Journal of Gerontology: SOCIAL SCIENCES 1991. Vol. 46, No. 6, S345-357

Copyright 1991 by The Cerontological Society of America

The Use of Health Services by Older Adults Fredric D. Wolinsky1 and Robert J. Johnson2 'Department of Medicine, Indiana University School of Medicine, department of Sociology and Anthropology, Kent State University. Using baseline data on the 5,151 respondents surveyed as part of the panel design of the Longitudinal Study on Aging (LSOA), this article estimates, cross-sectionally, the relationships hypothesized in the behavioral model of health services utilization. In addition to the traditional indicators of the predisposing, enabling, and need characteristics, the richness of the LSOA permits the inclusion of measures of multigenerational living arrangements, kin andnonkin social supports, health worries and the sense of health control, health insurance coverage, residential stability, and several multiple-item scales of functional limitations. Despite these innovations, the ability of the behavioral model to accurately predict the use of health services by older adults remains relatively unchanged. Important conceptual clarifications involving the hypothesized relationships, however, are identified and discussed.

I

T is well known that the number and proportion of elderly persons in the United States has been and is expected to continue increasing well into the 21st century (Rice and Feldman, 1981). It is also well known that, in the aggregate, older adults are disproportionately heavy users of health services (Fisher, 1980; Lubitz and Prihoda, 1984; Soldo and Manton, 1985; Waldo and Lazenby, 1984). As a result, although elderly Americans represent just one-eighth of the population, they account for more than one third of the total health care expenditures (Department of Health and Human Services, 1990). This has stimulated a considerable amount of research on the health and health services utilization of elderly Americans, which has been the subject of several detailed, critical reviews (see Wan, 1989; Wolinsky and Arnold, 1988). Almost without exception, that body of literature understandably suffers from one or more serious shortcomings (Wolinsky, 1990). The first problem, which we address here, involves the limited amount of information about the personal, economic, and health status characteristics of the individuals under study. For example, detailed data on health insurance coverage, social supports, health beliefs, and functional limitations are seldom (if ever) available for analysis in national data sets. To address this problem, we use baseline data taken from the Longitudinal Study on Aging (LSOA) to estimate, crosssectionally, the relationships hypothesized in Andersen's (1968) behavioral model of health services utilization, which is the most widely used conceptual framework in the field. This strategy serves two important purposes. First, it provides a comprehensive, national assessment of older adults' use of health services. Second, it provides baseline information to inform subsequent longitudinal analyses of these data. No other national data source has such a full and detailed assortment of multiple indicators of the predisposing, enabling, need, and health services utilization characteristics as the LSOA. Thus, we will be able to obtain rather good estimates of the net, direct effects of these characteristics. Conceptual Model The behavioral model of health services utilization was first presented by Andersen in 1968, and subsequently re-

vised with his colleagues (Aday and Andersen, 1974, 1975; Aday, Andersen, and Fleming, 1980; Aday, Fleming, and Andersen, 1984; Andersen and Newman, 1973). Because it is the most widely used model for studying health services utilization (Hulka and Wheat, 1985; Wan, 1989), it need not be discussed here at length (for a detailed, historical review, including its application to the special case of older adults, see Wolinsky, 1990). Basically, the behavioral model views the use of health services as a function of the predisposing, enabling, and need characteristics of the individual. The predisposing component is an abstraction from the proposition that some individuals have a greater propensity for using health services than do others. These propensities can be predicted from individual characteristics prior to an illness episode. The three dimensions of the predisposing characteristics include demographics, social structure, and health beliefs. Demographics are routinely measured by age, sex, marital status, and family size, which are all indicators of the individual's relative life cycle position. Social structure is routinely measured by employment, education, and ethnicity, which are all indicators of the individual's location in the social structure and reflect the behavioral patterns (i.e., life styles) to which people in such positions become socialized. Health beliefs, when measured, are typically assessed by questions about attitudes toward medical care, physicians, and disease, as well as worries about one's health. These three dimensions of the predisposing component represent the sociocultural element of the behavioral model. The enabling component is abstracted from the proposition that although the individual may be predisposed to use health services, he or she must nonetheless have some means for obtaining them. The enabling component, then, contains those factors which make health services available to the individual for consumption. This component is subdivided into two dimensions. The first consists of familial resources, routinely measured by income, the presence of health insurance, and having a regular source of health care. These measures tap the individual's ability to provide for him or herself. The second dimension, consisting of community resources, is routinely measured by physician- and hospitalS345

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WOLINSKY AND JOHNSON

bed-to-population ratios, as well as geographic location and population density indices. These two dimensions of the enabling characteristics represent the economic component of the behavioral model. Although the predisposing and enabling components are necessary conditions for the use of health services, they are not sufficient ones. To use health services the individual must have or perceive some illness (or its possibility). This need is specified as the most immediate cause of health services use. Need has two dimensions. The first represents the amount of illness that the individual perceives exists, and is routinely measured by a self-reported, global measure of health status. In contrast, the second dimension represents professionally evaluated need. Measures of activity limitations, especially those involving the basic activities of daily living, are routinely used as proxies of physicians' assessments of such limitations. These have been shown to yield more objective assessments of need than perceived health, which yields a more global and subjective evaluation (see Liang, 1986; Whitelaw and Liang, 1991; Wolinsky et al., 1984). These indicators of need tap the individual's recognition that a health problem either exists or is in the making.

A sample of 5,151 SOA individuals aged 70 years or more in 1984 were selected for follow-up interviews (by telephone if they had one, or by mail if they did not) in 1986, 1988, and 1990. This sample, which is the source of data for our research, was selected as follows. First, all SOA households with individuals aged 80 or over were selected, with all such individuals and any of their family members aged 70-79 interviewed. Second, all other households with an individual aged 70-79 were selected. From these households, Black individuals and any of their relatives aged 70-79 were selected. Finally, the remaining households with individuals aged 70-79 were randomly sorted, one half were selected, and all individuals aged 70-19 years were included. Greater detail on the design and execution of the LSOA can be found in Fitti and Kovar (1987). With only two exceptions (i.e., subsequent nursing home placement and death), all data used in the present analysis are based on self-reports given in the baseline interview, relying on household proxies in 8.0 percent of the cases, and on nonhousehold proxies in 1.6 percent of the cases. Because our analyses focus on assessing hypothesized relationships rather than on prevalence rates, we rely on the unweighted data (Blalock, 1968).

