Introduction 1 2 3 4 5 6 7 8 10 11 13 10 11 14 15 16 Data and Methods 17 18 Study population International Classification of Diseases 9th Revision Clinical Modification Preliminary analyses revealed that very few agency service records include diagnosis codes, but most Medicaid records do. Further investigation revealed that this is not an issue of Medicaid requiring a diagnosis for eligibility, but rather an issue of the data systems themselves. Agency services do not require a diagnosis code for reimbursement, so agency data systems do not track diagnosis. Conversely, Medicaid services often require a diagnosis for reimbursement, so Medicaid data systems track diagnosis. Because the presence or absence of a diagnosis is almost completely confounded by the use or nonuse of Medicaid services, information on service use differences associated with an opiate use disorder diagnosis is not presented. Client classification Any Medicaid service: individuals who have at least one Medicaid MH/SA service record, regardless of whether they have MH/SA state agency service records Agency services only with Medicaid enrollment: individuals who have at least one MH/SA state agency service record and were Medicaid-enrolled at some point between their first and last observed MH/SA service record but have no record of receiving an MH/SA service through Medicaid Agency services only without Medicaid enrollment: individuals who have at least one MH/SA state agency service record and were not Medicaid-enrolled at any time between their first and last observed MH/SA service record any Medicaid service any Medicaid service 17 19 The IDB client classification does not incorporate information on secondary diagnoses unless a primary MH/SA diagnosis is also present, nor does it incorporate drug-of-choice information from state agency intake records. Because both of these pieces of information were used to identify individuals with an opiate use disorder for this study, it is possible for individuals with an opiate use disorder to be classified as MH-only or having received no MH/SA service based on the IDB client classification. Service classification 20 21 17 Service encounters An individual’s total number of service encounter dates was defined as the count of unique dates within the MH/SA service window on which the individual had a record with at least one service of a given service category (MH, IP SA, residential SA, or OP SA). Within a single IP or residential stay, each daily service encounter date was counted separately. Using standard IDB definitions for the full SA population, this same information is presented for the broader SA population, excluding individuals with an indication of an opiate use disorder. Methods To characterize the level of contact individuals with an opiate use disorder have with the public treatment system, the analysis examined four key domains: (1) the proportion of clients using services, (2) the median length of the service window (i.e., the length of time between an individual’s first and last MH/SA service), (3) the number of days of Medicaid enrollment within the service window, and (4) the number of unique MH/SA encounter dates within the service window. \documentclass[12pt]{minimal}
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\begin{document}$$ \Pr {\text{ob}}{\left( {{\text{SERV}}_{i}  = 1} \right)} = f{\left( {\beta _{1} {\text{DEMOG}}_{i}  + \beta _{2} {\text{GROUP}}_{i}  + \beta _{3} {\text{WINDOW}}_{{\text{i}}}  + \beta _{4} {\text{ENROLL}}_{i} } \right)}, $$\end{document} i any Medicaid service i i any Medicaid service agency services only with Medicaid enrollment agency services only without Medicaid enrollment i i β \documentclass[12pt]{minimal}
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\begin{document}$$ {\text{In}}{\left( {{\text{DAYS}}_{i} } \right)} = \beta _{1} {\text{DEMOG}}_{i}  + \beta _{2} {\text{GROUP}}_{i}  + \beta _{3} {\text{WINDOW}}_{i}  + \beta _{4} {\text{ENROLL}}_{i}  + \varepsilon _{i} , $$\end{document} i 22 Results 1 1 17 any Medicaid service agency services only with Medicaid enrollment Table 1 Characteristics of individuals with a possible opiate use disorder compared with the non-opiate SA population Population characteristic Non-opiate SA population Opiate users N 83,793 15,652 Percentage of total SA population (%) 84.3 15.7 Gender (%)  Male 63.2 56.8  Female 36.8 43.2  Unknown 0.0 0.0 Age (%)  Youth (0–17) 19.9 3.1  Adult (18–64) 78.8 96.5  Elderly (65+) 1.3 0.4  Unknown 0.0 0.0 Race/ethnicity (%)  White 70.0 77.6  Black 9.0 10.6  Hispanic 8.5 4.3  Native American 7.8 4.5  Other 3.6 2.6  Unknown 1.0 0.3 IDB client category (%)  SA-only 68.8 59.9  Co-occurring (MH+SA) 31.2 34.7  MH-only 0.0 1.9  Neither MH nor SA 0.0 3.4 MH/SA service type (%)  SA services (%)     Any setting 94.9 94.6   Inpatient 3.0 6.2   Residential 31.9 57.7   Outpatient 80.9 71.4   No SA services 5.1 5.4   Any MH service 31.2 36.5  MH/SA service window length     25th percentile (days) 38.0 78.0   