Abstract

For some, community inclusion facilitates access to alcohol and drugs and, therefore, the potential for developing substance abuse disorders. However, little is known about substance abuse treatment use among people with intellectual disabilities. Using standardized performance measures, substance abuse treatment utilization was examined for Medicaid-covered people with intellectual disabilies and substance abuse (N  =  9,484) versus people without intellectual disabilies (N  =  915,070). The sociobehavioral model of healthcare use guides multivariate logistic regression analyses of substance abuse treatment utilization patterns, revealing disability-related disparities. Factors associated with utilization included being non-White, living in a nonurban area, having a serious mental illness, and living in a state with a generous Medicaid plan for substance abuse treatment. Implications relate to health policy, service delivery patterns, and the need for cross-system collaboration in the use of integrated treatment approaches.

People with intellectual disabilities have participated increasingly in community life since the deinstitutionalization era (Trent, 1994). This freedom has facilitated the potential for access to alcohol and other drugs and, thus, the potential for developing substance abuse disorders. Such disorders may impede community inclusion, general happiness, and well being in life (Edgerton, 1986). Although substance abuse is a documented concern among a small number of people with intellectual disabilities, little is known about treatment utilization (Barret & Paschos, 2006). This may stem from the common practice of excluding populations with intellectual disabilities from behavioral health studies to reduce sampling bias (Humphries & Weisner, 2000).

Existing research suggests that the prevalence of substance abuse among people with intellectual disabilities is both dated and very limited in scope (Westermeyer, Kemp, & Nugent, 1996; Westermeyer, Phaobtong, & Neider 1988). However, the potential for increased risks associated with the correlates of substance abuse among people with intellectual disabilities is of special concern (Rivinus, 1988; Slayter & Steenrod, 2009). People with intellectual disabilities can experience the standard consequences of substance abuse, including social isolation, the experience of stigma, reduced social functioning, and the development of serious health conditions. In addition to the standard concerns faced by all with substance abuse issues, such as blackouts, chronic illnesses, and family conflict, people with intellectual disabilities, many of whom use prescription psychotropic medications, may also be at increased risk for seizures with alcohol and other drug use, given the larger percentage of this population that uses prescribed psychotropic medications (Borthwick-Duffy & Eyman, 1990). People with intellectual disabilities, may also be at increased risk for victimization (i.e., assault, robbery, economic crimes) or justice system involvement (McGillivray & Moore, 2001; Petersilia, 2000). The U.S. Surgeon General's report (U.S. Surgeon General, 2002) on health disparities suggested that people with intellectual disabilities have poorer health in many areas, including behavioral health, and that difficulties in accessing appropriate and/or quality health care are common.

Despite the potential for additional risk as a result of substance abuse, little is known about substance abuse treatment utilization among people with intellectual disabilities, much less effective treatment approaches (Slayter, 2007, 2008). It has been posited that people with intellectual disabilities are less likely to be able to assess their problems associated with substance abuse and to find information and/or seek treatment compared with people without intellectual disabilities (Horwitz, Kerker, Owens, & Zigler, 2000). Researchers in the area of mental health treatment for people with intellectual disabilities have “emphasized the need for specialist, interdisciplinary community-based and accessible” (Bouras, Cowley, Holt, Newton, & Sturmey, 2003, p. 439) services for this population.

Access to substance abuse treatment is a noted concern for people with intellectual disabilities. In a study of one urban area, survey respondents at substance abuse treatment programs expressed willingness to provide treatment to people with intellectual disabilities but reported little experience in doing so (Lottman, 1993). Another study suggested, “Accessibility in theory does not necessarily equate to service utilization in practice” when referring to people with intellectual disabilities (Prout & Strohmer, 1998, p. 119). Access barriers for people with intellectual disabilities may extend beyond those of the general population, as a result of structural factors (i.e., bifurcated service systems, treatment paradigm clashes, funding issues) or a lack of evidence-based practices (Jerrell, 2000; McAuliffe, Laurie, Woodworth, Zhang, & Dunn, 2003; Menolascino, Gilson, & Levitas 1986; Slayter, 2008). However, it has been postulated that access problems may be compounded for people with intellectual disabilities, who may also experience unique, disability-related stigma (Lottman, 1993; Tyas & Rush, 1993). Combined with noted general health concerns and health disparities among people with and without intellectual disabilities, an examination of substance abuse treatment utilization is warranted. To establish baseline data, this study explored factors associated with substance abuse treatment utilization among people with substance abuse disorders, both with and without intellectual disabilities, in a Medicaid population.

Conceptual Model

Factors related to treatment utilization are explored through the use of Anderson's sociobehavioral model of health care utilization. Developed in the 1960s, this model elucidated the overarching social–structural, institutional, and individual-level factors that can be involved in creating pathways (or impediments) to health care utilization (Anderson 1995; Anderson & Berg, 1997). Three domains are included in the model: predisposing conditions, enabling resources, and need factors. Conceptually, predisposing conditions are those that could indicate an individual propensity toward or likelihood of health care utilization even in the absence of a particular illness (e.g., gender, age; Anderson & Newman, 1973). Enabling resources are those that allow for the “means and knowledge to get into treatment” (Pescosolido & Boyer, 1998, p. 400; e.g., geographic location, insurance status, or the existence of public programs to which a person has access), regardless of other factors. Need factors are related to a person's health status and can include mental illness or severity of a particular health condition and can explain individual variation in utilization. The sociobehavioral model has been widely applied to a spectrum of service utilization studies (Aday, 1984; Gelberg, Anderson, & Leake, 2000). Overall, the model allows for the assessment of factors that may impact different types of service utilization in different populations.

At least three studies that included people with intellectual disabilities have used the Anderson model (Beasley, 2000; Howard, 1990; Pruchno & McMullen, 2004). Although the model has not shown consistent findings related to predictors of utilization across an array of service types for people with intellectual disabilities, it has proven useful in learning about predictors of utilization in specific service domains. The model can be modified to fit issues specific to certain populations and service domains, making it flexible and adaptable to topic and data source while retaining an overall picture of factors that lead to utilization.

