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a Department of Psychology, Colorado State University, Fort Collins
b Florida Policy Exchange Center on Aging, University of South Florida, Tampa
c The Center on Aging, Florida International University, North Miami
Correspondence: Evelinn A. Borrayo, PhD, Department of Psychology, Colorado State University, Fort Collins, CO 80523. E-mail: borrayo{at}lamar.colostate.edu.
Decision Editor: Laurence G. Branch, PhD
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Abstract |
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Key Words: Public policy • Nursing facilities • Assisted living facilities • Home care • Public financing • Long-term care utilization
The rapid aging of the population, especially increases in the oldest old (those aged 85 and older), along with stagnation or decline in the availability of federal and state resources for health and long-term care (LTC) services (G. D. Cohen 1994
; Rothman 1993; Steinmo and Watts 1995) has pressed states to address ways to serve the growing frail population in less expensive and less institutional settings. Overall, institutions such as nursing homes consume 75%–85% of the public LTC dollars.
LTC services are typically needed when a person has an ongoing, long-term disability as indicated by increases in physical and cognitive impairments or loss of function as measured by impairments in activities of daily living (ADLs). Investigation of utilization across the continuum of services is important in understanding where these adults are likely to receive services. To date, most research on LTC utilization is limited to the study of predictors of institutional or community-based LTC services rather than simultaneously considering the entire LTC continuum, including assisted living facilities (ALFs). The purpose of this study is to provide an analysis of frail older adults' utilization of nursing facilities (NFs), ALFs, and home- and community-based services (HCBS) on the basis of factors commonly associated with health care utilization (Andersen 1995).
This research adapts the behavioral model of health services use (Andersen 1995; Andersen and Newman 1973) for understanding how predisposing, enabling, and need factors affect use of LTC services (Bass and Noelker 1987; Wolinsky 1990). This model implies that both system factors, such as public policies, and population characteristics are associated with the use of formal LTC services (Wallace, Levy-Storms, Kington, and Andersen 1998).
Predisposing characteristics reflect the propensity to use services, independent of personal circumstances and experiences that may cause the need for service use. Typically, they are sociodemographic variables. Enabling characteristics explain differences in the resources available to the individual in using LTC services. Income and health insurance are likely to enhance service use while other sources of help from social support networks or publicly financed LTC may either facilitate or impede use of formal services (Miner 1995). Need factors include perceived or evaluated physical, cognitive, functional, and mental health status.
Need-based factors such as serious health problems and conditions are often recognized as the most immediate predictors of service utilization and have explained the largest share of variance in health care utilization (Andersen 1995; Mercier and Shelley 1997). Researchers have found that these characteristics significantly contribute to older adults' use of discretionary services such as in-home and community services and nondiscretionary services such as nursing home placement (when it is not the consumer's choice; Calsyn and Winter 2000; Temkin-Greener and Meiners 1995; Wolinsky, Callahan, Fitzgerald, and Johnson 1992).
Briefly, among predisposing factors, advanced age predicts LTC utilization, especially NF use, although age loses its saliency as a predictor of LTC among high-risk individuals (Coughlin, McBride, and Lui 1990; Mittleman, Ferris, Shulman, Steinberg, and Levin 1996). Still, nursing home admission rates nearly double for each additional 5-year increase in age (Hanley, Alecxih, Wiener, and Kennell 1990). Race and ethnicity discriminate between use of institutional and community-based LTC. Overall, African Americans and Hispanic older adults are significantly less likely than non-Hispanic Whites to use institutional LTC services and more likely to use both formal and informal community-based LTC services (Greene and Ondrich 1990; Murtaugh, Kemper, and Spillman 1990; Liu, McBride, and Coughlin 1994; Wallace, Levy-Storms, Andersen, and Kington 1997; Wallace, Levy-Storms, and Ferguson 1995). This is not a function of other demographic characteristics, of enabling factors such as insurance, or even of need as indicated by functional status. Instead, race and ethnicity have a significant independent effect in LTC use that may be related to one or more social processes such as culture, class, institutionalized discrimination, and geopolitical differences (Wallace et al. 1998).