METHODS

Measurement

Data. — The data are taken from the LSOA, which is a collaborative effort of the National Center for Health Statistics (NCHS) and the National Institute on Aging. The LSOA involves a special six-year follow-up to the 1984 Health Interview Survey (HIS). The HIS is an ongoing, annual survey that first began in 1956. To this day, the HIS remains the principal source of all "official" statistics on the health and health services utilization of the United States. Because it is such a well-known data source, it will not be described at length. A more detailed description of the history, design, and logistics of the HIS can be found in several readily available publications (NCHS, 1975, 1985). Suffice it to say that the HIS uses a multistage sampling process that results in 52 weekly replicated samples that are pooled at the end of the calendar year to provide data on approximately 110,000 individuals residing in the 42,000 selected households. The pooled sample is fully representative of all noninstitutionalized individuals from all places during all seasons, and thus requires no adjustment for seasonal and geographic fluctuations. The 1984 HIS was augmented by two special supplements. One (referred to as the Health Insurance Supplement) involved the collection of detailed information on health insurance coverage among all respondents. The other (referred to as the Supplement on Aging [SOA]) involved the collection of detailed information by way of a 30-minute add-on interview concerning the health, social functioning, and living arrangements of 16,148 individuals aged 55 and older (this included half of the respondents aged 55 to 64, and all of those aged 65 or more). All individuals selected to receive the SOA are being followed for at least six years through matching with the National Death Index (NDI). In addition, the 11,197 individuals who were age 65 and over in 1984 are being followed for at least six years through matching with Medicare Part A and Part B records.

Predisposing characteristics. — Table 1 contains the means, standard deviations, coding algorithms, and psychometric properties of the data. There are 12 measures of the predisposing characteristics. Although many of these are standard, straightforward indicators that need little discussion (such as age, sex, race, living arrangement, marital status, and education), others are not. We include a dichotomous measure of the number of generations that live in the household to further characterize life cycle position. Basically, the concern here is that it is not just whether the older adult lives alone or is widowed, but also whether she or he lives in an extended family situation that has bearing on health services utilization (Bengtson et al., 1985). We expect those elderly persons living in extended families to use fewer health services because of the increased social support available to them. The presence of a telephone has traditionally been included when modeling data from the HISs, because in them a telephone contact with a physician is considered the same as a visit to the doctor. Accordingly, having a telephone is usually considered to be an enabling characteristic, and generally has its greatest impact on the number of physician visits made during any particular catchment period. Although that logic is relatively straightforward, it seems to be somewhat shortsighted when considering older adults. We believe that having a telephone predisposes the use of two kinds of support mechanisms, both of which are driven by socioemotional needs (see Homan et al., 1986, for an elaboration of this point). Although we do not expect either of these effects to be large, especially in the presence of other measures of social supports (see below), they appear to be more appropriately viewed under the domain of the predisposing rather than the enabling characteristics. As indicated above, one of the major shortcomings of prior assessments of health services utilization has been the ab-

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USE OF HEALTH SERVICES BY OLDER ADULTS

Table 1. Means, Standard Deviations, Coding Algorithms, and Psychometric Properties of the Baseline LSOA Data Variables Predisposing characteristics Age Female Black Lives alone Widowed Multigenerational family Telephone Education Kin supports Nonkin supports Health worries Health control

M 78.006 .632 .103 .368 .447 .191 .970 9.782 1.621 2.352 .335 .699

5.882 .482 .305 .482 .497 .393 .172 3.750 .641 1.344 .472 .459

Enabling characteristics Private insurance

.648

.478

Has Medicaid card Residentially stable Population density

.057 .839 7.368

.231 .368 2.524

.650

.477

.659

.474

.626 .670 .167 2.005 .451

1.251 1.184 .526 1.983 .835

.361 19.410 .115 .836 .217 5.175

.480 19.971 .455 .370 .413 4.038

Number of hospital nights in past year

8.853

4.982

Nursing home contact within 2 years of interview" Deceased within 2 years of interview"

.039 .118

.194 .322

Social Security dependence Need characteristics Perceived health Basic ADLs Household ADLs Advanced ADLs Lower body limitations Upper body limitations Health services utilization Bed-disability days: Contact Bed-disability days: Volume Home health services Physician contact in past year Hospital contact in past year Number of physician visits

Coding Algorithms

SD

J \ctual number of years = yes, 0 = no = yes, 0 = no = yes, 0 = no = yes, 0 = no = yes, 0 = no = yes, 0 = no i\ctual number of years :'-item scale, 1 = has support, !5-item scale, 1 = has support, = worries about health, 0 = = has control over health, 0

Psychometric Properties

n/a n/a n/a n/a

0 = no 0 = no no = does not

n/a n/a n/a n/a alpha = .531 alpha = .600 n/a n/a

= has private physician and hospital insurance, 0 = no = has Medicaid card, 0 = no = same address for 5 or more years, 0 = no O-point 1980 county adjacency code, ranging from 0 = thinly populated not adjacent to 9 = core SMSA county 1 = yes, 0 = no

n/a

1 = excellent, very good, or good, 0 = fair or poor 5-item scale, 1 = needs help, 0 = no 4-item scale, 1 = needs help, 0 = no 3-item scale, 1 = needs help, 0 = no 5-item scale, 1 = has limit, 0 = no 4-item scale, 1 = has limit, 0 = no

n/a

n/a n/a n/a

n/a

alpha alpha alpha alpha alpha

= = = = =

.827 .828 .641 .863 .588

1 = yes, 0 = no Actual number in past year, truncated at 61 days 4-item scale, 1 = receives services, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no Actual number of visits in past year, among those with visits, truncated at 13 Actual number in past year, among those with nights, truncated at 15

n/a n/a alpha = .609 n/a n/a n/a

1 = yes, 0 = no 1 = yes, 0 = no

n/a n/a

n/a

"These data were obtained at the first follow-up in 1986.

sence of measures of social support (for an elaboration of this problem, see Ward, 1978). The LSOA contains seven items that tap the structural or network aspects (Cohen and Syme, 1985) of social supports. These include whether the respondent had done volunteer work in the past 12 months, and whether in the past two weeks the respondent had socialized with friends and neighbors (or relatives, not in the household), talked with friends and neighbors (or relatives, not in the household) on the telephone, gone to church or temple, or gone to a group event (e.g., movies, sports events, classes). Extensive factor and principal components analyses of the

data revealed that these seven items formed two meaningful scales, one for the five items tapping nonkin social supports (alpha = .600), and one for the two items tapping kin social supports (alpha = .531). Both scales are unidimensional and produce factor loadings greater than .53. Health beliefs are also absent in most applications of the behavioral model; for an elaboration of this point, see Mechanic (1979). The LSOA contains two very important markers of an individual's health beliefs. First, respondents were asked whether their overall health status for the past 12 months had caused them a great deal of worry, some worry,

