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Predicting four types of service needs in older adults
Evaluation and Program Planning 24 (2001) 157±166
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Predicting four types of service needs in older adults Robert J. Calsyn*, Joel P. Winter MA University of Missouri-St. Louis, Gerontology Program, 406 Tower, 8001 Natural Bridge Road, St. Louis, MO 63121 4499, USA
Abstract Logistic regression analysis was used to predict four service need variables. A sample of nearly 5000 older Missourians were assessed on a comprehensive set of variables, representing all of the categories of the behavioral model. Variables in the behavioral model predicted perceived need for frail elderly services better than they predicted unmet need for frail elderly services, perceived need for community services, and unmet need for community services. Health need variables were better predictors of all of the service need variables than predisposing or enabling variables. Although the inclusion of interaction terms in the prediction models did not increase model ®t, some of the interaction terms were signi®cant and helped to clarify the relationship between certain predictor variables and the four service need variables. q 2001 Elsevier Science Ltd. All rights reserved. Keywords: Perceived service needs; Needs assessment; Behavioral model
1. Introduction State Units on Aging and their af®liated Area Agencies on Aging are federally mandated to assess the needs of the older adults living in their catchment area. As Diwan and Moriarity (1995) point out, the task of de®ning needs is not a simple one. Perceived service need and unmet service need are the two most common de®nitions of service needs used in previous research predicting service needs of the elderly. Although these two concepts have often been used interchangeably in previous articles, they are conceptually and computationally quite different. Perceived service need measures simply ask respondents if they need speci®c services (e.g. home health care or telephone reassurance), regardless of whether they are currently receiving any services aimed at alleviating those needs. Unmet service need measures, on the other hand, only count those services which respondents report needing, but which they are not receiving currently. Both service need measures are worthy of study. For example, measure of perceived service needs often correlate as high with service utilization as objective health needs (Calsyn & Roades, 1993; Mindel & Wright, 1982; Starrett, Todd, & DeLeon, 1989b). Similarly, research has shown that a signi®cant percentage of the older adult population report having unmet service needs even when they report no objective health needs (Jackson & Mittlemark, 1997). Although agency planners are primarily inter* Corresponding author. Tel.: 11-314-516-5421; fax: 11-314-516-5210. E-mail address: [email protected] (R.J. Calsyn).
ested in measuring and predicting unmet service needs, other gerontological researchers are more interested in predicting perceived service needs. This paper compares the ability of the behavioral model (Andersen, 1995) to predict both perceived service need and unmet service need. Although the behavioral model was originally developed to predict service utilization, several studies have used the behavioral model to predict either perceived service need (Calsyn & Roades, 1993; Coulton & Frost, 1982; Mindel & Wright, 1982; Richardson, 1992; Starrett, Decker, Araujo, & Walters, 1989a; Starrett, et al., 1989b) or unmet service need (Calsyn, Roades, & Klinkenberg, 1998; Jackson & Mittlemark, 1997). Unfortunately, these studies have only explained 10±15% of the variance of either perceived service needs or unmet service needs. Prior researchers have also failed to distinguish between needs for different types of services. With the exception of a study by Coulton and Frost (1982), previous studies have predicted an aggregated measure of service needs. These aggregated measures combine needs for frail elderly services (e.g. meals on wheels, home health) with needs for community services (e.g. senior centers, health screening). In hopes that the behavioral model would explain more of the variance of speci®c service need variables than total service need, this study predicted four service need variables: perceived need for frail elderly services, unmet need for frail elderly services, perceived need for community services, and unmet need for community services. For this study, predictor variables were classi®ed into the following categories based on the behavioral model
0149-7189/01/$ - see front matter q 2001 Elsevier Science Ltd. All rights reserved. PII: S 0149-718 9(01)00006-4
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(Andersen, 1995): predisposing; enabling; and health needs. Predisposing variables include demographic characteristics as well as beliefs and attitudes about the causes of problems. Enabling variables include individual resources (e.g. income) and family and community resources (e.g. social support). Health need variables include physical health status, functional impairment, and mental health. In this study we were particularly interested in determining what percentage of the variance of perceived service needs could be explained solely on the basis of health needs versus other variables. We expected health need variables would be the best predictors of all four service need variables. In addition, we hypothesized that predisposing and enabling variables would explain a signi®cant percentage of the variance of both perceived need and unmet need for community services, but very little variance of both perceived need and unmet need for frail elderly services. Our rationale for these hypotheses is based on service utilization research which has found that predisposing and enabling variables have explained very little variance of the use of more non-discretionary services such as hospitalization and home health care; nearly all of the explained variance was accounted for by health need variables (Wolinsky & Johnson, 1991). In contrast, predisposing and enabling variables have explained a signi®cant percentage of the variance of the use of more discretionary services such as attendance at a senior center (Mitchell & Krout, 1998). In the only study which has examined the prediction of multiple service need variables, Coulton and Frost (1982) did ®nd that health need variables explained nearly all of the variance of more non-discretionary service needs such as outpatient medical care and in-home personal care, whereas predisposing and enabling variables explained a signi®cant percentage of the variance of the need for recreational services, a discretionary service. Another improvement of this study over previous research was the inclusion of interaction terms in the prediction models. Several authors have suggested that the performance of the behavioral model in predicting service utilization could be improved if interaction terms were included in the model (Kosloski & Montgomery, 1994; Rundall, 1981). Similarly, we believed that the inclusion of interaction terms would improve the prediction of the service need variables. All of the interaction terms in our models included health need as one of the variables. In general, we postulated that terms would have a multiplicative effect under conditions of greater health needs. Below, we review more speci®c ®ndings of the studies predicting total service needs (perceived and unmet). Unfortunately, the study results are often contradictory. Differences in samples and operational de®nitions of constructs are also probably responsible for the lack of consistency in the study ®ndings. 1.1. Predisposing As a group, predisposing variables have explained very
little of the variance of total service needs. Moreover, none of the speci®c predisposing variables have exhibited a strong and consistent relationship with service needs. Although age was positively related to service needs in two studies (Jackson & Mittlemark, 1997; Mindel & Wright, 1982), three other studies found no relationship between age and service need (Calsyn & Roades, 1993; Calsyn et al., 1998; Richardson, 1992). We hypothesized that age would be positively related to need for frail elderly services, but unrelated to need for community services. African-Americans have reported greater service needs than Caucasians in some studies (Calsyn et al., 1998; Coulton & Frost, 1982; Jackson & Mittlemark, 1997), but race was unrelated to service needs in other studies (Calsyn & Roades, 1993; Mindel & Wright, 1982). Women reported more service needs than men in two studies (Calsyn & Roades, 1993; Mindel & Wright, 1982), but two other studies found no gender differences (Calsyn et al., 1998; Richardson, 1992). In the Coulton and Frost (1982) study, women reported greater need for medical, mental health, and recreational services, but men reported greater need for frail elderly services. Education was not correlated with service needs in any of the previous studies (Calsyn & Roades, 1993; Calsyn et al., 1998; Mindel & Wright, 1982; Richardson, 1992). 1.2. Enabling Enabling variables also have not explained much of the variance of total service needs of the elderly. Individuals with less income reported more service needs in one study (Calsyn et al., 1998), but income was unrelated to service needs in two other studies (Calsyn & Roades, 1993; Richardson, 1992). Social contact variables have not explained much of the variance of service needs (Calsyn & Roades, 1993; Calsyn et al., 1998; Richardson, 1992), with the exception of the study by Coulton and Frost (1982) which found that persons who were less isolated were more likely to report needing the more discretionary mental health and recreational services. Thus, we predicted that persons who reported more social contacts would also report more need for community services, but not frail elderly services. On the other hand, we predicted that persons who lived alone and/or reported no one to assist them in an emergency would report a greater need for frail elderly services; however, we did not think living situation and the availability of emergency services would affect need for community services. Based on Mitchell's (1995) ®nding that length of time in the community affects service awareness, we predicted that respondents who had lived in their community for a shorter period of time, would be more likely to report more service needs. In this study we also examined the impact of perceived age discrimination on service needs. We hypothesized that respondents who felt that they had experienced age discrimination would report more service needs, consistent with the results of a previous
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
study which had found that older adults who reported sex discrimination indicated having more needs (Starrett et al., 1989a). 1.3. Health needs Measures of health need (e.g. functional impairment, physical health status, and low morale) have typically been the strongest predictors of service needs (Calsyn & Roades, 1993; Coulton & Frost, 1982; Jackson & Mittlemark, 1997; Mindel & Wright, 1982; Richardson, 1992; Starrett et al., 1989a), with only one study ®nding no significant relationship between health need indicators and service needs (Calsyn et al., 1998). 1.4. Interactions As noted earlier we postulated that the inclusion of interaction terms in the prediction models would explain additional variance of service needs. We created interaction terms between two of the social contact variables, living situation (alone, with others) and availability of emergency support (yes, no) with the four health need variables. We predicted that the impact of health needs on both perceived and unmet need for frail elderly and community services would be greater for those respondents who lived alone and those respondents who reported having no one to rely on in the case of an emergency. This prediction is based on prior research predicting service utilization among older adults which ®nds that in high need situations, respondents who live alone and/or have less emergency support available, are more likely to use formal services (Biegel, Bass, Schulz, & Morycz, 1993; Logan & Spitze, 1994; Mitchell, 1995).
