Abstract

Purpose:

Based on the medical offset effect, the goal of the study was to examine the extent to which users and nonusers of adult day care centers (ADCC) differ in frequency of use of out-patient health services (visits to specialists) and in-patient health services (number of hospital admissions, length of hospitalizations, and visits to emergency departments).

Design and methods:

A case–control study was used with a sample of 800 respondents, of whom 400 were users of 13 day care centers in the southern region of Israel and 400 were nonusers, matched by age, gender, and active family physician. Data collection included face-to-face interviews using a structured questionnaire. Data on health care service utilization were drawn from the central computerized data of one of the health care organizations in Israel.

Results:

Although users of ADCC significantly differed from nonusers in socioeconomic characteristics, they did not significantly differ from nonusers in the magnitude of health care services’ utilization. Utilization of health care services was rather connected with morbidity rather than with use of ADCC. Therefore, no offset effect was found.

Implications:

The current form of ADCC in Israel focuses mainly on meeting social needs of the participants and therefore do not meet the their actual health needs. Therefore, inclusion of health services within ADCC may have an offset effect, but this necessitates further examination.

Older people use substantial health care services. Age-related chronic diseases represent globally the major disease burden on the cost and use of health care services. A study conducted in eight countries in the Organization for Economic Cooperation and Development (Anderson & Hussey, 2001) showed that between one third and one half of total health care expenditure is attributed to older people. In the United States, for example, older adults more than age 65 consume 36% of health care, despite representing only 13% of the population (Center for Medicaid and Medicare Services, 2005). Data from England (Wanless, 2006) show that older people (aged 65 years and older) account for nearly two thirds of bed days, and the rate of growth of emergency admission was highest in older age groups. Thus, economic evaluation of services is becoming more and more important as a means to assist policy makers in choosing the best interventions against scarcity of resources relative to the demands (Guk-Heea & Changsub, 2008). For this reason, many countries seek strategies such as managed care (Crane & Christenson, 2008) to control spending for health care services by developing less-expensive community-based services for frail older adults that will offset expensive health services.

An offset effect occurs when the provision of one type of service leads to a reduction in the use of other related services. The decrease in health care use following psychosocial interventions is often referred to as the “offset effect” (Shemo, 1985–1986). There is growing evidence (Wanless, 2006) that hospital admission and length of stay could be reduced by a range of social care interventions, such as early transfer of people from hospital to the community and providing ongoing home-based care and other community-based services. This can limit some of the common causes of hospitalizations among functionally dependent older people. Furthermore, older persons who are hospitalized are exposed to infections and other negative outcomes that can further complicate their medical conditions and can result in functional deterioration or even death as well as excessive expenditures.

Studies (Gladman et al., 1994; Hui et al., 1995) also show that geriatric day hospitals can effectively substitute hospitalization and even achieve better recovery results compared with inpatient hospitalization, even if there are no cost savings. From the perspective of the patients themselves also, it is better for them to stay for the least possible time in hospitals. However, studies (e.g., Preville et al., 2009) show that psychological distress among older adults can increase use of health services. Therefore, interventions that are aimed to decrease social isolation and promote social contacts can positively affect health behavior and well-being and consequently reduce health service use (Greaves & Farbus, 2006).

Indeed, a broad array of home- and community-based services is now available for older adults, including adult day care centers (ADCC) for those who are functionally disabled. ADCCs are one of the core community-based services frail older adults can attend during daytime and receive a variety of social, personal, and some health services. It is assumed that this relatively low-cost service can offset the need for more expensive long-term care services. However, the extent to which ADCCs can offset utilization of health services among frail older adults has been barely examined.

The goal of this study is to examine the extent to which users and nonusers of ADCCs differ in frequency of use of out-patient health services (visits to specialists) and in-patient health services (number of hospital admissions, length of hospitalizations, and visits to emergency departments). Based on the medical offset effect (Crane & Christenson, 2008), it is hypothesized that nonusers of ADCCs will use health services significantly more than users of ADCCs and that the magnitude of health service use among current users of ADCCs will show reduced health services pre- and post-use of ADCCs.