Method

Data Source

Using a retrospective, cross-sectional design, research questions related to the treatment utilization patterns of people with intellectual disabilities and substance abuse disorders were examined through the analysis of Medicaid claims and eligibility data for 1999. Data from the Medicaid Statistical Information System (MSIS) were obtained from the Centers for Medicare and Medicaid Services (CMS) through a data reuse agreement with J.E.N. Associates, Inc., and Brandeis University. MSIS claims data consist of bills for health, behavioral health, or allied services (e.g., diagnostic codes, place-of-service codes) paid for by the Medicaid program. MSIS files also include eligibility data (which can be linked to claims data), demographic variables (e.g., age, gender), and information about the type of Medicaid coverage individuals have (e.g., managed care vs. fee for service). MSIS data were drawn from all U.S. states and the District of Columbia, with the exception of Hawaii (as no data were reported for that state in 1999) when the analytic file was created.

Medicaid claims data have been used to study a range of health care utilization and expenditure patterns. With respect to the accuracy and validity of claims data from a clinical perspective, one set of behavioral health services researchers stated, “Medicaid claims offer a potentially rich source of clinical and services data but ready access to the data has to be coupled with a reliable and credible information base” (Walkup & Boyer, 2000, p. 136). However, data from another reliability assessment comparing Medicaid claims on six indicator conditions from over 2,000 people with Medicaid coverage to medical records revealed that outpatient claims data agreed 90% of the time (Steinwachs et al., 1998). However, this study also determined that claims data substantially underreported visits, for those providers that were classified as being low cost or for patients with low use rates. This suggests the potential for “undercounting” in the present study.

Data Assessment

The presence of detailed data in managed care–oriented health insurance plans is often limited due to the use of capitated, or “lump sum,” payments to the entities providing such care to cover all costs, without need for individual claims submissions (Garnick, Hendricks, & Comstock, 1996). Therefore, for people with Medicaid managed care plans, procedurally, information reported to CMS may not always include details about providers who contract with behavioral health care “carve-out” entities under capitated payment agreements (for details, see Garnick, Hodgkin, & Horgan, 2002). To determine the presence of claims data in managed care plans, a two-part data assessment of the MSIS data was conducted while the analytic file was being created.

First, people enrolled in Medicaid managed care plans that reported eligibility data but did not report claims (due to the use of capitated payment approaches) were excluded from the file because diagnostic data were not present. This represents a potential threat to external validity vis-à-vis generalizability to all people with Medicaid coverage. Second, some people with managed care coverage who were represented in the analytic file came from states that did not report data during specific months. These people were excluded from the file on a month-by-month basis. To identify states with missing data of this nature, a manual review of annualized prevalence figures for individuals with substance abuse treatment claims were examined by state and within-state plan types (see Slayter et al., 2006, p. 214, for a detailed description of this review process). This represents a potential limitation with respect to generalizing findings across an entire calendar year, due to potential state-specific seasonal effects. However, given that Medicaid is a primary source of health insurance for people with intellectual disabilities, the potential generalizability of this study's findings is significant (Anderson, Larson, Lakin, & Kwak, 2003).

Sampling Approach

Starting with a data file consisting of Medicaid claims and eligibility files for all people with Medicaid coverage aged 12–99 years old, all who had received at least one substance abuse diagnosis in 1999 (derived from bills submitted by providers in outpatient, inpatient, and long-term care settings) were considered. If a person did not have a claim that included a substance abuse diagnosis, they were not included in the study, suggesting a possible undercount with respect to substance abuse prevalence. It is important to distinguish between the identification of substance abuse through the presence of a diagnostic code on a claim (regardless of whether treatment was received for that claim or whether it was just noted in the record) and the identification of substance abuse treatment receipt through the presence of procedure codes (e.g., assessment, therapy session) or place-of-service codes. The former type of code was used to identify the study populations, and the latter groupings were used to assess substance abuse treatment utilization. Substance abuse was identified through a set of International Classification of Diseases, Ninth Revision Clinical Modification (ICD-9-CM; World Health Organization, n.d.) diagnosis codes used by the organization Washington Circle (a behavioral health services research collaborative that develops and tests performance measures, some of which have been adopted by the National Committee on Quality Assurance) in previous research. These codes included the following: 291, 292, 303, 304, 305, 535.3, 571.1, 648.3, 790.3, 965, 968–970, and 980 (alcohol/illicit drug psychosis, alcohol/drug abuse/dependence excluding remission, opioid dependence excluding remission, alcoholic gastritis, acute alcoholic hepatitis, drug dependency in pregnancy, excessive blood level of alcohol, poisoning by alcohol or drugs with a secondary diagnosis of dependence, and the toxic effects of alcohol; Garnick et al., 2002; McCory, Garnick, Bartlett, Cotter, & Chalk, 2000).

The sample (N  =  9,484) was identified through the use of a set of diagnostic codes for intellectual disabilities diagnoses. Characteristics and treatment utilization patterns were compared with people without intellectual disability diagnoses (N  =  915,070; hereinafter, “the comparison group”). Intellectual disabilities were identified ICD-9-CM Diagnostic Codes 317, 318.0, 318.1, 318.2, and319 (mild, moderate, severe, and profound “mental retardation” and “mental retardation”, unspecified). (For additional discussion of the validity of Medicaid claims for intellectual disability, please see Slayter, in press). In the analytic file-creation process, individual claims files were searched for the first mention of one of the five ICD-9-CM codes for “mental retardation,” looking at both primary and secondary fields, which are the two most commonly used fields out of a potential of five diagnosis code fields depending on setting type (e.g., inpatient, outpatient, long-term care). For example, in outpatient treatment settings, diagnostic codes rarely exceed one, limiting the ability to accurately determine co-occurring disorders within a single claim (Horner, Paris, Purvis, & Lawler, 1991; Lurie, Popkin, Dysken, Moscovice, & Finch, 1992).