159
dents who began the interview failed to complete the interview. Thus, the ®nal needs assessment sample was 4985; the 82 cases for which a family member or someone else had served as a proxy interviewee were eliminated from this study, leaving a sample of 4903. Due to listwise deletion of missing data, the sample size for the analyses was 4221. 2.2. Variables Refer to Table 1 for the coding and relevant descriptive statistics of all the variables in the study. The variables are divided into four sets: health needs, predisposing, enabling, and perceived and unmet service need variables. 2.2.1. Health need variables Health need variables included: perceived overall health, need for assistance with ADL (activities of daily living) or IADLs (instrumental activities of daily living), poor mental health days, and perceived loneliness. Perceived overall health was measured on a 5 point scale with 1 indicating excellent health and 5 indicating poor health. Possible ADLs were eating, dressing, toileting, bathing, getting in/ out of bed, getting around, and getting outside. Possible IADLs included using the telephone, normal housework, heavy cleaning, shopping, managing money, preparing meals, and taking medication. If respondents needed help with any one of these activities, then they were coded as needing help with I/ADL's. Mental health needs were assessed by two variables: number of days out of the last 30 that mental health was not good and extent of loneliness.
2.1. Sample and procedure
2.2.2. Predisposing variables Predisposing variables included age, gender, education, and race. Because of small numbers of respondents from other ethnic minority groups, race was coded as a dichotomous variable, Caucasian and African-American. The 116 other cases (2.4% of the total sample) were not used in the analysis (i.e. they were part of the listwise deletion).
Data were collected for the 1994 needs assessment study of the Missouri Department of Social Service's Division of Aging and the 10 Missouri Area Agencies on Aging (Drainer, 1994). The sample was strati®ed by the 10 AAA regions in Missouri using the optimum allocation or disproportionate sampling method. Telephone interviews were conducted by trained interviewers at the Center for Advanced Social Research at the University of Missouri-Columbia. Questions were asked of the person in the household age 60 or over who had the most recent birthday. A total of 102,500 phone numbers were dialed by computer using random digit dialing. There was no answer for 24,863 calls; the phone had been disconnected for 21,762 calls; a business number was reached for 10,009 calls. There was a total of 28,456 noneligible contacts, 6842 respondents refused the interview before eligibility could be determined, 1488 refused the interview after eligibility was determined, and 4095 respon-
2.2.3. Enabling variables Enabling variables consisted of income, time lived in community, perceived discrimination and several social contact measures. Due to the large amount of missing data with regards to the income item (26% of the total sample), missing data were replaced with the median value (i.e. 3) for this variable. Social contact variables in this set were: amount of social contact; whether respondents had someone who could care for them as long as needed; living situation; number of formal service information sources; and number of informal sources of service information. Amount of social contact was derived by combining and transforming responses to two survey items assessing time spent talking on the phone with friends or relatives and time spent visiting with friends or relatives. Both items were measured on scales from 0 (not at all) to 3 (once a day or more). These items were combined by assigning twice the weight to the
2. Method
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Table 1 Descriptive statistics for variables in the study Mean a
Coding Health variables Perceived Health I/ADL Needs Perceived Loneliness Poor mental Health Days Predisposing Age Gender Education Race Enabling Income Social Contact Emergency Support Formal Information Source Informal Information Source Time in Community Perceived Age Discrimination Living Situation Service Need Perceived Need, Frail Services Unmet Need, Frail Services Perceived Need, Community Services Unmet Need, Community Services
1±5 with: 1 Excellent, 5 Poor 0 No assistance needed, 1 Some assistance needed 1 Quite often, 2 Sometimes, 3 Almost never Number of day in the last 30 that mental health was not good
Standard deviation
2.88 0.37 2.69 1.62
1.19 0.48 0.57 5.44
Age in years 0 Male, 1 Female 1±7 with: 1 Never attended school, 4 High school graduate, 7 4 Years of college or more 0 Caucasian 1 African-American
70.84 0.69 4.17
7.37 0.46 1.53
0.06
0.23
0±9 with: 0 Less than $5000, 9 Greater than $50,000 Composite of time spent on phone and visiting away from residence 0 No, 1 Yes 0 No, 1 Yes 0 No, 1 Yes Years 0 No, 1 Yes 0 Alone, 1 With others
3.11 2.19 0.53 0.62 0.51 34.42 0.29 0.59
1.78 0.67 0.50 0.49 0.50 22.59 0.46 0.49
0.25 0.07 0.37 0.16
0.43 0.26 0.48 0.36
0 No needs, 1 Some needs 0 No needs, 1 Some needs 0 No needs, 1 Some needs 0 No needs, 1 Some needs
a
For dichotomous variables which are coded 0, 1, the mean indicates the percentage of respondents who were coded 1. For example, a mean of 0.69 for gender indicates that 69% of the sample were women.
visiting item (due to the comparatively greater richness of face-to-face interaction) and averaging. Living situation was a dichotomous variable assessing whether respondent lived with others or alone. Formal and informal information sources were measured as the number of either formal (i.e. physician, social worker, hospital) or informal (i.e. friend, relative) resources listed when respondents were asked two open-ended questions inquiring from whom they would seek information and help in making service decisions. 2.2.4. Service need variables Respondents were ®rst asked if they needed 20 different services. Then they were asked if they were receiving each of those services. For this study, four dichotomous variables were created: perceived need for frail elderly services, unmet need for frail elderly services, perceived need for community services, and unmet need for community services. Respondents were classi®ed as having a perceived need if they mentioned a need for any of the services listed, in that category regardless of whether they were currently receiving a service that addressed that need. On the other hand, respondents were considered to have an unmet need only if they were not receiving a service for which they reported a need. Frail elderly services were de®ned as reassurance services, in-home personal care, homemaker/chore assistance, in-home nursing care, respite care, in-home therapy, home-delivered meals, information on caring for the
elderly, assistance with ®nding information, and adult day care centers. Community services were de®ned as assistance with ®lling out forms, counseling services, transportation assistance, senior centers, health screening, assistance with ®nding employment, information on staying healthy, and home repair services. The list of services focused on services that could be accessed either directly or indirectly through the Missouri Division of Aging and the Area Agencies on Aging, sponsors of the study. 2.3. Analysis strategy For each of the analyses, hierarchical logistic regression modeling was used. Logistic regression is more appropriate than ordinary least-squares regression for dichotomous variables that deviate from a 50/50 split. Logistic regression also allows a hierarchical approach so that the relative contribution to model ®t of pre-determined sets of variables may be assessed. Because the authors wanted to determine whether predisposing and enabling variables explained any additional variance beyond health needs, health needs were entered into the equation ®rst followed by predisposing and enabling variables. First-order interaction terms were created as simply the product of two variables and are entered into the equations on the ®nal step. For interpretation purposes, logistic regression weights are reported for models with and without interaction terms. Thus, incremental increases in model ®t was assessed at four stages: health
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
need, predisposing, enabling, and ®rst-order interactions. Model ®t is assessed with the chi-square goodness of ®t test, and the Cox & Snell R 2 (SPSS Inc., 1997). 3. Results As Table 1 indicates, 25% of the sample reported perceived need for frail elderly services, but only 7% of the sample had an unmet need for frail elderly services. Similarly, 37% of the sample reported a perceived need for community services, but only 16% of the sample reported an unmet need for community services. Correlations between the four service need variables ranged from 0.24 to 0.57 (see Table 2). 3.1. Prediction of need for frail elderly services From Table 3, it is clear that the behavioral model is much better in explaining perceived need for frail elderly services than unmet need for frail elderly services. This is not surprising given that the distribution of scores for unmet need was more uneven (93/7%) than perceived need (75/ 25%). As Table 4 indicates all of the health need variables, particularly I/ADL needs, were signi®cant predictors of both perceived service need and unmet service need for frail elderly services. Age was positively associated with both perceived need and unmet need for frail elderly services. Gender was not related to perceived need for frail elderly services, but women were more likely than men to report unmet need for frail elderly services. African-Americans were more likely than Caucasians to report more perceived need and unmet need for frail elderly services. Income was not related to perceived service need, but lower income respondents were more likely than others to report unmet need for frail elderly services. Similarly, years in community was not related to perceived need, but respondents who had lived in the community longer were less likely to report unmet need for frail elderly services. Respondents who reported having a formal information source were more likely than those without a formal source to report a perceived service need, but not more likely to have an unmet service need. Likewise, perceptions of discrimination were related to perceived service need but not unmet service need. Although inclusion of interaction terms did not dramatically improve ®t of the model for either service need variable, they do indicate some important multiplicative effects. For example, living situation was signi®cantly related to perceived service need in the model without interactions, but was not signi®cantly related in the model with interaction terms. Instead, living situation interacted with I/ADL need. Living situation had little effect for those with no I/ ADL problems. However, for those with I/ADL problems, perceived needs was greater for those who lived alone. For those who live with someone, the relationship between poor
