The Utility of the State Buy-In Variable in the Medicare Denominator File to Identify Dually Eligible Medicare-Medicaid Beneficiaries: A Validation Study

Dually eligible Medicare and Medicaid beneficiaries (referred to as “duals”), comprise the most vulnerable subgroup in the elderly population, accounting for a disproportionate share of Medicaid and Medicare utilization and payments (Lied 2006). In addition to their socioeconomic disadvantage, duals are disproportionately represented among those with chronic illnesses and functional limitations, as well as among nursing home residents (Kaiser Family Foundation 2003). In cancer-related health services research, studies have documented disparities in the receipt of colorectal cancer screening services by dual status (Koroukian et al. 2006), as well as disparities in cancer stage, treatment, and outcomes (Bradley et al. 2007a; Bradley, Clement, and Lin 2008a; Bradley, Luo, and Given 2008b; Bradley et al. 2008c;). Despite these important differences, examining disparities by dual status is challenging because of uncertainties surrounding the best method to identify duals in population-based databases. As defined in a report by the Research Data Assistance Center (ResDAC), a contractor with Centers for Medicare & Medicaid Services (CMS) to provide assistance to researchers using Medicare and Medicaid data, dual enrollees “(1) are Medicare enrollees; (2) might have their Medicare Part B premium paid by their states' Medicaid program (Specified Low-Income Beneficiaries (SLMBs); (3) might have both their Medicare Part B premium and their Medicare cost-sharing amounts paid by their states' Medicaid program (Qualified Medicare Beneficiaries (QMBs); (4) might receive full Medicaid benefits in addition to (2) or (3)” (Barosso 2006). One potential source for identifying duals is the state buy-in (SBI) variable in the Medicare denominator file, used previously in population-based studies (Koroukian et al. 2006; O'Leary, Sloss, and Melnick 2007;). The alternative approach has been to link Medicare and Medicaid files, a laborious process employed in other studies (Bradley et al. 2007a; Bradley, Clement, and Lin 2008a; Bradley, Luo, and Given 2008b; Bradley et al., 2008c). Sparse information is available on the data limitations associated with using Medicare data alone to identify or count duals (Rosenbach and Lamphere 1999; Baugh 2004;). Baugh (2004) emphasized the need to link Medicare and Medicaid data to estimate the number of full Medicaid benefit dual enrollees because neither Medicare nor Medicaid data alone provide an accurate count of duals. The reason why Medicare data undercount duals is because Medicaid does not pay Medicare premium for all duals (Baugh 2004). However, the extent to which this undercount occurs has not been estimated previously. Rosenbach and Lamphere (1999) found severe underreporting in the SBI variable in 10 states relative to the number of duals reported by these states' Medicaid officials during a telephone interview. This finding prompted ResDAC to alert the research community about the inadequacy of the SBI variable to identify duals (Barosso 2006). The lack of empirical data on how to identify duals and whether to derive claims-based measures for duals from Medicare data, Medicaid data, or both sources combined has contributed to a significant hiatus in research relevant to this vulnerable population. In addition to identifying a patient as a dual, it is important also to determine when, in relation to an index health event (e.g., cancer, stroke), and for how long a dual has been enrolled in the Medicaid program. For example, the timing of enrollment in Medicaid in relation to a cancer diagnosis is significantly associated with cancer stage at diagnosis, with patients enrolled in Medicaid several months before diagnosis being more likely to be diagnosed at earlier stages than their counterparts enrolled in Medicaid immediately preceding or following cancer diagnosis (Perkins et al. 2001; Bradley, Given, and Roberts 2003; Koroukian 2003; O'Malley et al. 2006;). In this study, we examine (1) in a cross-sectional analysis, the sensitivity and positive predictive value (PPV) of the SBI variable to identify duals in a subgroup of older cancer patients from Michigan and Ohio; and (2) in a longitudinal analysis, the extent to which Medicare and Medicaid sources agree on the length of enrollment in Medicaid relative to their month of cancer diagnosis. We conducted these studies using data from cancer surveillance systems from Michigan and Ohio that were linked with Medicare and Medicaid data.