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Outcomes as measured by the HCBS outcome indicators vary considerably by subpopulation and participant attributes, but the variation takes on different forms for different measures. Table 7 presents the outcome indicator rates by subpopulation and Table 8 by key demographic attributes, also broken down by subpopulation.

Outcomes by Subpopulation

As shown in Table 7, for the vast majority of measures, admission rates are substantially higher among the dual eligibles than the Medicaid-only population. The differences in rates are dramatic, with dual eligibles experiencing rates that are often 50 to 100 percent higher than the Medicaid-only population. The exceptions are Short-Term Complications of Diabetes and Infection Due to Device or Implant, where rates are higher among Medicaid-only participants.

Not surprisingly, roughly the same pattern, with the same exceptions, is found when the 65+ subpopulation is compared with the other subpopulations. However, many measures also exhibit high rates among the 18-64 subpopulation. The contrast between the relatively high rates in the 65+ population and the other subpopulations is starkest for the I/DD subpopulation, which exhibits a pattern of strikingly lower rates than all the other groups on almost all measures.

In addition to variation by subgroup, tremendous State variation in outcome indicator rates is evident from the minimum and maximum rates shown. Indicators with the largest State range in percentage terms are Short-Term Complications of Diabetes, Injurious Falls, and Asthma or Chronic Obstructive Pulmonary Disease (COPD). Indicators with the smallest State range include Bacterial Pneumonia, Potentially Preventable Infections Composite, and ACSC Acute Conditions Composite. Total indicator rates for Short-Term Complications of Diabetes, for example, range from a low of 68 to a high of 1,188, a 17-fold difference. Rates for Bacterial Pneumonia range from 3,368 to 8,903, less than a threefold difference.

Outcomes by HCBS Individual Participant Attributes

The HCBS population as a whole exhibits differences in outcome indicator rates by race, gender, age, and urban/nonurban status as measured by location in a metropolitan statistical area (MSA) or non-MSA (Table 8).

• Rates for a slight majority of outcome indicators are higher for African Americans than for other races. The exceptions are Asthma or COPD, Potentially Preventable Infections Composite, Bacterial Pneumonia, ACSC Acute Conditions Composite, and Injurious Falls, where non-Hispanic whites have higher rates. Hispanics tend to have lower rates than non-Hispanic whites for almost all measures.
• Women tend to have worse outcomes than men, with higher rates on all measures other than Short-Term Complications of Diabetes, Bacterial Pneumonia, and Pressure Ulcer.
• Most of the outcome indicators exhibit unsurprising findings related to age, with lowest rates in the 18-64 age category, higher rates in the 65-84 category, and highest rates in the 85+ category. Two indicators, Short-Term Complications of Diabetes and Infection Due to Device or Implant, exhibit the reverse pattern, with rates decreasing with age. Another two indicators, Asthma or COPD and the ACSC Chronic Conditions Composite, exhibit a peak in rates in the 65-84 category, with lower rates among HCBS participants 85 and older.
• Finally, rates are almost all lower in urban areas (MSAs) than in nonurban areas. The exceptions are Pressure Ulcer, which is effectively equal in urban and nonurban areas, and Infection Due to Device or Implant, where the difference is trivial.

The overall rates of outcome indicators by participant attributes vary somewhat by dual status and by subpopulation. As dual eligibles constituted two-thirds of the HCBS population, not surprisingly, the rates for dual eligibles look very similar to the rates overall. Among the Medicaid-only population, however, differences by race and gender are generally more pronounced:

• Rates for African Americans in the Medicaid-only population are always higher than for whites and Hispanics except in Injurious Falls and Bacterial Pneumonia. In contrast to the dually eligible, Hispanics in this population do not exhibit lower rates, falling rather in the middle of the distribution and experiencing rates comparable to whites.
• Rates for women in the Medicaid-only population are always higher than for men except in Pressure Ulcer.
• Similarly, in the I/DD subpopulation, differences between African Americans and whites are more pronounced than in the overall population, and in both the I/DD and the SMI subpopulations, Hispanics tend to fall in the middle.
• The distribution of rates by race in the 65+ subpopulation and the 18-64 subpopulation without I/DD or SMI look similar to the rates for the overall HCBS population.