Data Analysis

The Anderson model guided bivariate analyses (i.e., rate calculations with odds ratios derived from logistic regression modeling, independent samples t tests) as well as multivariate logistic regression modeling. Multivariate modeling was conducted to examine the effect of an intellectual disability diagnosis on treatment utilization as well as factors associated with utilization among the sample alone. All independent variables chosen are commonly used in research related to substance abuse treatment utilization. Independent variables used in the Predisposing Characteristics domain included gender, age, and race/ethnicity (White and non-White, including Latino ethnicity). Variables included in the Enabling Resources domain included dual eligibility for Medicare, Medicaid plan type (fee for service vs. managed care), and urban/nonurban residence. The latter variable was created by using county-level data from the 1999 Area Resource Files (ARF) to link with individuals as represented in claims data via a zip code algorithm (e.g., zip codes in the claims data were linked to county codes, which were composed of sets of zip codes in the ARF data file). ARF data are “designed to be used by planners, policymakers, researchers, and others interested in the nation's health care delivery system and factors that may impact health status and health care in the U.S.” (Health Resources and Services Administration, 2009, p. 1) Much variation exists in state Medicaid programs regarding which services are covered as well as the scope and duration of those services (CMS 2000). Although each state program is required to provide mandatory services, they also have the choice of covering additional services. States fell into three categories regarding substance abuse treatment coverage in 1999. A control variable was added to this domain to assess for a low level of substance abuse treatment coverage by Medicaid, operationalized as coverage that does not include substance abuse treatment benefits or solely detoxification services (CMS, 2000).

Two variables were included in the Need Factors domain: serious mental illness and substance abuse-related disorders. First, an aggregate serious mental illness variable was based on existing studies that used ICD-9-CM codes, resulting in a combined list of Codes 295–299 (schizophrenia, affective psychoses, paranoid states, nonorganic psychoses, and childhood-onset psychoses; Daumit, Pratt, Crum, Powe, & Ford, 2002; Fujii, Wylieb, & Nathan, 2004; Gianfrancesco, Wang, & Yu, 2005). Second, a variable for substance abuse–related disorders was created. These disorders, including, for example, alcoholic liver damage, were considered as they may be a proxy indicator of longer term use or abuse of alcohol and/or other drugs. A grouping of substance abuse–related diagnostic codes was created, based on a list of codes previously compiled by the Washington Circle: 265.2, 357.5, 357.6, 425.5, 571.0 (alcoholic pellagra, alcohol polyneuropathy, polyneuropathy due to illicit drugs, alcoholic cardiomyopathy, alcoholic fatty liver/cirrhosis of the liver or alcoholic liver damage).

Creation of Dependent Variables

Two nominal dependent variables, substance abuse treatment initiation (yes/no) and engagement (yes/no), were defined via the use of three substance abuse treatment performance measures created by Washington Circle (Garnick et al., 2002; McCory, Garnick, Bartlett, Cotter, & Chalk, 2000). These treatment-episode–based performance measures focus on the early stages of substance abuse treatment (i.e., identification, initiation, and engagement) and allow for the calculations of rates and, of particular importance, for a study focused on treatment utilization, namely initiation and engagement (see Table 1 for measure specifications).

Table 1

Substance Abuse Treatment Performance Measures

Substance Abuse Treatment Performance Measures
Substance Abuse Treatment Performance Measures

The first performance measure, substance abuse identification, was used to determine whether a given person could be counted in other performance-measure rate calculations. Specifically, this measure required that each person have at least 4 consistent months of eligibility for Medicaid (to ensure rate calculability via the presence of data). After the population of people who were identified were selected, through the use of dates, service setting codes, and diagnostic codes, a determination of whether a given individual had initiated substance abuse treatment could be made. Then, using the same approach, looking only at people who initiated treatment, rates could be calculated for how many people went on to engage in treatment. This rate reporting approach differs from the original Washington Circle measure, which calculates both initiation and engagement through the use of the identified denominator. The present approach to rate reporting allows for an easy interpretation of how many people engaged in treatment after initiating treatment through the use of the number of people initiating as the denominator for the engagement rate calculation.

To a large extent, the analyses used the specifications set out by Washington Circle, with two slight modifications due to data limitations. First, for rate calculations only, in the original measures, “person years” from all people in the file were used for a certain time period to derive a weighted denominator. A person year refers to the total number of months that a person was eligible for Medicaid during the time period studied. If a person was eligible for 12 months of this study, that would count as 1 person year, but if a person was only eligible for Medicaid for 3 months out of the year, that would count as one fourth of a person year. In an equivalent approach, the actual number of persons represented in the analytical file were used, weighted by the amounts of missing data in the denominator, as discussed above in the Data Assessment section. Second, the types of codes used to identify a substance abuse treatment service were limited to the use of ICD-9-CM diagnostic codes. Significant state-by-state variation existed with respect to the use and/or reporting of current procedural terminology (CPT) codes, diagnosis-related groups (commonly referred to as DRGs) codes, and local codes derived by individual plans and/or state Medicaid programs. Because only diagnostic codes were used consistently across states and substate entities, the choice to use these codes from the primary and secondary positions in claims files was made, thus allowing for increased consistency.

Results

Of all people with Medicaid coverage aged 12–99 years, 3.3% (n  =  924,554) had substance abuse claims in 1999, with the sample constituting 1.0% of this group. People with intellectual disabilities and substance abuse claims constituted 2.6% of all people with intellectual disability claims (who did not have substance abuse claims). On average, the sample was 2 years younger than the comparison group, with no difference in reported rates of race/ethnicity. Males with intellectual disabilities and substance use disorders who were categorized as non-White constituted a significantly larger proportion than did their counterparts (60.7% vs. 47.6%, odds ratio [OR]  =  1.7, p < .001). Looking at each non-White racial and ethnic category, the comparison group was less likely to be Asian (0.4% vs. 0.5%, OR  =  0.5, p < .001), Latino/a (3.4% vs. 8.0%, OR  =  0.4, p < .001), or Native American (1.6% vs. 2.1%, OR  =  0.7, p < .01) but was slightly more likely to be Black (28.5% vs. 25.4%, OR  =  1.1, p < .001).