Living arrangements, Medicaid eligibility, and geopolitical region are enabling characteristics identified as significant predictors for institutionalization, but are highly dependent on the nature of the study population. Impaired older adults who live with a spouse are half as likely to be placed in a nursing facility as those who live alone, are not married, or are cared for by another relative or friend (Boaz, Muller, and Hu 1994; Freedman, Berkman, Rapp, and Ostfeld 1994; Jette, Tennstedt, and Crawford 1995; Montgomery and Kosloski 1994; Weissert and Cready 1989). Medicaid eligibility increases the risk of nursing home placement (Branch and Jette 1982; M. A. Cohen, Tell, and Wallack 1986; Kelman and Thomas 1990; Mittleman et al. 1993). Individuals who are able to pay for LTC and not use Medicaid have a considerably lower probability of nursing home admission, even when faced with the same set of health conditions and cognitive impairment (Temkin-Greener and Meiners 1995). Regional differences in utilization are due to variations in the supply of LTC options: number of nursing home beds, number of available slots for alternative LTC services, or both (Coughlin et al. 1990; Dwyer, Barton, and Vogel 1994; Egleston, Rudberg, and Brody 1999; Rudberg, Sager, and Zhang 1996). For example, higher state Medicaid spending on HCBS was significantly associated with a longer time before NF admission among unmarried elders (Miller et al. 1998).
ADLs, with the exception of mobility, are the best need predictors of LTC utilization by frail older adults (Kemper 1992; Morris, Sherwood, and Gutkin 1988; Wolinsky, Callahan, Fitzgerald, and Johnson 1993; Wolinsky and Johnson 1991) and especially of nursing home placement (Egleston et al. 1999; Rudberg et al. 1996). As a critical need factor, ADL impairment frequently coexists with cognitive impairment, particularly later loss ADLs such as toileting, transferring, and eating. The compounding of risk factors places an individual at greater risk of LTC service use (Weissert and Cready 1989). Persons with cognitive impairment and ADL limitations experience a two- to threefold increased risk of institutionalization (Foley et al. 1992), and the severity of cognitive impairment is invariably found to be a significant factor for NF admissions (Coughlin et al. 1990; Heyman et al. 1987; Murtaugh 1994). Older adults with Alzheimer's disease or dementia are institutionalized at significantly higher rates (e.g., 50%, Severson et al. 1994; 75%, Welch, Walsh, and Larson 1992). Psychiatric and emotional problems increase the risk of LTC utilization. Depression, in particular, is a significant predictor of LTC placement (Steele, Rovner, Chase, and Folstein 1990; Stern et al. 1997). The presence of chronic health conditions also increases the probability of nursing home use (Kart and Palmer 1987; Mitchell, Matthews, and Hack 2000), especially when associated with recent hospitalizations or Medicaid eligibility (Temkin-Greener and Meiners 1995). However, private payers who experienced heart or respiratory disease or cancer were more likely to die in the community without having been admitted to a nursing home.
In summary, predisposing, enabling, and need characteristics are important in the prediction of NF utilization and, to a lesser extent, of HCBS utilization by frail older adults. There has been much less systematic analysis regarding how these factors are associated with the full continuum of services available (NFs, ALFs, and HCBS; Polivka, Dunlop, and Brooks 1997). Such analyses would aid policymakers in setting criteria for providing older adults who receive publicly funded LTC access to the full continuum of services on the basis of their needs and living arrangements. For example, if individuals with a high level of need prefer to live at home, then public funding of LTC should make home-based options as available as NFs are for low-income elders. This would be further demonstrated if need, rather than enabling factors such as income or medicaid eligibility, differentially predicted the utilization of the various LTC services available (Andersen 1995). On the other hand, enabling factors are often the most mutable and can be affected by changes in public policy. Need is often considered to be less mutable. However, people's perceived need for LTC may be changed through education about available programs or use of devices to maintain independence (Klerk, Huijsman, and McDonnell 1997).