161
mental health days and perceived service need is stronger than for those who live alone. Living situation was also unrelated directly to unmet need. Rather, living situation interacted with two health variables (I/ADL and perceived health). Living situation had no effect on unmet need for respondents who were in better health, but for respondents in poorer health, those who lived alone reported more unmet need than those who lived with others. Poor mental health days also had no direct effect on unmet need. Rather, poor mental health days interacted with the availability of emergency support. The availability of emergency support had little effect on unmet need for those respondents with many poor mental health days. However, the availability of emergency support reduced unmet need for persons with fewer bad mental health days. The availability of emergency support also interacted with perceived health. Perceived health had little effect on unmet need for respondents who had someone to rely on in an emergency; however, for those respondents who had no one to rely on in an emergency, unmet need increased with greater perceived health problems. 3.2. Prediction of perceived need for community services Again, refer to Table 3 for the model ®t statistics; the behavioral model performs equally in predicting both perceived and unmet need for community services. Again, the health variables are the most important in predicting both types of need for community services, with little additional variance being explained by predisposing and enabling variables. Table 5 displays the speci®c regression coef®cients for all of the regression models. In general, variables which predict perceived service need also predict unmet need, but there are some notable exceptions. In addition, although the interaction terms did not increase model ®t, the inclusion of the interaction terms does help to clarify some of the relationships between variables. All of the health need variables predict both perceived need and unmet need for community services in the models without interaction terms. Although age was positively related to perceived service need, age was negatively related to unmet need for community services, suggesting that the community service needs of older respondents were met better than the needs of younger respondents. AfricanAmericans were more likely to report more perceived and unmet needs for community services than Caucasians. Lower income respondents also reported more perceived and unmet need for community services than higher income respondents. Respondents who did not have someone to rely on in an emergency were more likely to report more perceived and unmet community service needs than those who had someone to rely on in an emergency. Respondents who listed a formal information source were more likely than those without a formal source to report more perceived need for community services, but not more likely to report unmet need. Respondents who reported age discrimination
1. Perceived Health 2. Poor Mental Health Days 3. I/ADL Needs 4. Perceived Loneliness 5. Age 6. Gender 7. Education 8. Race 9. Income 10. Social Contact 11. Emergency Support 12. Formal 13. Informal 14. Time in Community 15. Discrimination 16. Living Situation 17. Perceived Need, Frail Services 18. Unmet Need, Frail Services 19. Perceived Need, Community Services 20. Unmet Need, Community Services 0.18 20.38 20.01 0.03 20.04 20.01 20.06 20.09 20.10 0.04 20.05 20.02 0.13 20.06 0.17 0.14 0.14 0.17
0.16 0.19
0.19
±
2
0.19 0.42 20.20 0.10 0.05 20.26 0.08 20.29 20.09 20.12 0.03 20.02 0.01 0.23 20.08 0.26
±
1
0.24
0.22 0.27
± 20.22 0.22 0.17 20.12 0.08 20.22 20.05 20.13 0.03 20.02 0.01 0.19 20.15 0.45
3
20.17
20.17 20.16
± 20.09 20.10 0.11 0.00 0.17 0.11 0.16 0.01 0.01 0.03 20.15 0.24 20.21
4
0.02
0.08 0.12
0.10 20.09 20.04 20.17 20.02 20.14 20.07 0.10 0.15 0.07 20.33 0.22
±
5
0.09
0.08 0.09
± 20.11 0.02 20.26 0.07 20.07 0.07 0.02 0.00 20.03 20.23 0.11
6
20.08
20.07 20.08
± 20.06 0.40 0.06 0.09 0.08 20.04 20.06 20.11 0.08 20.07
7
0.12
0.07 0.08
± 20.10 20.05 20.01 0.02 20.04 20.07 20.01 20.01 0.08
8
Table 2 Pearson correlations for variables in the study. (Note: if uru . 0.03, p , 0.05; if uru . 0.04, p , 0.01)
20.18
20.12 20.16
0.03 0.16 20.01 0.01 0.00 20.09 0.30 20.17
±
9
20.04
20.06 20.01
0.08 0.06 0.06 0.07 20.06 20.10 20.04
±
10
20.16
20.10 20.16
0.01 0.08 20.01 20.07 0.23 20.14
±
11
0.01
0.02 0.06
± 20.16 0.02 0.00 0.04 0.05
12
20.04
20.03 0.00
0.04 20.01 20.05 20.01
±
13
20.03
20.04 20.02
0.00 20.02 0.00
±
14
0.11
0.08 0.12
± 20.03 0.14
15
20.09
20.06 20.13
± 20.19
16
18
19
20
0.29 0.29 0.57 ±
0.48 ± 0.35 0.24 ±
±
17
162 R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
163
Table 3 Incremental improvements in ®t of the behavioral model for frail and community services and perceived and unmet need Frail services a
2 2LL b Health Need Chi-Square b Cox & Snell R 2b Predisposing Chi-Square b Cox & Snell R 2b Enabling Chi-Square b Cox & Snell R 2b Interaction Terms Chi-Square b Cox & Snell R 2b
Community services a
Perceived Need
Unmet Need
Perceived Need
Unmet Need
4748.88
2179.97
5546.96
3670.43
940.79** 0.20
272.64** 0.06
380.78** 0.09
332.42** 0.08
101.21** 0.22
29.21** 0.07
46.40** 0.10
53.79** 0.09
49.72** 0.23
26.47** 0.08
88.69** 0.12
92.82** 0.11
21.23** 0.23
16.9* 0.08
8.62 0.12
7.87 0.11
a
*p , 0.05, **p , 0.01. 2 2LL is analogous to the ®t of the null model. Subsequent chi-square values can be interpreted as improvement in ®t against a null model. The Cox & Snell R 2 is analogous to the R 2 from ordinary least square regression. b
Table 4 Logistic regression coef®cients for models predicting need for frail services Perceived Need a
Perceived Health Poor Mental Hlth. Days I/ADL Needs Perceived Loneliness Age Gender Education Race Income Social Contact Emergency Support Formal Informal Time in Community Discrimination Living Situation Live by Perc'd Health Live by I/ADL Live by Loneliness Live by Poor Mental Hlth. Days Emer. Support by Perc'd. Health Emer. Support by I/ADL Emer. Support by Loneliness Emergency Support by Poor Mental Health Days Constant a
Unmet Need a
Without interaction
With interaction
Without interaction
With interaction
b
S.E.
b
S.E.
b
S.E.
b
S.E.
0.156*** 0.026*** 1.764*** 20.249*** 0.043*** 0.071 0.039 0.640*** 20.031 0.005 20.271** 0.267** 0.033 20.002 0.220* 20.362***
0.0402 0.0073 0.0924 0.0737 0.0059 0.1006 0.0301 0.1586 0.0299 0.0630 0.0858 0.0882 0.0850 0.0018 0.0898 0.0944
0.158* 0.012 2.031*** 20.194 0.042*** 0.061 0.039 0.657*** 20.030 0.012 20.317 0.255** 0.033 20.002 0.220* 0.440 20.068 20.508** 20.137 0.041** 0.077 20.024 20.058 20.018
0.0643 0.0110 0.1484 0.1109 0.0060 0.1011 0.0303 0.1594 0.0300 0.0635 0.4883 0.0888 0.0857 0.0018 0.0904 0.4961 0.0786 0.1866 0.1502 0.0150 0.0785 0.1860 0.1482 0.0156
0.171** 0.018* 1.207*** 20.409*** 0.022* 0.463** 20.036 0.624** 20.105* 20.114 20.379** 0.050 20.148 20.006* 0.120 0.203
0.0632 0.0086 0.1571 0.1006 0.0088 0.1684 0.0472 0.2073 0.0478 0.0913 0.1348 0.1341 0.1296 0.0028 0.1338 0.1430
0.185 0.014 1.588*** 20.374* 0.021* 0.454** 20.036 0.657** 20.105* 20.108 0.455 0.065 20.128 20.006* 0.125 0.150 0.249* 20.658* 20.117 20.019 20.318** 20.086 0.074 0.039*
0.0973 0.0124 0.2629 0.1480 0.0089 0.1699 0.0474 0.2094 0.0480 0.0924 0.7249 0.1352 0.1309 0.0028 0.1347 0.7207 0.1239 0.3228 0.2048 0.0180 0.1249 0.3160 0.2115 0.0188
24.929***
0.5514
25.198***
0.6241
23.807***
0.8202
24.192***
0.9239
*p , 0.05, **p , 0.01, ***p , 0.001.
were also more likely to report more perceived need and unmet need for community services. There was also a signi®cant interaction between living situation and I/ADL assistance on perceived need for
community services that had the same pattern for frail elderly services. Although none of the interaction terms were signi®cant for unmet need, inclusion caused a reduction in three zero-order terms: perceived health, perceived
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Table 5 Logistic regression coef®cients for models predicting need for community services Perceived Need a
Perceived Health Poor Mental Health Days I/ADL Needs Perceived Loneliness Age Gender Education Race Income Social Contact Emergency Support Formal Informal Time in Community Discrimination Living Situation Live by Perc'd. Health Live by I/ADL Live by Loneliness Live by Poor Mental Hlth. Days Emer. Support by Perc'd. Health Emer. Support by I/ADL Emer. Support by Loneliness Emer. Support by Poor Mental Health Days Constant a
Unmet Need a
Without interaction
With interaction
Without interaction
With interaction
b
S.E.
b
S.E.
b
S.E.
b
S.E.
