Abstract

This study examined need, predisposing, market, and regional factors that predicted the likelihood of individuals with developmental disabilities living in state developmental centers (DCs) compared with living at home, in community care, or in intermediate care (ICFs) and other facilities. Secondary data analysis using logistic regression models was conducted for all individuals ages 21 years or older who had moderate, severe, or profound intellectual disability. Client needs were the most important factors associated with living arrangements, with those in DCs having more complex needs. Men had higher odds of living in DCs than in other settings, whereas older individuals had lower odds of living in DCs than in ICFs for persons with developmental disabilities and other facilities. Asians/Pacific Islanders, African Americans, and Hispanics were less likely to live in DCs than to live at home. The supply of residential care beds for the elderly reduced the likelihood of living in DCs, and the odds of living in a DC varied widely across regions. Controlling for need, many other factors predicted living arrangements. Policymakers need to ensure adequate resources and provider supply to reduce the need by individuals with intellectual disability to live in DCs and to transition individuals from DCs into other living arrangements.

State-operated institutions for individuals with intellectual and developmental disabilities include developmental centers, training centers, state schools, and developmental disability units in state psychiatric hospitals (Braddock et al., 2005). These state institutions, developed in the late 1800s, reached a peak in 1967, with 194,650 residents. Between 1960 and 2005, there were 130 closures of state institutions and the number of residents declined steadily to 41,214 individuals (Braddock et al., 2005). In addition to those individuals with developmental disabilities living in state-operated facilities, there were 89,064 individuals with developmental disabilities living in nursing facilities, private institutions, group homes, and other institutions; 180,060 individuals living in small homes; and 155,047 individuals living in supportive living arrangements across the country in 2004 (Braddock et al., 2005). Increasing numbers of individuals with intellectual and developmental disabilities are living at home (101,143 individuals, or 24.6%, in 2005), especially children and youth (Lakin & Stancliffe, 2007).

The reasons for the dramatic reduction in state developmental disabilities institutions have been widely discussed. Media exposés of poor care and custodial treatment in state developmental disabilities facilities and state mental hospitals brought attention to the need for reform. The demand for home and community based services (HCBS) has increased, especially with the growth of state Medicaid waiver programs, which spent $15.7 billion on 416,546 developmental disabilities participants in 2004 (Braddock et al., 2005). Class-action litigation (e.g., Wyatt v. Stickney, 1970, in Alabama) was another important factor that encouraged states to develop programs in the community, which intensified after the 1999 Olmstead Supreme Court ruling that prohibited states from refusing to provide HCBS when it is available and appropriate (Braddock & Fujiura, 1991; Braddock et al., 2005).

Although the number of individuals living in state institutions has declined, most states (all but nine states) continue to operate state facilities (Braddock et al., 2005). Some argue that state facilities are needed to care for individuals with profound developmental disabilities, even though there are individuals with the same level of disability and need living in the community. Some parents and advocates consider state institutions to be more stable than HCBS and have advocated for state facilities to remain open (McTernan & Ward, 2005). State hospital employees have also been important stakeholders who have attempted to block the closure of state facilities in some states (Castellani, 1992).

Moreover, many states have waiting lists for HCBS, which may unnecessarily force individuals with HCBS into institutional care or prevent their transition to the community (Kitchener, Ng, Miller, & Harrington, 2005). Community resistance to placement, fragmentation of services, and insufficient and inadequate community housing are also barriers to deinstitutionalization. These complex factors pose serious challenges for transitioning individuals from institutions to the community (Fujiura, 1994). Thus, state facilities continue to operate because of a complex set of factors.

Little is known about the factors associated with living in state-operated facilities compared with those living in private, institutional, residential settings or at home. Although some descriptive statistics are available from states showing the differences in the characteristics of individuals with developmental disabilities who are living in institutions versus the community (Burwell, Clauser, Hall, & Simon, 1987; California Department of Developmental Services, 2005; Freedman & Chassler, 2004; University of Minnesota Research and Training Center on Community Living, 2006), these studies have not explained which factors are the most important in predicting institutionalization. In addition, no studies on the effects of supply and demand on the use of development centers (DCs) or other living arrangements have been identified.