In this study, our goal was to use the LTC continuum model as a conceptual framework to identify specific population characteristics that determine enrollment in LTC programs. In this effort, we tested three hypotheses. First, we tested whether need variables differentially contribute to explaining LTC utilization across the continuum of services. We expected that those with high need would be in the most institutional settings (NFs), followed by ALFs and HCBS. Second, we assessed whether enabling variables, especially mutable variables such as Medicaid eligibility and geographic region, significantly discriminate the utilization of LTC services, specifically because of geopolitical differences in the supply of NF beds and alternatives. Last, we tested whether the moderating influence of predisposing and enabling variables on need (e.g., race on cognitive impairment) significantly predicts the differential utilization of LTC services. This model has traditionally served to investigate how population characteristics influence LTC use. However, the utilization of different LTC services is a fairly complex issue that is related to the context of states' policy and programs and the private LTC market. Although we were not able to directly measure these complexities, we discuss our findings within this relevant context and make recommendations accordingly.
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Methods |
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Sampling.
The representative cross-sectional sample was selected to achieve a sampling error of no more than ±3% at the 95% confidence level. The total sample size included 600 NF residents, 650 ALF residents, and 1,000 clients from HCBS programs for a total of 2,250. Because NF and ALF populations reside in facilities, we used a two-stage random sampling procedure. First, facilities were randomly selected, and second, residents were randomly selected within facilities. Of the NF residents, long-stay residents were included in this analysis, and short-stay NF residents were excluded. The long-stay residents had been in the nursing home at least 90 days or, if admitted more recently, had no plans for discharge from the NF. The ALF sample included 350 private-pay residents plus an oversample of 300 ALF residents who received public funding through Optional State Supplementation. At the time, Optional State Supplementation was the only source of public financing of ALFs. Although NFs are standard across the states, ALFs have no standard definition. In 1995, the state of Florida officially changed the designation of adult congregate living facility to assisted living facility and defined it to be any building or part of a building that provides housing, meals, and one or more personal services for a period exceeding 24 hr to one or more adults not related to the owner or administrator.
For the HCBS population, a representative sample of 350 active cases from the Home Care for the Elderly, the Community Care for the Elderly (CCE), and the Medicaid Waiver programs was targeted. In addition, the following alternative demonstration programs were oversampled to ensure sufficient cases to determine program characteristics: the Channeling Program (n = 175), the Medicaid Prepaid HMO Frail Elderly Option program (n = 175) and the Medicaid Waiver program (n = 300). There were no program exclusions for the home-care sample.
Subpopulations were stratified geographically on the basis of the most recent state agency data for each region (north, central, west, and south Florida), and the sample was weighted to reflect the proportion of clients in each state region and in each program. For example, the CCE program is the largest (in terms of client census) of the HCBS programs, yet a larger sample of Medicaid Waiver clients than CCE clients was drawn. Thus, we weighted each CCE case to count more heavily, and the Medicaid Waiver cases were downweighted to adjust for the intentional oversampling of this relatively smaller population.
Data Collection.
Trained graduate nursing students extracted data from standardized assessment instruments used by state agencies and providers to assess clients in all three settings. Data were drawn from (a) the admission Minimum Data Set (MDS) Resident Assessment Instrument in NFs, (b) the most recently completed Department of Elder Affairs Uniform Assessment Instrument for HCBS and OSS-supported ALF populations, and (c) the ALF admission screening instrument (Department of Elder Affairs Form 1823) for private-pay ALF residents.
The data collection instrument included common elements from each of these assessment instruments and identified the location of each element on each standard form to minimize errors. Missing information was obtained using earlier assessments and complemented as necessary by interviews with administrators or staff regarding the characteristics of the residents.