Thus, while the overall HCBS indicators exhibit a general pattern of higher rates of adverse outcomes for African Americans relative to whites and lower rates among Hispanics, the I/DD and SMI subpopulations exhibit a starker contrast between African Americans and whites and the Hispanic advantage disappears. Across all subpopulations, rates of outcome indicators are generally higher for women, older adults, and nonurban areas, but the number of exceptions varies slightly by subpopulation.

Outcomes by HCBS Availability, HCBS Use, and HCBS Policy Environment

Much attention has been paid in the last few decades to policies that promote the availability and use of home- and community-based long-term care services, as these services may address unmet need in the community and avoid costly institutional care. We therefore display in Tables 9-13 the overall outcome indicator rates by the potential availability and use of specific services.

Table 9 displays outcome indicators by whether a State offers selected services through its State plan, regardless of whether individuals receive the service. Similarly, Table 10 displays outcome indicators by whether a State offers selected services through at least one waiver, regardless of whether the waiver service is universally available or whether an individual receives the service. Tables 11 and 12 display the outcome indicators broken down by receipt of the State plan or waiver service, respectively. Finally, Table 13 exhibits the outcome indicators by key attributes of State Medicaid and long-term care policy.

The numbers in all of these tables are subject to caveats and have several plausible interpretations. First, the services listed are generally offered or used in conjunction with other services, so differences in rates are unlikely to be due to differences in that one service alone. Second, differences in rates may be attributable to effectiveness of care—HCBS participants who receive more services have fewer adverse events—or to differences in case mix of people from group to group. For example, some services are more appropriate for individuals at a lower level of need, while other services are directed at a higher level of need, and outcome indicator rates are likely to be higher in groups with a higher level of need. Similarly, States that offer more services or invest more in HCBS may include more individuals with lower levels of need, so their outcome indicator rates should also be lower. The numbers in these tables do not adjust for differences in case mix.

Outcomes by HCBS Availability

Outcomes by Availability of State Plan Services

Table 9 depicts outcome indicators by coverage or availability of services provided under State plans. While State plan services must be offered to all Medicaid beneficiaries who qualify, actual supply and quality of services may vary within a State. Our data reflect inclusion in the State plan as a measure of potential availability.

• Generally, Table 9 shows that outcome indicator rates across most indicators are lower in States with State plan services that include home health therapies, hospice care, and transportation (and almost all States offer these services, as shown in Table 1).
• Rates are also lower in States that offer personal care services, adult day care and, to a less consistent degree, residential care, services that are not offered by all States.
• In contrast, higher rates of most outcome indicators are found in States that offer private duty nursing (offered by more than half of States), and no systematic results are found for targeted case management (offered by almost all States).

Outcomes by Availability of Waiver Services

Table 10 presents parallel data to Table 9 but for 1915(c) waiver services only. In contrast to State plan service coverage, the potential availability of a service through a 1915(c) waiver is more often associated with higher hospital admission rates.

• Table 10 shows that outcome indicator rates across most indicators are higher in States that offer personal care, case management, adult day/health care, and durable medical equipment and supplies through at least one 1915(c) waiver in the State.
• Rates are lower only in the case of transportation services being offered, and results for residential care are mixed.

The generally higher rates in States that offer most of these services may reflect differences in case mix or may reflect waiver services being offered in response to high need. In addition, waiver services may be limited in geography or capacity such that the waiver is not really available to large numbers of people in a State's HCBS population.

Outcomes by HCBS Use

Outcomes by Use of State Plan Services

Table 11 shows outcome indicators by use of State plan services, which are roughly consistent with outcomes by availability of State plan services.

• Admission rates are lower among people who use home health therapies, personal care services, targeted case management, and adult day care.
• Rates are higher for people using hospice care, transportation services, and private duty nursing through the State plan than for people who do not use these State plan services.
• Results for residential care are mixed.

Outcomes by Use of Waiver Services

Table 12 displays outcome indicator rates by use of specific waiver services. Whereas outcomes by availability and use are similar for State plan services, the results for waiver service use presented in Table 12 provide more of a contrast to results for waiver service availability.

• Indicator rates are dramatically lower for individuals who use adult day care, residential care, or transportation through a 1915(c) waiver.
• Those using personal care or durable medical equipment and supplies still exhibit higher rates.
• Results for case management are mixed.