0.099** 0.026*** 0.735*** 20.175** 0.015** 0.056 20.006 0.576*** 20.075** 0.079 20.435*** 0.263*** 0.100 20.002 0.226** 20.140
0.0333 0.0068 0.0780 0.0653 0.0050 0.0799 0.0249 0.1416 0.0244 0.0526 0.0707 0.0724 0.0700 0.0015 0.0764 0.0790
0.067 0.030** 0.894*** 20.193* 0.015** 0.057 20.007 0.592*** 20.076** 0.080 20.536 0.260*** 0.106 20.003 0.230** 20.194 0.096 20.416** 20.020 0 0.001 20.037 0.130 0.063 20.011
0.0547 0.0107 0.1271 0.0982 0.0051 0.0801 0.0250 0.1417 0.0244 0.0528 0.4200 0.0726 0.0702 0.0015 0.0766 0.4276 0.0652 0.1559 0.1330 0.0140 0.0650 0.1547 0.1304 0.0143
0.125** 0.029*** 0.846*** 20.249** 20.017* 0.270* 0.017 0.840*** 20.179*** 0.029 20.628*** 20.063 20.085 20.002 0.216* 20.054
0.0447 0.0071 0.1040 0.0778 0.0067 0.1128 0.0337 0.1574 0.0339 0.0685 0.0964 0.0957 0.0930 0.0020 0.0981 0.1031
0.029 0.042*** 0.920*** 20.207 20.017* 0.272* 0.017 0.851*** 20.178*** 0.031 20.868 20.061 20.084 20.002 0.226* 20.075 0.163 20.129 20.151 20.019 0.039 20.042 0.068 20.015
0.0675 0.0103 0.1588 0.1121 0.0067 0.1135 0.0337 0.1575 0.0340 0.0689 0.5398 0.0960 0.0934 0.0020 0.0983 0.5288 0.0872 0.2072 0.1586 0.0147 0.0884 0.2106 0.1637 0.0160
21.680***
0.4605
21.590**
0.5211
20.189
0.6004
20.072
0.6610
*p , 0.05, **p , 0.01, ***p , 0.001.
loneliness, and emergency support. Thus, it seems that these variables are important, but they may have interactive effects that are dif®cult to detect. 4. Discussion 4.1. Relationship to prior research The main conclusions of the study are: (1) the behavioral model predicts perceived need for frail elderly services moderately well, but predicts the following less well: unmet need for frail elderly services, perceived need for community services, and unmet need for community services; (2) most of the predictor variables were related to all four service need variables in the same way, but there were some interesting exceptions; (3) inclusion of interaction terms does not increase model ®t, but interactions do clarify the relationship between some variables. Below we relate our speci®c ®ndings to previous studies. 4.1.1. Health needs Consistent with previous research, physical health needs were signi®cant predictors of all four service need variables.
I/ADL impairment was the strongest predictor. Unlike previous studies (Calsyn & Roades, 1993; Calsyn et al., 1998) this study did ®nd a signi®cant relationship between poorer mental health and greater perceived need and unmet need for both frail and community services. The most likely explanation for the discrepancy in ®ndings is the different mental health measures used in the studies. The prior studies asked about morale, whereas the current study speci®cally asked about, `how many days during the past 30 days was your mental health not good?'. 4.1.2. Predisposing Age had not consistently predicted total perceived service needs in previous studies. We had hypothesized a positive relationship between age and need for frail services, but no relationship between age and need for community services. Our hypothesis was not supported; age was negatively related to unmet need for community services, but positively related to the other three service need variables. Women had reported more service needs in about half of the previous studies. In the current study there were no gender differences in either perceived service need variable, but women report more unmet needs for both frail elderly services and community services. Consistent with 60% of the previous studies,
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
African-Americans were more likely to report more needs than Caucasians on all four dependent variables. Although race was not highly correlated with the dependent variables, it made an important contribution to the prediction because of the lack of multicollinearity with the other predictors. Education was not signi®cantly related to any of the service need variables, replicating past research. 4.1.3. Enabling With the exception of perceived need for frail elderly services, respondents in this study with lower income were more likely than other respondents to report service needs. One previous study had also found an effect of income on service needs, but two other studies found no effect of income. We suspect that study sample differences explain the results. The two studies which found no effect of income sampled mainly low income individuals (Calsyn & Roades, 1993; Richardson, 1992). Because there was a restricted range on the income variable, it is less likely that income would be a signi®cant predictor. In contrast, the current study and Calsyn et al. (1998) interviewed samples which had more variability in income, and both studies found that income predicted service needs. Similar to past research on perceived sex discrimination (Starrett et al., 1989a), respondents who reported age discrimination were more likely to report service needs, except perceived need for frail elderly services. We had predicted that residents who had lived in their communities for a shorter period of time would report more service needs, because they would be less aware of both formal and informal help. Although length of time in the community was related to unmet need for frail elderly services as predicted, length of time in the community was not related to other service need variables. In general, social interaction variables had not predicted total service needs in previous studies. In this study, social contact also had no effect on any of the service need variables. Perceived availability of emergency support, on the other hand, did affect perceived service needs. Respondents who had no one to rely on in an emergency were more likely to report more service needs, particularly if they had more health problems. Respondents who lived alone were more likely to report more perceived and unmet need for frail elderly services, but not more need for community services. Living situation also interacted with health variables in affecting perceived need for both frail elderly and community services. In general, the relationship between need for services and health needs was stronger for respondents who lived alone. This ®nding mirrors ®ndings from service utilization studies which have found that health problems increase service utilization more for respondents who live alone than respondents who live with others (Soldo, 1985). 4.2. Implications for agency planners Given that health need indicators were the best predictors of the service need variables, should agency planners
165
continue to include measures of perceived and unmet service needs in their needs assessment studies of the elderly? Harlow and Harlow (1989) have argued that estimating service needs of the elderly should be based primarily on objective health needs (e.g. functional impairment). Given that this study found that the health need variables explained most of the variance of all four service need variables, agency planners can rely on archival measures of health status and functional impairment which will provide them with a relatively good estimate of the total service needs of the older adult population in their area. Archival measures of health status and functional impairment will be even more useful if they have been routinely gathered in a particular community, so that the researcher can examine time trends (Harlow & Turner, 1993). Similarly, the researcher can make normative comparisons of the local community with other communities if the archival indicators of health and functional impairment have been assessed in other communities. However, archival data on the health status and functional impairment of older adults in a local community may not exist, or there may be other needs assessment questions (e.g. perceived service barriers) which require a survey of local older adults. If the agency planner has decided to collect data from a representative sample of the elderly, the additional costs associated with including measures of perceived and unmet service needs are minimal. For example, the Missouri Division of Aging learned that home repair assistance was the most needed service in the eyes of older Missourians based on the data used in this study. They could not have discovered this information by simply relying on archival measures of health status and functional impairment. 4.3. Future research This study had many strengths: (1) a large sample from a state with urban, suburban, and rural elderly populations; (2) a large number of predictor variables including interaction terms; (3) equations predicting both perceived and unmet service needs; and (4) separate equations predicting frail elderly service needs and community service needs. Nevertheless, one must always be careful in generalizing the study results to other samples, particularly older adults who are more frail with greater health needs. Another strength of this study was the use of the most popular theory in service utilization research, the behavioral model, in choosing predictor variables. However, the behavioral model has not explained much of the variance of either perceived or unmet service need. Future researchers should either adopt other theoretical frameworks or expand the behavioral model to include other variables in predicting perceived service need. For example, the fact that the mental health need variables were strong predictors of the service need variables in this study suggests that one's overall personality, psychological adjustment, and general satisfaction with
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life may be important predictors of perceived service needs. This position is supported by an earlier study which found that perceived service needs of the elderly correlated significantly with perceived quality of life (Murrell, Brockway, & Schulte, 1982). Finally, although this study has demonstrated that both perceived and unmet service needs are highly correlated with health needs, more research is needed to see how the service need variables examined in this study correspond to the other indicators of unmet need discussed by Diwan and Moriarty (1995). Acknowledgements This study would not have been possible without the cooperation of those older adults who consented to be interviewed; we appreciate their time and thoughtfulness. We also wish to thank the staffs of the Missouri Division of Aging and the Missouri Department of Social Services, particularly Dr Ann Deaton and Ms Becky Viet, who made the data available to the authors. However, the analyses and conclusions presented in this paper are the sole responsibility of the authors and do not necessarily re¯ect the views of either agency. The authors also appreciate the editorial and word processing assistance of Ms Dorothy Gano. Dr Calsyn and Mr Winter are af®liated with the University of Missouri-St. Louis. Correspondence should be directed to Dr Calsyn at University of MissouriSt. Louis, 406 Tower, 8001 Natural Bridge Rd., St. Louis, MO 63121-4499, USA. References Andersen, R. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36, 1±10. Biegel, P. E., Bass, D. M., Schulz, R., & Morycz, R. (1993). Predictors of in-home and out-of-home service use by family caregivers of Alzheimer's disease patients. Journal of Aging and Health, 5, 419±438. Calsyn, R. J., & Roades, L. A. (1993). Predicting perceived service need, service awareness, and service utilization. Journal of Gerontological Social Work, 21, 59±76. Calsyn, R. J., Roades, L. A., & Klinkenberg, W. D. (1998). Using theory to improve needs assessment studies of the elderly. Evaluation and Program Planning, 21, 277±286.
Coulton, C., & Frost, A. K. (1982). Use of social and health service by the elderly. Journal of Health and Social Behavior, 23, 330±339. Diwan, S., & Moriarty, D. (1995). A conceptual framework for identifying unmet health care needs of community dwelling elderly. Journal of Applied Gerontology, 14, 47±63. Drainer, M. D. (1994). Needs assessment study: 1994 Statewide Report. Missouri Department of Social Services. 221 W. High Street, Jefferson City, MO 65101. Harlow, K., & Harlow, D. (1989). Unmet needs: the challenge for planning and targeting of resources, Indianapolis: Heartland Center on Aging, Disability and Long Term Care, Indiana University. Harlow, K. S., & Turner, M. J. (1993). State units and convergence models: needs assessment revisited. Gerontologist, 33, 190±199. Jackson, S., & Mittlemark, M. (1997). Unmet needs for formal home and community services among African-American and White older adults: the Forsyth County aging study. Journal of Applied Gerontology, 16, 298±316. Kosloski, K., & Montgomery, R. J. V. (1994). Investigating patterns of service use by families providing care for dependent elders. Journal of Aging and Health, 6 (1), 17±37. Logan, J. R., & Spitze, G. (1994). Informal support and the use of formal services by older Americans. Journal of Gerontology: Social Sciences, 49, 525±534. Mindel, C. H., & Wright Jr., R. (1982). The use of social services by black and white elderly: the role of social support systems. Journal of Gerontological Social Work, 4, 107±125. Mitchell, J. (1995). Service awareness and use among older North Carolinians. Journal of Applied Gerontology, 14, 193±209. Mitchell, J., & Krout, J. A. (1998). Discretion and service use among older adults: the behavioural model revisited. The Gerontologist, 38, 159± 168. Murrell, S. A., Brockway, J. M., & Schulte, P. (1982). The Kentucky elderly need assessment: concurrent validity of different measures of unmet need. American Journal of Community Psychology, 10, 117± 132. Richardson, V. (1992). Service use among urban African American elderly people. Social Work, 37, 47±54. Rundall, T. G. (1981). A suggestion for improving the behavioral model of physician utilization. Journal of Health and Social Behavior, 22, 103± 104. SPSS, Inc. (1997). SPSS Professional Statistics TM 7.5, Chicago: SPSS. Soldo, B. (1985). In home services for the dependent elderly. Research on Aging, 7, 281±304. Starrett, R. A., Decker, J. T., Araujo, A., & Walters, G. (1989a). The Cuban elderly and their service use. Journal of Applied Gerontology, 8, 69±85. Starrett, R. A., Todd, A. M., & DeLeon, L. (1989b). A comparison of the social service utilization behavior of the Cuban and Puerto Rican elderly. Hispanic Journal of Behavioral Sciences, 11, 341±353. Wolinsky, F. D., & Johnson, R. J. (1991). The use of health services by older adults. Journal of Gerontology: Social Sciences, 46, 5345±5357.
