Full length articleTreatment outcome disparities for opioid users: Are there racial and ethnic differences in treatment completion across large US metropolitan areas?
Introduction
Illicit opioid use represents one of the most harmful drug problems globally, responsible for an estimated 70% of the world’s burden of disease attributable to drug use disorders as well as 66% of the 63,632 US drug overdose deaths in 2016 (Seth et al., 2018; United Nations Office on Drugs and Crime (UNODC, 2017). Although the US is the global leader in both absolute numbers (one quarter of the world’s total) and rates of overdose deaths, other nations such as Canada, Australia, Ireland, Turkey, England, Wales, and Scotland have all seen recent substantial increases in overdose mortality, primarily due to opioids (European Monitoring Centre for Drugs and Drug Addiction (EMCDDA, 2017a; United Nations Office on Drugs and Crime (UNODC, 2017). An important pillar for policy strategies to address the current opioid overdose crisis has been to provide increased access and capacity for treatment for those with opioid use disorders (OUDs) (Evans and Farrelly, 2017; Franklin et al., 2015; Murphy et al., 2016). One of the most widely used proximal measures of treatment effectiveness for substance use disorders (SUDs) is treatment completion (Brorson et al., 2013), generally defined as successfully completing treatment goals (Greenfield et al., 2007). Despite evidence showing sustained recovery may involve multiple episodes over time (Guerrero, 2013; McKay and Weiss, 2001), individual treatment completion episodes can serve as an important indicator associated with longer term abstinence, fewer relapses, higher levels of employment, higher wages, fewer readmissions, less future criminal involvement, and better health (Brorson et al., 2013).
Black and Hispanic people in the US tend to have lower treatment utilization rates, greater barriers to receiving treatment, and poorer outcomes, including treatment completion, compared to white clients (Alegría et al., 2006, 2011; Arndt et al., 2013; Guerrero et al., 2013a; National Research Council, 2003; Saloner and Le Cook, 2013). Similarly, in Europe, there has been a recent recognition that ethnic minorities, migrants, refugees, and asylum seekers who have substance use problems may be particularly vulnerable to barriers in accessing needed treatment services (European Monitoring Centre for Drugs and Drug Addiction (EMCDDA, 2017b). Evidence suggests that race/ethnicity also interacts with other factors, such as drug of choice and treatment modality, to produce disparities in treatment completion (Mennis and Stahler, 2016; Stahler et al., 2016), and these differences in outcomes vary considerably across different areas of the country (Arndt et al., 2013; Cummings et al., 2011; Guerrero et al., 2013a). The use of aggregate national level data may therefore mask geographic variations that may have important implications for SUD treatment policies that are generally implemented at the regional or municipal level in the US. This is particularly important when examining treatment outcomes for OUDs because of geographic variations in demographic characteristics, prevalence, overdose incidence, treatment systems, illicit opioid availability, and insurance coverage that exist in the US (Cummings et al., 2014; Hand et al., 2017; Martins et al., 2017; Rossen et al., 2014; Substance Abuse and Mental Health Services Administration (SAMHSA, 2017). Even in nations with more centralized national treatment systems, geographic variations in prevalence patterns and treatment access may be substantial (Morley et al., 2017; United Nations Office on Drugs and Crime (UNODC, 2017). While epidemiologic research has found important geographic and demographic differences in patterns of opioid use cross nationally (United Nations Office on Drugs and Crime (UNODC, 2017), among rural and urban users (Rigg and Monnat, 2015), across regions (Hand et al., 2017; Martins et al., 2017), and across different metropolitan areas (Roberts et al., 2010), few studies have focused on the geographic variation in racial/ethnic treatment outcome disparities (Arndt et al., 2013).
The present study examined racial and ethnic disparities in successfully completed treatment episodes for first time adult clients reporting opioids as a primary substance of use across the largest US metropolitan areas (populations greater than one million) using data extracted from the 2013 Treatment Episode Dataset-Discharge (TEDS-D) (Substance Abuse and Mental Health Services Administration (SAMHSA, 2015). We limited our sample to large metropolitan areas because there are considerable differences between urban and rural drug use patterns, treatment systems, and demographic characteristics, especially regarding opioid use (Wang et al., 2013). In addition, there are particularly large concentrations of minority groups in the largest metropolitan areas, and many smaller metropolitan areas have few OUD treatment discharges for certain racial/ethnic groups in the TEDS-D dataset, limiting statistical analyses for these areas.
Our major research questions were: (1) Are there racial/ethnic disparities in first time treatment episode completion for clients reporting opioid use as their primary substance of use in large metropolitan areas in the US? Based on prior research demonstrating racial/ethnic disparities (e.g., Guerrero et al., 2013a, b; Mennis and Stahler, 2016), we hypothesize that black and Hispanic opioid users will have a lower likelihood of treatment completion compared to whites. (2) If there are racial/ethnic disparities in first time treatment episode completion for clients reporting opioid use as their primary substance of use, do these disparities vary across large metropolitan areas in the US? Based on prior research showing geographic variation in disparities in treatment completion (Arndt et al., 2013; Saloner et al., 2014), we hypothesize that metropolitan areas will vary significantly in their level of racial/ethnic disparity.
Section snippets
Data source and sample
The data used for this study were extracted from the 2013 TEDS-D dataset (Substance Abuse and Mental Health Services Administration (SAMHSA, 2015). This dataset is compiled from an annual survey conducted by the federal government of state agencies concerning SUD treatment programs. It includes most publicly funded treatment admissions in the US during a given year and is considered generally representative of a national sample (Substance Abuse and Mental Health Services Administration (SAMHSA,
Sample characteristics
Table 1 displays the descriptive statistics for the outcome and explanatory variables. For the total sample of 34,380 discharges, only 27.8% completed treatment. The majority of the sample (70%) was white, with the remainder almost exclusively comprised of black (14%) and Hispanic (12%) people. About one quarter of the sample had less than a high school education (28%), most were not employed (79%), two thirds (69%) lived in independent housing, and the majority received treatment in outpatient
Discussion
To the best of our knowledge, this is the first analysis to examine racial/ethnic disparities in treatment episode completion for opioid using clients across and within the largest US metropolitan areas. Our results concerning racial/ethnic disparities in OUD treatment completion is consistent with a large body of research that has examined national level SUD treatment completion data (Arndt et al., 2013; Mennis and Stahler, 2016; Saloner et al., 2014; Stahler et al., 2016) as well as regional
Conclusion
As the opioid overdose crisis continues to impact people across the ethnic and racial spectrum in the US, it is important to better understand disparities in treatment outcomes. While large scale national data sets serve an important function in providing public health policymakers with information about national trends, our results suggest that there are important geographic variations in patterns of SUD treatment outcomes. In treatment systems with high levels of ethnic/racial outcome
Role of the funding source
Nothing declared.
Contributors
Both authors contributed to the conception and design of the study. Stahler drafted the Introduction and Discussion sections, and parts of the Methods and Results sections. Mennis developed the analytic plan, was responsible for analyzing the data, and drafted parts of the Methods and Results sections. Both authors read and approved the final manuscript.
Conflict of interest
No conflict declared.
Acknowledgements
The authors wish to thank Andrew Rosenblum, Noa Krawczyk, and Stephen Magura for sharing pre-publication drafts of their own papers and helpful discussion related to the findings of this study.
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