In this study, we address this knowledge gap by examining the factors associated with the likelihood of individuals with developmental disabilities living in state institutions compared with living at home or in other settings. Understanding these factors should bring greater attention to the variation in living arrangements and support both clinical practice and policy options that might facilitate the movement of individuals with developmental disabilities out of state institutions and into less restrictive settings. Thus, the purpose of this study was to examine the client predisposing and need factors along with the supply, demand, and state administrative structures associated with different living arrangements for individuals with developmental disabilities. The hypothesis was that client needs, client characteristics, provider supply, population demand, and state administrative structures would be important predictors of living arrangements.

All individuals age 21 years and over who were living in California state-owned or operated DCs (i.e., institutions) were compared with those living in three other settings: (a) at home, (b) in community care settings, and (c) in intermediate care facilities (ICFs) for the developmentally disabled and other private facilities in 2005. Those individuals with moderate to profound intellectual disability were included in the study because they were considered to be at risk for institutionalization (and those with mild intellectual disability were excluded).

California was selected for study because it had over 3,000 individuals living in state developmental centers (5 large facilities and 2 smaller treatment facilities) in 2005, even though the state had a well-established system of 21 state-funded regional centers to administer HCBS to individuals with developmental disabilities. The DCs were located throughout the state and varied somewhat in the services they provided, although they served the entire state population. The study was facilitated by California's large public-use database of developmental disability clients, which allowed for the comparison of different living arrangements. In California, developmental disability services (institutional as well as HCBS) are an entitlement, available to all individuals with developmental disabilities without regard to income eligibility.

Conceptual Framework

The conceptual model for this study draws on two frameworks: (a) Andersen's (1995) behavioral model and (b) economic theory. Andersen's behavioral model is a comprehensive approach to identify factors that influence health care utilization and outcomes. Recently, the Andersen model has been used in studies of access to and utilization of services for individuals with developmental disabilities and has been found to be a useful framework (Harrington & Kang, in press; Kang & Harrington, 2008; Magaña, Seltzer, & Krauss, 2002; Pruchno & McMullen 2004). The factors in Andersen's model that predict access and utilization of services include predisposing factors (e.g., client age, gender, and race–ethnicity), enabling factors (e.g., client income, insurance, marital status, and education), client-need factors that are either perceived/self-reported or evaluated by professionals (e.g., mental illness and physical disability), and health behaviors (e.g., practices such as drug and alcohol use). The Andersen model also includes the sociopolitical context and policy issues.

At the same time, economic models have been used to examine long-term care utilization by focusing on the supply of providers and demand (sociodemographic factors) for services, along with geographic variations in access for individuals with developmental disabilities (Harrington & Kang, in press; Kang & Harrington, 2008; Kitchener, Carrillo, & Harrington, 2003). Increases in the supply of alternatives to DCs should decrease the demand for DCs. Decreases in demand for DCs should result in reductions in the use of DCs, all other factors being equal. Because there are wide variations within most states in provider supply, sociodemographic (population) characteristics, and administrative structures, these factors could be expected to explain state DC utilization. Many other factors are probably related to deinstitutionalization, such as political issues, prison needs and construction, and union advocacy, but data on these factors are difficult to obtain and measure. The following section describes the specific factors examined in this study.

Client Characteristics

Need

Andersen's (1995) behavioral model predicts that need factors should account for most access to and utilization of services rather than predisposing characteristics. Decisions about living arrangements should be related to client needs and preferences rather than other factors such as age, race, and geographic region. Need factors include both physical and cognitive or intellectual and developmental disabilities. Because individuals assessed with severe or profound intellectual disability may have higher service needs, they may be more likely to use institutional services (Burwell, Clauser, Hall, & Simon, 1987; Freedman & Chassler, 2004; Pruchno & McMullen, 2004). Individuals with a dual diagnosis of developmental disablity and mental illness have complicated needs and may also be more likely to be institutionalized or to need community services (Bongiorno, 1996; Lunsky et al., 2006). Individuals with special behaviors (e.g., violent behaviors) may be challenging to manage in community settings, may use more services, may be more difficult to place in the community, and may be more likely to be admitted to and retained in psychiatric facilities (Essex, Seltzer, & Krauss, 1997; Freedman & Chassler, 2004; Pruchno & McMullen, 2004). Persons with developmental disablities and functional impairments, such as vision, hearing, and mobility impairments, are more likely to be living in institutional facilities (Freedman & Chassler, 2004).

