An Investigation of Interstate Variation in Medicaid Long-Term Care Use and Expenditures Across 40 States in 2006. A. Progress in Measuring and Understanding Long-Term Care System Performance


1. Measuring Progress

The desire to transform Medicaid LTC systems has led to the need for meaningful measures of the extent to which state LTC systems have met the aims set forth by Olmstead. In theory, such measures should capture the degree to which people needing LTC services are being served in the most integrated setting appropriate for their needs. In practice, such refined measures are expensive to develop, in part because of the limitations of available data. Furthermore, such measures initially were not needed because more basic measures could quantify progress and meet policy needs. As states continue balancing their systems, policymakers can now benefit from more refined measures to identify areas for program improvement.

The most commonly used indicators of LTC system performance -- the percentage of LTC spending allocated to HCBS and increases in the number of people receiving HCBS -- typically have relied on two readily available and annually updated sources of state-level data on Medicaid expenditures and HCBS use. These include aggregate spending data by service type reported by states in CMS Form 64 (Eiken et al. 2010), and counts of waiver enrollees reported in CMS Form 372 combined with state survey-based counts of personal care and home health users, as summarized each year by the Kaiser Commission on Medicaid and the Uninsured and University of California/San Francisco (Ng et al. 2009). Although these data convey important information on system performance, they cannot be used to conduct subgroup analyses (except for some waiver populations). This limits the ability to measure the extent to which HCBS have reached the people that need them. For this reason, researchers and policymakers have begun using CMS person-level administrative data in the Medicaid Analytic eXtract (MAX) system to explore who is being served by Medicaid LTC and to better understand system transformation.3 These projects include efforts to measure HCBS and institutional use and spending for various subgroups eligible for the Money Follows the Person (MFP) demonstration (Brown et al. 2009; Irvin and Ballou 2009); the AARP state LTC system scorecard that includes some person-based measures of system performance (Reinhard 2010; Reinhard et al. 2011); and CMS's development of a more comprehensive set of indicators of Medicaid re-balancing, which aims to measure LTC system performance and quality (Urdapilleta 2010). Although some reports have been issued, these measures are under development or are being further refined.

These previous and ongoing efforts to develop state LTC systems performance indicators have focused, on the one hand, on what can best be described as summary "outcome" measures of "re-balancing;" and, on the other hand, on codifying expert opinion concerning desirable systems attributes (i.e., "best practices") and scoring state systems accordingly without, however, conducting research to find out whether (which ones and how many) of these best practices predict or correlate with better scores on the summary outcome measures. The present study differs from these others, first, by developing more varied summary outcome measures, including ones for subpopulations, and, second, by looking for descriptive attributes (both the non-malleable or less malleable state systems characteristics that state government has little or no control over and the "policy" variables that they can influence) that correlate with desirable outcome measures.

2. What Do We Know About the Progress in Long-Term Care System Transformation?

As we noted earlier, LTC systems are becoming more balanced in favor of HCBS (Ng et al. 2009; Doty 2010). However, studies have shown substantial interstate and intrastate variations. Some states -- for example, Alaska, New Mexico, Oregon and Washington -- have been identified as successes, whereas others have received low rankings (Howes 2010; Kassner et al. 2008). Within states, performance indicators suggest that HCBS use is much more common among young disabled beneficiaries than older LTC recipients (Wenzlow et al. 2008), but our understanding of how components of the LTC systems function for people with physical disabilities compared to those for people with ID/DD is quite limited.

Factors that may be related to systems judged more successful (because they have been "re-balanced" in favor of HCBS) include single access points; availability of person-centered services; participant involvement; precipitating events or crises; and effective state leadership, planning, and processes (Eiken 2004). In their review of the literature, Mollica and Reinhard (2005) also identified as critical components the availability of broad HCBS, single global LTC budgeting, standardized assessment tools, transition programs, and quality improvement. In a study of MFP grantees, Irvin and Ballou (2009) found two additional features -- the depth of HCBS experience and coverage of optional state plan personal care -- among more balanced systems in terms of LTC spending. A recent survey of state programs confirmed many of these factors as facilitating re-balancing and also highlighted the broad fiscal challenges states face in maintaining and improving LTC systems in hard economic times (Rose et al. 2010).

Many important questions about LTC system performance remain unanswered. Among states identified as successes, are they providing HCBS to more people or are they providing more services? Who remains without access to appropriate HCBS? Can successful policies implemented in some states work for others? How do fiscal constraints and other state characteristics hinder or facilitate system transformation? For example, we would expect that rural states, in which the distance between service providers and recipients is large, may find it more challenging to provide their clients with LTC in home and community-based settings; hence, the lessons learned in more urban states may not apply. Insights into these questions would be particularly helpful to states as they face budget crises and as some consider cuts rather than expansions of Medicaid.

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