www.elsevier.com/locate/evalprogplan
Predicting four types of service needs in older adults Robert J. Calsyn*, Joel P. Winter MA University of Missouri-St. Louis, Gerontology Program, 406 Tower, 8001 Natural Bridge Road, St. Louis, MO 63121 4499, USA
Abstract Logistic regression analysis was used to predict four service need variables. A sample of nearly 5000 older Missourians were assessed on a comprehensive set of variables, representing all of the categories of the behavioral model. Variables in the behavioral model predicted perceived need for frail elderly services better than they predicted unmet need for frail elderly services, perceived need for community services, and unmet need for community services. Health need variables were better predictors of all of the service need variables than predisposing or enabling variables. Although the inclusion of interaction terms in the prediction models did not increase model ®t, some of the interaction terms were signi®cant and helped to clarify the relationship between certain predictor variables and the four service need variables. q 2001 Elsevier Science Ltd. All rights reserved. Keywords: Perceived service needs; Needs assessment; Behavioral model
1. Introduction State Units on Aging and their af®liated Area Agencies on Aging are federally mandated to assess the needs of the older adults living in their catchment area. As Diwan and Moriarity (1995) point out, the task of de®ning needs is not a simple one. Perceived service need and unmet service need are the two most common de®nitions of service needs used in previous research predicting service needs of the elderly. Although these two concepts have often been used interchangeably in previous articles, they are conceptually and computationally quite different. Perceived service need measures simply ask respondents if they need speci®c services (e.g. home health care or telephone reassurance), regardless of whether they are currently receiving any services aimed at alleviating those needs. Unmet service need measures, on the other hand, only count those services which respondents report needing, but which they are not receiving currently. Both service need measures are worthy of study. For example, measure of perceived service needs often correlate as high with service utilization as objective health needs (Calsyn & Roades, 1993; Mindel & Wright, 1982; Starrett, Todd, & DeLeon, 1989b). Similarly, research has shown that a signi®cant percentage of the older adult population report having unmet service needs even when they report no objective health needs (Jackson & Mittlemark, 1997). Although agency planners are primarily inter* Corresponding author. Tel.: 11-314-516-5421; fax: 11-314-516-5210. E-mail address: [email protected] (R.J. Calsyn).
ested in measuring and predicting unmet service needs, other gerontological researchers are more interested in predicting perceived service needs. This paper compares the ability of the behavioral model (Andersen, 1995) to predict both perceived service need and unmet service need. Although the behavioral model was originally developed to predict service utilization, several studies have used the behavioral model to predict either perceived service need (Calsyn & Roades, 1993; Coulton & Frost, 1982; Mindel & Wright, 1982; Richardson, 1992; Starrett, Decker, Araujo, & Walters, 1989a; Starrett, et al., 1989b) or unmet service need (Calsyn, Roades, & Klinkenberg, 1998; Jackson & Mittlemark, 1997). Unfortunately, these studies have only explained 10±15% of the variance of either perceived service needs or unmet service needs. Prior researchers have also failed to distinguish between needs for different types of services. With the exception of a study by Coulton and Frost (1982), previous studies have predicted an aggregated measure of service needs. These aggregated measures combine needs for frail elderly services (e.g. meals on wheels, home health) with needs for community services (e.g. senior centers, health screening). In hopes that the behavioral model would explain more of the variance of speci®c service need variables than total service need, this study predicted four service need variables: perceived need for frail elderly services, unmet need for frail elderly services, perceived need for community services, and unmet need for community services. For this study, predictor variables were classi®ed into the following categories based on the behavioral model
0149-7189/01/$ - see front matter q 2001 Elsevier Science Ltd. All rights reserved. PII: S 0149-718 9(01)00006-4
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(Andersen, 1995): predisposing; enabling; and health needs. Predisposing variables include demographic characteristics as well as beliefs and attitudes about the causes of problems. Enabling variables include individual resources (e.g. income) and family and community resources (e.g. social support). Health need variables include physical health status, functional impairment, and mental health. In this study we were particularly interested in determining what percentage of the variance of perceived service needs could be explained solely on the basis of health needs versus other variables. We expected health need variables would be the best predictors of all four service need variables. In addition, we hypothesized that predisposing and enabling variables would explain a signi®cant percentage of the variance of both perceived need and unmet need for community services, but very little variance of both perceived need and unmet need for frail elderly services. Our rationale for these hypotheses is based on service utilization research which has found that predisposing and enabling variables have explained very little variance of the use of more non-discretionary services such as hospitalization and home health care; nearly all of the explained variance was accounted for by health need variables (Wolinsky & Johnson, 1991). In contrast, predisposing and enabling variables have explained a signi®cant percentage of the variance of the use of more discretionary services such as attendance at a senior center (Mitchell & Krout, 1998). In the only study which has examined the prediction of multiple service need variables, Coulton and Frost (1982) did ®nd that health need variables explained nearly all of the variance of more non-discretionary service needs such as outpatient medical care and in-home personal care, whereas predisposing and enabling variables explained a signi®cant percentage of the variance of the need for recreational services, a discretionary service. Another improvement of this study over previous research was the inclusion of interaction terms in the prediction models. Several authors have suggested that the performance of the behavioral model in predicting service utilization could be improved if interaction terms were included in the model (Kosloski & Montgomery, 1994; Rundall, 1981). Similarly, we believed that the inclusion of interaction terms would improve the prediction of the service need variables. All of the interaction terms in our models included health need as one of the variables. In general, we postulated that terms would have a multiplicative effect under conditions of greater health needs. Below, we review more speci®c ®ndings of the studies predicting total service needs (perceived and unmet). Unfortunately, the study results are often contradictory. Differences in samples and operational de®nitions of constructs are also probably responsible for the lack of consistency in the study ®ndings. 1.1. Predisposing As a group, predisposing variables have explained very
little of the variance of total service needs. Moreover, none of the speci®c predisposing variables have exhibited a strong and consistent relationship with service needs. Although age was positively related to service needs in two studies (Jackson & Mittlemark, 1997; Mindel & Wright, 1982), three other studies found no relationship between age and service need (Calsyn & Roades, 1993; Calsyn et al., 1998; Richardson, 1992). We hypothesized that age would be positively related to need for frail elderly services, but unrelated to need for community services. African-Americans have reported greater service needs than Caucasians in some studies (Calsyn et al., 1998; Coulton & Frost, 1982; Jackson & Mittlemark, 1997), but race was unrelated to service needs in other studies (Calsyn & Roades, 1993; Mindel & Wright, 1982). Women reported more service needs than men in two studies (Calsyn & Roades, 1993; Mindel & Wright, 1982), but two other studies found no gender differences (Calsyn et al., 1998; Richardson, 1992). In the Coulton and Frost (1982) study, women reported greater need for medical, mental health, and recreational services, but men reported greater need for frail elderly services. Education was not correlated with service needs in any of the previous studies (Calsyn & Roades, 1993; Calsyn et al., 1998; Mindel & Wright, 1982; Richardson, 1992). 1.2. Enabling Enabling variables also have not explained much of the variance of total service needs of the elderly. Individuals with less income reported more service needs in one study (Calsyn et al., 1998), but income was unrelated to service needs in two other studies (Calsyn & Roades, 1993; Richardson, 1992). Social contact variables have not explained much of the variance of service needs (Calsyn & Roades, 1993; Calsyn et al., 1998; Richardson, 1992), with the exception of the study by Coulton and Frost (1982) which found that persons who were less isolated were more likely to report needing the more discretionary mental health and recreational services. Thus, we predicted that persons who reported more social contacts would also report more need for community services, but not frail elderly services. On the other hand, we predicted that persons who lived alone and/or reported no one to assist them in an emergency would report a greater need for frail elderly services; however, we did not think living situation and the availability of emergency services would affect need for community services. Based on Mitchell's (1995) ®nding that length of time in the community affects service awareness, we predicted that respondents who had lived in their community for a shorter period of time, would be more likely to report more service needs. In this study we also examined the impact of perceived age discrimination on service needs. We hypothesized that respondents who felt that they had experienced age discrimination would report more service needs, consistent with the results of a previous
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
study which had found that older adults who reported sex discrimination indicated having more needs (Starrett et al., 1989a). 1.3. Health needs Measures of health need (e.g. functional impairment, physical health status, and low morale) have typically been the strongest predictors of service needs (Calsyn & Roades, 1993; Coulton & Frost, 1982; Jackson & Mittlemark, 1997; Mindel & Wright, 1982; Richardson, 1992; Starrett et al., 1989a), with only one study ®nding no significant relationship between health need indicators and service needs (Calsyn et al., 1998). 1.4. Interactions As noted earlier we postulated that the inclusion of interaction terms in the prediction models would explain additional variance of service needs. We created interaction terms between two of the social contact variables, living situation (alone, with others) and availability of emergency support (yes, no) with the four health need variables. We predicted that the impact of health needs on both perceived and unmet need for frail elderly and community services would be greater for those respondents who lived alone and those respondents who reported having no one to rely on in the case of an emergency. This prediction is based on prior research predicting service utilization among older adults which ®nds that in high need situations, respondents who live alone and/or have less emergency support available, are more likely to use formal services (Biegel, Bass, Schulz, & Morycz, 1993; Logan & Spitze, 1994; Mitchell, 1995).