In this article, we do not suggest that individuals with higher needs should be in institutions but that those with higher needs may be more likely to be placed or maintained in state institutional services. At the same time, in this study, we examine individuals who lived at home and in the community with the same level of need as those in DCs.

Predisposing Characteristics

Gender is an important predisposing factor. Within the developmental disabilities population, men have a greater risk of dying from disease at a younger age (Patja, Molsa, & Livanainen, 2001) and have shorter life expectancy than women (Bittles et al., 2002). Women with developmental disabilities are less likely to be employed or educated, more likely to suffer from a range of mental health–related problems (including isolation and depression), and may have inadequate access to mental health services due to low income and few social supports (Brown & Gill, 2002). On the other hand, men may be more likely to have behavioral problems and other special needs that could result in their living in institutions.

Age may also be an important predisposing factor. Individuals with developmental disabilities, like those without disabilities, may experience age-related disorders. Older individuals with developmental disabilities may not have parents or family members alive or able to care for them (Doka & Lavin, 2003). In the past, individuals with developmental disabilities were more likely to be institutionalized than today because there were fewer options available then; thus, it would be expected that fewer young people with developmental disabilities would be living in state institutions. Some older individuals with developmental disabilities may have been living in state institutions for many years and may be more difficult to transition to the community than younger individuals. Old age (for those without developmental disabilities) is also associated with greater limitations in functional capacity, which can significantly increase the risk of institutionalization (Miller & Weissert, 2000). For all of these reasons, we expected that older aged individuals with developmental disabilities would be more likely to be in state institutions.

Race and ethnicity are important predisposing factors in Andersen's (1995) model. Mental health services generally are broadly underutilized by many racial–ethnic minorities (Cauce et al., 2002; McCallion, Janicki, & Grant-Griffin, 1997) compared with White individuals. Lower utilization of nursing homes by Hispanics, African Americans, and other minority populations has been found (Wallace, Levy-Storms, Kington, & Andersen 1998). African Americans adults with developmental disabilities are more likely to have unmet needs for services (Pruchno & McMullen 2004) and lower utilization of professional services than Whites (McCallion et al. 1997). Asians and Hispanics may have lower utilization of mental health service use in general, perhaps because of language and cultural barriers (McCallion et al., 1997). Race–ethnicity may reflect culturally determined attitudinal and belief systems that affect the use of services. Disparities in access may also be related to discrimination and/or limited income compared with Whites. Thus, we expected minority groups with developmental disabilities to be less likely to be living in state institutions.

Supply and Demand Factors

In terms of demand, communities with larger minority populations may have higher needs for care and, therefore, have a higher demand for institutional care. Communities with higher income levels should have less demand for services because individuals living in these areas may be able to afford alternatives to institutionalization (Kemper, 1992). Areas with a higher supply of alternative beds to state institutions should have less use of state institutions. Because U.S. nursing homes have some residents with intellectual disabilities (Lakin & Stancliffe, 2007), nursing home bed supply may be a factor. Communities with more nursing home beds, ICFs for those with intellectual disabilities (ICF-MR), and residential (or community) care beds for elderly or nonelderly persons may reduce state hospital use. In California, residential care beds are either licensed for elderly (age 65 years and over) or nonelderly persons.