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W0L1NSKY AND JOHNSON

hardly any worry, or no worry at all. To maximize the contrasts involved, we dichotomized the respondents' answers into expressing worries vs not expressing worries. Second, respondents were asked how much control they think they had over their future health: would they say a great deal, some, very little, or none. Here again, we dichotomized the responses into expressions of control vs no control in order to maximize the contrasts. Based on prior studies, we expect that those who are more worried about their health (Newman, 1975) and who feel that they are less in control of their health (Rodin, 1986; Rodin, Timko, and Harris, 1985) are more likely to use health services. Enabling characteristics. — There are five measures of the enabling characteristics. Two of them address the issue of health insurance. One of those is a dichotomous indicator of whether the respondent has private (excluding Medicare and Medicaid) insurance that covers at least some portion of physician and hospital charges. The other is also a dichotomous indicator and reflects whether the respondent has a valid Medicaid card. We do not use a measure of Medicare coverage for two reasons. First, all respondents in this sample are eligible for Medicare, and over 97 percent of them are covered. Second, the two measures of health insurance that are included capture the key elements of coverage status (i.e., private personal expenditures and public welfare transfers). Accordingly, the inclusion of a Medicare coverage measure would not add much to the analyses, either conceptually or statistically. Two of the other measures of the enabling characteristics tap residential stability and population density. If respondents have lived at the same address for five or more years, we consider them to be geographically stable. Geographically stable persons are more likely to be aware of the health services available in their community and to have established relationships with health care providers, both of which should facilitate the use of health services (Snider, 1980a, 1980b). The population density measure is the 10point 1980 county adjacency code provided by the Census Bureau. It ranges from thinly populated counties that are not adjacent to Standard Metropolitan Statistical Areas (SMS As) to the core county of an SMS A. We use the county adjacency codes as proxies for the supply of health services in the community, and expect respondents in areas of greater supply to use more health services (Aguirre et al., 1989). Income and poverty status are not included in Table 1 as measures of the enabling characteristics, and their absence warrants some discussion. As with most surveys, the problem with income data, or the poverty measures that can be constructed based on them and the number of persons in the household, is the large item nonresponse (922 persons in the LSOA either elected not to provide information on income, or were unable to do so). This poses a considerable dilemma to researchers, who must decide whether (a) to use missing data imputation techniques (frequently involving the substitution of conditional means), (b) to exclude those without income data, or (c) to drop income from their models. Given the longstanding concern over potential relationships between income and the use of health services (Bice, Eichhom, and Fox, 1972), we chose to conduct detailed analyses of the

optional treatments of this problem. Those analyses revealed two important pieces of information: (1) income had no significant net effects on the use of health services when the full model was run among only those respondents with income data (i.e., using listwise deletion in assembling the correlation matrix); and, (2) when income was deleted from the full model, and the full model was estimated separately among those with income data vs all respondents, significant changes in the parameter estimates for the predisposing, enabling, and need characteristics were observed. Taken together, those analyses indicate that although income did not have a significant effect on health services utilization, omitting respondents without income data produced biased estimates of the other parameters. Accordingly, the most appropriate strategy in this case is to delete income and its poverty derivatives from the analysis. We have, however, included an alternative indicator of financial status. Respondents were asked whether they received retirement income from a variety of potential sources. Using the information obtained from that series of questions, we classified these older adults as having either no source of retirement income or only Social Security, vs having Social Security and some other source of retirement income. Accordingly, this indicator taps the respondent's financial dependence on public retirement sources of minimal magnitude. As such, we expect those who are dependent on Social Security to consume fewer health services (especially of the discretionary variety) given their limited purchasing power. Need characteristics. — There are six measures of the need for health services. One is the standard dichotomy obtained from asking respondents to rate their overall health. Following Wolinsky and Arnold's (1988) suggestions, excellent, very good, and good responses were classified as indicating good perceived health, and fair or poor responses were classified as indicating bad perceived health. The remaining measures of health need are multiple-item scales that emerged from a series of theoretically directed factor and principal components analyses of 21 questions routinely taken from or modeled after various measures of activities of daily living. These included the Activities of Daily Living (ADL) scale developed by Katz et al. (1963), the Instrumental Activities of Daily Living (IADL) scale developed at the Duke University Center for the Study of Aging and Human Development (1978), and the Nagi (1976) disability scale. Five unidimensional scales emerged. We call the first the basic activities of daily living scale (basic ADL; minimum factor loading = .718; alpha = .827). It consists of five items from the traditional ADL, including the need for help with such personal activities as bathing, dressing, getting out of bed, walking, and toileting. We call the second scale household activities of daily living (household ADL; minimum factor loading = .727; alpha = .828). It consists of four items taken from the IADL, including the need for help with such household chores as meal preparation, shopping, and light and heavy housework. The third scale is called advanced activities of daily living (advanced ADL; minimum factor loading = .680; alpha = .641), and is composed of the three items from the original ADL and IADL

