Journal of Gerontological Nursing

Research Brief 

Factors Associated With Health Service Use Among Older Adults in a Mobile Veterans Program

Mary Elizabeth Bowen, PhD; Beatrice Gaynor, PhD, APRN, FNP-C; Lorraine J. Phillips, PhD, RN, FAAN, FGSA; Elizabeth Orsega-Smith, PhD; Angela Lavery, PhD, LCSW, FT; Anne Bradley Mitchell, PhD, ANP-BC; Heather K. Basehore, PhD

Abstract

The current retrospective, longitudinal study applied Andersen's Behavioral Model of Health Services Use to examine how demographic characteristics (age), available resources (e.g., a caregiver, the Mobile Veterans Program [MVP]) and health needs (e.g., cognitive and physical functioning, depressive symptoms) affect hospitalization and institutionalization outcomes among older adults using the MVP. Fifty-four Veterans (age 55 to 95) participating in the MVP for up to 2 years were examined using hierarchical linear modeling (HLM) and growth curve model. In the final HLM model, each 1-point increase in depressive symptoms was associated with 76% (p ≤ 0.05) greater risk of institutionalization and 40% (p ≤ 0.01) greater risk of hospitalization. Each 1-point increase in cognitive functioning was associated with 24% (p ≤ 0.05) lower risk of institutionalization. The relationship between caregiver burden and hospitalization was attenuated by frequency of MVP visits. Services focused on reducing depressive symptoms may influence health service use and reduce caregiver burden in this population. [Journal of Gerontological Nursing, 46(5), 15–22.]

Abstract

The current retrospective, longitudinal study applied Andersen's Behavioral Model of Health Services Use to examine how demographic characteristics (age), available resources (e.g., a caregiver, the Mobile Veterans Program [MVP]) and health needs (e.g., cognitive and physical functioning, depressive symptoms) affect hospitalization and institutionalization outcomes among older adults using the MVP. Fifty-four Veterans (age 55 to 95) participating in the MVP for up to 2 years were examined using hierarchical linear modeling (HLM) and growth curve model. In the final HLM model, each 1-point increase in depressive symptoms was associated with 76% (p ≤ 0.05) greater risk of institutionalization and 40% (p ≤ 0.01) greater risk of hospitalization. Each 1-point increase in cognitive functioning was associated with 24% (p ≤ 0.05) lower risk of institutionalization. The relationship between caregiver burden and hospitalization was attenuated by frequency of MVP visits. Services focused on reducing depressive symptoms may influence health service use and reduce caregiver burden in this population. [Journal of Gerontological Nursing, 46(5), 15–22.]

The U.S. population is aging; the number of adults age 65 and older is projected to double to >98 million by 2060 (Administration for Community Living, 2018). The aging Baby Boomer cohort will lead to a 75% increase in the number of older adults requiring health care services, including hospitalization and institutionalization (Mather, 2016). This increase may be due to the greater number of older adults with dementia and disabilities (Mather, 2016).

Forty-three percent of the 22 million Veterans in the United States are 65 and older, compared to 15% of the general population (Administration for Community Living, 2018; National Center for Veterans Analysis and Statistics, 2019). Older Veterans have more complex physical and cognitive health conditions than their counterparts, including higher rates of dementia, disease, and disability (Campbell et al., 2018; Gould et al., 2014; Haq & Dunn, 2018). Older Veterans report higher rates of social isolation, depression, and mental health disorders (Campbell et al., 2018; Gould et al., 2014). These Veterans are also at increased risk for hospitalization and institutionalization (Guthrie et al., 2016; Thorpe et al., 2010; Toot et al., 2017).

To address the health needs of older Veterans, the U.S. Department of Veterans Affairs (VA) offers a variety of in-home community, virtual health, and formal care coordination programs to older Veterans and their caregivers. The VA Mobile Veterans Program (MVP) is a nurse-led mobile team (e.g., a RN and licensed practical nurse, recreational therapist, and nursing aide) that operates out of five Veterans of Foreign Wars (VFW) post locations in central Pennsylvania. The MVP provides caregiver respite (1 day per week for 4 hours at each of the five sites). Basic nursing care (e.g., vital sign assessment), physical exercises, cognitive activities (e.g., trivia, reminiscence), music and art therapy, socialization with other Veterans (e.g., coffee social time, current events discussions), and nutritious lunches are provided during the 4-hour sessions. The MVP also supports caregivers by providing dementia-related behavioral information, prescription and health condition management, and care coordination with the VA Geriatric Clinic (a geriatric primary care provider).

