HCBS Population
Based on 2005 data, the national HCBS population includes 2.2 million people, which is almost 4 percent of the total Medicaid population. Two-thirds are dually eligible for Medicare and Medicaid, representing about one-sixth of the total dual eligible population. The population is quite diverse nationally, with much variation within and between States and by subpopulation. Differences in age and underlying type of disability may present different risk profiles and call for different types of services and management. We defined the HCBS population through enrollment in a 1915(c) waiver and/or use of a Medicaid State plan or 1915(c) waiver services that can be classified as HCBS. Almost one-half of the HCBS population is enrolled in one or more 1915(c) waivers nationwide, but this varies from 17.5 percent (California) to 100 percent (Maine). Further work at the State level would be useful to understand how States support their HCBS populations through a combination of State plan and waiver programs, as well as the specific types of services offered and used under each program.
To date, most efforts around quality improvement and the HCBS population have focused on HCBS participants who receive 1915(c) waiver services and not on people who receive HCBS State plan services. Yet, half of HCBS participants use State plan services only (are neither enrolled in a 1915(c) waiver nor use 1915(c) waiver services). Thus, future work needs to include or focus on those using State plan services to form a more comprehensive picture.
HCBS Programs and Services
The types of HCBS covered by Medicaid in States varies greatly, whether as optional State plan services or through 1915(c) waivers. When the availability of similar types of services through either State plan or 1915(c) waivers is considered, the variation among States appears to diminish. For example, only 8 States offer adult day care as a State plan option, but 37 States offer adult day/health care as either a State plan option or a 1915(c) waiver service.
Services available through 1915(c) waivers are restricted to subpopulations, however (by disability, health condition, geography, or waiting list), whereas State plan services must be available to all. Because State plan and waiver services must not be duplicative, these comparisons hide other differences (e.g., limitations on amount of services provided ). Findings that health outcome indicators vary by coverage and use of HCBS suggest that further work is needed to understand the specific nature of these services and their impact on outcomes.
HCBS Outcome Indicators
Analysis of the HCBS outcome indicators suggests that they reflect meaningful variation in the underlying health and outcomes of the HCBS population. Rates of potentially avoidable hospital admissions that are captured in the measures vary dramatically by measure, ranging from just a few hundred admissions per 100,000 HCBS participants to almost 18,000 admissions. In each case, the rate for the HCBS population is dramatically higher than for the Medicaid population as a whole.
Although hospital admission is an important outcome of great policy interest recently, it is important to view this outcome as just one of many ways to measure health and well-being in the HCBS population. This indicator reflects a combination of underlying health status and the quantity and quality of health care services generally, not just HCBS. These measures are not intended to measure the performance of HCBS providers or policy but rather to measure the health and welfare of the HCBS population on an important dimension of health outcomes.
Health and Welfare of the HCBS Population as Measured by the Outcome Indicators
Although the underlying rates of the outcome indicators vary substantially by measure, by subpopulation, and by State—up to a 17-fold difference by State—we find evidence of systematic patterns across outcome indicators. These patterns tend to hold across measures even though each measure has a different average rate.
The rates of outcome indicators vary dramatically by population subgroup. Rates among dual eligibles tend to be much higher than among other subgroups. Rates generally are lowest in the I/DD population. These findings are consistent with expectations based on age differences and the likely underlying health condition.
The findings may be explained by the choice of measures, because the outcome indicators tend to relate to problems with frail physical health, rather than to conditions that may affect people with generally good physical health but other disabilities (I/DD, mental health conditions). Other outcome indicators than those we consider here may exhibit different patterns. Accordingly, this set of outcome indicators may not serve all parts of the HCBS population equally well and should be considered a subset of a broader range of health outcomes of interest.
Outcome indicator rates appear to be associated with demographics. Across all subpopulations, rates of outcome indicators are generally higher for women, older adults, and people in nonurban areas.
With respect to race, we considered both individual-level race and area-level race, and the findings were roughly consistent across the two. While the overall HCBS indicators exhibit a general pattern of higher rates of adverse outcomes for African Americans relative to whites, the I/DD and SMI subpopulations exhibit a starker contrast between African-Americans and whites than in the overall HCBS population. Furthermore, although Hispanics (and areas with a higher proportion of Hispanics) exhibit lower rates of most outcome indicators overall, this advantage disappears in the I/DD and SMI subpopulations. These findings suggest that these indicators may be useful measures of health disparities, further work may be needed to develop appropriate risk adjustment.