State Administrative Units: Regional Centers

California developed a comprehensive system for providing services to individuals with developmental disabilities by passing legislation in 1969. The legislation made services an entitlement for all individuals in California with developmental disability. The legislation created private, nonprofit organizations called regional centers to assess clients, plan, coordinate, and provide services through contracts to meet the needs of individuals with developmental disability (California Department of Developmental Services, 2007). The 21 regional centers have their own state funding stream, and state DCs have separate funding. Persons may be referred to developmental centers in California by the regional centers, the county mental health departments, and/or the judicial system. Because these regional centers vary in terms of the number and types of clients, funding levels, and administrative procedures, we expected that there would be variation in state DC utilization patterns across regional centers. Regional differences in the utilization of state institutional services are of concern because they can represent geographic inequities to alternative services in the community.

Method

As noted above, this study conducted a secondary analysis of public data to examine factors associated with individuals living in state developmental centers compared with individuals living (a) at home, (b) in community care settings, and (c) in ICFs/MR and other private institutions in California in 2005. To be eligible for developmental disability services in California, a person must have a disability that begins before the person's 18th birthday, that is expected to continue indefinitely, and that presents a substantial disability. Eligibility includes individuals with intellectual disability, cerebral palsy, epilepsy, autism, and other disabling conditions closely related to intellectual disability or that require treatment similar to individuals with intellectual disability; however, eligibility does not include other handicapping conditions that are solely physical in nature. Eligibility is established through diagnosis and assessment performed by the California regional centers.

Data

The data included all individuals who were living in developmental centers or who were assessed and determined to be eligible to receive developmental disability services through the regional centers in California as of June 30, 2005. The state client master file had a total of 204,777 individuals registered with the California Department of Developmental Services (DDS), and, of those, 178,936 had received a client assessment. Within this group, we examined only individuals age 21 years and over in the client master file who had a client assessment on file, because most individuals living in DCs and other institutional were 21 and over. After removing all individuals age 20 or under, there were 91,727 individuals remaining, which included 3,047 who were living in state DCs. Because the study focused on those individuals who were at risk for institutionalization, we then removed all individuals (51,611) who were assessed with mild or no intellectual disability specified because they had a low risk for being in a DC. This left a total of 40,116 individuals in the analysis.

Data Sources

Two secondary data sets on developmental disability clients were used for this analysis. The first data set was the client master file, which contained demographic and living arrangement data on all persons served by the California DDS. This information was matched to the client assessment data, and then all identifying information was removed before the files were given to the investigators. Information on regional centers was included.

The second data set was the developmental disability client development evaluation report (CDER; i.e., assessment) file, the largest developmental disability diagnostic information database in the world. It includes detailed data on each client's physical abilities, language, vision, cognitive functioning, psychological status, social functioning, behavioral problems, medical conditions, special conditions, special aids, and care needs. This is a uniform database completed for each developmental disability client in the state when the individuals enters the developmental disability system; it is updated generally every 3 years. These two data sets were linked together for the analysis.

In addition, we used the U.S. Census (2000) data on demand variables in the population (i.e., race and income per capita). For area provider supply, facility data from the California Departments of Health Services and Social Services were used. Because of lack of access to information on the county for each client, the demand and provider supply variables were calculated for each of the 21 regional center areas. The regional centers generally encompassed more than one county (58 counties), except in the case of Los Angeles, which was broken into 7 regional centers because of its large population. It should be noted here that limitations in the California client database did not allow us to examine enabling factors as defined by Andersen (1995), such as parent income, education, health insurance, and parent marital status.

Data Analysis

Using the client master file, the living arrangement was identified for each client age 21 years and older who was classified as having moderate, severe, or profound intellectual disability. Of the total 40,116 individuals, 2,595 were living in DCs (Group 4). There were 19,649 individuals living at home or in independent living or supportive arrangements (Group 1). Group 2 was identified as those individuals (10,438) who were living in community (residential) care facilities with 3 to 15 beds, family homes, or foster homes. Last, Group 3 consisted of 7,434 individuals living in ICFs, ICFs for developmentally disabled persons, ICF for developmentally disabled persons/habilitation, ICFs for developmentally disabled persons/nursing facilities, skilled nursing facilities, large community care facilities (16 beds or more), acute care hospitals, and other types of facilities.