Clinical Diagnoses

With respect to serious mental illness diagnoses, the comparison group was more likely to have a serious mental illness diagnosis. Differences were also noted with regard to specific diagnoses of schizophrenia (31.8% vs. 8.6%, OR  =  5.1, p < .001), affective psychoses (29.9% vs. 17.5%, OR  =  2.0, p < .001), paranoid states (1.6% vs. 0.4%, OR  =  3.7, p < .01), and nonorganic psychoses (18.3% vs. 4.2%, OR  =  5.1, p < .001). Gender-specific assessments suggested that females with intellectual disabilities and substance abuse were more likely to have affective psychoses than were their male counterparts (35.6% vs. 26.2%, OR  =  1.2, p < .001). With respect to substance abuse–related disorders, the sample was less likely to have any of these conditions. The most prevalent substance abuse–related disorder among the sample was alcoholic fatty liver, cirrhosis, or liver damage, at 3.0%.

About two thirds (67.3%; n  =  6,390) of the sample met criteria for measure calculations. Of this group, 24.6% (n  =  1,575) initiated treatment, just over half of whom (52.6%, n  =  746) went on to engage in treatment. The comparison group was slightly more likely to meet criteria for performance-measure calculations and had higher initiation 29.3% (n  =  169,227) and engagement (55.9% of all who initiated; n  =  74,583) rates. The sample was slightly more likely to drop out of treatment before initiation (71.9%, OR  =  1.3, p < .001) or after initiation but before engagement (47.4%, OR  =  1.1, p < .001). Outpatient settings were the most common initiation and/or engagement sites.

Logistic regression modeling was used to assess for any potential disability-related disparities in access to substance abuse treatment. Members of the sample were 30% less likely to initiate and 32% less likely to engage in substance abuse treatment (see Table 2). Using members of the sample alone, regression modeling was conducted to identify factors predictive of initiation or engagement in substance abuse treatment. Factors associated with initiation among the sample included being male, non-White, not being dually eligible, living in a nonrural area or a state with low Medicaid coverage for substance abuse treatment, or having a serious mental illness. Factors associated with engagement shifted slightly, with increased likelihood of males, non-Whites, and urban dwellers engaging in substance abuse treatment along with the addition of two new factors: having fee-for-service coverage and/or having a substance abuse–related diagnosis (see Table 3).

Table 2

Predictive Modeling of Substance Abuse Treatment Initiation and Engagement Among Medicaid-Covered People (Aged 12–99 Years) With Substance Abuse Claims

Predictive Modeling of Substance Abuse Treatment Initiation and Engagement Among Medicaid-Covered People (Aged 12–99 Years) With Substance Abuse Claims
Predictive Modeling of Substance Abuse Treatment Initiation and Engagement Among Medicaid-Covered People (Aged 12–99 Years) With Substance Abuse Claims
Table 3

Predictive Modeling of Substance Abuse Treatment Initiation and Engagement Among Medicaid-Covered People (Aged 12–99 Years) With Intellectual Disabilities and Substance Abuse Claims

Predictive Modeling of Substance Abuse Treatment Initiation and Engagement Among Medicaid-Covered People (Aged 12–99 Years) With Intellectual Disabilities and Substance Abuse Claims
Predictive Modeling of Substance Abuse Treatment Initiation and Engagement Among Medicaid-Covered People (Aged 12–99 Years) With Intellectual Disabilities and Substance Abuse Claims

Discussion

It may be helpful for practitioners and policymakers in the arenas of substance abuse treatment and disability policy to note that the results of this study suggest the presence of disability-related disparities, point to health policies that may inhibit treatment utilization, and provide possible pathways to treatment via intersystem collaboration. That 2.6% of the population of Medicaid-covered people with intellectual disabilities received some care related to substance abuse during 1999 supports existing estimates of substance abuse treatment involvement. One national study of the noninstitutionalized population with intellectual disabilities and/or developmental disabilities found that 2.2% of the population had received any substance abuse treatment in 1995 (Larson, Lakin, Anderson, & Kwak, 1999). One other study examined the prevalence of developmental disability among people receiving treatment in a range of settings in Pima County, Arizona, also revealing a 2.2% prevalence rate (NAADD, 1999). Comparisons with two other studies based in treatment settings are not feasible due to differences in sampling and single site of care settings (Westermeyer, Kemp, & Nugent, 1996; Westermeyer, Phaobtong, & Neider 1988). Despite this apparent comparability, these rates are likely an undercount of actual prevalence.

Disparities in Treatment Access

Despite what may appear to be a low prevalence of substance abuse treatment utilization, findings suggest the presence of significant disability-related disparities in substance abuse treatment utilization between people with intellectual disabilities and substance abuse and their counterparts without intellectual disabilities. Especially concerning is that fact that a third of people with intellectual disabilities and substance abuse did not have 4 months of consistent Medicaid eligibility in 1999. This finding suggests the potential presence of many more people with intellectual disabilities and substance abuse concerns who may not be accessing or utilizing substance abuse treatment services at all. For those with consistent eligibility, study findings revealed a set of characteristics that may impact utilization.

To a great extent, both populations have parallel experiences, especially when comparing the actual rates of initiation and engagement regardless of the statistical differences noted, which are suggestive of disparities for people with intellectual disabilities. Unfortunately, equality in this regard does not suggest that either population is being well served, as only between a fifth and a quarter of all people with new substance abuse treatment episodes are initiating treatment, for example. In general, these findings suggest that existing systems must improve their services for applicability to populations with intellectual disabilities, as well as the role that various systems or entitlement programs impact access to substance abuse treatment. The major theme derived from analyses of substance abuse treatment initiation and engagement supports existing research, which suggests the presence of barriers to substance abuse treatment utilization for people with intellectual disabilities. Equality of access to treatment is not the only concern, but reduced treatment utilization also has important implications regarding cost savings (both fiscal and social) in a population with high health costs and potential for social isolation that could be exacerbated by substance abuse.

For those who did initiate and/or engage in substance abuse treatment, several findings related to factors associated with both conditions are of note. As with documented utilization trends in the overall Medicaid-covered population, women with intellectual disabilities and substance abuse were less likely to initiate and engage in substance abuse treatment than were their male counterparts (Buck & Miller, 2002). Given what appear to be gender disparities in treatment utilization, advocacy for increased gender-accessible treatment with appropriate ancillary services (e.g., transportation, child care, parenting support) is vital.