159
dents who began the interview failed to complete the interview. Thus, the ®nal needs assessment sample was 4985; the 82 cases for which a family member or someone else had served as a proxy interviewee were eliminated from this study, leaving a sample of 4903. Due to listwise deletion of missing data, the sample size for the analyses was 4221. 2.2. Variables Refer to Table 1 for the coding and relevant descriptive statistics of all the variables in the study. The variables are divided into four sets: health needs, predisposing, enabling, and perceived and unmet service need variables. 2.2.1. Health need variables Health need variables included: perceived overall health, need for assistance with ADL (activities of daily living) or IADLs (instrumental activities of daily living), poor mental health days, and perceived loneliness. Perceived overall health was measured on a 5 point scale with 1 indicating excellent health and 5 indicating poor health. Possible ADLs were eating, dressing, toileting, bathing, getting in/ out of bed, getting around, and getting outside. Possible IADLs included using the telephone, normal housework, heavy cleaning, shopping, managing money, preparing meals, and taking medication. If respondents needed help with any one of these activities, then they were coded as needing help with I/ADL's. Mental health needs were assessed by two variables: number of days out of the last 30 that mental health was not good and extent of loneliness.
2.1. Sample and procedure
2.2.2. Predisposing variables Predisposing variables included age, gender, education, and race. Because of small numbers of respondents from other ethnic minority groups, race was coded as a dichotomous variable, Caucasian and African-American. The 116 other cases (2.4% of the total sample) were not used in the analysis (i.e. they were part of the listwise deletion).
Data were collected for the 1994 needs assessment study of the Missouri Department of Social Service's Division of Aging and the 10 Missouri Area Agencies on Aging (Drainer, 1994). The sample was strati®ed by the 10 AAA regions in Missouri using the optimum allocation or disproportionate sampling method. Telephone interviews were conducted by trained interviewers at the Center for Advanced Social Research at the University of Missouri-Columbia. Questions were asked of the person in the household age 60 or over who had the most recent birthday. A total of 102,500 phone numbers were dialed by computer using random digit dialing. There was no answer for 24,863 calls; the phone had been disconnected for 21,762 calls; a business number was reached for 10,009 calls. There was a total of 28,456 noneligible contacts, 6842 respondents refused the interview before eligibility could be determined, 1488 refused the interview after eligibility was determined, and 4095 respon-
2.2.3. Enabling variables Enabling variables consisted of income, time lived in community, perceived discrimination and several social contact measures. Due to the large amount of missing data with regards to the income item (26% of the total sample), missing data were replaced with the median value (i.e. 3) for this variable. Social contact variables in this set were: amount of social contact; whether respondents had someone who could care for them as long as needed; living situation; number of formal service information sources; and number of informal sources of service information. Amount of social contact was derived by combining and transforming responses to two survey items assessing time spent talking on the phone with friends or relatives and time spent visiting with friends or relatives. Both items were measured on scales from 0 (not at all) to 3 (once a day or more). These items were combined by assigning twice the weight to the
2. Method
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Table 1 Descriptive statistics for variables in the study Mean a
Coding Health variables Perceived Health I/ADL Needs Perceived Loneliness Poor mental Health Days Predisposing Age Gender Education Race Enabling Income Social Contact Emergency Support Formal Information Source Informal Information Source Time in Community Perceived Age Discrimination Living Situation Service Need Perceived Need, Frail Services Unmet Need, Frail Services Perceived Need, Community Services Unmet Need, Community Services
1±5 with: 1 Excellent, 5 Poor 0 No assistance needed, 1 Some assistance needed 1 Quite often, 2 Sometimes, 3 Almost never Number of day in the last 30 that mental health was not good
Standard deviation
2.88 0.37 2.69 1.62
1.19 0.48 0.57 5.44
Age in years 0 Male, 1 Female 1±7 with: 1 Never attended school, 4 High school graduate, 7 4 Years of college or more 0 Caucasian 1 African-American
70.84 0.69 4.17
7.37 0.46 1.53
0.06
0.23
0±9 with: 0 Less than $5000, 9 Greater than $50,000 Composite of time spent on phone and visiting away from residence 0 No, 1 Yes 0 No, 1 Yes 0 No, 1 Yes Years 0 No, 1 Yes 0 Alone, 1 With others
3.11 2.19 0.53 0.62 0.51 34.42 0.29 0.59
1.78 0.67 0.50 0.49 0.50 22.59 0.46 0.49
0.25 0.07 0.37 0.16
0.43 0.26 0.48 0.36
0 No needs, 1 Some needs 0 No needs, 1 Some needs 0 No needs, 1 Some needs 0 No needs, 1 Some needs
a
For dichotomous variables which are coded 0, 1, the mean indicates the percentage of respondents who were coded 1. For example, a mean of 0.69 for gender indicates that 69% of the sample were women.
visiting item (due to the comparatively greater richness of face-to-face interaction) and averaging. Living situation was a dichotomous variable assessing whether respondent lived with others or alone. Formal and informal information sources were measured as the number of either formal (i.e. physician, social worker, hospital) or informal (i.e. friend, relative) resources listed when respondents were asked two open-ended questions inquiring from whom they would seek information and help in making service decisions. 2.2.4. Service need variables Respondents were ®rst asked if they needed 20 different services. Then they were asked if they were receiving each of those services. For this study, four dichotomous variables were created: perceived need for frail elderly services, unmet need for frail elderly services, perceived need for community services, and unmet need for community services. Respondents were classi®ed as having a perceived need if they mentioned a need for any of the services listed, in that category regardless of whether they were currently receiving a service that addressed that need. On the other hand, respondents were considered to have an unmet need only if they were not receiving a service for which they reported a need. Frail elderly services were de®ned as reassurance services, in-home personal care, homemaker/chore assistance, in-home nursing care, respite care, in-home therapy, home-delivered meals, information on caring for the
elderly, assistance with ®nding information, and adult day care centers. Community services were de®ned as assistance with ®lling out forms, counseling services, transportation assistance, senior centers, health screening, assistance with ®nding employment, information on staying healthy, and home repair services. The list of services focused on services that could be accessed either directly or indirectly through the Missouri Division of Aging and the Area Agencies on Aging, sponsors of the study. 2.3. Analysis strategy For each of the analyses, hierarchical logistic regression modeling was used. Logistic regression is more appropriate than ordinary least-squares regression for dichotomous variables that deviate from a 50/50 split. Logistic regression also allows a hierarchical approach so that the relative contribution to model ®t of pre-determined sets of variables may be assessed. Because the authors wanted to determine whether predisposing and enabling variables explained any additional variance beyond health needs, health needs were entered into the equation ®rst followed by predisposing and enabling variables. First-order interaction terms were created as simply the product of two variables and are entered into the equations on the ®nal step. For interpretation purposes, logistic regression weights are reported for models with and without interaction terms. Thus, incremental increases in model ®t was assessed at four stages: health
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
need, predisposing, enabling, and ®rst-order interactions. Model ®t is assessed with the chi-square goodness of ®t test, and the Cox & Snell R 2 (SPSS Inc., 1997). 3. Results As Table 1 indicates, 25% of the sample reported perceived need for frail elderly services, but only 7% of the sample had an unmet need for frail elderly services. Similarly, 37% of the sample reported a perceived need for community services, but only 16% of the sample reported an unmet need for community services. Correlations between the four service need variables ranged from 0.24 to 0.57 (see Table 2). 3.1. Prediction of need for frail elderly services From Table 3, it is clear that the behavioral model is much better in explaining perceived need for frail elderly services than unmet need for frail elderly services. This is not surprising given that the distribution of scores for unmet need was more uneven (93/7%) than perceived need (75/ 25%). As Table 4 indicates all of the health need variables, particularly I/ADL needs, were signi®cant predictors of both perceived service need and unmet service need for frail elderly services. Age was positively associated with both perceived need and unmet need for frail elderly services. Gender was not related to perceived need for frail elderly services, but women were more likely than men to report unmet need for frail elderly services. African-Americans were more likely than Caucasians to report more perceived need and unmet need for frail elderly services. Income was not related to perceived service need, but lower income respondents were more likely than others to report unmet need for frail elderly services. Similarly, years in community was not related to perceived need, but respondents who had lived in the community longer were less likely to report unmet need for frail elderly services. Respondents who reported having a formal information source were more likely than those without a formal source to report a perceived service need, but not more likely to have an unmet service need. Likewise, perceptions of discrimination were related to perceived service need but not unmet service need. Although inclusion of interaction terms did not dramatically improve ®t of the model for either service need variable, they do indicate some important multiplicative effects. For example, living situation was signi®cantly related to perceived service need in the model without interactions, but was not signi®cantly related in the model with interaction terms. Instead, living situation interacted with I/ADL need. Living situation had little effect for those with no I/ ADL problems. However, for those with I/ADL problems, perceived needs was greater for those who lived alone. For those who live with someone, the relationship between poor