The data analysis consisted of two parts. First, descriptive statistics were calculated for all the variables that compared those individuals living in each setting. Second, logistic regression analyses were conducted using SAS (Version 9.1) to examine the factors that predicted those individuals who lived in DCs compared with those in each of the three groups identified above.

The factors were divided into the following five categories: (a) client need characteristics, (b) client predisposing characteristics (age, gender, race–ethnicity), (c) provider supply (nursing homes and residential care facilities [RCFs] for elderly and nonelderly beds per population and whether there was a DC in the area), (d) demand variables (population characteristics of living in regional center areas [i.e., race–ethnicity and income]), and (e) regional centers. Client race–ethnicity variables were categorized as White (the comparison group), Asian or Pacific Islanders, African American, Hispanic, or other (which included those who were unknown, Native American, or mixed-race categories). For the demand variables, we used race–ethnicity and income in the RC area but excluded age and gender in the population because they were highly related to race–ethnicity and income.

Three logistic regression models were conducted to compare those individuals living in DCs with individuals living in each of three settings: the home and independent living, community care facilities, and in ICFs, ICFs for developmentally disabled persons' facilities and other institutions. These analyses were compared separately and not simultaneously.

Each analysis was conducted by examining groups of variables separately to compare the effects of each group. In all regressions, need characteristics were entered into the model first, because these variables should account for most of the variation in service use and, ideally, these need factors should be considered first in making decisions about living arrangements before other factors, such as predisposing factors, are considered. Client predisposing factors were entered next. Area demand factors and provider supply were entered next into the model. We could not include the supply of ICF in the model because many of the DC beds are licensed as ICFs, and these were highly correlated with nursing home and residential care beds.

The three regression analyses were repeated, substituting the specific RCs for the provider supply and area population characteristics. We only show the combined model for the DCs compared with each of the three living arrangements in the tables, but we report the pseudo-R2 for each set of variables that were entered into the models.

As Menard (2000) suggested, we calculated a pseudo-R2, described by McFadden (1974) as the coefficient of determination. Because the logistic regression output in SAS does not provide this pseudo-R2, it was calculated manually and was defined as −2{ln(L0) − ln(L1)}/{−2 ln(L0)} or 1 − ln(L1)/ln(L0), where −2ln(L0) is the −2 log-likelihood of the intercept-only model, and −2ln(L1) is the −2 log-likelihood for the specified model.

Results

Descriptive Statistics

Table 1 shows the descriptive statistics for the sample of individuals age 21 and over comparing those living in a DC with those living in each of the three alternative living arrangements. The 2,595 individuals living in DCs had higher needs than those living at home, in community settings, or in private institutions. For example, 73% of those in DCs had profound intellectual disability compared with 10% of those living at home, 17% of those living in community care settings, and 46% of those living in private institutions. On the other hand, there were 1,977 individuals with profound intellectual disability living at home, 1,783 living in community care, and 3,406 living in private institutional settings compared with 1,903 living in DCs. Table 1 also shows that men were (61.3%) more likely to be living in DCs than women. Whites were more likely to be living in DCs than Asians, African Americans, and Hispanics, who were most likely to be living at home. The mean age of individuals was higher in DCs than in other settings.

Regression Analyses

Using three separate logistic regression analyses, the odds ratios for living in a DC compared with living in each of the three group settings were calculated. The results are shown in Table 2.

Client Need Characteristics

Table 2 shows that many need variables were significant predictors of living in a DC compared with living in other settings. Individuals with profound intellectual disability were 27 times more likely to be living in a DC than at home, 4.6 times more likely to be living in a DC compared with living in community care, and 3.6 more likely to be living in a DC compared with living in ICFs for developmentally disabled persons and other facilities. In general, this table shows that need increased from those living at home to living in community care, and to ICFs for developmentally disabled persons and other facilities, with DCs having the highest need.

