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Assisted Living and Special Populations: What Do We Know About Differences in Use and Potential Access Barriers?

Correspondence: Address correspondence to Mauro Hernandez, PhD, Department of Social and Behavioral Sciences, University of California, San Francisco, 3333 California Street, Suite 455, San Francisco, CA 94118. E-mail: mauro.hernandez{at}ucsf.edu

	Abstract

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Purpose: The purpose of this article was to review existing knowledge about assisted living (AL) use by traditionally underserved populations, including people of color, low-income people, and those living in rural communities. Design and Methods: We reviewed methodologies and findings of research on AL and residential care up to June 2004.  Results: Some studies suggested that low-income people and people of color either are less likely to utilize AL or receive AL in settings with less space and amenities and/or lower scores in selected quality measures. Generalizations from national or multistate studies to the population have methodological limitations. Numerous topics remain unstudied, including the pricing of AL, the experience of choosing an AL facility, the presence of discriminatory admission and retention practices, and the role of resident preferences. Data on access to AL in rural areas are inconclusive. Implications: We offer recommendations for national surveys to better classify supportive housing settings. Furthermore, we suggest quantitative and qualitative studies to illuminate the experience of low-income, racial/ethnic minority, and rural populations in AL settings.

Key Words: Aged • Ethnic groups • Income • Home- and community-based services • Residential care • Rural health

The concerns of this article are whether state policies and industry development efforts, particularly those favoring apartment-type AL, are increasing or reducing access to supportive housing for racial/ethnic minorities, persons with low income, and those in rural settings. (Special populations outside the scope of this review include people with developmental disabilities, people younger than age 65 with disabilities, and gay and lesbian seniors.) We examined these questions through a review of research about AL (and other licensed housing). We qualify the findings and conclusions drawn from this review, because many data limitations effect the interpretation and reliability of the available information.

	Methods

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We based this article on a selected initial review of 75 articles and reports published between 1990 and mid-2004 that described AL use, as well as industry and state policy developments. We identified these using title and keyword Internet searches of databases such as PubMed and the Social Sciences Citation Index. We used different combinations of search terms, including, but not limited to, assisted living, residential care, Medicaid, income, race, ethnicity, African American, Hispanic, and rural. Reports identified from federal agency and industry Web sites, conference proceedings, and reference lists within the articles and reports augmented the published article search. We included for discussion selected findings from 51 publications that reported resident, organizational, and state characteristics that provide insights into differential use by traditionally underserved populations. We excluded studies that examined organizational practices (e.g., resident-centered care) and processes (e.g., entry and adjustment) that were outside the scope of this review. Space constraints required excluding case studies and limiting citations with similar findings.

The first major section of this article describes existing knowledge about general AL resident characteristics, use by lower income residents and racial/ethnic groups, as well as organizational factors that may explain differences in use. This section also reviews what is known about AL supply availability in rural areas. The next section proposes options for improving both national and special studies data, followed by suggestions for further research to inform policy, improve practice, and expand options for underrepresented LTC users.

	What We Know

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Most available data about LTC users and providers in the United States are oriented to persons living in the community or in nursing facilities. The supportive housing component of the LTC system (which includes AL, residential care, and board and care, among other labels) is much less understood. Knowledge about supportive housing operations and residents has generally come from case studies and convenience or purposive samples of facilities. Typically, these studies have been limited to single or a few states. Even the so-called national surveys have used facility and resident samples that do not permit national or statewide estimates. Industry-sponsored surveys, another source of information, have been compromised by low response rates and samples limited to association membership. The few attempts to develop probability samples within states have been underpowered to address subpopulations, such as racial/ethnic minorities, varied housing types, or geographic regions. Further complicating matters, most studies have been cross-sectional and have not tracked outcomes or behaviors among cohorts or panels over extended periods.

In spite of the foregoing disclaimer about the state of the science, the accumulated knowledge is beginning to reveal the "what" of possible differences among subgroups, but so far there is little understanding of the "why" such patterns might exist or of the number of persons affected.