Trade-offs Between Services

The literature provides evidence for two types of trade-offs; one trade-off is between health care services, such as out-patient health care services that offset in-patient health services and the second trade-off is between health and social services, suggesting that social services can offset expensive health care services. For example, use of community-based health services can result in less hospitalization or long-term institutionalization (Greene, Ondrich, & Laditka, 1998; Hughes et al., 1997; Stessman, Ginsberg, Hammerman-Rozenberg, Friedman, & Ronen, 1996), and care provided in out-patient clinics, in community-based primary care clinics, and home health care services can reduce the use of emergency departments (McCusker et al., 2001). An experimental study (Schneider, Duggan, Cordingley, Mozley, & Hart, 2007) in England showed that including a program of occupational therapy for older persons reduced use of health services compared with a control group who did not participate in this program. Another study conducted in the United Kingdom (Roderick et al., 2001) that examined costs and effectiveness of home-based rehabilitation for patients who underwent a stroke compared with a geriatric day hospital showed that although no significant differences were found between the two groups in terms of overall costs per patient, the costs for health services were lower in the home-based program because patients used more social services. In Italy (Aimonino et al., 2008), older patients in general wards were compared with those in geriatric wards. A follow-up after 6 months showed fewer readmissions and lower costs for those who were admitted to geriatric wards compared with those who were in general wards. In Australia (Byles, Powers, Chojenta, & Warner-Smith, 2006), increased use of social services reduced use of health services. Although there were no significant differences in health status between those who lived in urban areas compared with those who lived in remote rural areas, those who lived in urban regions visited more family physicians, specialists, and other health care services compared with those who lived in rural regions but used more community-based and alternative health services. This suggests that lack of appropriate responses to needs in one domain can lead to increased utilization of expensive services in another domain and that patients with similar health problems may use different services due to their place of residence. In an experimental project in rural regions in Canada (Mitton, O’Neil, Simpson, Hoppins, & Harcus, 2007), home care was provided by nurses, and family physicians was found to decrease number of hospital admissions, bed days, and number of applications to emergency departments, which resulted in decreased expenditures per person. A meta-analytic study (Hughes, 1997) that examined the impact of day care on utilization and costs of hospitalizations compared with control groups found that day care had a slight to moderate impact on reducing the number of bed days. Another meta-analytic study (Chiles, Lambert, & Hatch, 1999) found a medical cost-offset effect and that average savings resulting from psychological interventions was estimated to be about 20%.

However, to curtail high expenditures on expensive health services, various western countries (e.g., England, Canada) put more emphasis on developing community care (McDaniel, 1997). Yet, the trade-off between use of health services and use of ADCCs has been barely examined. Examining the extent to which ADCCs offset use of health services, specifically out-patient (visits to specialists) and in-patient services (number hospitalizations, bed days, and emergency departments) can add to our knowledge and point to the effectiveness of ADCC in reducing use of health services.

ADCCs in Israel

ADCCs are a core community-based service for frail older persons that started to develop in Israel in the early 1980s. After the enactment of the Long-Term Care Insurance Law in 1986, many new ADCCs were established, which were aimed at enabling disabled elderly persons to age in place. To date, there are 172 day care centers that serve approximately 16,000 people (Brodsky, Shnoor, & Be’er, 2011). The vast majority of them operate according to a social model (Dabelko & Zimmerman, 2008), focusing on providing social and recreational activities, personal services, and to some extent also health promotion programs. However, health services are not provided within these ADCCs, as they are in many in the U.S. (Nadash, 2003). Most of the centers operate five days a week, five to six hours a day, but there are also a few ADCCs that operate 6 days a week and are open 8 hours a day. They are heterogeneous in terms of their physical size, number of participants, the characteristics of the participants, variety of services, auspices, and operators. Most of the expenses are covered by the long-term care benefits to which disabled older persons are entitled under the Long-Term Care Insurance Law. This enables the participants to replace 2–3 hr of homecare services per day with a daily visit to an ADCC. Thus, people who are entitled to receive 10–18 weekly hours of homecare can replace that with day care visits 5 days a week. The ADCCs provide transportation and two meals a day (breakfast and lunch) for which the participants co-pay about $5 a day. For each participant, an individual care plan is prepared and provided by a multidisciplinary team. The core services provided at the ADCCs include personal care, social activities, health promotion, meals, physical activities, and laundry. No medical services are provided within the ADCCs.