Among the home- and community-based sector of long-term care, the MVP model most closely parallels adult day service centers that aim to support community living and delay institutionalization for persons with chronic conditions and disabilities (Lendon & Rome, 2016). The MVP is a Veteran-centric program designed to incorporate positive perceptions of military service (Borowsky et al., 2002; Kuwert et al., 2014; Southwick et al., 2016) and encourage social engagement with peers (Chinman et al., 2012; Rozanova et al., 2015). The opportunity for social engagement with other Veterans is particularly important to this unique subculture of older adults who report increased loneliness due to their distinctive experiences and interactions as Veterans (Carr et al., 2018; Hobbs, 2008; Sayer et al., 2010). There is some evidence that the use of in-home care and other community-based services early in disease progression delays institutionalization (Gaugler et al., 2005). The consistent use of adult day services is also associated with a reduced risk of institutionalization among older adults (Kelly et al., 2016).

The current retrospective, longitudinal study examined the extent to which cognitive and physical functioning, depressive symptoms, and caregiver burden predicted the odds of hospitalization and institutionalization outcomes among older Veterans using the MVP. In Andersen's (1995) Behavioral Model of Health Services Use, demographic characteristics (age), available resources (e.g., a caregiver, the MVP), and health needs (e.g., cognitive and physical functioning, depressive symptoms) affect the use of health care services. Thus, it is expected that decreased cognitive and physical functioning, increased depressive symptoms and caregiver burden, and less frequent MVP use will be associated with increased rates of hospitalization and institutionalization among older Veterans. Findings from the current study may be used to develop or improve other community programs, including nurse-led programs that serve similarly vulnerable older adult populations and their caregivers.

Method

The current study was approved by the VISN 4 Multi-Site Institutional Review Board and the Coatesville VA Medical Center Research and Development Committee.

Participants

Participants were community-dwelling older Veterans (age 55 to 95) using the MVP who had primary caregivers. Veterans were referred to the MVP from a VA Geriatric Clinic and had corresponding physical and cognitive assessments from the Geriatric Clinic over the course of the study. Retrospective chart reviews were conducted on older Veterans using the MVP from December 31, 2014 to December 31, 2016. All participants were male, consistent with the older Veteran population. Of the 115 Veterans using the MVP, 61 were missing multiple time points of data on key variables (e.g., cognitive and/or physical functioning), including 15 participants who did not have a caregiver and nine participants who were institutionalized at baseline and/or Time 1 and were thus excluded from the study. The resultant sample included 54 older Veterans with three or four continuous assessments over the course of the study.

The current study explored how Veterans who were excluded due to lack of a caregiver might differ from participants and found no significant differences in age (t = −0.96; p = 0.32), depressive symptoms (t = 0.72; p = 0.47), or cognitive impairment (CI; t = −1.50; p = 0.15). The current study also explored how Veterans excluded due to lack of repeated measures might differ from participants with complete data and found significant differences in age (t = −2.65; p ≤ 0.05), but not depressive symptoms (t = 0.87; p = 0.38), CI (t = 0.97; p = 0.34), or caregiver burden (t = 0.05; p = 0.95).

Measures

Dependent Variables. The dependent variables of interest were hospitalization and non-respite institutionalization (yes/no; assessed at baseline and every 6 months).

Independent Variables. To measure health needs, the current study used age, cognitive and physical functioning, and depressive symptoms. Available resources were measured by caregiver burden and the average number of days between MVP visits (frequency of MVP use). Independent variables were measured at baseline and every 6 months unless otherwise noted below. Age was calculated from birth year.

Cognitive functioning was measured by the Saint Louis University Mental Status (SLUMS) examination, a 30-point, 11-question tool that assesses orientation, memory, attention, and executive function (Tariq et al., 2006) at baseline and every 6 months. Items on the SLUMS examination included animal naming, digit span, figure recognition, clock drawing, and size orientation. Previous work with older Veterans showed a cut off score of 22.3 for mild CI and 14.9 for dementia (Tariq et al., 2006).