USE OF HEALTH SERVICES BY OLDER ADULTS

scales that do not load with the other items. These are the questions about the need for help with managing money, in using the telephone, and in eating. We believe that the advanced ADL taps a set of activities of daily living that focuses more precisely on cognitive capacity. This belief is derived from two propositions. First, none of these activities requires much physical activity. Indeed, the underlying construct is more likely to be cognitive. Cognitive impairments common to this factor include an inability to communicate (on the telephone), to remember (both telephone numbers and the necessity of eating), and to plan for the future (as in scheduling meals or managing money). Second, and equally noteworthy, these advanced ADLs are orthogonal to upper body limitations (see below), further eschewing any interpretation of an underlying physical problem. Accordingly, we expect the advanced ADL to be especially important in the prediction of long-term institutionalization, such as nursing home placement, or death. Using a three-step process, additional construct validation for the advanced ADL was obtained using information contained in the LSOA about difficulties the respondents experienced concerning remembering things or being confused. First, we conducted analyses of variance using the responses to the memory and confusion items (e.g., never, frequent, sometimes, or rarely) as the grouping factors, and the basic, household, and advanced ADLs as the outcome variables. This yielded sets of means on the three ADLs by memory and confusional state. Next, we calculated the ratio of those response means (on the ADLs) for those who reported frequent problems with memory or confusion, to those who reported no problems. These ratios reflect the ability of the ADLs to differentiate between the extremes of the memory and confusion response categories. Finally, we calculated the ratios of the ratio obtained for the advanced ADL, to the ratio obtained for the basic and household ADLs. These ratios of ratios indicate the relative discriminatory power of the advanced ADL compared to the basic and household ADLs. Our calculations show that the advanced ADL discriminates between the extremes of the memory response categories 56 to 67 percent more than do the household and basic ADLs, respectively. Moreover, the advanced ADL discriminates between the extremes of the confusion response categories 142 to 164 percent more than do the basic and household ADLs, respectively. We repeated this method of construct validation using the companion questions of whether current memory and confusional states represent changes from a year ago. In these analyses we calculated the ratios of the response means for those who reported that their troubles with memory and confusion had increased, to those who reported that no change had occurred. Again, the results showed that the advanced ADL discriminates between these memory response categories 35 to 44 percent more than do the household and basic ADLs, respectively. For the confusion measure, the advanced ADL discriminates 39 to 56 percent more than the household and basic ADLs, respectively. When taken together, these results clearly show that the advanced ADL is markedly more discriminating with respect to memory and confusional states than are the basic and household ADLs.

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The two remaining scales are drawn from Nagi's (1976) disability items. One taps lower body limitations (5 items; minimum factor loading = .744; alpha = .863), and includes difficulties in walking a quarter of a mile, walking up 10 steps without rest, standing or being on your feet for 2 hours, stooping, crouching, or kneeling, and lifting or carrying 25 pounds. The other taps upper body limitations (4 items; minimum factor loading = .463; alpha = .588), and includes difficulties in sitting for 2 hours, reaching up over your head, reaching out as if to shake hands, and using fingers to grasp objects. Although both scales should correlate with the use of health services, we expect that upper body limitations will be more important in the prediction of long-term institutionalization, such as nursing home placement. Recently, important statistical interactions involving ethnicity and the need characteristics in predicting the use of health services have been identified (Wolinsky et al., 1989; also see Blendon et al., 1989, and Freeman et al., 1987, for similar conclusions based on a more descriptive approach). Basically, it has been shown that minority elderly adults' use of health services is far more constrained by and sensitive to the need characteristics. Moreover, the behavioral model is more robust among those groups. Inasmuch as the LSOA is essentially limited to Whites and Blacks (there are only 79 non-White non-Black individuals), we address this issue by constructing (but not showing in Table 1) a set of multiplicative interaction terms involving the race and need measures. Based on prior research, we expect the interaction terms to reflect the disadvantages faced by older Black adults in using health services. Health services utilization. — There are nine measures of health services utilization. Three of these tap informal services, five tap formal services, and the last involves two-year mortality experience. Following Mechanic (1979) and Wolinsky et al. (1983), the first two measures of informal health services utilization are whether any bed-disability days were taken during the past 12 months and, among those having taken bed-disability days, how many were taken. The other measure of informal health services utilization is a unidimensional scale that reflects how many of any of four home health services were used (minimum factor loading = .575; alpha = .609). This includes the use of meal delivery, homemaker, health aide, and visiting nurse services. Because of the distortion introduced into the distribution of the number of bed-disability days by the positive skew, that measure is truncated such that 61 or more days are statistically treated as 61 days. Building on the established literature for the treatment of non-normally distributed dependent variables in regression analysis (Lewis-Beck, 1980), Wolinsky and Coe (1984) have shown that simple truncation of such "count" measures of the volume of health services utilization at about the 95th percentile is as effective as the taking of natural logarithms. And because these simple truncations retain the original and meaningful units of measurement, they are more easily interpreted than the taking of natural logarithms. Moreover, it has been shown that the predictors of exceptionally high levels of health services utilization are either not found in or not well measured by individual based models like the behavioral

S350

WOUNSKY AND JOHNSON

model (Wolinsky and Coe, 1984). The reason for this is that a substantial component of the variation in the use of high users' health services is due to both exceptionally problematic illness episodes, which standard survey measures of need are not sufficiently sensitive to capture, and to delivery system characteristics, such as physician practice patterns, that are not tapped in most surveys (Wolinsky, 1990). The five measures of formal health services utilization include both contact and volume measures of physician and hospital utilization, and a contact measure of nursing home placement. The physician and hospital contact measures are more general indicators of access to health care, and indicate whether a physician has been seen, or a night spent in the hospital, during the past 12 months. In contrast, the volume measures of physician and hospital utilization reflect the extent of health services used during the past 12 months, among those with contact. To correct for the positive skew of these count variables, the distributions are truncated at the 95th percentile such that 13 or more visits to physicians are statistically treated as 13 visits, and 15 or more nights spent in the hospital are statistically treated as 15 nights (Wolinsky and Coe, 1984). The last two measures of formal health services utilization differ from those just described in that they are measured prospectively. One indicates whether nursing home placement has occurred within the first two years since the baseline (1984) interview. Because of the noninstitutionalized nature of the LSOA sample at baseline, and given the absence of a distinct catchment period associated with the question about prior nursing home episodes that was included in the baseline interviews, we rely on this prospective index for measuring nursing home placement. The other prospectively assessed measure of formal health services utilization taps whether the respondent died within two years since the baseline interview, as reported at the follow-up interview by a surviving collateral. We use this measure of mortality status as a proxy for formal health services utilization because there is considerable evidence to suggest that the most extensive use of formal health services occurs in the year or two preceding death at any age (Roos, Montgomery, and Roos, 1987; Roos, Shapiro, and Tate, 1989). Moreover, even if death does not result in the extensive use of health services, it almost always results in some formal services consumption. Indeed, in these data about two-thirds of the deaths occurred either in hospitals or nursing homes. Thus, prospectively assessed decedent status is an important, albeit oblique, marker of the use of health services, especially those services of a more institutional nature. Nonetheless, it would be remiss not to note that decedent status is the least direct of all of our measures of health services utilization and that, substantively interpreting the results of statistically modeling, it should be viewed with caution. Analysis Ordinary least squares (OLS) regression analysis is used to estimate the parameters specified in the behavioral model. Although more sophisticated statistical procedures have been developed for dealing with dichotomous dependent variables, it has been shown that if (a) the split on such dichotomies falls within the .10 to .90 range (with some