It is important to note that the Naegelkerke R2 values were very low for both models, suggesting that the models do not include the most salient factors related to initiation and engagement, perhaps related to lack of evidence-based practices for this population and/or the impact of stigma, variables that were unable to be measured in these models. Anderson (1995) has critiqued this model for evidencing a bias toward individual characteristics versus societal factors. Given this, results should be interpreted with caution.

Possible Racial Disparities in Treatment Access

Also of concern were findings suggestive of increased rates of initiation and engagement among members of the sample who were categorized as non-White. Explanations could relate to a chain of realities already documented in the literature. Research suggests that there is potential for increased (and potentially inappropriate) rates of intellectual disabilities diagnoses among youth of color (Murphy et al., 1998). This, in turn, could relate to disproportionate rates of criminal court involvement among non-White populations, especially males. This argument is supported by estimates that a majority of people with criminal court cases have some alcohol or other drug component and are often treated in diversion programs (Pettit & Western, 2004).

Impact of Dual Eligibility for Medicare and Medicaid

Resource-related findings suggested implications for health policy decision making that impacts people with intellectual disabilities. That sample individuals were more likely to be dually eligible for Medicare mirrored existing knowledge related to this population's categorical eligibility via receipt of supplemental security insurance (SSI) or social security disability insurance (SSDI). Although sample members with dual eligibility were only 6% less likely to initiate or engage in treatment, this population may be especially vulnerable and in need of treatment (Bachman, Drainoni, &Tobias, 2003). This variable may represent a proxy for disabling conditions that are difficult to accommodate in treatment programs. However, the Medicare program itself may also actually function as a barrier to obtaining treatment, given the program's limited coverage for substance use disorders (e.g., outpatient treatment, 50% copayments; CMS, 2007; West, 2007). Provider-level confusion about billing for substance use disorder treatment among people with disabilities who are dually eligible has been documented, warranting reconsideration of benefit structures (Bachman, Drainoni, & Tobias, 2004).

High Risk for Treatment Drop Out

Although all substance abuse treatment clients experience high rates of dropping out, the present study's findings are of special concern, given what may be increased vulnerabilities faced by community-based people with intellectual disabilities and substance abuse. Despite the identification of factors that may be associated with not initiating or engaging in treatment, this study's findings can be further elucidated by other research examining the role of person-level factors that impact utilization. A person-level qualitative examination of the factors that may impact dropout is necessary, modeled, perhaps, on research related to the broader population of people with disabilities by Krahn, Farrell, Gabriel, and Deck (2007). Such a study would ideally assess the role of stigma on the part of both clinicians and people without intellectual disabilities as well as an assessment of personal factors that may have contributed to leaving treatment (e.g., willingness to remain in treatment, relationships with other clients), clinical factors (e.g., treatment matching concerns, engagement with clinicians), and an assessment of organizational and/or insurance-related factors (e.g., transportation, insurance coverage structure).

Factors Associated With Initiation and Engagement

In addition to the identification of factors that may be associated with not initiating or engaging in treatment, these data suggest the presence of three factors that are associated with both conditions. One factor was associated with initiation, with two other factors that were associated with engagement in substance abuse treatment. That people with intellectual disabilities, substance abuse, and co-occurring serious mental illness were more likely to initiate substance abuse treatment suggests a potential pathway to substance abuse treatment access, although this factor was not associated with engagement. Given the higher prevalence of serious mental illnesses among populations with intellectual disabilities and the presence of evidence-based therapeutic approaches for people with intellectual disabilities and mental illness (including but not limited to serious mental illnesses), these may be starting points for developing population-specific substance abuse treatment approaches (Bouras, Cowley, Holt, Newton, & Sturmey, 2003). Given the special needs of people with co-occurring substance use disorders and serious mental illness in the general population, a full assessment of how existing integrated treatment models (considered a best practice) can be adapted is warranted (Jerrell, 2000). This finding suggests that an important line of defense in substance abuse prevention, identification, intervention, screening, and assessment lies with the clinicians who are most commonly involved in the ongoing support of people with intellectual disabilities (e.g., primary care physicians, nurse practitioners, psychologists, psychiatrists, and social workers). These parties could partner with substance abuse treatment agencies to learn more about this condition and screening and management approaches (Slayter & Steenrod, 2009).

With respect to engagement, members of the sample who had fee-for-service coverage were slightly more likely to engage in substance abuse treatment. Although people with disabilities have increasingly enrolled in Medicaid managed care programs, findings related to an increased likelihood of fee-for-service coverage among the sample were not surprising due to the propensity for special health care needs not best supported via managed care approaches (Drainoni, Tobias, & Dreyfus, 1995; Fossett & Thompson, 1999; Kaiser Commission on Medicaid and the Uninsured, 1999). State Medicaid programs have resisted the use of managed care in behavioral health for people with disabilities because not much is known about the benefits of such approaches for this population (Bachman, Drainoni, & Tobias 2004; Hanson & Huskamp, 2001). In fee-for-service plans, gate-keeping mechanisms and level of service limitations may not be as strong a factor in utilization. Behavioral health care under a managed care approach may be especially problematic for people with intellectual disabilities and substance abuse, given the clinical view that this population can benefit from treatment at a rate slower and longer than that of the general population (Selan, 1976). Last, the presence of substance abuse–related disorders also appeared to be associated with engagement. The presence of these chronic disorders may suggest a level of medical need that would create a pathway to receiving–accepting substance abuse treatment, perhaps a “treatable moment.” This study's findings are a bridge from Lottman's (1993) earlier work on barriers to substance abuse treatment for all people with intellectual disabilities from the provider viewpoint, in that these data support provider's views that access barriers are a reality.

Although this study focuses on the early stages of substance abuse treatment, findings pave the way for additional research on the ways in which existing health policies as well as treatment modalities and related wraparound services (e.g., aftercare, recovery homes, relapse prevention planning) can be optimized for people with intellectual disabilities. Clinically, findings related to engagement appear to support existing research, which suggests the presence of logistical, theoretical, and clinical barriers to substance abuse treatment for people with intellectual disabilities. Although the Americans with Disabilities Act of 1990 (1994, 2005) has been tested with regard to physical accessibility of substance abuse treatment programs, it has not, to my knowledge, been applied to the ‘cognitive accessibility’ of substance abuse treatment approaches for people with intellectual disabilities. Existing research and commentary in clinical journals, much of which is dated, regarding this population suggest that substance abuse treatment providers do not know what to do with this population in treatment, given the lack of evidence-based practices, even though existing knowledge about the process of psychotherapy with people with intellectual disabilities suggests that learning takes place at a slower pace than in other populations (McGillicuddy & Blane, 1999; Selan 1976; Sengstock, Vergason, & Sullivan 1975; Wenc, 1980).