Sample
The total LTC sample used for this analysis was 1,968 clients aged 60 and older, which included 474 residents from NFs, 599 residents from ALFs, and 895 HCBS clients. Table 1 displays sample characteristics by LTC setting. Overall, our 1995 Florida sample was less likely to be non-Hispanic White (75%) than were elders in the 1994 National Long-Term Care Survey (87%; Spector, Fleishman, Pezzin, and Spillman 2000). The Florida sample was also more likely to be female (75%) than the national sample (71%) and not married (77% vs 66%). On average, the Florida sample had impairments in 2.65 ADLs, whereas 43% of the national sample had impairments in 3 or more ADLs. In Florida, elders scored 1.6 on a 0–3 scale of cognitive impairment, and 40% of the national sample was cognitively impaired. These differences were expected given the demographic characteristics of Florida compared with the nation.
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), our sample was comparable in age. In our sample, 37% were between 75 and 84 years old compared with 37% of the frail Floridians, and 42% were 85 years and older compared with 27% of the frail Floridians. Our sample consisted of 75% women versus 69% of the frail Floridians. The sample was also quite impaired. On average, they had 2.70 chronic diseases and needed assistance with 2.65 ADLs (out of 5). As shown in Table 2 , 77% of the consumers needed help with bathing, 62% with dressing, 43% with transferring, 48% with using the toilet, 36% with eating, and 47% with mobility.
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Enabling resources available to the individual for LTC were measured by marital status, Medicaid eligibility, and geopolitical region of the state. The Medicaid eligible variable was coded "yes" if the individual was currently on Medicaid, eligible for supplemental security income, or participating in any welfare program. It was coded "no" if none of the conditions were true. Marital status was used to measure the enabling effects of social support. Individuals who are eligible for Medicaid and do not have a spouse are at higher risk for institutionalization. We expected to find these variables associated with NF utilization because Medicaid covers this option for low-income elders; it did not, however, cover ALFs at the time of the study. Region of the state (i.e., north, central, west, or south Florida) was included to represent geopolitical differences that affect LTC supply such as availability of nursing home beds and other LTC options. In a separate analysis of LTC supply and utilization data among Florida's counties, a significant inverse relationship was found between the percentage of low-income older adults in nursing homes and state expenditures on HCBS at the county level (Oakley 2000). This negative correlation was strongest in the regions of Florida where nursing home bed supply was at or below the state median of beds per 1,000 Medicaid-eligible population aged 65 and older. Institutionalization rates were twice as high among Medicaid-eligible elders in the Florida counties with nursing home bed supply above the state median than in the counties with supplies below the state median (32% and 16% respectively). Overall, we expected that frail elders who lived in regions outside of South Florida would be more likely to receive services in a nursing home, particularly in regions where the nursing home bed supply was above the state median and the availability of alternative LTC programs was more limited.
Measures of need included Alzheimer's disease, chronic bladder incontinence, assistance with mobility, ADL impairment, chronic diseases, and a separate measure of severity of cognitive impairment. Mobility was not included in the ADL score because it has a distinct predictive value from other ADLs in LTC models (e.g., Weissert and Cready 1989). Chronic diseases were counted if they were recorded as a client's diagnosis, whether or not these diseases were identified as the reason for receiving LTC services. Sixteen chronic diseases were included in the score: arthritis, emphysema/asthma, diabetes, gallbladder problems, gastritis/ulcers, heart disease, high blood pressure, cancer, Parkinson's, stroke, osteoporosis, anemia, urinary tract infection (past 30 days), renal failure, hypothyroidism, and pneumonia. Severity of cognitive impairment was used to provide a more precise level of need that is not represented by physical health, a diagnosis of Alzheimer's disease, or ADL measures (Wiener, Hanley, Clark, and Van Nostrand 1990). A variable was constructed that matched the Department of Elder Affairs Uniform Assessment Instrument Mental Status Questionnaire's score (severely impaired, moderately impaired, mildly impaired, and independent), based on the short Blessed Test, with the four-level scale of the Cognitive Skills for Daily Decision-Making scale from the MDS Resident Assessment Instrument for the NF and private-pay ALF residents.