161
mental health days and perceived service need is stronger than for those who live alone. Living situation was also unrelated directly to unmet need. Rather, living situation interacted with two health variables (I/ADL and perceived health). Living situation had no effect on unmet need for respondents who were in better health, but for respondents in poorer health, those who lived alone reported more unmet need than those who lived with others. Poor mental health days also had no direct effect on unmet need. Rather, poor mental health days interacted with the availability of emergency support. The availability of emergency support had little effect on unmet need for those respondents with many poor mental health days. However, the availability of emergency support reduced unmet need for persons with fewer bad mental health days. The availability of emergency support also interacted with perceived health. Perceived health had little effect on unmet need for respondents who had someone to rely on in an emergency; however, for those respondents who had no one to rely on in an emergency, unmet need increased with greater perceived health problems. 3.2. Prediction of perceived need for community services Again, refer to Table 3 for the model ®t statistics; the behavioral model performs equally in predicting both perceived and unmet need for community services. Again, the health variables are the most important in predicting both types of need for community services, with little additional variance being explained by predisposing and enabling variables. Table 5 displays the speci®c regression coef®cients for all of the regression models. In general, variables which predict perceived service need also predict unmet need, but there are some notable exceptions. In addition, although the interaction terms did not increase model ®t, the inclusion of the interaction terms does help to clarify some of the relationships between variables. All of the health need variables predict both perceived need and unmet need for community services in the models without interaction terms. Although age was positively related to perceived service need, age was negatively related to unmet need for community services, suggesting that the community service needs of older respondents were met better than the needs of younger respondents. AfricanAmericans were more likely to report more perceived and unmet needs for community services than Caucasians. Lower income respondents also reported more perceived and unmet need for community services than higher income respondents. Respondents who did not have someone to rely on in an emergency were more likely to report more perceived and unmet community service needs than those who had someone to rely on in an emergency. Respondents who listed a formal information source were more likely than those without a formal source to report more perceived need for community services, but not more likely to report unmet need. Respondents who reported age discrimination
1. Perceived Health 2. Poor Mental Health Days 3. I/ADL Needs 4. Perceived Loneliness 5. Age 6. Gender 7. Education 8. Race 9. Income 10. Social Contact 11. Emergency Support 12. Formal 13. Informal 14. Time in Community 15. Discrimination 16. Living Situation 17. Perceived Need, Frail Services 18. Unmet Need, Frail Services 19. Perceived Need, Community Services 20. Unmet Need, Community Services 0.18 20.38 20.01 0.03 20.04 20.01 20.06 20.09 20.10 0.04 20.05 20.02 0.13 20.06 0.17 0.14 0.14 0.17
0.16 0.19
0.19
±
2
0.19 0.42 20.20 0.10 0.05 20.26 0.08 20.29 20.09 20.12 0.03 20.02 0.01 0.23 20.08 0.26
±
1
0.24
0.22 0.27
± 20.22 0.22 0.17 20.12 0.08 20.22 20.05 20.13 0.03 20.02 0.01 0.19 20.15 0.45
3
20.17
20.17 20.16
± 20.09 20.10 0.11 0.00 0.17 0.11 0.16 0.01 0.01 0.03 20.15 0.24 20.21
4
0.02
0.08 0.12
0.10 20.09 20.04 20.17 20.02 20.14 20.07 0.10 0.15 0.07 20.33 0.22
±
5
0.09
0.08 0.09
± 20.11 0.02 20.26 0.07 20.07 0.07 0.02 0.00 20.03 20.23 0.11
6
20.08
20.07 20.08
± 20.06 0.40 0.06 0.09 0.08 20.04 20.06 20.11 0.08 20.07
7
0.12
0.07 0.08
± 20.10 20.05 20.01 0.02 20.04 20.07 20.01 20.01 0.08
8
Table 2 Pearson correlations for variables in the study. (Note: if uru . 0.03, p , 0.05; if uru . 0.04, p , 0.01)
20.18
20.12 20.16
0.03 0.16 20.01 0.01 0.00 20.09 0.30 20.17
±
9
20.04
20.06 20.01
0.08 0.06 0.06 0.07 20.06 20.10 20.04
±
10
20.16
20.10 20.16
0.01 0.08 20.01 20.07 0.23 20.14
±
11
0.01
0.02 0.06
± 20.16 0.02 0.00 0.04 0.05
12
20.04
20.03 0.00
0.04 20.01 20.05 20.01
±
13
20.03
20.04 20.02
0.00 20.02 0.00
±
14
0.11
0.08 0.12
± 20.03 0.14
15
20.09
20.06 20.13
± 20.19
16
18
19
20
0.29 0.29 0.57 ±
0.48 ± 0.35 0.24 ±
±
17
162 R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
163
Table 3 Incremental improvements in ®t of the behavioral model for frail and community services and perceived and unmet need Frail services a
2 2LL b Health Need Chi-Square b Cox & Snell R 2b Predisposing Chi-Square b Cox & Snell R 2b Enabling Chi-Square b Cox & Snell R 2b Interaction Terms Chi-Square b Cox & Snell R 2b
Community services a
Perceived Need
Unmet Need
Perceived Need
Unmet Need
4748.88
2179.97
5546.96
3670.43
940.79** 0.20
272.64** 0.06
380.78** 0.09
332.42** 0.08
101.21** 0.22
29.21** 0.07
46.40** 0.10
53.79** 0.09
49.72** 0.23
26.47** 0.08
88.69** 0.12
92.82** 0.11
21.23** 0.23
16.9* 0.08
8.62 0.12
7.87 0.11
a
*p , 0.05, **p , 0.01. 2 2LL is analogous to the ®t of the null model. Subsequent chi-square values can be interpreted as improvement in ®t against a null model. The Cox & Snell R 2 is analogous to the R 2 from ordinary least square regression. b
Table 4 Logistic regression coef®cients for models predicting need for frail services Perceived Need a
Perceived Health Poor Mental Hlth. Days I/ADL Needs Perceived Loneliness Age Gender Education Race Income Social Contact Emergency Support Formal Informal Time in Community Discrimination Living Situation Live by Perc'd Health Live by I/ADL Live by Loneliness Live by Poor Mental Hlth. Days Emer. Support by Perc'd. Health Emer. Support by I/ADL Emer. Support by Loneliness Emergency Support by Poor Mental Health Days Constant a
Unmet Need a
Without interaction
With interaction
Without interaction
With interaction
b
S.E.
b
S.E.
b
S.E.
b
S.E.
0.156*** 0.026*** 1.764*** 20.249*** 0.043*** 0.071 0.039 0.640*** 20.031 0.005 20.271** 0.267** 0.033 20.002 0.220* 20.362***
0.0402 0.0073 0.0924 0.0737 0.0059 0.1006 0.0301 0.1586 0.0299 0.0630 0.0858 0.0882 0.0850 0.0018 0.0898 0.0944
0.158* 0.012 2.031*** 20.194 0.042*** 0.061 0.039 0.657*** 20.030 0.012 20.317 0.255** 0.033 20.002 0.220* 0.440 20.068 20.508** 20.137 0.041** 0.077 20.024 20.058 20.018
0.0643 0.0110 0.1484 0.1109 0.0060 0.1011 0.0303 0.1594 0.0300 0.0635 0.4883 0.0888 0.0857 0.0018 0.0904 0.4961 0.0786 0.1866 0.1502 0.0150 0.0785 0.1860 0.1482 0.0156
0.171** 0.018* 1.207*** 20.409*** 0.022* 0.463** 20.036 0.624** 20.105* 20.114 20.379** 0.050 20.148 20.006* 0.120 0.203
0.0632 0.0086 0.1571 0.1006 0.0088 0.1684 0.0472 0.2073 0.0478 0.0913 0.1348 0.1341 0.1296 0.0028 0.1338 0.1430
0.185 0.014 1.588*** 20.374* 0.021* 0.454** 20.036 0.657** 20.105* 20.108 0.455 0.065 20.128 20.006* 0.125 0.150 0.249* 20.658* 20.117 20.019 20.318** 20.086 0.074 0.039*
0.0973 0.0124 0.2629 0.1480 0.0089 0.1699 0.0474 0.2094 0.0480 0.0924 0.7249 0.1352 0.1309 0.0028 0.1347 0.7207 0.1239 0.3228 0.2048 0.0180 0.1249 0.3160 0.2115 0.0188
24.929***
0.5514
25.198***
0.6241
23.807***
0.8202
24.192***
0.9239
*p , 0.05, **p , 0.01, ***p , 0.001.
were also more likely to report more perceived need and unmet need for community services. There was also a signi®cant interaction between living situation and I/ADL assistance on perceived need for
community services that had the same pattern for frail elderly services. Although none of the interaction terms were signi®cant for unmet need, inclusion caused a reduction in three zero-order terms: perceived health, perceived
164
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
Table 5 Logistic regression coef®cients for models predicting need for community services Perceived Need a
Perceived Health Poor Mental Health Days I/ADL Needs Perceived Loneliness Age Gender Education Race Income Social Contact Emergency Support Formal Informal Time in Community Discrimination Living Situation Live by Perc'd. Health Live by I/ADL Live by Loneliness Live by Poor Mental Hlth. Days Emer. Support by Perc'd. Health Emer. Support by I/ADL Emer. Support by Loneliness Emer. Support by Poor Mental Health Days Constant a
Unmet Need a
Without interaction
With interaction
Without interaction
With interaction
b
S.E.
b
S.E.
b
S.E.
b
S.E.