Methods

Sample

Data were drawn from a study that was aimed to compare utilization of health care services between users and nonusers of ADCCs (Iecovich & Biderman, 2011). The sample included 400 users of day care centers for frail older adults and 400 paired nonusers who could be ADCC users. The sample of users was recruited through 13 ADCCs in the Southern region of Israel who together serve about 1,000 older persons who are physically frail but mostly cognitively intact. Inclusion criteria were: age more than 60, speak Hebrew or Russian (about a third of the respondents were immigrants from former Soviet Union countries who immigrated to Israel after the collapse of the Soviet regime in 1989), frail in terms of having physical difficulties in performing activities of daily living (ADL), cognitively intact, and members of Clalit Health Maintenance Organization (HMO), which is the largest HMO in Israel. In Israel, there are four HMOs that operate under the National Health Insurance Law enacted in 1994 and provide a wide range of universal health care services to all its citizens. The Clalit HMO provides health care services to more than half of the population, and the vast majority of older adults are insured in this health organization (Bendelak, 2011). Only those who met the inclusion criteria and gave their consent to participate in the study were included in the sample. Thus, among users of day care centers, 400 were interviewed, 165 refused, 75 were not members of the Clalit HMO, and the remainder was either unable to be interviewed due to language barriers, deafness, cognitive impairment, or were unavailable.

The 400 nonusers composed the comparison group and were matched with users by gender, age (within 5 years difference between user and nonuser), functional status, and same family physician, to control for clinical treatment approaches of the family physicians. Based on the matching criteria, the family physicians were asked to provide names of cognitively intact nonusers for each user of an ADCC. In case a nonuser was unavailable or reluctant to participate in the study, the physician was asked to provide another name that met the same criteria. Thus, 400 nonusers were interviewed, 111 were refused, 65 were unavailable, and 91 were unable to be interviewed due to hospitalization, illness, language barriers, and seven died.

Data Collection

In the first stage, a letter was posted to managers of the ADCCs explaining the goals of the study and asking their permission for the interviewers to present the research goals to the users of the ADCCs. In the second stage, visitors of the ADCCs were approached by interviewers and were asked to volunteer to be interviewed. Those respondents who gave their consent and were Clalit HMO members underwent a short mini-mental test (Mini-Mental State Examination [MMSE], Folstein, Folstein, & McHugh, 1975) that included only 13 questions that related to memory and orientation to make sure that they were not cognitively impaired; if they gave three incorrect answers or more (e.g., date of birth, age, what day is it today, who is the current prime minister, who was the former prime minister, etc.), the interview was stopped. After completing the MMSE, the research goals were explained to the respondents, and if they agreed to be interviewed, they were asked to sign a consent form. It should be noted that some of the ADCC participants did not participate in the study because they refused to sign consent forms. Interviews were conducted in one of the rooms at the ADCC to assure confidentiality. After terminating the data collection at each ADCC, an application was made to the family physicians of the respondents and they were asked to provide lists of patients that matched the characteristics of the respondents who were interviewed at the ADCC and that met the previous criteria (age, gender, functional status, cognitively intact, and speak Hebrew or Russian).

After receiving lists from each family physician, a prenotification letter was sent to the patients explaining the goals of the study, asking their consent to be interviewed, assuring them of confidentiality, and notifying them that an interviewer would contact them the next week via telephone. A week later, interviewers telephoned the older persons and asked their consent to be interviewed; once they agreed, appointments were made at their homes. At the beginning of the interview, they underwent a short MMSE to make sure that they were not cognitively impaired. Data were collected during 2009–2010 through face-to-face interviews, using a structured questionnaire. All interviewers were trained to interview older people and in use of the specific questionnaire.

After completing all the interviews, medical information was retrieved from the central computerized database of the Clalit HMO. As patients are seen by their family physicians at the community health clinics, diagnoses were made and are recorded on a master diagnostic list in patients’ medical records. The study underwent an institutional board review and was approved by the ethics committees of the Clalit HMO and the University Medical Center. IRB protocol approval number is 0036-08 (K), and informed consent was obtained from all participants.

Measures

Outcome Variable.—

Use of health services. 

Use of several in-patient and out-patient health services was examined. For each service, data were drawn from the central computerized database and related to two time points: 1 year prior to ADCC use and 1.5 year after starting use of ADCC.

  1. Visits to specialists—total number of visits to specialists (e.g., dermatologist, cardiologist, nephrologist, dermatologist, and neurologist).

  2. Admissions to hospitals—number of admissions and total number of bed days in all admissions.

  3. Visits to emergency departments—number of visits to emergency departments in hospitals.

Independent Variables.—

Instrumental activities of daily living. 

The measure by Fillenbaum (1985) was used to examine the ability to perform instrumental activities of daily living (IADL). The measure includes eight items relating to home chores, laundry, cooking, etc. Scores for each item ranged from 1 (no difficulty at all) to 5 (very much difficulty). The final index was based on the sum of scores, ranging from 8 to 40. The internal consistency in this study (Cronbach’s alpha) was .94.

