Data from a large sample of publicly-funded substance abuse treatment centers indicated that the percentage of minority clients in the center’s caseload was negatively associated with the odds of medication adoption, even after controlling for a number of other organizational characteristics. The significance of this association, after controlling for patient diagnostic characteristics indicative of clinical need, is suggestive of a disparity rather than a mere difference in service delivery (Rathore and Krumholz 2004
These findings suggest that there may be merit in conceptualizing disparities as an organizational phenomenon, rather than only as inequalities that occur at the individual-level. This line of reasoning has been supported by recent research about the quality of services delivered by other types of healthcare organizations, such as hospitals (Barnato et al. 2005
; Groeneveld et al. 2005
; Hasnain-Wynia et al. 2007
; Skinner et al. 2005
), dental practices (Gilbert, Litaker, and Makhija 2007
), and nursing homes (Miller, Papandonatos, Fennell, and Mor 2006
; Smith et al. 2007
). An organizational approach is also supported in recent work on racial and ethnic differences in access to residential drug treatment programs (Blumenthal, Jacobson, and Robinson 2007). Much more research is needed on whether the racial and ethnic composition of patients is associated with other services within addiction treatment and in other types of healthcare organizations.
It is important to note that associations between minority composition and service quality have implications for all individuals served by those organizations. Work by Groeneveld et al. (2005)
documented how both black and white patients were disadvantaged by the lower quality of care delivered by hospitals with higher concentrations of minority patients. Clarke et al. (2007)
reported that such differences increased risks of mortality for all patients. In the context of substance abuse treatment, the failure to adopt medications has implications for the quality of treatment received by all patients in those facilities.
Identifying disparities is only the first step in understanding inequalities within the U.S. healthcare system. In addition to research that describes the different organizational contexts in which disparities exist, there is a need for research that untangles the complicated question about why organizations differ in their ability to deliver high-quality, evidence-based healthcare. The persistence of residential segregation and neighborhood disadvantage in the US may represent one important explanation for disparities (Kirby and Kaneda 2005
; Williams and Collins 2001
). Patterns of residential segregation are linked to disadvantages in terms of education, employment, income, and health insurance coverage as well as the quality of services within these communities (Williams and Jackson 2005
). Residential segregation in communities may also translate into segregation within healthcare organizations such as hospitals (Clarke et al. 2007
) and nursing homes (Smith et al. 2007
). Recent research indicates that the degree of disadvantage in the neighborhood surrounding alcohol treatment facilities explains a substantial amount of variance in differential treatment completion between African Americans and whites (Jacobson, Robinson, and Blumenthal 2007b
). However, there is an ongoing need for more research on the linkages between residential segregation, segregation in healthcare organizations, and neighborhood disadvantage in modeling the quality of available services.
These data indicated that the association between minority composition and medication adoption remained significant after controlling for a variety of other organizational and patient diagnostic characteristics. Future research might consider how program administrators construct meanings about the race and ethnicity of their clients, and how those perceptions influence organizational decision-making. The current study cannot address whether organizational decisions to not adopt medications reflect biases about minority clients (Burgess et al. 2006
; van Ryn and Burke 2000
; van Ryn and Fu 2003
), such as stereotypes that minorities would be less compliant with medication regimens (Bogart et al. 2001
). Alternatively, decisions to not adopt may be reflective of the structural conditions that Jacobson (2004)
calls the “treatment ecology,” meaning the local area surrounding treatment centers. Some research suggests that pharmacies in minority neighborhoods may differentially stock prescription medications (Morrison et al. 2000
); in this type of treatment ecology, treatment organizations may be unwilling to adopt medications if they perceive that their clients will be unable to actually access these medications via local pharmacies. These are research questions that cannot be addressed in the current study, but represent important directions for future research.
From the perspective of public policy, addressing racial and ethnic inequalities in healthcare will clearly require a complex array of approaches. Some have argued that broader movements towards quality improvement in healthcare organizations have the potential to decrease the magnitude of disparities at the individual-level (Trivedi et al. 2005
), particularly if those efforts are targeted at the types of organizations that treat predominantly minority populations. This line of argumentation suggests the need for studies of organization-level quality improvement interventions that also measure whether such efforts reduce disparities. The current study focused on publicly-financed treatment programs, this research minimized the impact of differences in insurance coverage which accounts for some disparities in other healthcare contexts (Schmidt et al. 2006
). The reliance on governmental funding among these programs suggests that public policymakers may be uniquely positioned to address the lack of medication adoption by programs that are funded through governmental resources.
Several limitations are implicit within these data. First, these analyses draw on cross-sectional data, so causality cannot be established. Second, all data were self-reported by program administrators so there are potential limitations due to errors in recall and desirability bias. The measures of racial and ethnic composition were framed as what was typical for the center’s caseload; no verification of patient records was required. Comparing these self-reported data on racial and ethnic composition to other sources of information about addiction treatment in the US is difficult. For example, the large-scale federal data collection system on program-level characteristics, called the National Survey of Substance Abuse Treatment Services (N-SSATS), uses administrator self-reports but does not measure racial and ethnic composition (SAMHSA 2007
). The racial and ethnic composition in our data was similar to a survey of over 300 drug treatment units participating in a large research network known as the National Drug Abuse Treatment Clinical Trials Network (CTN); the average CTN-affiliated program reported that 29% of their clients were African American, 17% were Latino, and 2.5% were American Indians (McCarty et al. 2008
Other limitations are related to the measures and sample. This analysis only considered one category of evidence-based addiction treatment, so it is not known whether similar findings would result from studying the adoption of other categories of evidence-based practices, such as behavioral interventions or wraparound services. An additional limitation is that these analyses cannot speak to the likelihood that a given individual will receive pharmacotherapies within these centers. Further research is needed on the rates of implementation, or the extent to which these medications are routinely used, within these programs. Additionally, these data are only representative of the publicly-funded addiction treatment system. It is not known if these findings would generalize to treatment programs that are privately funded or located within specific systems such as the Veterans’ Health Administration or correctional-based treatment programs. It would also be inappropriate to generalize these findings to other types of healthcare organizations. Continued research on organizational- and system-level disparities in the availability of quality care for the treatment of addiction is warranted.
Although we attempted to control for a variety of factors that have been previously associated with medication adoption, there are likely additional variables that we were unable to consider in our analysis. Perhaps the largest factor that we could not address is the issue of consumer demand and patient preferences. Other studies of individuals have found racial and ethnic differences in perceptions of mental health needs (Kimerling and Baumrind 2005), beliefs about alcoholism as a moral weakness (Caetano 1989
; Caetano 2003
), and in rates of filling prescriptions for antidepressant medications (Harman, Edlund, and Fortney 2004
). The inclusion of measures of the patients’ substance abuse and co-occurring mental health diagnoses were intended as proxies for consumer demand, but these are imperfect measures at best. Future research should explore the role of consumer demand in organizational decision-making about adopting medications, perhaps by including data from program managers as well as clients.
It has been argued that the availability of evidence-based treatment practices is a sound indicator of an organizational orientation toward treatment quality, even if a particular intervention is not medically appropriate for all or even a majority of patients. These data suggest that the odds of organizational adoption of medications as a treatment technology are negatively associated with the racial and ethnic composition of its caseload. Rectifying such inequalities may require system-level efforts to enhance program quality and increase rates of adoption. Improvements in the quality of services delivered by these organizations have the potential to benefit all patients served by these organizations.