Median (days) 168.0 348.0   75th percentile (days) 446.0 770.0  MH/SA service data source (%)     Any Medicaid 46.0 51.0   Agency services only with Medicaid enrollment 17.3 22.7   Agency services only without Medicaid enrollment 36.8 26.3 SA MH 1 any Medicaid service agency services only with Medicaid enrollment agency services only without Medicaid enrollment any Medicaid service any Medicaid service at least one Figure 1 Median MH/SA service window length and days of medicaid enrollment among individuals with a possible opiate use disorder by data source category  2 3 2 3 any Medicaid service 2 agency services only 3 agency services only with Medicaid enrollment agency services only without Medicaid enrollment any Medicaid services Figure 2 Percentage of individuals with a possible opiate use disorder using MH/SA services by service type and data source category  Figure 3 Median days of care conditional on service use among individuals with a possible opiate use disorder by service type and data source category  2 2 2 any Medicaid service agency services only with Medicaid enrollment agency services only without Medicaid enrollment Table 2 Logistic regression results for the probability of service use Parameters Probability of residential SA service use Probability of OP SA service use Probability of any MH service use Agency services only with Medicaid enrollment 1.079*** −1.167*** −1.526*** Standard error 0.050 0.061 0.052 Odds ratio 2.941 0.311 0.217 Agency services only without Medicaid enrollment 0.319*** −1.819*** −2.210*** Standard error 0.052 0.069 0.072 Odds ratio 1.376 0.162 0.110 Control variables Intercept 0.446*** 1.091 −0.274*** Standard error 0.059 0.078 0.065 Female −0.519*** 0.344 −0.064 Standard error 0.036 0.046 0.042 Race/ethnicity (referent is non-Hispanic White) Black −0.269*** 0.371 −0.493*** Standard error 0.057 0.075 0.069 Hispanic −0.081 0.036 −0.402*** Standard error 0.086 0.097 0.116 Native American 0.342*** −0.067 −0.221** Standard error 0.087 0.101 0.101 Other −0.357*** 0.229 −0.169 Standard error 0.110 0.145 0.128 Unknown −1.656*** −1.122** 0.863* Standard error 0.507 0.441 0.459 Age (referent is 36 to 40) 0 to 17 −0.384*** 0.765 −0.058 Standard error 0.103 0.146 0.124 18 to 20 −0.032 −0.037 −0.126 Standard error 0.112 0.132 0.134 21 to 25 0.361*** −0.098 0.097 Standard error 0.075 0.086 0.085 26 to 30 0.279*** −0.060 0.027 Standard error 0.063 0.076 0.072 31 to 35 0.175*** 0.034 0.093 Standard error 0.058 0.072 0.066 41 to 45 −0.205*** −0.047 −0.116* Standard error 0.055 0.069 0.065 46 to 50 −0.465*** −0.010 −0.268*** Standard error 0.065 0.082 0.078 51 to 55 −0.537*** 0.001 −0.425*** Standard error 0.099 0.129 0.121 56 to 60 −1.096*** 0.443** −0.718*** Standard error 0.159 0.220 0.183 61 to 64 −0.656*** −0.713** −0.729** Standard error 0.241 0.297 0.288 65 plus −1.417*** −0.285 −0.675** Standard error 0.301 0.344 0.322 Service window length (days) 0.000 0.003 0.001*** Standard error 0.0001 0.0001 0.0001 Medicaid enrollment (months) −0.005 −0.017** 0.053*** Standard error 0.004 0.007 0.005 IP SA OP MH p p p 3 agency services only with Medicaid enrollment agency services only without Medicaid enrollment agency services only agency services only with Medicaid enrollment agency services only without Medicaid enrollment agency services only without Medicaid enrollment any Medicaid service 2 Table 3 Linear Regression Results for Unique Health Care Service Dates  Parameters Unique dates of residential SA service Unique dates of OP SA service Unique dates of MH service Agency services only with Medicaid enrollment 0.248*** 0.169*** −0.009 Standard error 0.030 0.045 0.063 Percentage change 0.282 0.185 −0.009 Agency services only without Medicaid enrollment −0.818*** 0.472*** 0.448*** Standard error 0.038 0.077 0.094 Percentage change −0.559 0.604 0.564 Control variables Intercept 3.310*** 2.552*** 2.337*** Standard error 0.042 0.065 0.067 Female −0.017 0.322*** −0.045 Standard error 0.025 0.039 0.041 Race/ethnicity (referent is non-Hispanic White) Black −0.007 0.121** −0.010 Standard error 0.040 0.061 0.074 Hispanic 0.208*** 0.013 −0.153 Standard error 0.057 0.106 0.133 Native American 0.254*** −0.039 −0.700*** Standard error 0.052 0.084 0.106 Other −0.057 −0.016 0.211* Standard error 0.079 0.123 0.128 Unknown −1.268*** 0.000 −1.340*** Standard error 0.483 0.000 0.443 Age (referent is 36 to 40) 0 to 17 0.549*** 0.374*** 0.224* Standard error 0.076 0.112 0.123 18 to 20 0.193*** 0.135 0.366** Standard error 0.074 0.123 0.143 21 to 25 −0.028 0.188*** 0.062 Standard error 0.045 0.072 0.085 26 to 30 0.003 0.174*** 0.071 Standard error 0.039 0.061 0.071 31 to 35 −0.026 0.039 0.040 Standard error 0.037 0.057 0.065 41 to 45 −0.031 0.137** 0.115* Standard error 0.037 0.058 0.064 46 to 50 0.037 0.115 0.309*** Standard error 0.046 0.075 0.078 51 to 55 0.040 0.317*** −0.080 Standard error 0.073 0.122 0.123 56 to 60 −0.229* 0.264 −0.037 Standard error 0.134 0.210 0.196 61 to 64 −0.618*** 0.283 −0.093 Standard error 0.190 0.357 