Activities of daily living. 

ADL was measured by using Katz, Down, Cash, and Grotz’s (1970) index that includes eight items (washing, dressing, toileting, indoor mobility, eating, etc.), with scores for each item ranging from 1 (no difficulty at all) to 5 (very much difficulty). The sum of scores produced an index ranging from 8 to 40. The internal consistency in this study (Cronbach’s alpha) was .91.

Comorbidity. 

Chronic health conditions were drawn from the central computerized medical records of the respondents. The diagnoses that are coded, using the International Classification of Diseases (9th version, World Health Organization, 2007), include the medical history of the patients and are available for computerized retrieval.

Thirteen chronic illnesses were included: cancer, diabetes, hypertension, heart attack, other heart disease, cardiovascular accident, circulatory, respiratory, gastrointestinal, osteoporosis, thyroid problems, arthritis, and urinary/nephrologic problems. Score for each condition was 1 = yes and 0 = no. Scores were summed with higher scores indicating more morbidity.

ADCC use. 

Respondents were divided into two groups—those who visited an ADCC (=1) and those who did not (=0).

Economic status. 

The respondents were presented with seven categories of income and were asked to choose the category that related to them. Thus, 1indicated the lowest monthly income, which was the poverty line in Israel, whereas 7 indicated the highest level of income. In addition, respondents were asked to rate their perceived economic status with scores ranging from 1 (very good) to 5 (very poor).

Covariates.—

Included age, gender, education (included seven categories ranging from 1 = partial elementary school to 7 = graduate degree and over), ethnicity (coded as 1 = Europe/America, 2 = Asia/Africa, and 3 = born in Israel), marital status (coded 1 = married and 0 = unmarried), living arrangements (coded 1 = live alone and 2 = otherwise), number of children, number of children living in proximity, household size, and length of time living in Israel.

Analyses

A range of descriptive analyses (percentages, means, and SDs) were initially performed to present the characteristics of the respondents and the dependent and independent variables; t test/χ2 were carried out to examine the associations between the dependent and independent variables, respectively, specifically to examine differences between users and nonusers of ADCCs with regard to demographics and utilization of health services. In addition, correlation coefficients were calculated to examine the intercorrelation between utilization of the various health services examined in this study.

To examine the factors that explain utilization of health services 6 months after using the ADCCs, linear regression analyses were performed that included a dichotomous variable of use and nonuse of the ADCC, respondents’ sociodemographics, morbidity, economic status (monthly income), and use of the specific health service a year prior to using the ADCC. Only those variables that were found significant in the bivariate analyses were included in the equations. SPSS package version 17 data storage and analysis was used to perform the statistical analyses.

Results

Participants’ Characteristics

Respondents’ characteristics by ADCC utilization are presented in Table 1. The findings show that the vast majority (∼76%) were women with an average age of about 78 years. No significant differences were found between users and nonusers of ADCCs in functional status in terms of ability to perform ADL and IADL tasks and comorbidity. Also, no significant differences were found between the two groups of respondents in the number of children living in near proximity and perceived economic status. However, significant differences were found between the two groups of respondents in ethnicity, marital status, level of education, number of children, living arrangements, household size, length of stay in Israel, and monthly income. Among those who attended ADCCs, there were significantly more widowed persons, with lower levels of education, born in Asian/African countries, living alone compared with their counterparts in the control group many more of whom were married, highly educated, lived with somebody, and were born in European/American countries. In addition, users of ADCCs had significantly more children, lived longer in Israel, lived in smaller households, and had a lower monthly income compared with their peers in the control group. In other words, those who visited in ADCCs were of a lower socioeconomic status compared with nonusers. Among those who attended ADCCs, the average length of time of visiting the day care center was 49.17 months (SD = 50.35) and the average number of weekly visits was 3.83 (SD = 1.23).

Table 1.