General Resident Characteristics
Recent national and state studies have mostly focused on AL that serves a primarily older population. Looking across these studies, the typical resident seems to be a White, widowed woman in her mid-80s. Compared to the nursing facility (NF) population and the U.S. population aged 85 and older, AL residents seem to be more educated, less likely to be married, and more likely to be White (Gabrel & Jones, 2000; Hawes, Phillips, & Rose, 2000; Spillman, Liu, & McGilliard, 2002; U.S. Census Bureau, 2000). Although AL residents have significant LTC needs, they are generally less impaired than NF residents. Analyses of Medicare Current Beneficiary Survey (MCBS) data indicated that 1 in 2 AL residents needed supervision or personal assistance with three or more activities of daily living, compared to 3 in 4 NF residents (Spillman et al., 2002). (These analyses broadly defined AL to include retirement homes, personal care, continuing care, AL, and other forms of supportive housing.) A smaller proportion of AL residents (35%) were in this category 6 years earlier. Administrators in a national probability sample of more narrowly defined AL reported that on average, 1 in 4 residents were considered "heavy care" (Hawes, Rose, & Phillips, 1999). (Facilities included in this sample had to be larger than 10 beds; serve a primarily elderly population; and be a self-described AL facility or offer all of the following: 24-hr staff oversight, housekeeping, 2 or more meals per day, and personal assistance.) This more impaired category of residents needed hands-on assistance with locomotion, transfers, toileting, or eating. AL use by residents with cognitive impairments may also be increasing. For example, the proportion of AL residents with Alzheimer's disease and other dementia grew from 28% in 1992 to 35% in 1998 (Spillman et al., 2002). These trends are consistent with policy changes permitting higher levels of impairment in licensed housing (Mollica, 2002).

Use by Lower Income Residents
Knowledge about AL use by low- and moderate-income residents is fragmented and incomplete. At most, we can say that the proportion of low- and moderate-income residents varies depending on the type of facility included in the AL sample. Early studies reported that as many as half of board and care facility residents relied on public payment sources (Hawes, Wildfire, & Lux, 1993; Mor, Sherwood, & Gutkin, 1986). More recent studies have given more attention to subsets of the supportive housing sector and may disproportionately represent new and more expensive housing units (e.g., Hawes et al., 2000; Wylde, 1998). Findings in these latter studies suggest a decline in use among lower income residents. It is unclear whether such a downward trend may be related to sampling bias favoring newer, larger, and more expensive AL. Less ambiguous is that state Medicaid AL programs have not grown at the same rate as private supply (Mollica, 2002). Complicating matters, the most recent national studies (Hawes et al., 1999; Spillman et al., 2002) did not use comparable measures in classifying Supplemental Security Income (SSI) and/or Medicaid recipients. Similar problems limit comparison with earlier study findings.

AL resident income may provide some indication of accessibility by moderate- and low-income individuals. Hawes and colleagues (2000) collected income data from about half of the residents interviewed in a national probability sample of high-service or high-privacy AL. Compared to the U.S. population aged 75 and older (U.S. Census Bureau, 1999), a smaller proportion of study respondents had incomes less than $25,000 per year—82% and 71%, respectively (Hawes et al., 2000). However, the income distribution in this sample may have been skewed toward more affluent clients because resident income data were not collected from residents of the lower priced low-service and low-privacy facilities in the larger study (Hawes et al., 1999). Analyses of MCBS data suggested a widening income gap during the 1990s. Specifically, the proportion of residents with annual incomes less than $10,000 decreased from 55% in 1992 to 48% in 1998, while the proportion of residents with incomes greater than $20,000 almost doubled from 14% to 26% (Spillman et al., 2002). This trend may be due to a combination of factors, including a failure to adjust the income threshold for differences in the cost of living between the two periods, and the rather imprecise definition of AL that can be derived from the MCBS. It is also possible (although unexamined) that the expansion of other home- and community-based services during this period facilitated the aging in place of low-income housing residents, thus reducing the number of moves to AL among low-income persons.

One finding that seems reliable is that low-income residents may have less access to the newer facilities developed since 1995, which have targeted a more affluent clientele (Golant, 1999; Kane & Wilson, 2001). During the 1990s, institutional lending practices defined the target market for project viability at a minimum of $25,000 to $35,000 annual income per year (ProMatura Group, 1999). Public payment sources may represent a smaller portion of total revenues among newer, purpose-built AL facilities compared to traditional board and care facilities and smaller facilities (Stearns & Morgan, 2001). National data tracking changes in bed supply among these various types of facilities did not measure facility rent or whether facilities targeted to moderate-income individuals are leaving the housing supply or have shifted their enrollment.