Individuals with severe intellectual disability, autism, an increasing number of medical problems, dual diagnosis, special behavioral problems, who did not understand spoken words, severe behavior problems, and special health care needs were all more likely to be living in a DC. Individuals taking behavior-modifying drugs were less likely to be living in a DC than in a community care facility or living in ICFs for developmentally disabled persons and other facilities. As expected, need variables accounted for most of the variation in living in a DC (ranging from 81% of the pseudo-R2 value compared with those living at home, 68% for those living in community care, and 58% for those living in ICFs for developmentally disabled persons and other facilities; Table 2).

Client Predisposing Characteristics

Table 2 shows the effects of the predisposing characteristics. Men were more likely to be in DCs than women (ranging from 1.9 times more likely than living at home to 1.5 times more likely than living in ICFs for developmentally disabled persons and other facilities). Older aged individuals were more likely to be living in a DC than living at home.

Asian or Pacific Islanders and African Americans were less likely to be living in DCs than at home. Hispanics were less likely to live in DCs than at home (0.27 odds) and in community care settings (0.71 odds), but there were no differences between living in DCs and living in ICFs for developmentally disabled persons and other facilities. Other racial–ethnic groups were more likely to be living in DCs than in community settings or in ICFs for developmentally disabled persons and other facilities. The client predisposing factors accounted for a 2.5% to 0.4% increase in the pseudo-R2 value over the need variables (Table 2) across the three models.

Provider Supply Variables

Table 2 shows that provider supply influenced utilization of DCs. Individuals in areas with more nursing home beds per 1,000 were more likely to be living in DCs than at home (1.45 odds), in a community care facility (1.41 odds), and in ICFs for developmentally disabled persons and other facilities (1.47 odds). Individuals living in areas with more community care beds for the elderly had lower odds of living in DCs than at home (0.57 odds), in a community setting (0.73 odds), and in ICFs for developmentally disabled persons and other facilities (0.84 odds). The supply of nonelderly community care beds in an area had no effect on living arrangements. Supply variables accounted for a 0.1% to 0.7% increase in the pseudo-R2 value over need and predisposing variables (Table 2).

Population Characteristics

Table 2 shows that individuals living in geographical areas with a higher percentage of Hispanics were less likely to be living in DCs than in community care facilities or in ICFs for developmentally disabled persons and other facilities. Individuals living in areas with higher percentages of African Americans in the population were more likely to have individuals living in DCs than living at home and more likely to be living in DCs than living in ICFs for developmentally disabled persons and other facilities. Individuals living in areas with higher incomes were more likely to be living in DCs than living at home (1.18 odds), than in community care (1.08 odds), or in ICFs for developmentally disabled persons and other facilities (1.08 odds). Last, individuals living in areas that had a developmental center had increased odds of living in a DC rather than living at home. The population characteristics increased the pseudo-R2 value between 1.1 and 1.5 over the models with need, predisposing, and supply factors (Table 2).

For the comparison of living in DCs with living at home (Group 1), the total model explained 85% of the variation. For comparison of living in DCs with living in community care facilities (Group 2), the model explained 69% of the variation, and the model explained 61% of the variation comparing DCs with ICFs for developmentally disabled persons and other facilities (Group 3).

Regional Centers

Table 3 shows that when regional centers were substituted for provider supply and area population characteristics, significant differences were found in regards to whether individuals were living in DCs. Controlling for need and predisposing characteristics, some regional centers had higher odds of clients living in a DC compared with the regional center with the lowest odds. For example, one regional center had odd ratios 20 times higher than the comparison center for living in a DC than living at home, odds 24 times higher than living in a community care setting, and odds 9.6 times higher than living in an ICFs for developmentally disabled persons and other facilities, controlling for need and predisposing factors. Regional centers as a group predicted an additional 2.1%–3.2% of the variation in living in DCs, after taking into account need and predisposing characteristics (Table 3).