Physical functioning was measured by the Katz Independence in Activities of Daily Living (ADL) Index at baseline and every 6 months. The ADL Index was used to rate the level of dependence or independence in the performance of six ADL tasks (i.e., bathing, dressing, toileting, transferring, continence, and feeding), each ranging from 1 to 3 with a score of 18 indicating independence (Katz et al., 1970).

Depressive symptoms were measured by the Geriatric Depression Scale (GDS) at baseline. The GDS includes 15 questions on life satisfaction, happiness, boredom, memory problems, and trying new things (Yesavage et al., 1982–1983). More than 5 points on the GDS suggests depression, ≥10 indicates depression. Work prior to the current study suggests a mean GDS score of 3.5 among community-dwelling older adults age ≥65 (Marc et al., 2008).

Caregiver burden was measured by the Zarit Burden Interview (ZBI) at baseline. In t his 22-item tool, caregivers were asked to define (score 0 to 4 on each item) the extent to which there are emotional, physical, social, and financial consequences associated with caring for their loved one, with higher scores associated with increased burden. Normative data suggest a mean of 24 on the Zarit among caregivers of older persons with dementia; 28 among caregivers of persons with stroke; 20 among caregivers of persons with chronic obstructive pulmonary disease, and 30 among caregivers of persons with physical disabilities (Schreiner et al., 2006; Wang et al., 2008).

Average number of days between visits was measured by the number of days between the first and last recorded visits within the 2-year period of the study divided by the number of visits. For example, if the Veteran attended the MVP 24 times over the course of 1 year, the average number of days between visits would be 15.2 (365 days divided by 24).

Statistical Analysis

Descriptive analyses were completed using SPSS Statistics 25 statistical package. Hierarchical linear modeling (HLM) software, version 7.03, was used to examine institutionalization and hospitalization over time among older Veterans using the MVP. HLM examined individual and aggregate levels of data over time. This multilevel modeling technique conceived each Veteran as having his own regression equation but incorporated information from each measurement simultaneously in the same model. As many health service use distributions were non-normal, reflecting relatively low institutionalization and hospitalization rates among older adults over time, alternative distributions were considered and nonlinear models that modeled Poisson distributions of institutionalization and hospitalization data fit better than the linear models. The results of the nonlinear analyses are reported below.

The models presented are a series of nested models, one for each level of the hierarchy. At the first level, each Veteran's trajectory of change in institutionalization or hospitalization risk is represented as a function of person/time specific parameters (e.g., cognitive and physical functioning) plus random error. Level 1 variables are time-varying, accounting for intra-individual changes across the cognitive and physical measures. Level 2 statistically models individual variations in growth parameters across a population of persons for the non-time-varying variables (e.g., caregiver burden, frequency of MVP use, depressive symptoms). Multilevel models account for between-participant heterogeneity, within-individual correlations, and model cluster-induced errors in the intercepts and coefficients to increase the efficiency of the estimates.

A growth curve model was examined to determine whether there is a nonlinear change in the odds for institutionalization and hospitalization over time and whether the rate of change varies across participants. Linear and quadratic components were included in all institutionalization and hospitalization models. The linear slope was allowed variation across individuals to examine rate of change. Robust standard errors were used to account for non-normality.

In the analyses presented in Table 1, a model-building sequence was used so that the effects of each new variable could be examined while accounting for controls. This model-building sequence was followed with each variable; age and cognitive and physical functioning were added first, followed by caregiver burden and depressive symptoms. The final model added the average number of days between MVP visits.

Hierarchical Linear Models Examining the Use of Health Services Within the Andersen Model of Health Services Use Framework Among Older Veterans Using a MVP

Table 1:

Hierarchical Linear Models Examining the Use of Health Services Within the Andersen Model of Health Services Use Framework Among Older Veterans Using a MVP

Results

Table 2 shows descriptive characteristics of the sample. The average score on the SLUMS examination was 12.5, which meets criteria for dementia (56.6% of the sample). Approximately 32% of participants met the criteria for mild CI. In contrast, the average score on the ADL Index was 16.8, suggesting a sample of higher physical functioning, with an average of ≤1 ADL impairment. The level of burden reported by caregivers was low, with an average score of 7.3 on the ZBI. Veterans reported few depressive symptoms, with an average of 2.9 on the GDS; 15.7% of participants scored >5 on the GDS, indicating depression. Within the first 6 months of the study, 5.7% of older Veterans had a hospitalization event. Over the 24-month study period, 56% of older Veterans in the sample had been hospitalized at least once.