Summary of Outcomes by Use of State Plan Services and Waiver Services

Together, Tables 9 and 11 represent a tendency for lower outcome indicator rates to be associated with potential availability and use of many but not all State plan services relevant to HCBS participants.

Tables 10 and 12 represent a more mixed picture: the existence of a 1915(c) waiver offering a particular service (in at least one of the States waivers) is not associated with lower rates of outcome indicators, but use of selected services is associated with dramatically lower rates. These dramatically lower rates may reflect effectiveness of the services or differences in the underlying health of individuals who use and do not use these services.

Outcomes by HCBS Policy Environment

Of interest as commonly used summary measures are outcome rates with respect to the State policy variables captured in Table 13. First, we consider the restrictiveness of State eligibility policies with respect to programs for medically needy people. We classified States into one of three categories for this variable: (1) the State has no program for medically needy people; (2) the State has a program but it is quite restrictive in terms of income and asset requirements; or (3) the State has a program that is less restrictive in terms of income and asset requirements.

Second, we consider the nursing home level of care (LOC) necessary for individuals to qualify for many State HCBS waiver programs, the definition of which varies by State: high (most restrictive), middle, and low (least restrictive), based on a previously published categorization (Mollica & Reinhard, 2005). Finally, we consider the percentage of State Medicaid long-term care funds that are spent on HCBS as opposed to institutional care. This is a commonly used metric to indicate the extent of State support for HCBS. We compare rates of outcome indicators for each of these State policy attributes.

For the overall HCBS population, the results in terms of outcome indicators are strikingly consistent:

• Across all measures, the outcome indicator rates monotonically increase with the restrictiveness of the program for medically needy people. States without a program have the highest rates of admissions and those with the least restrictive policies have the lowest rates.
• Similarly, States with high requirements for nursing home LOC have the highest admission rates and States with low requirements have the lowest rates.
• Finally, States that spend more on HCBS as a percentage of total long-term care spending (Burwell, et al., 2006) have lower rates of adverse outcomes as reflected in the outcome indicators.

Despite the consistent results, interpretation is difficult. 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 relationship of the State policy variables considered in Table 13 to the outcome indicators is roughly consistent across subpopulations and by dually eligible status. For each subgroup, the results exhibit the same basic pattern but are slightly less uniform than the pattern found in the overall HCBS population. The main deviations from the overall pattern are in the nursing home LOC results:

• In the SMI subpopulation, the reverse pattern for nursing home LOC is found for Asthma or Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and Infection Due to Device or Implant. More restrictive policies are associated with lower rates of admission.
• Among the 65+ subpopulation and the 18-64 without I/DD or SMI subpopulation, the high LOC groups sometimes have lower rates than the middle LOC groups.

These deviations from the overall pattern may be related to specific provisions of the LOC criteria that do not translate well to restrictiveness for each population for each outcome indicator.

Outcomes by Area Characteristics

Whereas Table 8 displayed HCBS outcome indicator rates by attributes of the individuals in the HCBS population, Tables 14-19 display outcome indicator rates by aggregated area characteristics (county and/or State). These characteristics correspond to important individual attributes such as sociodemographics and health status. The motivation behind these comparisons is twofold:

1. First, it is useful to identify the types of areas and populations in which high rates of adverse outcomes as represented by the HCBS outcome indicators are likely to be present. These areas can potentially be targeted with policies aimed at reducing hospital admissions among HCBS participants.
2. Second, differences by individual and area-level attributes can be used to identify predictors of risk that should be included in risk adjustments if the indicators are eventually to be used to compare policies.

Outcomes by Supply of Health Care Providers

In Table 14, we consider the relationship between the outcome indicators and the supply of health care providers of various types per capita: acute care hospital beds, nursing home beds, home health agencies, intermediate care facilities for the mentally retarded (ICFs-MR), and inpatient psychiatric beds. These rates are calculated first at the county level and then at the State level.

We also consider whether the county is "underserved" by mental health providers or by primary care providers, two variables defined by the Health Resources and Services Administration in the Area Resource File. These variables are based on number of providers per capita only (not adjusted for need). These "underserved" variables are only available at the county level, not the State level.

