid use which was in turn tested for a direct association with hospital readmissions. We also tested whether hospital access as measured by the proportion of people per hospital moderates the relationship between opioid use and hospital readmissions. We found significant associations between Negative Economic Factors and Opioid Use, between Environmental Factors and Opioid Use, and between Opioid Use and Hospital Readmissions. We found that Hospital Access positively moderates the relationship between Opioid Use and Readmissions. A priori assumptions about factors that influence hospital readmissions must extend beyond just individualistic factors and must incorporate a holistic approach that also considers the impact of macrolevel environmental factors.

Social Vulnerability and the Prevalence of Opioid Use Disorder Among Older Medicare Beneficiaries in U.S. Counties

OBJECTIVES

Recent research has investigated the factors associated with the prevalence of opioid use disorder (OUD) among older adults (65+), which has rapidly increased in the past decade. However, little is known about the relationship between social vulnerability and the prevalence of OUD, and even less is about whether the correlates of the prevalence of OUD vary across the social vulnerability spectrum. This study aims to fill these gaps.

METHODS

We assemble a county-level data set in the contiguous United States (U.S.) by merging 2021 Medicare claims with the CDC's social vulnerability index and other covariates. Using the total number of older beneficiaries with OUD as the dependent variable and the total number of older beneficiaries as the offset, we implement a series of nested negative binomial regression models and then analyze by social vulnerability quartiles.

RESULTS

Higher social vulnerability is associated with higher prevalence of OUD in U.S. counties. This association cannot be fully explained by the differences in the characteristics of older Medicare beneficiaries (e.g., average age) and/or other social conditions (e.g., social capital) across counties. Moreover, the group comparison tests indicate correlates of the prevalence of OUD vary across social vulnerability quartiles in that the average number of mental disorders is positively related to OUD prevalence in the least and the most vulnerable counties and social capital benefits the less vulnerable counties.

DISCUSSION

A perspective drawing upon contextual factors, especially social vulnerability, may be more effective in reducing OUD among older adults in U.S. counties than a one-size-fits-all approach.

Predictors of Five-year Readmission to an Inpatient Service among Patients with Opioid Use Disorders

Background Opioid use disorder (OUD), a relapsing-remitting chronic medical disease, accounts for a sizable proportion of all-cause adult inpatient stays. We evaluated the incidence and predictors of any and multiple readmissions to inpatient care for OUD. Methods This retrospective, register-based cohort study assessed consecutive patients with OUD admitted to a federally-funded inpatient service of an addiction treatment center in North India between January 2007 and December 2014. Binary logistic regression was used to determine independent readmission predictors based on demographic, clinical, and treatment variables that significantly differed in bivariate analysis. Results Among 908 patients, 306 (33.7%) and 106 (11.7%) had any and multiple readmissions, respectively. Injection drug use (Odds ratio [OR] 2.92, 95% confidence interval [CI] 1.90-4.49), comorbid severe mental illness (OR 2.80, 95% CI 1.42-5.55) and common mental disorder (OR 3.4 95% CI 1.65-6.95), antagonist treatment (OR 1.6 95% CI 1.14-2.27), and urban residence (OR 1.38 95% CI 1.01-1.90) increased odds of readmission. 'Improved' discharge status (OR 0.48 95% CI 0.34-0.70) in first admissions reduced odds of any readmission. Similar risk factors also influenced multiple readmissions with higher odds ratios. Conclusions Identification and adequate treatment of risk factors may reduce the chances of readmission.

Demystifying hospital charges for hospital readmissions in 2017 in the United States for psychosis (DRG = 885)

Existing research on hospital charges is primarily focused on hospital admissions, but not on hospital readmissions. Our research fills this gap. We utilize the 2017 Hospital Readmissions database from the Agency for Healthcare Research and Quality (AHRQ) to empirically study factors that impact hospital charges for hospital readmissions. We focus on psychosis (DRG = 885) which has 609,360 records in 2017 in the AHRQ database. We employ regression analyses using patient demographics, inpatient care variables, and hospital characteristics to explain variance in hospital charges. Results show that inpatient care (diagnoses, procedures, length of stay), hospital ownership, and younger patients result in higher hospital charges.