Preventing Hospital Readmission for Patients With Comorbid Substance Use Disorder : A Randomized Trial

A 7 Year Summary of Emergency Department Visits by Patients With Mental Health Disorders

OBJECTIVES

Emergency departments (EDs) have been increasingly utilized over time for psychiatric care. While multiple studies have assessed these trends in nationally representative data, few have evaluated these trends in state-level data. This investigation seeks to understand the mental health-related ED burden in North Carolina (NC) by describing trends in ED visits associated with a mental health diagnosis (MHD) over time.

METHODS

Using data from NC DETECT, this investigation describes trends in NC ED visits from January 1, 2008 through December 31, 2014 by presence of a MHD code. A visit was classified by the first listed MHD ICD-9-CM code in the surveillance record and MHD codes were grouped into related categories for analysis. Visits were summarized by MHD status and by MHD category.

RESULTS

Over 32 million ED visits were recorded from 2008 to 2014, of which 3,030,746 (9.4%) were MHD-related visits. The average age at presentation for MHD-related visits was 50 years (SD 23.5) and 63.9% of visits were from female patients. The proportion of ED visits with a MHD increased from 8.3 to 10.2% from 2008 to 2014. Annually and overall, the largest diagnostic category was stress/anxiety/depression. Hospital admissions resulting from MHD-related visits declined from 32.2 to 18.5% from 2008 to 2014 but remained consistently higher than the rate of admissions among non-MHD visits.

CONCLUSION

Similar to national trends, the proportion of ED visits associated with a MHD in NC has increased over time. This indicates a need for continued surveillance, both stateside and nationally, in order to inform future efforts to mitigate the growing ED burden.

Patients with substance use disorders receiving continued care in skilled nursing facilities following hospitalization

Background: As hospitals in the US face pressures to reduce lengths of stay, healthcare systems are increasingly utilizing skilled nursing facilities (SNFs) to continue treating patients stable enough to leave the hospital, but not to return home. Substance use disorder (SUD) can complicate care of patients transferred to SNFs. The objective of this paper is to understand SNF experiences for this population of patients with comorbid SUD transferred to SNFs and examine care experiences in these facilities. Methods: This secondary mixed-methods analysis focuses on SNF experiences from a clinical trial of patient navigation services for medically-hospitalized adults with comorbid opioid, cocaine, and/or alcohol use disorder. This study compared baseline assessments and medical record review for participants (N = 400) with vs. without SNF transfer, and analyzed semi-structured qualitative interviews with a subsample of 15 participants purposively selected based on their transfer to a SNF. Results: Over 1 in 4 participants had a planned discharged to a SNF (26.8% sub-acute, 3.3% acute). Compared to participants with other types of discharge, participants discharged to a SNF had longer initial hospitalizations (4.9 vs. 11.8 days, p < 0.001), and were more likely to be White (38.6 vs. 50.8%; p = 0.02), female (38.9 vs. 52.5%; p = 0.01), have opioid use disorder (75.7 vs. 85.0%, p = 0.03), and be hospitalized for infection (43.6 vs. 58.3%; p = 0.007), and less likely to have worked prior to hospitalization (24.3 vs. 12.5%; p = 0.006). Qualitative narratives identified several themes from the SNF experience, including opioid analgesic dosing issues, challenges to the use of opioid agonist treatment of OUD, illicit opioid dealing/use, and limited access to addiction recovery support services during and following the SNF stay. Conclusions: SNFs are a common disposition for patients in need of subacute services following hospitalization but may be ill-equipped to properly manage patients in need of new or continuing SUD treatment.

Assessing and Augmenting Predictive Models for Hospital Readmissions With Novel Variables in an Urban Safety-net Population

BACKGROUND

The performance of existing predictive models of readmissions, such as the LACE, LACE+, and Epic models, is not established in urban safety-net populations. We assessed previously validated predictive models of readmission performance in a socially complex, urban safety-net population, and if augmentation with additional variables such as the Area Deprivation Index, mental health diagnoses, and housing access improves prediction. Through the addition of new variables, we introduce the LACE-social determinants of health (SDH) model.

METHODS

This retrospective cohort study included adult admissions from July 1, 2016, to June 30, 2018, at a single urban safety-net health system, assessing the performance of the LACE, LACE+, and Epic models in predicting 30-day, unplanned rehospitalization. The LACE-SDH development is presented through logistic regression. Predictive model performance was compared using C-statistics.

RESULTS

A total of 16,540 patients met the inclusion criteria. Within the validation cohort (n=8314), the Epic model performed the best (C-statistic=0.71, P<0.05), compared with LACE-SDH (0.67), LACE (0.65), and LACE+ (0.61). The variables most associated with readmissions were (odds ratio, 95% confidence interval) against medical advice discharge (3.19, 2.28-4.45), mental health diagnosis (2.06, 1.72-2.47), and health care utilization (1.94, 1.47-2.55).

CONCLUSIONS

The Epic model performed the best in our sample but requires the use of the Epic Electronic Health Record. The LACE-SDH performed significantly better than the LACE and LACE+ models when applied to a safety-net population, demonstrating the importance of accounting for socioeconomic stressors, mental health, and health care utilization in assessing readmission risk in urban safety-net patients.

Social Determinants of Health in Neurology

Neurologic health disparities are created and perpetuated by structural and social determinants of health. These factors include, but are not limited to, interpersonal bias, institutional factors that lead to disparate access to care, and neighborhood-level factors, such as socioeconomic status, segregation, and access to healthy food. Effects of these determinants of health can be seen throughout neurology, including in stroke, epilepsy, headache, amyotrophic lateral sclerosis, multiple sclerosis, and dementia. Interventions to improve neurologic health equity require multilayered approaches to address these interdependent factors that create and perpetuate disparate neurologic health access and outcomes.