arguing that even the .05 to .95 range is acceptable), and (b) sample sizes in excess of 1,000 are used, then (c) OLS provides equivalent, unbiased estimates of the regression coefficients (Cleary and Angel, 1984; Cox and Snell, 1989). The R2 levels, however, are sensitive to OLS procedures. They can become so significantly restricted by the range of the binary split that they may no longer be suitable for judging the fit of the overall model (Cox and Wermuth, 1990). Thus, with the possible exception of those predicting nursing home contact, the effect parameters (i.e., individual coefficients) obtained from our models should be rather robust, although the goodness-of-fit indices may be underestimated. Nonetheless, as an added safeguard we also estimated the models for the dichotomous measures using logistic regression analyses. As expected, those results (available on request) are fundamentally equivalent to the OLS estimates reported here (the few exceptions are noted below and in footnotes to the tables). Following procedures outlined by Lewis-Beck (1980), all of the statistical assumptions of OLS regression analyses were submitted to testing. Although no meaningful violations were detected, further comment about the multicollinearity issue seems warranted given the multiple indicator approach used here, especially for the need characteristics. To assess multicollinearity, we regressed each independent variable on all of the other independent variables (excluding the interaction terms). In no circumstance did the R2 levels obtained exceed standard R2 cutoff levels (i.e., .64) indicative of collinearity problems. Thus, our reliance on multiple indicators has not introduced what could be characterized as "harmful multicollinearity" (Gordon, 1967).

RESULTS

Table 2 contains the R2 results obtained from the hierarchical modeling of the measures of health services utilization. In these hierarchical models, the predisposing characteristics were entered on the first step of the regression analysis, the enabling characteristics were added next, and the need characteristics were added on the third step. As shown, the results are entirely consistent with the extant literature. In 13 of 18 comparisons, the net contribution of the need characteristics is larger than the individual contributions of either the predisposing or enabling characteristics. The five comparisons (involving physician and hospital contact, the number of nights spent in the hospital, nursing home contact, and mortality) where this is not the case (a) all involve the predisposing characteristics, which, in addition to their net effects, receive credit for their joint effects with the enabling and need characteristics, and are thus somewhat inflated, and (b) all involve rather marginal differences. Moreover, in many cases the net contribution of the need characteristics is equivalent to or larger than the combined contributions of the predisposing and enabling characteristics. The unstandardized coefficients obtained from the final stage of the hierarchical OLS regression modeling of the health services utilization measures are shown in Table 3. For clarity, coefficients are omitted if they do not significantly differ from zero (p ^ .05). The appearance of footnotes in the table denotes cases in which the coefficients

USE OF HEALTH SERVICES BY OLDER ADULTS

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Table 2. R1 Results Obtained From the Hierarchical Modeling of the Measures of Health Services Utilization Hierarchical Models Health Services Utilization Measures Bed-disability days: Contact Bed-disability days: Volume (among those with days) Home health services Physician contact Number of physician visits (among those with visits) Hospital contact Number of hospital nights (among those with nights) Nursing home contact within 2 years of interview Deceased within 2 years of interview

Predisposing Characteristics

Predisposing and Enabling Characteristics

Predisposing, Enabling, and Need Characteristics

.074 .114 .049 .033 .081 .054 .054 .038 .062

.079 .121 .059 .051 .093 .060 .064 .044 .063

.149 .252 .136 .069 .173 .102 .107 .054 .100

Table 3. Unstandardized Coefficients Obtained From the Final Stage of the Hierarchical Regression Modeling of the Measures of Health Services Utilization* Measures of Health Services Utilization Independent Variables Predisposing characteristics Age Female Black Lives alone Widowed Multigenerational family Telephone Education Kin supports Nonkin supports Health worries Health control Enabling characteristics Private insurance Has Medicaid card Residentially stable Population density Social Security dependent Need characteristics Perceived health Basic ADLs Household ADLs Advanced ADLs Lower body limitations Upper body limitations Black * Perceived Health Black * Basic ADLs Black * Household ADLs Black * Advanced ADLs Black * Lower Body Limitations Black * Upper Body Limitations Intercept /?2 Number of cases

Bed Days: Contact

Bed Days: Volume

- .003"

-.163 -2.546

Home Health

Physician Contact

Physician Visits

Hospital Contact

Hospital Nights

-.313

-.050

-1.008

Nursing Home

Deceased

.003

.005 -.071

-.007

-.012

-.058 .074" .040

.137

.034 .017 .082

- 1.188 1.991

-.492 1.162

.113 -.033

.117 -.043

.085

2.002

-.149

.034 .023

-5.252 1.245 2.975 1.133 2.008

.071 .144

1.268

.005 -.044

.119

-.041

-1.657

.092 -.003 1.002

-.104 .009"

.038 .079

.308 .026

.236

.018" .047 .018

-.057 .014"

.541 .237

c

.058 .013

.524 .059 -.132 - .028"

.058" .114 -.036 .440 .149 4,603

26.430 .252 1,665

.067 -.170 .136 4,603

.468 .069 4,603

4.712 .173 3,856

.156 .102 4,603

-.023 9.342 .107 1,003

-.205 .054 4,603

-.192 .100 4,603

•Coefficients not significantly different from zero at the .05 level or beyond omitted for clarity. b In the logistic regression analyses, the coefficients obtained were of the same sign and magnitude, but were not significantly different from zero at the .05 level or beyond. c In the logistic regression analyses, the coefficient obtained was of the same sign and magnitude, but was significantly different from zero at the .05 level or beyond.