Characteristics of Respondents by Group of Respondents

Variable  Users (N = 400) Nonusers (N = 400)  χ2/t 
M SD Range M SD Range 
Use of ADCC          
    Gender          
        Women 75.7    75.6    0.52 
    Age  77.95 7.09 60–100  77.78 6.09 62–95 0.59 
    Ethnicity         17.24*** 
        Asia/Africa 62.9    50.6    
        Europe/America 35.6    44.1    
        Israel 1.5    5.2    
    Marital status         63.22*** 
        Married 21.3    46.4    
        Widowed 72.4    46.4    
        Divorced/separated 6.3    7.2    
    Education         25.85*** 
        Elementary 70.2    52.9    
        Secondary 20.1    29.7    
        High education 9.7    17.4    
    Number of children  4.45 3.03 0–15  3.92 2.90 0–17 2.56* 
    Number of children living in proximity  1.59 1.96 0–13  1.58 1.88 0–11 0.13 
    Living arrangements         52.87*** 
        Alone 56.1    36.2    
        With a spouse 21.3    44.4    
        With a child 15.8    11.4    
        Otherwise 6.8    8.0    
    Length of stay in Israel  45.17 17.12 0–84  41.85 18.12 0–80 2.61** 
Use of group of respondents          
    Household size  1.69 1.03 1–6  1.83 0.90 1–8 −1.99* 
    Monthly income  2.42 0.97 1–5  2.88 1.18 1–7 −0.16.13*** 
    Perceived economic status         8.85 
        Very good 9.7     8.3   
        Good 34.1     30.8   
        Moderate 48.0     46.8   
        Poor/very poor 8.2     14.1   
       Comorbidity  4.91 2.15 0–12  4.90 2.17 0–15 0.04 
    ADL  14.31 5.69 8–32  14.84 6.11 8–32 –1.30 
    IADL  20.13 7.35 8–38  20.60 7.46 8–36 –0.91 
Variable  Users (N = 400) Nonusers (N = 400)  χ2/t 
M SD Range M SD Range 
Use of ADCC          
    Gender          
        Women 75.7    75.6    0.52 
    Age  77.95 7.09 60–100  77.78 6.09 62–95 0.59 
    Ethnicity         17.24*** 
        Asia/Africa 62.9    50.6    
        Europe/America 35.6    44.1    
        Israel 1.5    5.2    
    Marital status         63.22*** 
        Married 21.3    46.4    
        Widowed 72.4    46.4    
        Divorced/separated 6.3    7.2    
    Education         25.85*** 
        Elementary 70.2    52.9    
        Secondary 20.1    29.7    
        High education 9.7    17.4    
    Number of children  4.45 3.03 0–15  3.92 2.90 0–17 2.56* 
    Number of children living in proximity  1.59 1.96 0–13  1.58 1.88 0–11 0.13 
    Living arrangements         52.87*** 
        Alone 56.1    36.2    
        With a spouse 21.3    44.4    
        With a child 15.8    11.4    
        Otherwise 6.8    8.0    
    Length of stay in Israel  45.17 17.12 0–84  41.85 18.12 0–80 2.61** 
Use of group of respondents          
    Household size  1.69 1.03 1–6  1.83 0.90 1–8 −1.99* 
    Monthly income  2.42 0.97 1–5  2.88 1.18 1–7 −0.16.13*** 
    Perceived economic status         8.85 
        Very good 9.7     8.3   
        Good 34.1     30.8   
        Moderate 48.0     46.8   
        Poor/very poor 8.2     14.1   
       Comorbidity  4.91 2.15 0–12  4.90 2.17 0–15 0.04 
    ADL  14.31 5.69 8–32  14.84 6.11 8–32 –1.30 
    IADL  20.13 7.35 8–38  20.60 7.46 8–36 –0.91 

Notes: ADCC = adult day care centers; ADL = activities of daily living; and IADL = instrumental activities of daily living.

*p < .05. **p < .01. ***p < .001.

Table 2 presents differences in utilization of health services by group of respondents. The findings show that no significant differences were found between users and nonusers of ADCCs, either prior to or after use of this type of service, except for visits to specialists 6 months after beginning use of the ADCC. That is, although no significant differences between users and nonusers of ADCCs were found prior to use of ADCC, those who started to visit ADCCs had fewer visits to specialists compared with nonusers after 6 months of using the ADCC. Users of ADCCs had relatively fewer visits to emergency departments, hospital admissions, and bed days after using the ADCCs compared with prior to use of the ADCCs, whereas among the control group of nonusers, there were no substantial differences during that time. In other words, the findings indicate that among users of ADCCs, there was a decline in use of in-patient health services after using ADCCs.

Table 2.