A serious limitation of most studies is the lack of adequate information about resident income, assets, or payment sources to examine how lower income residents are paying for monthly AL charges. Answering key policy questions about barriers to AL use by lower income residents requires a better understanding of the mix of payment sources—whether public (e.g., SSI, SSP, Medicaid, Veterans Administration) or private (e.g., personal assets, family supplementation, LTC insurance)—that are supplementing personal income. Although limited by sampling methods and a low response rate, one industry-supported national survey suggested that 3 in 4 sampled residents purchased their services using personal resources alone. Other supplemental payment sources included family (16%), SSI (9%), Medicaid (7%), other (4%), private insurance (3%), state plan benefits (3%), and veteran's assistance (0.5%; Wylde, 1998). Earlier studies that included a more representative proportion of smaller facilities than this study found that SSI- and Medicaid-eligible persons accounted for about 1 in 3 residents (Phillips et al., 1995).

Use by Racial and Ethnic Groups
Beyond noting the proportion of White residents or comparing White to Black residents (Hawes et al., 1999; Hedrick et al., 2003; Kopetz et al., 2000; Sikorska, 1999), few studies have examined issues of race/ethnicity in AL. As a result, questions of differential use, needs, access barriers, preferences, and quality for people of color remain largely unexamined. Readers should consider what has been reported (and is summarized below) about use rates among racial groups to be tentative. Typically, studies did not stratify sample frames over sample facilities located in minority communities or produce sample sizes of minority residents that were large enough to yield a statistically meaningful subsample within the study facilities. (Several recent studies did not include resident race/ethnicity among the reported AL resident demographic characteristics; e.g., Edelman, Kuhn, & Kasayka, 2000; Kopetz et al., 2000; Levin, 2000; Pruchno & Rose, 2000; Whitebird & Kane, 2000).

In the context of these limitations, a pattern among national, multistate, and single-state AL studies suggests disproportionately lower AL use by people of color compared to their proportions in the national and state populations. Specifically, although non-Hispanic Whites compose 89% of the oldest-old (85+) U.S. population (U.S. Census Bureau, 2000) and 85% of the NF resident population (Gabrel & Jones, 2000), national studies have suggested that between 96% (Spillman et al., 2002) and 99% (Hawes et al., 2000) of AL residents are White. Although reported racial differences in proportional use among NF residents seem to be narrowing, the proportion of White AL residents grew from 91% in 1992 to 96% in 1998 (Spillman et al., 2002). Researchers have yet to examine the extent to which this trend may be the result of varying housing definitions, supply changes, or affordability. Possible culturally based explanations for differential use include shared attitudes and preferences for family care versus AL (Mutran, Sudha, Reed, Menon, & Desai, 2001; Sudha & Mutran, 1999).

Findings from the Collaborative Studies of Long-Term Care suggested that racial "separation" occurs across and within facility types. For example, most African American AL residents tend to be concentrated in predominantly African American facilities. In this four-state sample, a large majority of homes across AL categories had no African American residents. The study also identified several community-, facility-, and resident-level characteristics that were associated with having any African American residents. These included the proportion of African Americans in the community, having an African American administrator, and having a higher proportion of male residents and younger residents (Howard et al., 2002). In Florida, facilities with a high proportion of minorities had younger residents, a greater proportion of developmentally disabled and mentally ill residents, and a higher proportion of residents relying on public financial assistance (Salmon, Hyer, Hedgecock, Zayac, & Engh, 2004). Patterns of racial separation or unequal racial distribution may be due to a combination of economic factors, exclusionary practices, and resident choice (Howard et al., 2002). Choice in this context may include preferences for facility size, racial homogeneity, geographic location, or proximity to home. Researchers have studied few of these preference factors or actual discrimination.

Organizational Characteristics and Differential Use
AL studies identified several organizational characteristics that seem to be linked to differences in use by lower income and/or minority residents. Recognizing the usual cautions in generalizing findings across states and settings, there is some evidence that lower income and non-White residents are more likely to live in smaller, older, and lower priced homes that have fewer residential and safety-enhancing architectural design features (Dietz & Wright, 2002; Hedrick et al., 2003; Morgan, Gruber-Baldini, & Magaziner, 2001; Newcomer, Breuer, & Zhang, 1994; Salmon et al., 2004). Additionally, residents in facilities with more favorable scores in most process-of-care measures (e.g., acceptance of problem behaviors, provision of privacy, resident control, and service provision) were more often White (Zimmerman et al., 2003). Other important factors that may influence cost and access among different groups (e.g., staffing levels and mix, ownership, affiliation, geographic location, admission/discharge policies, and unit size) remain largely unexamined in this context. Two of these factors—size and residency criteria—have received more attention, as discussed below.