Limitations

Medicaid claims are widely used in health services research to examine a range of conditions, including substance abuse (Sambamoorthi, Warner, Crystal, & Walkup, 2000). Claims data are especially helpful in locating hard-to-study populations (Quam et al., 1993). Although useful in examining prevalence and incidence of various conditions as well as utilization and expenditures, codes—the primary source of all clinical insight for a study reliant on claims data—are “not meant to tell stories, (but) rather to generate reimbursement” (Iezzoni, 2002, p. 348; see also Horner, Paris, Purvis, & Lawler, 1991). Undercounts may occur due to stigma-related underreporting, perceived irrelevance of ancillary diagnoses, lack of space on reporting forms, or a focus on another health condition during a visit (Garnick, Hendricks, & Comstock 1996; Iezzoni, 2002).

Summary

Overall, findings support a call for additional clinical-level research on substance abuse treatment utilization, with a particular focus on the individual clinical factors, treatment modalities, and social–structural factors that may impact treatment initiation, engagement, and recovery among people with intellectual disabilities. Last, this study also highlights one way that claims data can be harnessed to obtain information about rare and elusive populations for the development of smaller scale, in-depth, clinical studies.

Acknowledgments

I thank Drs. Marty Wyngaarden Krauss, Constance Horgan and Deborah Garnick, of the Heller School for Social Policy and Management at Brandeis University as well as Richard Saitz, of Boston University and Dan Gilden (Jen Associates, Inc.) for support in designing and implementing this study at the dissertation stage. Additional thanks go to Rosemary Hakim and M. Beth Benedict (Centers for Medicare and Medicaid Services) for access to these data and general guidance on the use of Medicaid claims data. Support for this research included a predoctoral training grant from the National Institute on Alcohol Abuse and Alcoholism as well as an American Dissertation Fellowship award from the American Association of University Women.