Although past research has suggested that older people with very similar health and functioning profiles live in all three kinds of settings, the LTC system is structured to provide a higher level of care in NFs, followed by ALFs and HCBS. We expected that those with higher impairments in functioning and health would be in one of the two facility settings compared with HCBS.
Methods of Analysis
A multinomial logistic regression model was used to estimate the effects of differentiating predisposing, enabling, and need variables on utilization of services in one of the three settings (NFs, ALFs, or HCBS) when comparing each with one another. Interaction terms were included to improve the model's ability to predict service utilization (Kosloski and Montgomery 1994). Some predisposing and enabling variables have maximum impact on service use under conditions of high need (Calsyn and Winter 2000). Thus, we tested the interactions for race/ethnicity, gender, and marital status with need factors of cognitive impairment, functional status, and chronic diseases. The interaction between incontinence and cognitive impairment was also tested.
Before computing the interaction terms, we created center scores (Aiken and West 1991) for continuous variables (cognitive impairment, functional status, and number of chronic diseases) to avoid problems with multicollinearity. All other variables in the interactions were dichotomies (female = 1, non-Hispanic White = 1, married = 1). Interactions were entered into the regression equation as the product of the two variables. Significant interactions were retained in the final analysis reported here. Dropping nonsignificant interactions simplified the final analysis and further decreased the collinearity inherent with interaction terms (Bass, Looman, and Ehrlich 1992).
The same multinomial logistic regression was run twice, first to compare NFs versus HCBS and ALFs versus HCBS and then to compare NFs versus ALFs. A hierarchical approach allowed us to compare the predictive power of the predisposing variables (race/ethnicity, age, gender), enabling variables (marital status, insurance, region of the state), need variables (functional disability, chronic diseases, and cognitive impairment), and significant interaction terms (e.g., chronic diseases and gender) on LTC utilization across the continuum of care.
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Results |
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2(3,524, N = 1,968) = 2,655.34, p = 1.000. Hierarchical comparison of the models as variables showed considerable improvement in explained variance, from .272 with the predisposing and enabling variables to .586 with the addition of the need variables. The interactions improved the model only slightly (.016; total R2 = .602). It is not unusual for interaction variables to improve explained variance by as little as 1% in field studies (McClelland and Judd 1993).
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Individuals who needed assistance with ADLs were more likely to be in a NF as well, but those with more chronic diseases were more likely to be in a NF or HCBS program than in an ALF. Those who needed assistance with mobility were more likely to be in HCBS. However, when NFs were compared with ALF settings, individuals who needed total assistance with mobility were more likely to be in a NF than in an ALF, and individuals who only needed some assistance were more likely to be in an ALF than a NF setting. Overall, individuals who had higher levels of need in terms of functional assistance were in NF and ALF settings, but those with higher numbers of chronic diseases and those in need of assistance with mobility were receiving HCBS. These findings are more likely to reflect program policies as most ALFs do not accept individuals with total impairment in mobility, whereas NFs are established to serve individuals with the highest functional impairment levels, including lack of mobility.
The second hypothesis—that enabling factors, especially mutable variables such as Medicaid eligibility and public policies associated with geographic region, would be significantly associated with the differential utilization of LTC services—was confirmed as anticipated. Individuals who were not married were more likely to be in a NF or ALF than in HCBS, and they were equally as likely to be in the two facility settings. Individuals who were living in north, central, and west Florida were more likely to be in a NF than in an ALF or HCBS, and those in south Florida were more likely to be in HCBS programs than in either an ALF or a NF setting. Individuals who were Medicaid eligible were more likely to be in a NF or HCBS program and not in an ALF. These findings may confirm that there are very clear LTC supply differences between south Florida and the rest of the state (Oakley 2000). For example, we believe that our findings reflect geopolitical differences in the supply of NF beds and alternatives. In addition, the limited supply of affordable ALFs and the limited scope of public programs that support the assisted living option constrain access to ALFs among low-income elders. All other variables being equal, individuals who were Medicaid-eligible or who lived in regions outside of south Florida were more likely to receive care in a NF than in an alternative setting.