Differences in Use of Health Services by Group of Respondents

 Users of ADCC (N = 400) Nonusers of ADCC (N = 400)  
Variable M SD M SD t 
Visits to specialists 1 year before use of ADCC 4.71 4.84 5.27 5.77 −1.47 
Visits to specialists half year after use of ADCC 2.35 2.87 2.79 3.19 −2.04* 
Visits to emergency department 1 year before use of ADCC 0.50 1.02 0.40 0.86 1.46 
Visits to emergency department half year after use of ADCC 0.18 0.51 0.21 0.52 −0.80 
Admissions to hospitals 1 year before use of ADCC 0.45 0.91 0.38 0.95 1.10 
Admissions to hospitals half year after use of ADCC 0.18 0.51 0.24 0.79 −1.36 
Number of bed days 1 year before use of ADCC 2.99 7.52 2.84 10.11 0.23 
Number of bed days half year after use of ADCC 1.07 3.86 1.47 5.35 −1.23 
 Users of ADCC (N = 400) Nonusers of ADCC (N = 400)  
Variable M SD M SD t 
Visits to specialists 1 year before use of ADCC 4.71 4.84 5.27 5.77 −1.47 
Visits to specialists half year after use of ADCC 2.35 2.87 2.79 3.19 −2.04* 
Visits to emergency department 1 year before use of ADCC 0.50 1.02 0.40 0.86 1.46 
Visits to emergency department half year after use of ADCC 0.18 0.51 0.21 0.52 −0.80 
Admissions to hospitals 1 year before use of ADCC 0.45 0.91 0.38 0.95 1.10 
Admissions to hospitals half year after use of ADCC 0.18 0.51 0.24 0.79 −1.36 
Number of bed days 1 year before use of ADCC 2.99 7.52 2.84 10.11 0.23 
Number of bed days half year after use of ADCC 1.07 3.86 1.47 5.35 −1.23 

Notes: ADCC = adult day care centers.

*p < .05.

Table 3 presents the correlation coefficients between morbidity and use of various health services. The findings indicate that higher morbidity was significantly associated with increased use of various health services. Also, use of various health services was significantly positively intercorrelated, suggesting that more frequent use of one type of service was connected with increased use of other health services.

Table 3.

Correlation Coefficients Between Utilization of Health Services Pre- and Post-Use of ADCCs (N = 800)

Variable 
1. Morbidity         
2. Visits to specialists 1 year before .31***        
3. Visits to specialists half year after .27*** .61***       
4. Visits to emergency department 1 year before .14*** .20*** .12***      
5. Visits to emergency department half year after .13*** .16*** .15*** .22***     
6. Admissions to hospitals 1 year before .24*** .20*** .06 .38*** .17***    
7. Admissions to hospitals half year after .10** .11*** .10** .15*** .31*** .37***   
8. Number of bed days 1 year before .15*** .12*** .05 .31*** .10** .70*** .20***  
9. Number of bed days half year after .09** .11** .10** .20*** .31*** .34*** .74*** .26*** 
Variable 
1. Morbidity         
2. Visits to specialists 1 year before .31***        
3. Visits to specialists half year after .27*** .61***       
4. Visits to emergency department 1 year before .14*** .20*** .12***      
5. Visits to emergency department half year after .13*** .16*** .15*** .22***     
6. Admissions to hospitals 1 year before .24*** .20*** .06 .38*** .17***    
7. Admissions to hospitals half year after .10** .11*** .10** .15*** .31*** .37***   
8. Number of bed days 1 year before .15*** .12*** .05 .31*** .10** .70*** .20***  
9. Number of bed days half year after .09** .11** .10** .20*** .31*** .34*** .74*** .26*** 

Notes: ADCC = adult day care centers.

*p < .05. **p < .01. ***p < .001.

Table 4 shows the multiple regression analyses for factors explaining visits to specialists, emergency department, number of hospital admissions, and total bed days 6 months after the start of visiting ADCCs. The findings show that use of ADCCs was not significantly connected with utilization of any of the health services, whereas morbidity and use of specific health services a year prior to ADCC use were significant in explaining visits to the specific services 6 months later. In other words, the magnitude of use of one type of service 1 year prior to ADCC use was a significant predictor of using that same service 6 months after beginning use of the ADCC.

Table 4.