Demographic Characteristics of Older Veterans Using a MVP at Baseline

Table 2:

Demographic Characteristics of Older Veterans Using a MVP at Baseline

Table 1 shows the results of institutionalization (Models 1 to 3) and hospitalization (Models 4 to 6). In Model 1, Veteran participants had lower risk of institutionalization, with each additional increase in age associated with 18% lower risk of institutionalization (odds ratio [OR] = 0.82; p ≤ 0.01). There was no relationship between cognitive and physical functioning and institutionalization. In Model 2, when depressive symptoms and caregiver burden were added to the model, participants continued to have lower risk of institutionalization, with each additional year of age associated with 21% lower risk of institutionalization (OR = 0.79; p ≤ 0.01). An increase in cognitive functioning was associated with 24% lower risk of institutionalization (OR = 0.76; p ≤ 0.05). Accounting for the other factors in the model, an increase in depressive symptoms was associated with 62% higher risk of institutionalization in Model 2. The strength of associations among age, cognitive functioning, depressive symptoms, and institutionalization risk were similar in Model 3 with the addition of the average number of days between visits. The average number of days between visits was not significantly associated with institutionalization.

Results from growth curve analyses suggest a significant linear (β = −0.13; p ≤ 0.001) decrease in institutionalization with this trend accelerating (OR = −0.001; p ≤ 0.001) over time. The linear slope did not vary significantly across participants (χ2 = 0.00; p > 0.50), suggesting a similar rate of change among participants over the course of the study. Given that these components were significant, post hoc analyses examined whether depression and CI (significant variables in the model) may have a more direct effect, effectively altering institutionalization trajectories over time. There were no significant interactions between depression, CI, and the linear and quadratic components. Taken together, these results suggest participants with depressive symptoms and/or CI are at a higher risk of institutionalization at study start and maintain higher risk over the study period compared to their counterparts (Figure 1).

The relationship between cognitive functioning, depressive symptoms, and institutionalization over time.

Figure 1.

The relationship between cognitive functioning, depressive symptoms, and institutionalization over time.

In Model 4, there was no relationship between age, cognitive or physical functioning, and hospitalization. In Model 5, when depressive symptoms and caregiver burden were added, an increase in depressive symptoms was associated with 34% greater risk of hospitalization. In Model 5, each increase in caregiver burden was associated with 26% lower risk of hospitalization (p ≤ 0.01). In Model 6, after the addition of the average number of days between visits, the relationship between depressive symptoms and hospitalization was strengthened (OR = 1.40; p ≤ 0.01), but there was no longer a significant relationship between caregiver burden and hospitalization. In post hoc analyses, the current study examined the potential for multi-collinearity between the average number of days between visits and caregiver burden and found there was no significant relationship (p = 0.98). This finding suggests the relationship between caregiver burden and hospitalization may be moderated by frequency of use of the MVP. Finally, in this model, there was no relationship between the average number of days between visits and hospitalization. Results from growth curve analyses suggest no linear or quadratic effect on the risk of hospitalization over time.

Discussion

This retrospective, longitudinal study examined the association of physical and cognitive functioning, depressive symptoms, caregiver burden, and frequency of MVP use with hospitalization and institutionalization among older Veterans. Increased age was associated with a decreased risk for institutionalization over time. This association may be due to selective survival effects among MVP attendees where frailer members die and/or are institutionalized at younger ages, leaving a more robust group of survivors at successively older ages. In addition, participants remaining in the study had higher physical functioning than other adult day service participants who need assistance with multiple ADL (National Center for Health Statistics, 2019). The current study's hypothesis was partially supported as decreased cognitive functioning and increased depressive symptoms were associated with increased institutionalization and increased depressive symptoms were associated with increased hospitalization. Concurrently, increased caregiver burden was associated with decreased hospitalization and there was some evidence to suggest that this may be due to the frequency of MVP use.