The last hypothesis—that the moderating influence of predisposing and enabling factors on need is significantly associated with the differential utilization of LTC services—was also supported. There were three significant interactions between predisposing and need variables. First, women who had more chronic diseases, compared with men with more chronic diseases, were more likely to be in a NF than they were to be in an ALF. When a comparison of NF and ALF with HCBS was done, women with chronic diseases were as likely to be in the two facility settings as they were to be at home. The other two interactions revealed that non-Hispanic Whites who had severe cognitive impairment, compared with ethnic minorities with severe cognitive impairment, were less likely to use NF or ALF services than they were to use HCBS.
Similarly, there were three significant interactions between enabling and need variables. Individuals who were not married and who had more chronic disease were less likely to be in an ALF than in HCBS. However, if they were not married and had severe cognitive impairment or needed assistance with ADLs, they were more likely to be in a NF than in HCBS. In other words, with the exception of women with chronic diseases and individuals who were not married and in need of functional assistance, most of the impaired LTC consumers with physical and mental health needs were likely to receive HCBS rather than 24-hr care in an ALF or NF.
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Medicaid eligibility and region of the state were two enabling variables that were associated with utilization without regard to specific need. Medicaid primarily covers nursing home services and, to a lesser extent, in-home care through the HCBS Medicaid Waiver. After data collection was completed, the state instituted a small-scale Medicaid Waiver program for ALFs. Although we expected that Optional State Supplementation–supported clients in assisted living who were on supplemental security income (and, generally, Medicaid eligible) might improve the impact of Medicaid on utilization across the full continuum, they did not. This is most likely because the Optional State Supplementation program is also relatively small compared with Medicaid support of NFs.
Regional differences in nursing home bed supply and the availability of publicly supported alternatives to nursing home care also determine differential LTC program utilization. Coughlin and colleagues 1990 found in a national sample that a person living in an area with a higher than average nursing home bed supply was almost twice as likely to have a permanent nursing home stay than a person living in an area with a lower bed supply.
Some very functionally impaired individuals in the sample were being cared for at home and not in NFs or ALFs. Individuals who were totally impaired in mobility or had multiple chronic diseases were in HCBS. Unmarried elders with chronic diseases were at home rather than in an ALF, but if they had severe cognitive impairment or needed assistance with ADLs, they were likely to be in a NF. A plausible explanation is that spousal caregivers are an important source of support in order to remain in the community (Jette et al. 1995; Weissert and Cready 1989). Finally, although non-Hispanic Whites were more likely to be in NFs or ALFs, those with higher levels of cognitive impairment were more likely to be in HCBS. This may be less of a difference by race/ethnicity than a difference in access to LTC options when faced with caring for a family member with severe cognitive impairment (Mitchell et al. 2000). Overall, our findings suggest that a similar tendency might exist nationwide in the population of consumers of publicly funded LTC services that have these characteristics; however, it is important to remember that not all consumers follow this pattern of LTC use.
In summary, inconsistent with state financing of LTC, highly functionally impaired individuals were also in HCBS settings, although reimbursement for HCBS is typically one-sixth of the NF reimbursement, and 89% of the 1995–1996 budget for LTC in Florida was spent on NF care (Polivka 2001). Our findings imply that public financing of LTC does not reflect the capacity of the system to serve frail elders at home.
Limitations
This cross-sectional field study made practical use of existing client records across the full continuum of LTC services for one state. Florida does not represent the country, but it is the bellwether state for aging policy issues in the country. Rather than developing a standardized instrument for data collection, we used common elements from three existing standardized client and resident assessment instruments. In a few instances, source variables were similar but not an identical match (e.g., Medicaid eligibility), and new variables were created for analysis that were based on reasonably equivalent measures (e.g., eligibility for supplemental security income and welfare were proxies for Medicaid eligibility). In addition, the data available to us included admission data for NF and private-pay ALF residents, and admission data from the most recently completed Department of Elder Affairs Uniform Assessment Instrument for HCBS residents and for publicly supported (Optional State Supplementation) ALF residents.