Discussion

In California, where the state DC population has declined substantially over the past 15 years, we expected that those individuals with developmental disabilities living in state DCs would have higher needs than those living in other settings. The regression models showed that client needs were the strongest predictive factors for living in DCs, consistent with the literature (Bongiorno, 1996; Essex et al., 1997; Krauss, Seltzer, & Jacobson, 2005; Lunsky et al., 2006; Pruchno & McMullen, 2004). On the other hand, individuals with high needs were also living at home, in community care settings, and in ICFs for developmentally disabled persons and other facilities. Individuals with comparable needs can live at home or in community settings when appropriate services and supports are available. Because of the complex, multiple needs of those living in DCs, the state would need to ensure adequate services to prevent institutionalization or support the transition of these individuals into the home or to community settings.

Controlling for all need factors in the model, men were more likely than women to live in DCs compared with the other three settings but especially more likely to be in DCs than at home. This finding raises questions about whether men were being offered adequate support to live at home and in the community.

As expected, the odds of older aged groups living in DCs were higher than living at home. This may be because some individuals living in DCs have aged in these settings. Others individuals may have been admitted to DCs before community alternatives were available and/or before judicial hearings were required. Perhaps, the older individuals living in DCs may have aging parents or have lost their parents and family members (Doka & Lavin, 2003). For older people, the lower odds of living in DCs than in ICFs for developmentally disabled persons and other facilities suggest that older individuals may be more likely to be placed or transferred to ICFs for developmentally disabled persons and other facilities than younger individuals.

Asians and Pacific Islanders, African Americans, and Hispanics all had lower odds of living in a DC than of living at home, and Hispanics had lower odds of living in DCs than in community care settings, controlling for need. These findings are consistent with studies that showed lower use of nursing homes and home and community based long-term care services for these groups (Miller & Weissert, 2000; Wallace et al., 1998). These patterns may reflect family preferences. Lower use of DCs may be a positive finding, as long as minorities with developmental disabilities receive appropriate home and community based services. Unfortunately, a recent study in California found that Asians–Pacific Islanders, African Americans, Hispanics, and other races were all less likely to receive home and community based services from state regional centers than Whites and that expenditures for those who did receive services were significantly lower for all minority groups (Harrington & Kang, in press).

Confirming our hypothesis, this study showed that provider supply and population characteristics in a region had an impact on the odds of living in a DC. A higher number of nursing home beds in an area increased the odds of living in a DC compared with living in other settings. Perhaps areas that are more generous in providing nursing home beds are also more likely to have DCs beds available. Or, individuals living in areas accustomed to placing individuals in institutions may be more accepting of placement in DCs. Having a DC in the area increased the odds of living in a DC compared with living at home. Perhaps if a DC is located in close proximity to an individual's home, families are less reluctant to have their family member live in a DC.

Although provider supply and population characteristics of a region do not account for a large percentage of the variation in living in DCs, RCF for elderly beds appeared, as expected, to substitute for living in a DC. Surprisingly, licensed residential care beds for nonelderly individuals had no effect on living in a DC, when controlling for population characteristics and other factors. Approximately 65% of residential care beds for nonelderly persons in California are dedicated to clients with developmental disability. Perhaps some of these nonelderly residential care facilities primarily serve children and are unavailable for adults, or perhaps the overall supply of such facilities is inadequate to meet the demand for residential care.

Although regional centers do not control provider supply for nursing homes, ICFs for developmentally disabled persons, and residential care facilities, they may influence supply by placement and/or contracting practices. Moreover, the regional centers have financial incentives to place individuals in ICFs for developmentally disabled persons, nursing homes, and DCs because Medicaid (Medi-Cal) pays for these basic services directly, although regional centers may provide some supplemental services to such clients. Inequities in supply across the state suggest the need for greater regional planning for community alternatives to DCs. Ways to reduce the financial incentives for regional centers to place individuals in DCs and other private facilities could be examined.