Size
The relationship between smaller AL size and higher use by lower income and non-White residents may be contingent on a number of factors. Among these are lower prices that are likely a function of geographic location, lower capital costs, and/or greater reliance on unpaid work by owners and/or staff. We found no studies examining how or whether public payment sources influence access to smaller versus larger AL facilities. However, considering the higher cost structures of larger, more recently developed AL facilities (Stearns & Morgan, 2001), it is unlikely that lower income residents are distributed across facility types unless public payment levels are adjusted accordingly. The experience of one state, Washington, is instructive. Washington has Medicaid rates tiered by facility type, location, and resident impairment level. One effect of this policy is that public pay residents represented almost half of the residents in both adult foster homes (average of 5 beds) and larger, non-apartment-style AL providers (average of 65 beds). However, apartment-style AL providers (average of 68 beds) had the lowest proportion (32%) of public pay residents (Curtis, Kiyak, & Hedrick, 2000).

Residency Criteria
Although studies have collected data about AL admission and discharge policies, few have examined whether these policies explain differential use by lower income or non-White residents. Providers may set criteria regarding permitted health, personal care, and behavioral needs that are more restrictive than required by state regulations (Chapin & Dobbs-Kepper, 2001; Newcomer et al., 1994). Restrictive residency criteria regarding activities of daily living, behavioral needs, and health needs apparently vary by state and facility type and size (Newcomer et al., 1994; Phillips, Hawes, & Rose, 2000; Salmon et al., 2004; Zimmerman et al., 2003). More restrictive criteria (although not directly tied to personal resources) are often associated with higher daily rates and may be more affordable for those with greater personal resources. For example, facility (and state) policies may allow exceptions to admission or discharge requirements that are contingent on an individual's ability to secure private duty care. This would not usually be an option for lower income residents. Further complicating matters, such exceptions for hospice or home health care may be contingent on a facility's access to third-party providers.

AL providers may also have residency requirements that are tied to individual financial resources and payment source. State regulations do not typically restrict facilities from defining such criteria (Mollica, 2002). Providers may enact such restrictions due to their aversion to public payment programs. Some providers believe that public programs are unresponsive to cost increases and are associated with increased regulatory oversight (Manard & Cameron, 1997; Mollica, 2002; O'Keeffe, O'Keeffe, & Bernard, 2003). Other possible explanations for state differences include a facility resident's access to supplemental payments for personal care services; the level of demand for supportive housing within the market area; and the availability of alternative services, such as home- and community-based services and nursing homes. We found few studies of such influences at a given point in time and none that monitored effects over time.

Rural AL Supply Availability
Many rural communities have a large proportion of older adults who face structural barriers to accessing a variety of health care and LTC services—including AL. Among these are a limited service supply and individual socioeconomic factors (Rogers, 2002). Consistent with this expectation, one study found that community-based Medicare beneficiaries (after controlling for age, socioeconomic status, and health) living in rural areas were at significantly greater risk of transitioning to an NF compared to those in metropolitan areas. There was, however, no significant difference for entering an AL facility (Waidmann & Thomas, 2003).

The actual distribution of AL among rural and nonrural settings is much less documented than the preceding finding would suggest. For example, Hawes, Phillips, Holan, and Sherman (2003) reported that 23% of AL facilities (size 11 beds) and 15% of AL beds in 1998 were located in rural areas, compared to about one fifth of the U.S. older population. They suggested that there was a substantial undersupply of rural AL beds relative to the distribution older adults. It is not clear whether one would reach such a conclusion with the inclusion of smaller (<11 beds) facilities, which may be more likely to be found in rural communities. Enumerations in other states have shown wide variation in the proportion of facility types located in rural communities. For example, 63% of small (<16 bed) facilities in North Carolina were located in rural communities compared to 3% in Maryland, 10% in New Jersey, and 7% in Florida (Zimmerman et al., 2001). More to the point of access to services, a study of five states found that between 18% and 30% of the counties in Kansas, Mississippi, Ohio, and South Dakota (and most of the rural counties) had no licensed AL facilities in 1995 (Newcomer et al., 2001).

Knowledge about how rural AL compares to nonrural AL in terms of the quality of care provided or other resident outcomes is limited by sample frame exclusion and sample size when included. Hawes and colleagues (2003) provided one of the few "national" studies of rural AL. They found that, compared to metropolitan AL facilities, nonmetropolitan AL facilities tended to be much smaller and less likely to be a self-described AL facility. Nonmetro facilities were also more likely to have a higher proportion of semiprivate accommodations and be categorized as low service in the study's nomenclature. (The low-service distinction should be qualified because the full-time registered nurse requirement to be categorized as high service was not adjusted for AL size—nonmetro facilities were about half the size of those in metropolitan markets.) Paradoxically, case-mix characteristics were fairly similar, and rural AL facilities appeared to be more willing to retain residents with temporary nursing needs and to have lower monthly rates.