State policies that reflect the generosity of HCBS programs (breadth of eligibility, share of dollars spent on HCBS) exhibit a strong relationship with the outcome indicators. More generous HCBS programs are very consistently associated with lower rates of hospitalization. It is impossible to distinguish with these descriptive results whether greater access to services reduces rates of adverse outcomes or whether States that are more supportive of HCBS extend services to healthier individuals, resulting in fewer adverse outcomes among those served. The relative stability of the State policy findings with respect to subpopulations suggests the importance of better understanding how these policies operate and whether the exhibited relationships are potentially causal.
Findings with regard to the potential availability (coverage) and use of specific HCBS, either State plan or waiver, are mixed. Most, but not all, of the results suggest that use of HCBS is associated with a lower rate of hospitalizations. Notable exceptions include State plan hospice, transportation, and private duty nursing services and 1915(c) waiver personal care and durable medical equipment, which are associated with higher rates of outcome indicators. These findings may indicate that these services attract sicker individuals.
Personal care as a State plan service is associated with lower rates of hospitalization. Availability of targeted case management does not exhibit an association with outcome indicator rates, but use of the service is associated with lower rates.
A caveat to these descriptive results is that we cannot separate out the effect of each service individually when multiple services may be received. Further work is needed to determine which services are most beneficial for which types of individuals.
Finally, outcome indicator rates appear to be associated with the supply of health care providers in the area. At the State level, higher rates are associated with more acute care hospital beds, nursing home beds, home health agencies, and inpatient psychiatric beds. These results are less consistent for ICFs-MR and for supply at the county level.
At the county level, there is a general pattern of higher indicator rates in areas with a greater supply of acute care hospital beds, but the pattern is not as consistent overall and seems to be driven by the Medicaid-only and I/DD groups. Overall, the supply results generally support the hypothesis that a greater number of health care providers is indicative of a sicker HCBS population that is more likely to have higher rates of potentially avoidable hospital admissions. The findings also support the hypothesis that greater attention, identification of problems, and care seeking result from a greater number of medical personnel in the area.
Suggestions for Further Research
Systematic variation associated with individual and area characteristics suggests that the outcome indicators could be used to identify subpopulations and areas with the greatest level of unmet need among HCBS participants and to target policies and services toward reducing hospitalizations in those groups.
Systematic variation associated with individual and area characteristics also suggests that the utility of the outcome indicators for any type of comparison across policies or geographic areas may be improved by adjusting them for the underlying health risk of the population. For example, the outcome indicator rates exhibit a strong age gradient, with higher rates in older individuals and in areas with a greater proportion of older individuals. Women tend to have higher rates than men and people in nonurban areas tend to have higher rates than people in urban areas.
The rates vary substantially by clinical subpopulation, with I/DD populations exhibiting the lowest rates for most indicators, perhaps due to differences in the underlying age distribution by subpopulation. Finally, the rates also appear to vary systematically with area health characteristics, with higher rates found in States with higher prevalence of chronic diseases and disability. In order to assess hospital admissions among the HCBS population and identify areas with the greatest need, risk adjustment may not be necessary, but it would be essential if outcome indicators were to be used for comparison across areas.
Furthermore, the analyses presented and conclusions reached are based entirely on examination of cross-sectional and unadjusted data. Interpretation of these results is limited, because multiple individual and policy attributes may be correlated with each other and with the outcome indicators simultaneously. These more complex relationships are impossible to identify in a descriptive table. Potentially strong associations between the outcome indicators and Medicaid policy suggest the need for a more rigorous statistical analysis to control for confounding variables. Such analysis could also be used to identify the strongest predictors of the outcome indicators while controlling for correlated factors and to examine causal pathways.
Finally, the results in this report are based on administrative (billing) records and solely on 2005 data, the latest year for which Medicaid data were available at the time of analysis. The use of administrative data entails limitations but no alternative captures data on a national scale. Given additional expansions in HCBS since 2005, it would be fruitful to update these analyses as newer data become available.