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obtained from the OLS and logistic regressions differed in estimated significance levels, but not in direction or magnitude. In all but one case the difference involves an OLS estimate that was marginally significant whereas the logistic estimate was not. Accordingly, although the footnoted coefficients are reported, our confidence in them is diminished. It is comforting to note that all but one of these cases involve the need characteristics or their interaction with race. Logistic regression is known to be especially more robust than OLS when predicting highly skewed dichotomies from repetitive and redundant measures (see Cox and Snell, 1989). The need characteristics, especially given the interaction terms involving race, are repetitive (but not redundant) and well correlated. Predisposing characteristics. — Age has significant effects for only four measures of health services utilization. Older adults are less likely to take bed-disability days at home and have fewer of them (although confidence in the former effect is diminished inasmuch as it was not replicated in the logistic regression analyses), but are more likely to be placed in a nursing home or to die. This suggests that because of their frailty (from both a physiological and psychosocial standpoint; see Streib, 1983), older adults are less able to rely on informal health services, especially self care, and must turn more rapidly toward the professional referral system and institutionally based services. Otherwise, health services utilization appears to be unrelated to age (Wolinsky, 1990). Sex has significant negative effects on half of the measures of health services utilization. Women take fewer beddisability days, and are less likely to have had contact with hospitals. Among those having seen a physician, women have fewer visits than men. When hospitalized, women are more likely to have shorter lengths of stay. Women are also less likely to die. These results are generally consistent with the extant literature (Wan, 1989), and probably reflect differences in life expectancy, the distribution of acute killer diseases, and fixed role obligations (Verbrugge, 1984, 1985). What is intriguing here is the absence of any direct effect of sex on physician contact rates. It would appear that when more comprehensive measures of the need characteristics and social supports are included in the behavioral model, gender differences in whether one goes to see the doctor at least once a year disappear, although the differences in the number of visits (among those with visits) remain. No significant effects on any measures of health services utilization are found for race, living alone, or being widowed. The absence of such effects is also intriguing, given the frequency with which they are reported in the literature (Cafferata, 1987; Homan et al., 1986; Mutran and Ferraro, 1988). Such effects are usually interpreted as reflecting differential access to health care and to social supports. It would seem plausible, then, that in the presence of more detailed measures of access and social support, as well as with the interaction terms involving race and the need characteristics, the salience of these factors (especially as proxies for access, social support, and need differentials) would diminish. Residing in a multigenerational household has a signifi-

cant effect on only one measure of health services utilization. Those older adults in multigenerational environments are less likely to use home health services. Although this probably reflects the increased potential for receiving informal services within multigenerational families (Bengtson et al., 1985), it suggests that the substitution of such services is limited to home health care (or other informal services). To be sure that the absence of effects of race, living alone, and being widowed, and the limited effect of residing in a multigenerational household did not result from repetitive measurement problems that minimize the unique effects attributable to any one of these indicators (Gordon, 1967), additional analyses were conducted (not shown). In those analyses we serially deleted the multigenerational household residence and living alone terms from the equations. This procedure did not alter the substance of the results reported herein. Thus, the absence of any effects of race, living alone, and being widowed is not the result of statistical artifact. Similarly, having a telephone has a significant effect only on physician contact, and then only in the OLS regression analysis. On the one hand, this is as expected, given that telephone consultation with a physician is treated as a visit to the doctor in the LSOA. On the other hand, it was expected that the presence of a telephone would serve as a proxy for access to social support networks, and thus have an impact on other measures of health services utilization. The absence of such effects, however, may have resulted from the inclusion of several other measures of social support, including telephone contacts with friends and relatives. Education has a significant effect on only one measure of health services utilization. Among older adults with at least one visit to a physician, educational attainment results in greater numbers of doctor visits. In the absence of an effect on physician contact, this probably reflects the well-known relationship of education with patient compliance (especially as it relates to follow-up visits; see Becker, 1974). Kin supports also have an effect on only one measure of health services utilization. The more access one has to kin supports, the more likely one is to have seen a physician in the past 12 months. This probably reflects the encouragement by family members to see a physician that is provided when older adults discuss their health problems in the context of routine contact with their kin. Contrary to prior expectations (Brody, 1985), such encouragement actually results in increased contact with physicians rather than the substitution of informal supports for their services. In a like fashion, nonkin supports also have a positive effect on physician contact. This provides further support for the notion that in routine contacts with kin and nonkin, the health of older adults becomes a topic of discussion, and that this discussion encourages physician contact. In contrast to the effects of kin supports, however, the presence of nonkin supports significantly reduces the number of bed-disability days taken (among those having taken such days), as well as the length of stay in hospitals, and the likelihood of being admitted to a nursing home or dying. These effects are consistent with the expectation that social supports (at least from nonkin) can substitute for formal health services (Brody, 1985; Wolinsky, Mosely, and Coe, 1986). They are

USE OF HEALTH SERVICES BY OLDER ADULTS

also consistent with the literature establishing the relationship between social supports and mortality (Berkman and Breslow, 1983). As expected, older adults who worry about their health are more likely to use a variety of health services. This includes whether bed-disability days are taken, and if so, how many are taken, physician and hospital contact, and the number of physician visits. Such findings provide considerable support for the importance of health beliefs in general, and of health worries in particular (Newman, 1975). What is not clear from these data, however, is whether the health worries caused the use of health services, or vice versa. This question is not easily resolved, even though health worries were not predictive of the two prospective measures (i.e., nursing home contact and mortality within 2 years of baseline). Feeling in control over one's health has a significant effect on only one measure of health services utilization. Older adults feeling some sense of control were less likely to die. This provides limited support for the view that those who sense control are less likely to give up and succumb to their ailments altogether (Rodin, 1986; Rodin, Timko, and Harris, 1985). It does not, however, provide support for the notion that perceptions of control over one's health have any impact on the use of traditional health services. Thus, the role of perceived control does not appear to be as global as originally expected. Enabling characteristics. — Having private health insurance coverage has a significant effect only on physician contact. Those with insurance are more likely to have contacted a physician in the past 12 months than those without it. This effect of having private health insurance probably reflects the reduction, if not elimination, of the limited financial barriers to access for physicians' services in this sample, of whom more than 97 percent have Medicare coverage. In contrast, having a valid Medicaid card has significant effects on whether bed-disability days are taken, the use of home health services, physician and hospital contact, and the number of physician visits. These effects consistently reflect the greater use of these services by those receiving Medicaid. What is not clear is how these effects should be interpreted. On the one hand, they may be seen as indicative of the greater need (especially of the pent-up variety) of older adults with lower socioeconomic status, and thus eligible for Medicaid. That would be consistent with the traditional literature documenting the delay among such groups in seeking help, and the increased consumption of services once contact is made (Koos, 1954; Stahl and Gardner, 1976; Wolinsky, 1982). It would also reflect the fact that among those with lower socioeconomic status, individuals applying for (and receiving) Medicaid are more likely to have greater health needs than those choosing not to apply. On the other hand, these effects may be more indicative of the difference in the nature, type, and quality of health care provided to Medicaid patients that results in greater fragmentation, less coordination, and more infrequent continuity, all of which are likely to result in higher levels of health services utilization (Wolinsky, 1990). Unfortunately, these data do not dictate a choice between these alternative explanations.