Although we relied on trained data collectors, there were different types of professionals who completed the assessments that provided the source of data for our study. Generally, these professionals included registered nurses in the NFs who completed the Minimum Data Set, state social worker/case managers in the HCBS and Optional State Supplementation programs who completed the Department of Elder Affairs Uniform Assessment Instrument, and licensed physicians or nurse practitioners who completed the Department of Elder Affairs Form 1823 required for assisted living. Future research that focuses on the utilization of LTC services across the continuum should consider collecting data by means of a single instrument and commonly trained evaluators to enhance assessment precision and data comparability.
As a cross-sectional study, this research demonstrates associations between predictors and setting but does not necessarily predict utilization. Our sample consisted of clients who were already receiving LTC services in one of three settings and not from the point when they first needed assistance. Follow-up research with the same study population would clarify how these predictors work. More research that follows individuals through the disablement process and LTC decision making is still needed.
Recommendations
The expansion and structuring of LTC programs is needed to attain a more equitable distribution of services by minimizing the influences on service use of predisposing factors such as ethnicity and enabling factors such as geographic region of the resident, while maximizing the influence of need factors such as functional disability, chronic diseases, and cognitive impairment (Andersen and Newman 1973; Spector and Fleishman 1998). Consistent with our findings, the absence of a clear policy-driven relationship between the level of services provided and the needs (e.g., functional impairment) across long-term programs is probably characteristic of LTC services in most states. The development of a closer link between services and needs will become increasingly important for policymakers as the population needing LTC services grows and the pressure on state and federal fiscal resources mounts over the next several years. This has important fiscal implications. We have evidence that targeting works (Greene, Lovely, Miller, and Ondrich 1995). We can spend more resources (such as are available through Medicaid waivers) on individuals who have higher impairments and save state and federal dollars. Yet, if targeting based on impairment is abandoned in favor of targeting based on financial eligibility, then we are not making effective use of these funds. In addition, more effective targeting should not neglect individuals who have fewer ADL impairments and need assistance in order to remain out of institutions. Targeting should also not neglect the need for General Revenue–funded programs that do not require a strict income and asset test.
The process of targeting could be informed by regional needs assessments that are conducted at regular intervals and include a wide range of health, functional status, and socioeconomic and service capacity information that goes well beyond the demographic data that are commonly available from secondary sources. This information, in combination with the latest findings on the relative cost-effectiveness of LTC programs, could be used to adjust resource allocation formulas, including those governing the nursing home certificate of need determinations. Costs of routinely conducted regional needs assessments could be more than offset by the cost savings generated from the rigorous tailoring of LTC programs to those who are seriously impaired and with relatively few economic and social support resources.
Conclusions
Findings from this study were in line with what was expected on the basis of other LTC utilization research. Essentially, the literature and our findings indicate that the best predictors of nursing home placement are functional impairment (i.e., ADLs), Alzheimer's disease, marital status (i.e., not married), and ethnicity (i.e., non-Hispanic White). There is also consistency in predicting that disadvantaged individuals are more likely to receive formal LTC services in their home, but we provide further insight into the severity of impairment of these consumers.
This study adds value to earlier research because we considered all the LTC programs available to frail elders in need of care. In other words, a novel contribution is the inclusion of ALFs to the common comparison of nursing homes to home- and community-based programs. Our findings clearly reflect the distinct admission policies of ALFs, which often specialize in dementia care and primarily serve clients who are mobile and continent and who can, for the most part, self-finance this care rather than depend on public funding. In essence, our analysis allows for a comparison of important factors that distinctly differentiate consumers' utilization of formal services along the LTC continuum.
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Acknowledgments |
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Received for publication October 30, 2001. Accepted for publication April 16, 2002.
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