0.308 65 plus 0.701*** −0.700 0.116 Standard error 0.271 1.321 0.353 Service window length (days) 0.001*** 0.003*** 0.002*** Standard error 0.000 0.000 0.000 Medicaid enrollment (months) −0.019*** −0.024*** 0.013*** Standard error 0.003 0.004 0.003 IP SA OP MH p p p Implications for Behavioral Health This analysis examined the service use patterns of individuals with an indication of an opiate use disorder using IDB data from Washington for the period 1996 through 1998. Among individuals with opiate use disorders, the receipt of at least one MH/SA service through Medicaid appears to be positively associated with IP SA service use in that only individuals who met this condition had any record of an IP SA service. The receipt of at least one MH/SA service through Medicaid was also positively associated with OP SA service and with MH service use among individuals with opiate use disorders. The use of only state MH/SA agency services, on the other hand, was positively associated with the use of residential SA services. The findings suggest that the regulatory restrictions faced by state agencies and Medicaid may drive observed patterns of care for individuals with opiate use disorders. Specifically, the finding that agency-only MH/SA service use is associated with higher rates of residential service use and with no IP SA service use is likely tied to regulatory differences between Medicaid and the state agencies in Washington. For example, SA block grant funds, which are a key source of funding for both DASA and MHD, could not be used for IP hospital care during the period covered by this analysis. This restriction most likely induced state agencies to route patients for whom outpatient care is insufficient to residential care. Similarly, the Medicaid Institutions of Mental Disorders exclusion prohibits payment for psychiatric services received by adults in residential care facilities with more than 16 beds and so may induce providers to route more severe patients to IP care rather than to residential care. This explanation suggests that using administrative data to track service use patterns may be misleading because the collection of service data is driven by regulatory environments and billing systems that may not capture the actual intensity of care given. The results are subject to several limitations. First, although the IDB represents one of the most comprehensive cross-system databases used to examine this critical issue to date, it does not capture all possible services that could be used by individuals with possible opiate use disorders in Washington. Other possible sources include self-pay, private insurance, and the Department of Veterans Affairs (VA) system, among others. Another important limitation common to all administrative databases is that the actual treatment need of the population studied is unknown, so definitive statements cannot be made about the appropriateness of the services received. Furthermore, because of the limited time frame of the IDB, data are unavailable for individuals who used MH/SA services either before 1996 or after 1998. As a result, it is possible that this study has not captured the full service use history of some individuals who appear in the treatment system briefly at the beginning or end of the study period. A final limitation of this study is that information on prescription drug use is not available in the agency service records contained in the IDB and is therefore not considered in this study. Despite these limitations, the analysis of service use patterns of individuals with possible opiate use disorders during the late 1990s offers key implications for today’s policy makers as they attempt to address issues surrounding the emergence of new opiates. First and foremost, if policy makers are trying to track the service use of a small but important segment of the overall SA population, using integrated data is a necessity. When funding for treatment services is cut in an effort to contain costs, it is important to determine if those services have simply been shifted to other state programs, reflecting little net cost savings for states overall. The present study combines state MH/SA agency data with state Medicaid data, but including additional data sources (e.g., VA or criminal justice system) would provide an even more complete picture of service use patterns. Second, the utility of administrative data for analyses such as these is often limited by the influence of the regulatory environment, clinical practice patterns, and the institutional history of the data systems used. Given that state and federal policy makers increasingly rely on administrative data to assess the performance of the treatment system, the results clearly highlight the need to better track service provision. SAMHSA’s IDB, therefore, represents the vanguard of a new, expansive, cross-agency philosophy regarding administrative data sources and serves as a model for new and more comprehensive data integration efforts.