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Residential stability has only one significant effect. Those who have lived in their neighborhood for five or more years are less likely to use home health services. This is the opposite of what was expected (Snider, 1980a, 1980b). Those expectations were based on residentially stable older adults having more knowledge of the services available in their community. It may be that the residentially stable have developed more diverse and facilitative social support networks that more readily substitute for home health services. Such an interpretation presumes statistical interaction between social supports and residential stability, however, and the absence of any effects of the former parent terms on home health services utilization diminishes enthusiasm for that explanation. Population density (as a proxy for the relative supply of physicians and hospitals in the community) has significant effects on three measures of health services utilization. Older adults in larger communities are more likely to have seen a physician in the past 12 months, and to have seen that physician more often. At the same time, elderly individuals in larger communities are less likely to be admitted to a nursing home. Although the effects on physician utilization are consistent with the provider-induced demand hypothesis (Eisenberg, 1986), the very modest effect on nursing home admissions is not. Unfortunately, these data do not provide a mechanism for exploring that relationship at greater length. Being dependent on Social Security for retirement income increases the number of bed-disability days taken (among those having taken such days), decreases the likelihood of physician contact, and increases the number of nights spent in the hospital (among those having been hospitalized). These effects are generally consistent with those reported above for having a valid Medicaid card. As such, they may be seen as indicative of the combination of greater need (especially of the pent-up variety) and decreased access to health care associated with lower socioeconomic status and less purchasing power. That would be consistent with the traditional literature documenting the delay in help-seeking among such groups, and the increased consumption of services once contact is made (Koos, 1954; Stahl and Gardner, 1976; Wolinsky, 1982). Need characteristics. — The additive effects of the need characteristics are entirely consistent with prior expectations (Hulka and Wheat, 1985; Wan, 1989; Wolinsky, 1990). Older adults with poorer perceptions of their health, and those reporting more problems with basic ADLs, household ADLs, advanced ADLs, and lower and upper body limitations are more likely to use health services. In particular, those who perceive their health to be less than good are more likely to take bed-disability days and to take more of them, to have contacted physicians and hospitals in the past 12 months, to have seen physicians more often, and to die. Needing help with basic ADLs results in using more home health services, and being placed in a nursing home or dying, although confidence in the two latter effects is diminished by their failure to be replicated in the logistic regression analyses. Needing help with household ADLs also results in the taking of more bed-disability days (among those having

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taken days) and the use of more home health services. In addition, however, problems with performing household ADLs result in a greater likelihood of hospitalization, and more physician visits and longer lengths of stay in hospitals, although confidence in the hospital contact effect is diminished by its failure to be replicated in the logistic regression analyses. Needing help with advanced ADLs also results in the taking of more disability days, but not in the use of home health services. Having problems performing advanced ADLs also results in a greater likelihood of hospital contact and mortality. This suggests the importance of separating the advanced ADLs from the basic and household ADLs, inasmuch as the advanced ADLs are the only ones predictive of hospital contact or mortality. Indeed, these effects would not have been detected had all of the ADLs been treated in the traditional fashion. The existence of lower body limitations has significant effects on several measures of health services utilization. Older adults with such limitations are more likely to take bed-disability days and to take more of them, to have contacted a physician or hospital within the past 12 months, to have spent more nights in the hospital, to have seen physicians more often, and to have died. In contrast, upper body limitations have a significant effect only on the two bed-disability days measures. Those with such limitations are more likely to take bed-disability days and to take more of them than those without upper body limitations. These results indicate that it is lower body limitations that are more salient in the demand for health services. The interaction of race and the need characteristics demonstrates support for the view that older adults' demands for health services in response to health status vary by ethnicity (Wolinsky et al., 1989). Absent from that support, however, is a clear and consistent pattern among the interaction effects. Not every interaction term produces significant effects. In particular, when the full panoply of need characteristics are included, no interactions involving perceived health status are identified. And among those interaction terms that do produce significant effects, those effects are not always consistent across the various measures of health services utilization. Having problems with the basic ADLs has more impact on the number of physician visits for Blacks than for Whites. Difficulties with household ADLs are more important for Blacks in the taking of bed-disability days and hospital contact, although confidence in the former effect is diminished inasmuch as it is not replicated in the logistic regression analyses. For Blacks, needing assistance with advanced ADLs results in the use of more home health services but decreases the likelihood of hospital contact. Lower body limitations are less important among Blacks for physician and hospital contact, as well as for mortality. Confidence in the effect of hospital contact, however, is diminished inasmuch as it is not replicated in the logistic regression analyses. Finally, the effect of upper body limitations on the use of home health services is greater among Blacks. DISCUSSION

Three themes emerge from these analyses that warrant further discussion. First, the R2 levels obtained from this

study are generally consistent with those reported in prior studies (Hulka and Wheat, 1985; Wan, 1989; Wolinsky, 1990). This is despite the fact that the present research includes a considerably broader and richer array of measures of the predisposing, enabling, and need characteristics. In particular, we incorporated an indicator of multigenerational living arrangements, two social support scales, measures of health worries and sense of health control, two indicators of health insurance coverage, an index of residential stability, and six measures of need, including five multiple-item scales. Although the expanded model and these improvements in measurement clarified several of the relationships with health services utilization (see below), they did not enhance the robustness of the behavioral model. Accordingly, we argue that substantial improvements in R2 will not likely result from further refinement or proliferation of the traditional measures of the predisposing, enabling, and need characteristics. This is not to suggest that the behavioral model is forever limited to its current level of predictive utility, nor to suggest that the model be abandoned altogether. Rather, two very different strategies seem eminently promising. One involves exploring the relationships among the various measures of health services utilization themselves. As indicated above, although there is much discussion of how informal services may substitute for more formal services, and how outpatient services may substitute for inpatient services, virtually no research exists that addresses these issues (Brody, 1985; Wolinsky, Mosely, and Coe, 1986). From our perspective (Wolinsky, 1990), this is a serious shortcoming, especially from the standpoint of the robustness of the behavioral model. If the presumptions underlying such discussions hold, then the simultaneous consideration of multiple measures of health services utilization using structural equation methods (Long, 1983a, 1983b) should considerably enhance our ability to predict any one of them. The other strategy involves moving the study of health services utilization beyond its reliance on cross-sectional data (Wolinsky, 1990). Such data make it difficult to disentangle competing causal processes and to incorporate prior levels of health services utilization into the behavioral model. Given that many older adults are relatively consistent in their use of health services over time, regardless of whether they are nonusers, modest users, or heavy users (Mossey, Havens, and Wolinsky, 1989), it would seem advantageous to incorporate prior use patterns into the model. Constructing linear panel analyses (Kessler and Greenberg, 1981) would enhance the fit of the models and allow an examination of the effects of the predisposing, enabling, and need characteristics on changes in health services utilization over time. Those models would show whether these characteristics are more predictive of changes in health services utilization than they are predictive of static levels. The second theme to emerge from these analyses that warrants further discussion involves the dominance of the need characteristics. Despite the introduction of the various measures of social supports, health beliefs, and health insurance coverage, need still drives the system. Moreover, the introduction of these additional measures did not apprecia-

