Psychiatric Comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) Measures of Alcohol and other Drug Treatment Initiation and Engagement across 7 Health Care Systems

Abstract

Background: Psychiatric comorbidity is common among patients with alcohol and other drug (AOD) use disorders. To better understand how psychiatric comorbidity influences AOD treatment access in health care systems, the present study examined treatment initiation and engagement among a large, diverse sample of patients with comorbid psychiatric and AOD use disorders. Methods: This study utilized data from a multisite observational study examining Healthcare Effectiveness Data and Information Set (HEDIS) measures of initiation and engagement in treatment (IET) among patients with AOD use disorders from 7 health care systems. Participants were aged 18 or older with at least 1 AOD index diagnosis between October 1, 2014, and August 15, 2015. Data elements extracted from electronic health records and insurance claims data included patient demographic characteristics, ICD-9 (International Classification of Diseases, Ninth Revision) diagnostic codes, and procedure codes. Descriptive analyses and multivariate logistic regression models were used to examine the relationship between patient-level factors and IET measures. Results: Across health care systems, out of a total of 86,565 patients who had at least 1 AOD index diagnosis during the study period, 66.2% (n ¼ 57,335) patients also had a comorbid psychiatric disorder. Among patients with a comorbid psychiatric disorder, 34.9% (n ¼ 19,998) initiated AOD treatment, and of those, 10.3% (n ¼ 2,060) engaged in treatment. After adjusting for age, sex, and race/ethnicity, patients with comorbid psychiatric disorders were more likely to initiate (odds ratio [OR] ¼ 3.20, 95% confidence interval [CI] ¼ 3.08, 3.32) but no more likely to engage (OR ¼ 0.56, 95% CI ¼ 0.51, 0.61) in AOD treatment, compared with those without a comorbid psychiatric disorder. Conclusions: Findings suggest that identification of comorbid psychiatric disorders may increase initiation in AOD treatment. However, innovative efforts are needed to enhance treatment engagement both generally and especially for individuals without diagnosed psychiatric conditions.

Predictors of Substance use Disorder Treatment Initiation and Engagement among Adolescents Enrolled in Medicaid

“…Our findings confirmed the results of some prior studies of HEDIS IET in other samples but also differed in several ways. First, our finding that adolescents with greater mental health comorbidity were more likely to initiate, but less likely to engage in treatment is consistent with prior studies among adults and adolescents 12 , 29 . However, we further extended these findings by also assessing the previous diagnosis for a drug overdose, poisoning, and self-harm.…”

“…First, our finding that adolescents with greater mental health comorbidity were more likely to initiate, but less likely to engage in treatment is consistent with prior studies among adults and adolescents. 12,29 However, we further extended these findings by also assessing the previous diagnosis for a drug overdose, poisoning, and self-harm. Given that adolescent drug-related overdoses and suicides are increasing in the U.S., [30][31][32] and SUDs are risk factors for both, it is critical to improve access to SUD treatment for these adolescents.…”

Understanding the role of financial capacity in the delivery of opioid use disorder treatment

“…The second variable was retention, defined as number of days clients spent in treatment (i.e., treatment duration in days). These outcomes are measured at the client level and have been used in several studies across datasets, time periods, and treatment systems [ 8 , 19 , 36 , 38 ].…”

Impact of Cluster B Personality Disorders in Drugs Therapeutic Community Treatment Outcomes: A Study Based on Real World Data

“…In the context of addictions, some authors have identified a high use of EHRs [25] in treatment centers (TCs), highlighting the potential utility of these records in research [26]. However, published studies with EHRs are relatively scarce [27][28][29][30] and even less frequent in TCs [31]. In this group of patients with high dropout rates, RWD analysis could help understand the variables associated with patient therapeutic dropout in general and the impact of comorbid mental disorders in particular.…”