Economies of scale, market opportunities, financing, and state policies likely affect the supply and distribution of facilities. There is some indication that facilities may be clustered in selected counties rather than being distributed among counties (Newcomer et al., 2001). Researchers have not studied either the factors influencing this, or the effects of this on residential relocation patterns or other regional service systems. Changes in the rural AL supply in recent years, either from new construction, NF conversions, or adaptation of preexisting housing, have not been reported, but it is known that during the late 1990s, at least two of the largest national chains (Mullen, 2003) targeted new development efforts in nonurban communities. Rural states such as Iowa and Nebraska have adopted programs to facilitate NF conversions to AL facilities (Mollica, 2002). Ten states participated in the Coming Home Program with the Robert Wood Johnson Foundation (2004). This program provided state grants, development funding, and technical assistance in support of development efforts for 31 existing and 73 new projects. Using illustrative case studies from four states, Jenkens, Carder, and Maher (2004) described some of the common challenges and successful strategies for developing affordable AL, including in rural communities. Challenges included the lack of affordable small-scale models to replicate, experienced providers and developers, commitment among state finance agencies, and interagency coordination. A range of strategies was used to support affordable AL development, such as providing technical assistance to states and organizations, facilitating expert assistance using operations and development consultants, facilitating cross-agency partnerships, and providing predevelopment loans.

	Enhancing Existing Knowledge

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Many of the limitations in knowledge about the distribution of the population by age, income, racial/ethnic group, and rural location in AL can be resolved if national, catchment area, and service inventory surveys and databases were modified to include sample frames and appropriate living quarters classifications. At least 6 of the more than 75 national and catchment area surveys have the potential to provide national estimates on populations in supportive housing and to monitor changes in these living quarters. None of these currently do so. (See Newcomer & Maynard, 2002, for a review of these data sets.) For example:

1. The U.S. Census and the American Housing Survey, two major surveys of housing and the population, have limited measures of disability.
   
2. Most national surveys of population health characteristics (including the National Health Interview Survey) have samples designed to produce state and national estimates of the community-dwelling population. Such samples exclude persons in noncommunity settings (e.g., nursing facilities, licensed residential care, and other group housing or living quarters).
   
3. The Medical Expenditure Panel Survey, which includes samples of the NF population, does not expressly sample persons in AL or supportive housing.
   
4. Most surveys (with the exception of the Medical Expenditure Panel Survey, the MCBS, and the Health and Retirement Study) provide cross-sectional descriptions of the community population. This limits measurement of changes in health status and functional support and how changing status may be related to one's living arrangements and living quarters.
   
5. Living quarters classification codes used by both population surveys and the panel studies do not distinguish the level of services provided by the housing facility or whether the facility is licensed as a form of supportive housing. Good definitions would eliminate the need to approximate supportive housing enumerations in the MCBS, for example.
   
6. Encouragingly, the National Center for Health Statistics, the Agency for Healthcare Research and Quality, and the Office of the Assistant Secretary for Planning and Evaluation are collaboratively designing a cross-sectional survey of a nationally representative sample of licensed housing serving older adults. It will likely be fielded for the first time in 2008. The planned sample frame will be stratified by facility size, but these strata may be too small to provide reliable estimates by racial and income subgroup. The American Housing Survey modified its sample design in 2007 to include licensed housing. Researchers can expect the first results from this survey in 2008.
   

Even as improved national data emerge, it is important to recognize that many policy, quality assurance, and other oversight needs require local facility-level information. The Centers for Medicare & Medicaid Services has implemented such data systems in cooperation with states for nursing homes. These data systems are the On-Line Survey, Certification, and Reporting System (OSCAR) and the Resident Assessment Instrument. Similar data systems do not exist for AL nationally. Researchers could compile from periodic housing licensing surveys a data system adapted from OSCAR methods. This would provide a relatively low-cost enumeration of the licensed housing supply and profile key facility and resident attributes. Resident-level data would be much more expensive to collect. Such a process would likely require adopting a standard uniform assessment and care planning tool. This has not received widespread support (Assisted Living Quality Coalition, 2000; Polivka, 2003), although some states have begun using a resident assessment instrument for persons participating in the Medicaid AL waivers (Hawes, 2002).