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bly enhance the robustness of the overall model. Although this is quite consistent with the extant literature (Hulka and Wheat, 1985; Wan, 1989; Wolinsky, 1990), care must be taken in the interpretation of this situation. Andersen and his colleagues (Aday and Andersen, 1974, 1975, 1981; Aday, Andersen, and Fleming, 1980; Aday, Fleming, and Andersen, 1984; Andersen, 1968; Andersen and Newman, 1973) argue that if the need characteristics are the principal determinants of health services utilization, then the system may be characterized as being equitable. Under such circumstances, especially when the predisposing and enabling characteristics have only minimal effects, they see no need for targeted, programmatic interventions. There are two reasons to be hesitant about reaching such a conclusion. One is that most of the variance in the use of health services remains unexplained. As a result, we do not really know what accounts for most health services use, and we should not placidly take refuge in the assumption that the error variance results from a truly random process (Wolinsky, 1990). The other reason to be hesitant in declaring the equity of the health care delivery system is that the need characteristics themselves have different effects for majority vs minority older adults. Indeed, this article joins several recent reports documenting the fact that older minority adults' use of health services is far more constrained by and sensitive to the need characteristics (see Blendon et al., 1989; Freeman et al., 1987; Wolinsky et al., 1989). The existence of such constraints and sensitivities is not consistent with a conclusion of equity. A final theme to emerge that warrants further discussion is the identification of important effects from the previously absent or improperly measured factors. Five of these seem especially noteworthy. These involve health worries, control over health, the separation of kin from nonkin social supports, the separation of advanced from basic and household ADLs, and the separation of upper from lower body functional limitations. Worrying about one's health resulted in greater levels of health services utilization. In the presence of repetitive measures of health status, this provides further evidence that both the worried well and the worried ill place greater demands on the system than the need characteristics alone would indicate (Newman, 1975). Having a sense of control over one's health reduced the risk of dying. This provides limited evidence that older adults with perceived control are less likely to give up and succumb to their ailments altogether (Rodin, 1986; Rodin, Timko, and Harris, 1985). Although the inclusion of social support measures was clearly advantageous, it was the separation of kin from nonkin social supports that is most important. Kin supports only affected physician contact, and then only modestly. In contrast, nonkin supports had similar effects on physician contact, but also had important effects that reduced the number of bed-disability days taken, the length of stay in hospitals, nursing home placement, and the risk of dying. Thus, contrary to prior expectations (Brody, 1985), it is nonkin rather than kin supports that appear to substitute for formal health services utilization. Moreover, both kin and nonkin supports actually had positive effects on physician contact rates, which is also contrary to prior expectations

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(Brody, 1985). This suggests that in the course of routine interactions with family and friends, the older adult's health is discussed and encouragement provided for going to see the doctor. The separation for the advanced ADLs from the basic and household ADLs also proved to be important. Having difficulties with the advanced ADLs was a significant predictor of the taking of bed-disability days, hospital contact, and mortality. Neither of the other ADLs had such consistent effects on these outcomes, which all represent varying degrees of a shift toward marked dependency. Indeed, only advanced ADLs produced significant effects on hospital contact and mortality in both the OLS and logistic regression analyses. These results underscore the primacy of cognitive factors in the functioning, placement, and prognosis of older adults (Folstein, Folstein, and McHugh, 1975). Perhaps more importantly, however, these results suggest that current proposals before Congress to use either traditional (e.g., basic) ADLs or instrumental (e.g., household) IADLs as the primary basis for determining coverage eligibility for home and community-based long-term care services are ill advised. Such measures will likely discriminate against cognitively impaired individuals (see Kasper, 1990, for a thoughtful and extended discussion of this issue). The reason involves the domino effect of how, for some diseases, cognitive limitations may be first to appear, followed by difficulties with household ADLs and, ultimately, by difficulties in basic ADLs. Traditional ADL and IADL measures mask the effects of these cognitive impairments by including only one or two such items along with six or more sociobiological items. As a result, the traditional ADL and IADL measures are not sufficiently sensitive to detect the early stages of cognitive decline. Therefore, their use as eligibility screens will likely result in increased health care costs resulting from treatment that was delayed until eligibility was subsequently obtained. And if intervention is delayed until the second (or third) domino falls, the potential for a return to prior functioning levels will likely be diminished as well. Finally, the separation of upper from lower body functional limitations also proved to be useful. Upper body limitations had a significant effect only on the taking of beddisability days. Those having such limitations were more likely to have taken such days, and to have taken more of them. In contrast, lower body limitations had significant effects on whether bed-disability days were taken and how many were taken, on physician and hospital contact, on the number of physician visits, and on mortality. For all of these outcomes, lower body limitations resulted in more health services utilization. Beyond identifying lower body limitations as the more important and consistent determinant of health services utilization, these results suggest that the inclusion of upper body limitations in the same scale may have masked the observed impact of lower body limitations in prior studies (see Kane and Kane [1981] for a detailed review of such studies). ACKNOWLEDGMENTS

This research was supported by grant R37-AG-09692 to Dr. Wolinsky from the National Institutes of Health, and by a grant from the Research

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Council of Kent State University to Dr. Johnson. The opinions expressed here are those of the authors, and do not necessarily reflect those of the funding agencies or academic institutions involved. Address correspondence to Dr. Fredric D. Wolinsky, Department of Medicine, Indiana University School of Medicine, 1001 West Tenth Street, Regenstrief Health Center, Fifth Floor, Indianapolis, IN 46202-2859.

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Received October 19, 1990 Accepted April 30, 1991