References

References
Anderson
,
L.
,
S.
Larson
,
C.
Lakin
, and
N.
Kwak
.
2003
.
Health insurance coverage and health care experiences of persons with disabilities in the NHIS-D.
DD Data Brief
.
5
(
1
):
1
12
.
Minneapolis
University of Minnesota, Institute on Community Inclusion
.
Andersen
,
S.
and
J.
Berg
.
1997
.
Rational drop-out from substance abuse treatment as a means to minimize personally felt risk?
Addiction Research
5
:
507
517
.
Anderson
,
R.
1995
.
Revisiting the behavioral model and access to medical care: Does it matter?
Journal of Health and Social Behavior
36
:
1
10
.
Anderson
,
R.
and
J.
Newman
.
1973
.
Societal and individual determinants of medical care utilization in the United States.
Milbank Memorial Fund Quarterly
51
:
95
124
.
Bachman
,
S.
,
M.
Drainoni
, and
C.
Tobias
.
2003
.
Substance abuse treatment services for people with disabilities.
Journal of Disability Policy Studies
14
:
154
162
.
Bachman
,
S.
,
M.
Drainoni
, and
C.
Tobias
.
2004
.
Medicaid managed care, substance abuse treatment and people with disabilities.
Health and Social Work
29
:
189
196
.
Barrett
,
N.
and
D.
Paschos
.
2006
.
Alcohol-related problems in adolescents and adults with intellectual disabilities.
Current Opinion in Psychiatry
19
:
481
485
.
Beasley
,
J.
1999
.
Coordinated community mental health care for individuals with mental illness and mental retardation: Four years of service outcomes and retrospective family caregiver service experiences
Doctoral dissertation, The Heller School for Advanced Studies in Social Welfare, Brandeis University, Waltham, MA.
Borthwick-Duffy
,
S.
and
R.
Eyman
.
1990
.
Who are the dually diagnosed?
American Journal on Mental Retardation
94
:
586
595
.
Bouras
,
N.
,
A.
Cowley
,
G.
Holt
,
J.
Newton
, and
P.
Sturmey
.
2003
.
Referral trends of people with intellectual disabilities and psychiatric disorders.
Journal of Intellectual Disability Research
47
:
439
446
.
Buck
,
J.
and
K.
Miller
.
2002
.
Mental health and substance abuse services in Medicaid, 1995
.
Rockville, MD
Center for Mental Health Services, Substance Abuse and Mental Health Administration
.
Centers for Medicare and Medicaid Services
2000
.
A profile of Medicaid: Chartbook 2000
.
Baltimore, MD
Centers for Medicare and Medicaid Services, U.S. Department of Health & Human Services
.
Centers for Medicare and Medicaid Services
2007
.
Your Medicare coverage
Retrieved December 28, 2007, from http://www.medicare.gov/Coverage/Home.asp
.
Daumit
,
G.
,
L.
Pratt
,
R.
Crum
,
N.
Powe
, and
D.
Ford
.
2002
.
Characteristics of primary care visits for individuals with severe mental illness in a national sample.
General Hospital Psychiatry
24
:
391
395
.
Drainoni
,
M.
and
S.
Bachman
.
1995
.
Overcoming treatment barriers to providing services for adults with dual diagnosis: Three approaches.
Journal of Disability Policy Studies
6
:
43
55
.
Drainoni
,
M.
,
C.
Tobias
, and
T.
Dreyfus
.
1995
.
Medicaid managed care for people with disabilities: Overview of the population
.
Boston, MA
Medicaid Working Group
.
Edgerton
,
R.
1986
.
Alcohol and drug use by mentally retarded adults.
American Journal of Mental Deficiency
90
:
602
209
.
Fossett
,
J.
and
F.
Thompson
.
1999
.
Back-off, not backlash in Medicaid managed care.
Journal of Health Politics, Policy and Law
24
:
1159
1172
.
Fujii
,
D.
,
A.
Wylieb
, and
J.
Nathan
.
2004
.
Neurocognition and long-term prediction of quality of life in outpatients with severe and persistent mental illness.
Schizophrenia Research
69
:
67
73
.
Garnick
,
D.
,
A.
Hendricks
, and
C.
Comstock
.
1996
.
Using health insurance claims data to analyze substance abuse charges and utilization.
Managed Care Research and Review
53
:
352
368
.
Garnick
,
D.
,
D.
Hodgkin
, and
C.
Horgan
.
2002
.
Selecting data sources for substance abuse services research.
Journal of Substance Abuse Treatment
22
:
11
22
.
Garnick
,
D.
,
M.
Lee
,
M.
Chalk
,
D.
Gastfriend
,
C.
Horgan
,
F.
McCorry
,
A.
McLellan
, and
E.
Merrick
.
2002
.
Establishing the feasibility of performance measures for alcohol and other drugs.
Journal of Substance Abuse Treatment
23
:
375
385
.
Gelberg
,
L.
,
R. M.
Andersen
, and
B. D.
Leake
.
2000
.
The behavioral model for vulnerable populations: Application to medical care use and outcomes for homeless people.
Health Services Research
34
:
1273
1302
.
Gianfrancesco
,
F.
,
R.
Wang
, and
E.
Yu
.
2005
.
Effects of patients with bipolar, schizophrenic, and major depressive disorders on the mental and other healthcare expenses of family members.
Social Science & Medicine
61
:
305
311
.
Hanson
,
K.
and
H.
Huskamp
.
2001
.
Behavioral health services under Medicaid managed care: The uncertain implications of state variation.
Psychiatric Services
52
:
447
450
.
Health Resources and Services Administration
2009
.
Area resource file (ARF): National county-level health resource information database
Retrieved December 20, 2009, from http://arf.hrsa.gov/
.
Horner
,
R.
,
J.
Paris
,
J.
Purvis
, and
F.
Lawler
.
1991
.
Accuracy of patient encounter and billing information in ambulatory care.
Journal of Family Practice
33
:
593
598
.
Horwitz
,
S.
,
B.
Kerker
,
P.
Owens
, and
E.
Zigler
.
2000
.
The health status and needs of individuals with mental retardation
.
New Haven, CT
Department of Psychology, Yale University, and Special Olympics, Inc
.
Howard
,
A.
1990
.
Adults with mental retardation living in Massachusetts communities: Health characteristics, ambulatory health service utilization and associated expenditures
Doctoral dissertation, The Florence Heller Graduate School for Advanced Studies in Social Welfare, Brandeis University, Waltham, MA.
Humphries
,
K.
and
C.
Weisner
.
2000
.
Use of exclusion criteria in selecting research subjects and its effect on the generalizability of alcohol treatment outcome studies.
American Journal of Psychiatry
157
:
588
594
.
Iezzoni
,
L.
2002
.
Using administrative data to study persons with disabilities.
Milbank Quarterly
80
:
347
380
.
Jerrell
,
J.
2000
.
Issues and outcomes in integrated treatment programs for dual disorders.
Journal of Behavioral Health Services Research
27
:
303
313
.
Kaiser Commission on Medicaid and the Uninsured
1999
.
Profiles of disability: Employment and health coverage
.
Washington, DC
Kaiser Family Foundation
.
Kasper
,
J.
,
R.
Elias
, and
B.
Lyons
.
2004
.
Issue brief: Dual eligibles: Medicaid's role in filling Medicare's gaps
.
Washington, DC
Kaiser Family Foundation
.
Krahn
,
G.
,
N.
Farrell
,
R.
Gabriel
, and
D.
Deck
.
2007
.
Access barriers to substance abuse treatment for persons with disabilities: An exploratory study.
Journal of Substance Abuse Treatment
31
:
375
384
.
Larson
,
S.
2001
.
Prevalence of mental retardation and developmental disabilities: Estimates from the 1994/1995 National Health Interview Survey Disability supplements.
American Journal on Mental Retardation
106
:
231
252
.
Larson
,
S.
,
C.
Lakin
,
L.
Anderson
, and
N.
Kwak
.
1999
.
Characteristics of and service use by persons with ID/DD living in their own homes or with family members: NHIS-D analysis.