Interaction of State Policy and Supply and Demand Factors
A key policy question is the relationship between state policy, other environmental changes, and LTC supply and demand. For example, if state AL policies alter the demand for and supply of LTC services, what might be the effects on bed supply, case mix, Medicaid budgets, and access to AL services by racial/ethnic and income groups? To what extent are changes in supply affected by state policy versus local conditions, or other factors? The OSCAR-type database proposed in the previous section would provide information for monitoring changes in supply, resident attributes, and other outcomes occurring after a policy introduction (e.g., LTC reimbursement, eligibility, and nursing home transition initiatives).

Explaining Use by Lower Income Residents
Comparative state studies are needed that examine the availability of public funding and use by lower income residents, particularly those with higher service needs. Such residents may find it more difficult to access AL in states where combined public payment sources (e.g., Medicaid, SSI, SSP) provide little incentive to admit such residents or retain them as their service needs increase. Natural experiments—represented by the varying state Medicaid, quality assurance, and service eligibility policy interventions—present opportunities to investigate these questions. Much work has documented the range of policy options that states have used to facilitate access for low-income residents using SSP, Medicaid funding, and development financing (Harrington, LeBlanc, & Wong, 2002; Mollica, 2002; O'Keeffe et al., 2003). This work has documented the statewide supply of selected supportive housing settings, but it has not monitored changes in supply at the community level, or the changes in access and use by target populations, such as those considered at risk for NF admission. Studies are also needed to improve understanding of the internal and external factors that have enabled states to adopt more generous public support programs for AL. Such work could look at the state policy-making process, as well as the consequences of AL policies (e.g., amount of public payment) on the number of individuals who have access to the payment and the number of providers willing to accept residents under these programs.

Understanding Differences in Use by Racial/Ethnic Groups
Another area of needed study would explore the relationships between race/ethnicity, attitude formation, decision-making processes, and AL use. For example, what are the different and shared assumptions, experiences, and attitudes of various racial/ethnic groups and subgroups toward potential and actual AL use? Would facility design and programmatic modifications improve the cultural acceptability or relevance of AL facilities? Are changes needed in basic areas such as dietary and social/recreational programming to match the expressed preferences of different racial/ethnic groups?

Although providing more culturally relevant or appropriate care may go a long way toward increasing AL use by non-White residents, the literature suggests that race differences will persist due to a number of other structural factors (Wallace, Abel, Stefanowicz, & Pourat, 2001; Wallace, Levy-Storms, Kingston, & Andersen, 1998). Among these are the role of public payment sources, income/asset differences, professional referral patterns, AL outreach strategies, and previous client experiences when considering residential LTC options. Variations among state programs can create natural experiments, one approach for addressing these questions.

	Concluding Comments

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The AL and residential care industry is in transition, attempting to adapt to changes in health and LTC policy, market opportunities, and consumer preferences. The most visible changes include practices by states and providers to redefine the levels of frailty that can be maintained in supportive housing settings, and a substantial private sector investment in the construction of AL facilities. These changes are occurring in an historical context in which relatively little is known about the population served in supportive housing or about the quality of assistance that might be available as frailty levels increase. This article has provided an overview of the even more limited knowledge about some of the subgroups potentially using AL. Among these are racial and ethnic minorities, persons with low income, and rural residents. The themes and trends suggested by this work raise concerns about declining or uneven access to AL by these groups. Studies have also suggested, but inadequately investigated, the factors that may be contributing to disparities in access. Experts can address some of the limitations in the current research with relatively minor modifications to existing data systems. With better data it will be possible to monitor the effects of both public and private sector changes on consumer demand, provider supply, and quality of care in supportive housing; to take advantage of the variation in practices among and within states; and to evaluate the effects.

	Footnotes

 
An earlier version of this article was presented at a working conference, Developing a Research Agenda to Shape and Approve Assisted Living, Arlington, Virginia, June 12–13, 2004, funded by a grant from the Agency for Healthcare Research and Quality and honorarium funding from the U.S. Department of Health and Human Services. The Minnesota Department of Social Services supported the commissioning of this article. Additional support was provided by Grant 5 T32 HS00086 from the Agency for Healthcare Research and Quality.

¹ Department of Social and Behavioral Sciences, University of California, San Francisco.

Decision Editor: Susan L. Hughes, DSW

Received for publication June 13, 2006. Accepted for publication April 11, 2007.

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