In general, one could generate multiple hypotheses about the expected direction of the association between the outcome indicators and supply. A greater supply of acute care hospital beds may enable or encourage more inpatient admissions, so admission rates would be expected to be higher. A greater supply of nursing home beds and ICFs-MR may mean that individuals who remain in the community are healthier on average, resulting in lower rates of admissions (the selection hypothesis). A greater number of home health agencies may be associated with greater access to home-based care, also leading to lower admissions.

On the other hand, a greater supply of all types of health care providers, including inpatient psychiatric beds, might indicate a sicker population such that outcome indicator rates would be expected to be higher. Higher rates of hospital admissions in areas with more health care providers might also reflect greater numbers of medical personnel identifying problems and encouraging care. In our data, we can observe the direction of the relationship but cannot distinguish among the multiple hypotheses that may underlie the observed association. The State-level results are largely consistent with the latter hypothesis:

• Higher rates of admissions as represented by the outcome indicators are associated with more acute care hospital beds, more nursing home beds, more home health agencies, and more inpatient psychiatric beds. This pattern is highly consistent across all types of providers.
• On the other hand, higher rates of the outcome indicators are associated with fewer ICFs-MR per capita for the overall HCBS population, more consistent with the selection hypothesis. However, this result is driven by the non-I/DD subpopulation for whom the number of ICFs-MR should be less relevant. Among the I/DD subpopulation, more ICFs-MR per capita is associated with higher rates of the outcome indicators.

Thus, overall, the State-level results 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 results also support the hypothesis that greater attention, identification of problems, and care seeking result from a greater number of medical personnel in the area.

The two county-level variables representing "underserved" status provide results that are counterintuitive at first glance:

• Counties that are "underserved" by mental health providers or by primary care providers are consistently associated with lower rates across the outcome indicators.

This phenomenon may also be explained by the sickness of the population, because areas with more need have more providers, consistent with the State-level results for supply described above. The other county-level results in Table 14, however, are less consistent than the State-level results and sometimes at odds with them.

• 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. It is not true of the SMI, 18-64 without I/DD or SMI, or 65+ subpopulations.
• Oddly, the county-level results for supply of psychiatric beds are in direct opposition to the State-level results, exhibiting higher rates of the outcome indicators in areas with fewer psychiatric beds.

Although the county-level variables should contain more specific and relevant information, they also contain more statistical noise, because individuals do not always use an array of health care providers based in the same county. In addition, county lines are somewhat arbitrary, so aggregating to the State level may provide a cleaner picture.

Outcomes by Area-Level Age and Race

Table 15 displays outcome indicators by the area-level distribution of age and race, first by county and then by State:

• Overall, counties with more non-Hispanic whites tend to have higher rates of the outcome indicators while areas with more Hispanics tend to have lower rates, although the latter result is less consistent when looking only at dual eligibles.
• The pattern for African Americans is inconsistent across measures overall, but a higher percentage of African Americans is associated with higher rates of most outcome measures in the I/DD subpopulation.
• The age pattern at the county level shows that a higher percentage of people age 65+ is associated with higher rates of outcome measures. The same pattern holds for the percentage of people age 85+. The main exception to the county-level age pattern is the Pressure Ulcer measure, for which rates are higher when the percentage of people age 65+ or age 85+ is lower.

The State-level results are very similar to the county-level results except that the overall pattern of higher rates in areas with a higher proportion African American is more pronounced and consistent, especially among older adults.

Outcomes by Area-Level Socioeconomic Status

Table 16 displays outcome indicators by several area-level indicators of socioeconomic status: median income, percentage of the population in poverty, and number of Federally Qualified Health Centers (FQHCs) per capita. The FQHC variable could be interpreted in two ways. First, it could be a sign of access to care in low-income groups, which would lead one to expect lower outcome indicator rates in areas with more FQHCs. Second, it could be a proxy for poverty and health status in that more FQHCs indicates more low-income individuals in need of health care.