Regression Analyses of Factors Explaining Use of Health Services 6 Months After Using ADCC

 Visits to specialists after 6 months Visits to emergency departments after 6 months Number of admissions after 6 months Number of bed days after 6 months 
Variable B SE β B SE β B SE β B SE β 
Ethnicity −.12 0.27 −.02 .10 0.06 .10 .09 0.07 .07 .80 0.51 .08 
Marital status .04 0.25 .01 −.03 0.05 −.03 .08 0.07 .06 .59 0.48 06 
Education −.03 0.07 −.02 .00 0.01 .01 .02 0.02 .04 .21 0.13 .07 
Length of living in Israel .00 0.01 .03 .00 0.00 .01    .03 0.01 .11* 
Living arrangements .26 0.31 .04 −.05 0.06 −.05 .00 0.08 .00 −.10 0.59 −.01 
Household size −.00 0.13 .00 .01 0.03 .02 −.02 0.03 −.03 −.23 0.25 −.25 
Number of children −.05 0.04 −.05 −.01 0.01 −.08 −.01 0.01 −.03 −.04 0.07 −.03 
Monthly income −.00 0.09 .00 −.02 0.02 −.05 −.02 0.02 −.03 −.07 0.18 −.02 
Morbidity .13 0.04 .10*** .03 0.01 .12*** .01 0.01 .02 .10 0.08 .04 
Use of ADCC .26 0.18 .04 .05 0.04 .05 .08 0.05 .06 .39 0.35 .04 
Visits to specialists a year prior to ADCC use .32 0.02 .58***          
Visits to emergency departments a year prior to ADCC use    .11 0.02 .21***       
Admissions a year prior to ADCC use       .26 0.03 .36***    
Bed days a year prior to ADCC use          0.13 0.02 .25*** 
R2 .38 .08 .16 .09 
F 42.80*** 5.95*** 12.90*** 6.94*** 
 Visits to specialists after 6 months Visits to emergency departments after 6 months Number of admissions after 6 months Number of bed days after 6 months 
Variable B SE β B SE β B SE β B SE β 
Ethnicity −.12 0.27 −.02 .10 0.06 .10 .09 0.07 .07 .80 0.51 .08 
Marital status .04 0.25 .01 −.03 0.05 −.03 .08 0.07 .06 .59 0.48 06 
Education −.03 0.07 −.02 .00 0.01 .01 .02 0.02 .04 .21 0.13 .07 
Length of living in Israel .00 0.01 .03 .00 0.00 .01    .03 0.01 .11* 
Living arrangements .26 0.31 .04 −.05 0.06 −.05 .00 0.08 .00 −.10 0.59 −.01 
Household size −.00 0.13 .00 .01 0.03 .02 −.02 0.03 −.03 −.23 0.25 −.25 
Number of children −.05 0.04 −.05 −.01 0.01 −.08 −.01 0.01 −.03 −.04 0.07 −.03 
Monthly income −.00 0.09 .00 −.02 0.02 −.05 −.02 0.02 −.03 −.07 0.18 −.02 
Morbidity .13 0.04 .10*** .03 0.01 .12*** .01 0.01 .02 .10 0.08 .04 
Use of ADCC .26 0.18 .04 .05 0.04 .05 .08 0.05 .06 .39 0.35 .04 
Visits to specialists a year prior to ADCC use .32 0.02 .58***          
Visits to emergency departments a year prior to ADCC use    .11 0.02 .21***       
Admissions a year prior to ADCC use       .26 0.03 .36***    
Bed days a year prior to ADCC use          0.13 0.02 .25*** 
R2 .38 .08 .16 .09 
F 42.80*** 5.95*** 12.90*** 6.94*** 

Notes: ADCC = adult day care centers.

*p < .05. **p < .01. ***p < .001.

Discussion

In brief, the goal of the study was to examine if there is an offset effect between use of ADCCs and health services. It was hypothesized that there will be significant differences between the two groups of respondents 6 months after beginning use of the ADCCs such that users of ADCCs will use less health services compared with the control group of nonusers. Surprisingly, the findings indicate that the hypothesis was refuted and that users of ADCCs did not use less health services compared with their counterparts who were nonusers. This is consistent with a study (Crane & Christenson, 2008) that examined the effect of pre- to post-family therapy intervention on use of health care services whereby no significant decreases were found in the use of different types of outpatient care. Yet, this finding is surprising given the extensive amount of research that supports medical offset with psychosocial interventions. Furthermore, the findings show that use of one type of health service was connected with increased use of other health services. For example, more visits to specialists were significantly connected with more visits to emergency departments, hospitalizations, and more bed days.