Individuals living in areas with higher African American populations had higher odds of living in a DC compared with living at home or in ICFs for developmentally disabled persons and other facilities (suggesting higher demand), but this is not consistent with the findings for individual African American clients who were less likely to be living in a DC than at home. Perhaps individuals in these areas have inadequate home and community based services, which may encourage placement in DCs. Individuals living in areas with more Hispanics had lower odds of living in DCs compared with living in community care setting or in ICFs for developmentally disabled persons and other facilities (suggesting lower demand), which was consistent with the lower use of DCs by individual Hispanic clients. Individuals living in high-income areas had greater odds of living in DCs. Perhaps, higher income populations are less tolerant of individuals with developmental disabilities living at home and in the community, which, in turn, encourages institutionalization, consistent with Mercer's (1965) classic study on labeling of persons with intellectual disability, where high-status parents were less optimistic and accepting of children with intellectual disability. Or individuals living in high-income areas may be more demanding of services and believe that the DCs may offer more comprehensive or stable services than those provided in other settings.

As expected, there were wide differences in the odds of living a DC in the 21 regional center areas in California, where some regional centers had very high odds of individuals living in DCs compared with other centers, controlling for need. The differences in provider supply and population within the regional center areas accounted for some of the variation as shown, but other variation was beyond these factors. Some variation may be related to differences in regional center resources and in administrative decision making and management (e.g., their expertise, commitment, and/or experience). These variations should be examined in more depth to understand their causes and the barriers to deinstitutionalization.

The level of funding for the regional centers in California may also encourage placement in DCs or be a barrier to transitioning individuals from DCs to living in the home and community. California ranked 39th among states in its fiscal effort to support developmental disability services in 2002 (having dropped from its ranking of 37th in 1997; Braddock & Hemp, 2004). The California waiver that focuses on spending for home and community based waiver services for developmental disabilities was $29 per capita in 2005, making it 45th among states (Burwell, Sredl, & Eiken, 2006). Although there are no formal, written waiting lists for the developmental disability waiver in California, there have been informal reports of waits for appropriate services and/or problems with the allocation of waiver services. The California waiver for nursing facilities has had a waiting list and limited slots, which also may result in some individuals using DCs unnecessarily. Thus, limited funds and limited availability of home and community based services may be important barriers to deinstitutionalization in California, and these can add to the regional variations within the state.

This study had several limitations. The lack of information on family preferences, family characteristics (e.g., number of children and marital status), social supports, family income, health insurance, primary care providers, and data at the county level limited the analysis. Decision making about institutionalization or residential care is clearly related to family situations (which were not included in this analysis), including the characteristics of parents and the family, extent of help from children, role demand, family relationships, and social supports (Pruchno & Patrick, 1999). California should consider adding this type of information to its client databases to allow for a more comprehensive analysis of access to developmental disability services. Moreover, the lack of information on case management and regional center administration did not allow for inclusion of these factors in the model to understand how these factors may influence client outcomes.

This study was also unable to examine other factors that may be associated with institutionalization, such as political pressures, state hospital unions, parent organizations, regional center financial differences, waiting lists for home and community services, fragmentation of services, insufficient and inadequate housing, and other barriers to deinstitutionalization. These can also explain the continued use of state DCs and need to be examined in future studies.

This study demonstrates that although client need was the strongest factor predicting living arrangements, there were a number of factors beyond client needs that predicted living in DCs. Policymakers need to consider these factors in establishing policies and/or allocating funds to the regional center programs to ensure access to home and community services. If regional center programs were allowed to use the funds for an individual in a DC (e.g. using a money-follows-the-person approach), then it is likely that more individuals in DCs could be deinstitutionalized. The costs of living in a DC (average of $258,000 per individual in 2006–2007; California Governor's Budget, 2007–2008) appear to be sufficient to ensure adequate home and community services.

This study points to the need for additional research about the use of DCs in states. It is likely that the inadequate supply of and geographic variation in the supply of home and community based services are major factors that limit deinstitutionalization. The waiting lists for services, limited funding for HCBS, and lack of appropriate services to maintain individuals at home and in the community need to be addressed by policymakers if states expect to move forward with deinstitutionalization.

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Charlene Harrington, PhD, RN (charlene.harrington@ucsf.edu), Professor Emeritus; Taewoon Kang, PhD, Research Assistant; and Jamie Chang, MA, Doctoral Student, Department of Social and Behavioral Sciences, University of California, San Francisco, 3333 California St., Suite 455, San Francisco, CA 94118.

Editor-in-Charge: Susan Parish