• At the county level overall, the results exhibit a striking inverse relationship between socioeconomic status and outcome indicator rates, with lower income and higher poverty consistently associated with higher rates of adverse outcomes.
• On average, median income is a stronger predictor of outcome indicator rates than the percentage in poverty. �The difference in rates by median income is larger across most measures than the difference in rates by percentage in poverty.
• More FQHCs is consistently associated with higher outcome indicator rates, consistent with the interpretation that the number of FQHCs is a proxy for poverty and poorer health status.
• The overall results for socioeconomic status at the county level are roughly consistent across dual eligibles, Medicaid-only participants, and the four subpopulations. The results for the percentage of the population in poverty are considerably less consistent, and even reversed for some measures (e.g., infection-related measures) among the elderly subpopulation and the group under 65 without I/DD or SMI. Median income remains a strong inverse predictor of outcome indicator rates.
• The State-level results for Table 16 are consistent with the county-level results in terms of median income.
• Percentage in poverty is a less consistent predictor across measures at the State level and again varies in consistency and even direction at the subpopulation level. For example, States with a higher proportion in poverty tend to have lower outcome indicator rates among HCBS participants who are 65 and older. It may be because the group in poverty is concentrated in younger populations. In this sense, median income may capture a broader sense of socioeconomic status than percentage in poverty.

The State section of the table includes two additional variables not available at the county level, the percentage of persons 65+ who are living alone and the percentage of female labor participation.

• The State-level results are consistent in exhibiting higher rates of outcome indicators in States with a higher percentage of older individuals living alone, which one would expect.
• In general, States with a higher percentage of female labor force participation have higher rates of outcome indicators.

These results are consistent with a lower availability of informal care, although other explanations are plausible.

Outcomes by Area-Level Health Status

Outcomes by Prevalence of Chronic Conditions

Table 17 displays outcome indicator rates by State-level prevalence of chronic conditions (diabetes, asthma, cardiovascular disease, high blood pressure, and serious mental illness). These were not available at a county level.

• Unsurprisingly, the results for the overall HCBS population show consistently that areas with higher prevalence of each chronic condition have higher rates of adverse outcomes as represented by the outcome indicators. The pattern is strong for all conditions except high blood pressure. Although generally exhibiting the same pattern, high blood pressure shows a smaller magnitude of difference between States with high and low prevalence and the direction of the difference is not consistent for all measures.
• These results are fairly consistent across subpopulations, with the pattern of results for high blood pressure being the least consistent across subpopulations but others showing a consistently strong relationship between chronic condition prevalence and outcomes.

Outcomes by Prevalence of Any Disability

As a parallel to Table 17, Table 18 presents outcome indicator rates by State-level prevalence of disability of several types. The table shows the percentage of the population in the following groups: ages 18-64 with any disability, age 65+ with any disability, ages 18-64 on Social Security Disability Insurance (SSDI), and people on SSDI with a diagnosis of mental retardation.

• Outcome indicator rates for the overall HCBS population are substantially higher in States with a higher prevalence of disability as indicated by three of the four disability categories.
• Among those age 65 and older, outcome indicator patterns are the opposite, with slightly higher rates in States with lower prevalence of disability. This anomalous result varies by subpopulation. Among the I/DD and SMI subpopulations, higher prevalence of disability is associated with higher outcome indicator rates for all four measures of disability, while the other subpopulations drive the overall, slightly less consistent result.

Outcomes by Prevalence of Specific Disabilities Among Older Adults

Table 19 presents outcome indicators by specific types of disability (sensory, physical, self-care, or mental disability and difficulty going outside home) among older adults (65+) only.

• Very consistently across almost all outcome indicators, somewhat higher rates of admissions are associated with higher rates of sensory, physical, and self-care disabilities and with difficulty going outside home.
• The pattern for mental disability is much less consistent and tends toward the reverse direction.
• The inconsistency of the overall results for prevalence of mental disability is driven by stark differences by dual status and subpopulation. Medicaid-only HCBS participants and the 18-64 subpopulation exhibit the more intuitive pattern of higher outcome indicator rates with higher prevalence of mental disability, although prevalence is measured for people 65 and older.
• Oddly, the results for the 65+ subpopulation are generally in the opposite direction from what would be expected for four of the five types of disability. Only for prevalence of sensory disability among those 65 and older does the 65+ subpopulation exhibit higher outcome indicator rates with higher prevalence.

The results for the 65+ subpopulation are counterintuitive and difficult to explain, but most of the results for the overall HCBS population remain intuitive and plausible.