0.099** 0.026*** 0.735*** 20.175** 0.015** 0.056 20.006 0.576*** 20.075** 0.079 20.435*** 0.263*** 0.100 20.002 0.226** 20.140
0.0333 0.0068 0.0780 0.0653 0.0050 0.0799 0.0249 0.1416 0.0244 0.0526 0.0707 0.0724 0.0700 0.0015 0.0764 0.0790
0.067 0.030** 0.894*** 20.193* 0.015** 0.057 20.007 0.592*** 20.076** 0.080 20.536 0.260*** 0.106 20.003 0.230** 20.194 0.096 20.416** 20.020 0 0.001 20.037 0.130 0.063 20.011
0.0547 0.0107 0.1271 0.0982 0.0051 0.0801 0.0250 0.1417 0.0244 0.0528 0.4200 0.0726 0.0702 0.0015 0.0766 0.4276 0.0652 0.1559 0.1330 0.0140 0.0650 0.1547 0.1304 0.0143
0.125** 0.029*** 0.846*** 20.249** 20.017* 0.270* 0.017 0.840*** 20.179*** 0.029 20.628*** 20.063 20.085 20.002 0.216* 20.054
0.0447 0.0071 0.1040 0.0778 0.0067 0.1128 0.0337 0.1574 0.0339 0.0685 0.0964 0.0957 0.0930 0.0020 0.0981 0.1031
0.029 0.042*** 0.920*** 20.207 20.017* 0.272* 0.017 0.851*** 20.178*** 0.031 20.868 20.061 20.084 20.002 0.226* 20.075 0.163 20.129 20.151 20.019 0.039 20.042 0.068 20.015
0.0675 0.0103 0.1588 0.1121 0.0067 0.1135 0.0337 0.1575 0.0340 0.0689 0.5398 0.0960 0.0934 0.0020 0.0983 0.5288 0.0872 0.2072 0.1586 0.0147 0.0884 0.2106 0.1637 0.0160
21.680***
0.4605
21.590**
0.5211
20.189
0.6004
20.072
0.6610
*p , 0.05, **p , 0.01, ***p , 0.001.
loneliness, and emergency support. Thus, it seems that these variables are important, but they may have interactive effects that are dif®cult to detect. 4. Discussion 4.1. Relationship to prior research The main conclusions of the study are: (1) the behavioral model predicts perceived need for frail elderly services moderately well, but predicts the following less well: unmet need for frail elderly services, perceived need for community services, and unmet need for community services; (2) most of the predictor variables were related to all four service need variables in the same way, but there were some interesting exceptions; (3) inclusion of interaction terms does not increase model ®t, but interactions do clarify the relationship between some variables. Below we relate our speci®c ®ndings to previous studies. 4.1.1. Health needs Consistent with previous research, physical health needs were signi®cant predictors of all four service need variables.
I/ADL impairment was the strongest predictor. Unlike previous studies (Calsyn & Roades, 1993; Calsyn et al., 1998) this study did ®nd a signi®cant relationship between poorer mental health and greater perceived need and unmet need for both frail and community services. The most likely explanation for the discrepancy in ®ndings is the different mental health measures used in the studies. The prior studies asked about morale, whereas the current study speci®cally asked about, `how many days during the past 30 days was your mental health not good?'. 4.1.2. Predisposing Age had not consistently predicted total perceived service needs in previous studies. We had hypothesized a positive relationship between age and need for frail services, but no relationship between age and need for community services. Our hypothesis was not supported; age was negatively related to unmet need for community services, but positively related to the other three service need variables. Women had reported more service needs in about half of the previous studies. In the current study there were no gender differences in either perceived service need variable, but women report more unmet needs for both frail elderly services and community services. Consistent with 60% of the previous studies,
R.J. Calsyn, J.P. Winter / Evaluation and Program Planning 24 (2001) 157±166
African-Americans were more likely to report more needs than Caucasians on all four dependent variables. Although race was not highly correlated with the dependent variables, it made an important contribution to the prediction because of the lack of multicollinearity with the other predictors. Education was not signi®cantly related to any of the service need variables, replicating past research. 4.1.3. Enabling With the exception of perceived need for frail elderly services, respondents in this study with lower income were more likely than other respondents to report service needs. One previous study had also found an effect of income on service needs, but two other studies found no effect of income. We suspect that study sample differences explain the results. The two studies which found no effect of income sampled mainly low income individuals (Calsyn & Roades, 1993; Richardson, 1992). Because there was a restricted range on the income variable, it is less likely that income would be a signi®cant predictor. In contrast, the current study and Calsyn et al. (1998) interviewed samples which had more variability in income, and both studies found that income predicted service needs. Similar to past research on perceived sex discrimination (Starrett et al., 1989a), respondents who reported age discrimination were more likely to report service needs, except perceived need for frail elderly services. We had predicted that residents who had lived in their communities for a shorter period of time would report more service needs, because they would be less aware of both formal and informal help. Although length of time in the community was related to unmet need for frail elderly services as predicted, length of time in the community was not related to other service need variables. In general, social interaction variables had not predicted total service needs in previous studies. In this study, social contact also had no effect on any of the service need variables. Perceived availability of emergency support, on the other hand, did affect perceived service needs. Respondents who had no one to rely on in an emergency were more likely to report more service needs, particularly if they had more health problems. Respondents who lived alone were more likely to report more perceived and unmet need for frail elderly services, but not more need for community services. Living situation also interacted with health variables in affecting perceived need for both frail elderly and community services. In general, the relationship between need for services and health needs was stronger for respondents who lived alone. This ®nding mirrors ®ndings from service utilization studies which have found that health problems increase service utilization more for respondents who live alone than respondents who live with others (Soldo, 1985). 4.2. Implications for agency planners Given that health need indicators were the best predictors of the service need variables, should agency planners
165
continue to include measures of perceived and unmet service needs in their needs assessment studies of the elderly? Harlow and Harlow (1989) have argued that estimating service needs of the elderly should be based primarily on objective health needs (e.g. functional impairment). Given that this study found that the health need variables explained most of the variance of all four service need variables, agency planners can rely on archival measures of health status and functional impairment which will provide them with a relatively good estimate of the total service needs of the older adult population in their area. Archival measures of health status and functional impairment will be even more useful if they have been routinely gathered in a particular community, so that the researcher can examine time trends (Harlow & Turner, 1993). Similarly, the researcher can make normative comparisons of the local community with other communities if the archival indicators of health and functional impairment have been assessed in other communities. However, archival data on the health status and functional impairment of older adults in a local community may not exist, or there may be other needs assessment questions (e.g. perceived service barriers) which require a survey of local older adults. If the agency planner has decided to collect data from a representative sample of the elderly, the additional costs associated with including measures of perceived and unmet service needs are minimal. For example, the Missouri Division of Aging learned that home repair assistance was the most needed service in the eyes of older Missourians based on the data used in this study. They could not have discovered this information by simply relying on archival measures of health status and functional impairment. 4.3. Future research This study had many strengths: (1) a large sample from a state with urban, suburban, and rural elderly populations; (2) a large number of predictor variables including interaction terms; (3) equations predicting both perceived and unmet service needs; and (4) separate equations predicting frail elderly service needs and community service needs. Nevertheless, one must always be careful in generalizing the study results to other samples, particularly older adults who are more frail with greater health needs. Another strength of this study was the use of the most popular theory in service utilization research, the behavioral model, in choosing predictor variables. However, the behavioral model has not explained much of the variance of either perceived or unmet service need. Future researchers should either adopt other theoretical frameworks or expand the behavioral model to include other variables in predicting perceived service need. For example, the fact that the mental health need variables were strong predictors of the service need variables in this study suggests that one's overall personality, psychological adjustment, and general satisfaction with
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life may be important predictors of perceived service needs. This position is supported by an earlier study which found that perceived service needs of the elderly correlated significantly with perceived quality of life (Murrell, Brockway, & Schulte, 1982). Finally, although this study has demonstrated that both perceived and unmet service needs are highly correlated with health needs, more research is needed to see how the service need variables examined in this study correspond to the other indicators of unmet need discussed by Diwan and Moriarty (1995). Acknowledgements This study would not have been possible without the cooperation of those older adults who consented to be interviewed; we appreciate their time and thoughtfulness. We also wish to thank the staffs of the Missouri Division of Aging and the Missouri Department of Social Services, particularly Dr Ann Deaton and Ms Becky Viet, who made the data available to the authors. However, the analyses and conclusions presented in this paper are the sole responsibility of the authors and do not necessarily re¯ect the views of either agency. The authors also appreciate the editorial and word processing assistance of Ms Dorothy Gano. Dr Calsyn and Mr Winter are af®liated with the University of Missouri-St. Louis. Correspondence should be directed to Dr Calsyn at University of MissouriSt. Louis, 406 Tower, 8001 Natural Bridge Rd., St. Louis, MO 63121-4499, USA. References Andersen, R. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36, 1±10. Biegel, P. E., Bass, D. M., Schulz, R., & Morycz, R. (1993). Predictors of in-home and out-of-home service use by family caregivers of Alzheimer's disease patients. Journal of Aging and Health, 5, 419±438. Calsyn, R. J., & Roades, L. A. (1993). Predicting perceived service need, service awareness, and service utilization. Journal of Gerontological Social Work, 21, 59±76. Calsyn, R. J., Roades, L. A., & Klinkenberg, W. D. (1998). Using theory to improve needs assessment studies of the elderly. Evaluation and Program Planning, 21, 277±286.
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