Several explanations can be provided to understand these findings. First, ADCCs in Israel do not provide health care services and actually are aimed to enable their participants to meet their social needs rather than their health needs. Users of ADCCs are a more vulnerable group compared with nonusers in terms of their lower socioeconomic status and social isolation (unmarried, live alone). Because the current ADCC programs do not address their health needs, they do not meet their health needs and therefore no significant differences were found between the two groups of respondents. Furthermore, the findings indicate that morbidity was significantly positively connected with use of any of the health services, reflecting their health needs. Indeed, previous studies (e.g., Bradley et al., 2002; Brown, Barner, Bohman, & Richards, 2009) based on the behavioral model by Andersen (1995, 2008) found that the need factors were more significant in explaining utilization of health services than any other factors. Therefore, it might be that if primary and rehabilitation health services would have been provided, ADCCs could better meet the participants’ health needs and thus decrease use of health services. For example, a study conducted in Canada (Savard, Lebel, Leduc, Beland, & Bergman, 2009) found low correspondence between program activities and participants’ needs, suggesting a need to review activity program components and finding ways to match better client needs. This issue, however, could not be examined because, as noted previously, almost all ADCCs in Israel operate according to the social model rather than the mixed model as in other countries such as the United States (Nadash, 2003).

Second, health services in Israel are provided under the National Health Insurance Law enacted in 1994 and are universal. Although for some health services (e.g., visits to specialists) patients have to co-pay out of pocket, these co-payments are relatively low and older patients with low income have exemptions from most of these co-payments and thus can more easily afford using expensive health services. However, low income is not a major impediment to access most health services. Moreover, most insurees also have supplemental insurances that enable them to use private health services, but this is less common among low-income groups (Brammli-Greenberg, Gross, Yair, & Akiva, 2011). It might be that because those who do not use ADCCs have a higher income may use more private health services through private health insurance schemes than those with low income who cannot afford it. The use of private health services are not recorded in the medical records in the HMOs and therefore data on this issue are unavailable. In this case, it might be that using private health services may offset use of public health services. However, this issue has not been examined in this study and merits further research.

Third, many more participants of ADCCs were unmarried, lived alone, and were poorer compared with their counterparts who were nonusers, suggesting lesser social support. Previous studies (Coe, Wollinsky, Miller, & Prendergast, 1985; Penning, 1995) showed that social networks play a significant role in reducing utilization of health services with those with stronger social support compared with those with weaker social networks used less various health services such as community-based clinics, home health care, and hospital admissions. Furthermore, there is growing evidence that relationship functioning can have a major impact on health services’ use (e.g., Jenkins, 1997). Addressing relationship problems may decrease excessive health care use. Also, research indicates that participation in mental health services can decrease subsequent health care use and costs (e.g., Crane et al., 2004; Law & Crane, 2000; Law et al., 2003). This is further supported by a study (Crane & Christenson, 2008) that examined the effect of pre- to post-family therapy intervention on health care services use. However, significant decreases were found with individual therapy under any of the different types of outpatient care. For example, there was a significant decrease of 47% in urgent care visits for those who participated in family therapy. It is therefore likely that ADCCs provide social support to their participants and thus serve as a substitute for lack of natural social support. This may consequently buffer utilization of health services, which otherwise would have resulted in overutilization of health services. Therefore, no significant differences between the two groups of respondents were found, although users of ADCCs were more socially deprived compared with their nonuser counterparts. However, these issues were not examined in this study and need further investigation.

Implications

Although the study refuted the offset effect with regard to ADCCs, there is need to reframe and modify the content of ADCCs to include some primary health and rehabilitation services. Instead of exclusively focusing on physical or social needs, integrated care models to address physical, mental, and social needs of elders can better meet the variety of needs of users of ADCCs (Dabelko & Balaswamy, 2000). This may encourage current nonusers to use this service, but it will also enable examining if health services provided in ADCCs offset use of other ambulatory and in-patient health services. For this, further longitudinal as well as quasi-experimental studies are necessary.

Limitations

There are several limitations to this study. First, the study is a cross-sectional study; thus, the causal relationship between utilization of ADCCs and health care services is warranted. Further investigation and evaluation studies that will include longitudinal as well as quasi-experimental designs to examine differences in health service utilization before and after attending ADCCs can throw light on this issue. This can enable better understanding of the association between these variables and identify factors that can make ADCCs more effective in reducing use of expensive health services. Second, generalization of the findings is limited because the sample and the sampling procedure do not guarantee representativeness of all visitors in ADCCs on a national level. This is because the sample was not randomly selected and included only ADCCs in the Southern region of Israel. Despite these limitations, the study adds to our knowledge in understanding use of services among frail older adults and points to the need for further research in order to understand how ADCCs can be more effective in offsetting health care services, in particular, hospital admissions.

Funding

This study was funded by the Israel National Institute for Health Policy Research.

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Author notes

Decision Editor: Rachel Pruchno, PhD