ID/DD Data Brief
3
:
1
12
.
Lottman
,
T.
1993
.
Access to generic substance abuse services for persons with mental retardation.
Journal of Alcohol and Drug Education
39
:
41
55
.
McAuliffe
,
W.
,
R.
LaBrie
,
R.
Woodworth
,
C.
Zhang
, and
R.
Dunn
.
2003
.
State substance abuse treatment gaps.
American Journal on Addictions
12
:
1
21
.
McCory
,
F.
,
D.
Garnick
,
J.
Bartlett
,
F.
Cotter
, and
M.
Chalk
.
2000
.
Developing performance measures for alcohol and other drug services in managed care plans.
Journal on Quality Improvement
26
:
633
643
.
McGillivray
,
J.
and
M.
Moore
.
2001
.
Substance use by offenders with mild intellectual disability.
Journal of Intellectual and Developmental Disability
26
:
297
310
.
Menolascino
,
F.
,
S.
Gilson
, and
A.
Levitas
.
1986
.
Issues in the treatment of mentally retarded patients in the community mental health system.
Community Mental Health Journal
22
:
314
327
.
Moore
,
D.
1998
.
TIP 29: Substance abuse disorder treatment for people with physical and cognitive disability
.
Rockville, MD
Substance Abuse and Mental Health Services Administration
.
Mullins
,
C.
,
S.
Snyder
,
J.
Wang
,
J.
Cooke
, and
C.
Baquet
.
2004
.
Economic disparities in treatment costs among ambulatory Medicaid cancer patients.
Journal of the National Medical Association
96
:
1565
1574
.
Murphy
,
C.
,
C.
Boyle
,
D.
Schendel
,
P.
Decouflé
, and
M.
Yeargin-Allsopp
.
1998
.
Epidemiology of mental retardation in children.
Mental Retardation and Developmental Disabilities Research Reviews
4
:
6
13
.
National Association on Alcohol, Drugs and Disability
1999
.
Access limited: Substance abuse services for people with disabilities: A national perspective
.
Washington, DC
Author
.
Pescosolido
,
B.
and
C.
Boyer
.
1998
.
How do people come to use mental health services? Current knowledge and changing perspectives.
In
Horwitz
,
A. V.
and
T. L.
Scheid
. (
Eds.
).
A handbook for the study of mental health
.
pp.
392
411
.
New York
Cambridge University Press
.
Petersilia
,
J.
2000
.
Invisible victims.
Human Rights
27
:
9
12
.
Pettit
,
B.
and
B.
Western
.
2004
.
Mass imprisonment and the life course: Race and class inequality in U.S. incarceration.
American Sociological Review
69
:
151
169
.
Prout
,
H.
and
D.
Strohmer
.
1998
.
Issues in mental health counseling with persons with mental retardation.
Journal of Mental Health Counseling
20
:
112
121
.
Pruchno
,
R.
and
W.
McMullen
.
2004
.
Patterns of service utilization by adults with a developmental disability: Type of service makes a difference.
Mental Retardation
109
:
362
378
.
Quam
,
L.
,
L.
Ellis
,
P.
Venus
,
J.
Clouse
,
C.
Taylor
, and
S.
Leatherman
.
1993
.
Using claims data for epidemiologic research: The concordance of claims-based criteria with the medical record and patient survey for identifying a hypertensive population.
Medical Care
31
:
498
507
.
Rivinus
,
T.
1988
.
Alcohol use disorder in mentally retarded persons.
Psychiatric Aspects of Mental Retardation
7
:
19
21
.
Rush
,
J.
and
A.
Frances
.
2000
.
Expert consensus guidelines for the use of treatment of psychiatric and behavioral problems in mental retardation.
American Journal on Mental Retardation
105
:
159
228
.
Sambamoorthi
,
U.
,
L.
Warner
,
S.
Crystal
, and
J.
Walkup
.
2000
.
Drug abuse, methadone treatment, and health services use among injection drug users with AIDS.
Drug and Alcohol Dependence
60
:
77
89
.
Selan
,
B.
1976
.
Psychotherapy with the mentally retarded.
Health & Social Work
23
:
73
85
.
Sengstock
,
W.
,
G.
Vergason
, and
M.
Sullivan
.
1975
.
Considerations and issues in a drug abuse program for the mentally retarded.
Education and Training of the Mentally Retarded
10
:
138
143
.
Simpson
,
D.
and
G.
Joe
.
2004
.
A longitudinal evaluation of treatment engagement and recovery stages.
Journal of Substance Abuse Treatment
27
:
89
97
.
Slayter
,
E.
2007
.
Balancing risk management with the dignity of risk: A case management framework for people with mental retardation and substance abuse.
Families in Society: Journal of Contemporary Human Services
88
:
651
671
.
Slayter
,
E.
2008
.
Understanding and overcoming barriers to substance abuse treatment access for people with mental retardation.
Journal of Social Work in Disability and Rehabilitation
7
:
63
80
.
Slayter
,
E.
,
D.
Garnick
,
J.
Kubisiak
,
C.
Bishop
,
D.
Gilden
, and
R.
Hakim
.
2006
.
Injury prevalence among children and adolescents with mental retardation.
Mental Retardation
44
:
212
223
.
Slayter
,
E.
and
S.
Steenrod
.
2009
.
Addressing alcohol and drug addiction among people with mental retardation: A need for cross-system collaboration.
Journal of Social Work Practice in the Addictions
9
:
71
90
.
Steinwachs
,
D.
,
M.
Stuart
,
S.
Scholle
,
B.
Starfield
,
M.
Fox
, and
J.
Wiener
.
1998
.
A comparison of ambulatory Medicaid claims to medical records: A reliability assessment.
American Journal of Medical Quality
13
:
63
69
.
Sturmey
,
P.
,
H.
Reyer
,
R.
Lee
, and
A.
Robeck
.
2003
.
Substance-related disorders in persons with mental retardation
.
Kingston, NY
NADD Press
.
Taylor
,
A.
,
S.
Larson
, and
R.
Correa-de-Araujo
.
2006
.
Women's health care utilization and expenditures.
Women's Health Issues
16
:
66
79
.
Trent
,
J.
1994
.
Inventing the feeble mind: A history of mental retardation in the United States
.
Berkeley
University of California Press
.
Tyas
,
S.
and
B.
Rush
.
1993
.
Treatment of disabled persons with alcohol and drug problems: Results of a survey of addiction services.
Journal of Studies on Alcohol
54
:
275
282
.
U.S. Surgeon General
2002
.
Closing the gap: A national blueprint to improve the health of persons with mental retardation
.
Washington, DC
U.S. Department of Health and Human Services
.
Walkup
,
J.
,
M.
Sambamoorthi
, and
S.
Crystal
.
1999
.
Characteristics of persons with mental retardation and HIV/AIDS: Infection in a state Medicaid population.
American Journal on Mental Retardation
104
:
356
363
.
Wenc
,
F.
1980
.
Special issue: The multidisabled: In focus: The developmentally disabled substance abuser.
Alcohol Health and Research World
5
:
42
46
.
West
,
S.
2007
.
The accessibility of substance abuse treatment facilities in the U.S. for persons with disabilities.
Journal of Substance Abuse Treatment
33
:
1
5
.
Westermeyer
,
J.
,
K.
Kemp
, and
S.
Nugent
.
1996
.
Substance disorder among persons with mild mental retardation.
American Journal on Addictions
5
:
23
31
.
Westermeyer
,
J.
,
T.
Phaobtong
, and
J.
Neider
.
1988
.
Substance use and abuse among mentally retarded persons: A comparison of patients and a survey population.
American Journal of Drug and Alcohol Abuse
14
:
109
123
.
World Health Organization. (n.d.)
International classification of diseases, ninth revision clinical modification
.
Geneva, Switzerland
Author
.

Author notes

Elspeth Maclean Slayter, PhD (E-mail: eslayter@salemstate.edu), Assistant Professor, Salem State University, School of Social Work, Salem, MA 01970.

Editor-in-Charge: Steven J. Taylor