These findings are consistent with Andersen's (1995) Behavioral Model of Health Services Use in that the older Veterans in the current study who had increased health needs and fewer resources used more health services. Some studies similarly report caregiver burden, depression, CI, and frequency of service use affecting institutionalization and/or hospitalization risk among adult day service participants (Cohen-Mansfield & Wirtz, 2007; Kelly, 2017). Notably, however, findings are mixed (Lendon & Rome, 2016). Further research is needed to better understand predictors of institutionalization and hospitalization among different older adult populations considering that resources for accessing long-term care services may also differ. The rate of hospitalization observed in the current study is also slightly higher than national results from adult day centers (showing a 90-day hospitalization rate of 4.4%) (Lendon & Rome, 2016). This higher rate may be because older Veterans have more complex health needs than their counterparts, including higher rates of dementia and depression (Campbell et al., 2018; Gould et al., 2014).

Consistent with the subculture of aging theory, attending the MVP may increase social engagement for Veterans (Rose, 1962; Russell & Russell, 2018). Veterans are a unique subculture with a shared sense of community formed from their experience as Veterans and reinforced by supportive organizations (Hobbs, 2008; Olenick et al., 2015). The MVP may also benefit caregivers who reported relatively low caregiver burden in the current study despite CI/dementia in the care recipient. When MVP frequency was controlled, the relationship between caregiver burden and hospitalization was attenuated, suggesting a moderating relationship between these factors. Although additional research is necessary, the MVP may be associated with reduced hospitalizations because caregivers can interact with health care staff (e.g., during vital sign assessment) who can answer questions, address concerns, and refer to outpatient care. Few respite programs examined in the literature reported similar resources for clients. Additional attention to the assessment and management of older adults using similar programs may be warranted to provide an opportunity to intervene and coordinate care. In terms of future work, a team approach to provide social engagement, physical and cognitive activity, mental health preventive services, case management, advocacy, and referrals may also effectively reduce health service utilization in this population.

Limitations

There are several limitations to consider while interpreting the findings from the current study. First, although the small non-probability sample limits the extent to which study findings are generalizable, the cognitive and physical levels of the older Veterans were consistent with some other adult day programs, such as adult day participants in the Program of the All-inclusive Care of the Elderly (PACE). Only older Veterans with consistent measures across variables of interest for 2 years were examined and thus less is known of less frequent users of the MVP. In addition, the current study did not account for chronic conditions, marital status, or caregiver characteristics (e.g., relationship to Veteran) that may be associated with study outcomes.

Conclusion

Despite limitations, findings from the current study address several key areas associated with aging in place for older Veterans. Few mobile programs, such as the MVP, are being modeled across the country, and thus, previous work has not examined health service utilization outcomes in the older Veteran population. Mobile programs have the potential to provide a few hours of care per week to community-based older adults and their caregivers using limited staff. Like their roles in adult day care settings, RNs could also serve as clinicians, health educators, and case managers in a mobile program to support aging in place (Jennings-Sanders, 2004). Such mobile programs may be more cost effective than traditional respite programs. In addition, mobile programs have the potential to reach rural older adults where other programs and services are scarce. The current study, taken together with some extant literature, suggests respite programs with content designed to address cognitive decline and depressive symptoms may reduce health service use in later life.

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Hierarchical Linear Models Examining the Use of Health Services Within the Andersen Model of Health Services Use Framework Among Older Veterans Using a MVP

CharacteristicOdds Ratio [95% Confidence Interval]
InstitutionalizationHospitalization
Model 1Model 2Model 3Model 4Model 5Model 6
Demographic
  Age (years)0.82** [0.71, 0.94]0.79 [0.66, 0.95]0.80 [0.67, 0.96]1.07 [0.96, 1.19]1.04 [0.91, 1.18]1.04 [0.91, 1.20]
Health needs
  Cognitive functioning (SLUMS Examination)0.85 [0.70, 1.03]0.76* [0.60, 0.95]0.73* [0.55, 0.98]1.02 [0.87, 1.20]0.95 [0.81, 1.12]0.94 [0.78, 1.14]
  Physical functioning (Katz ADL Index)0.94 [0.78, 1.14]0.84 [0.67, 1.04]0.75 [0.72, 1.05]1.17 [0.74, 1.86]1.11 [0.74, 1.66]1.12 [0.75, 1.68]
  Depressive symptoms (GDS)1.62* [1.13, 2.34]1.76* [1.12, 2.75]1.34*** [1.15, 1.55]1.40** [1.12, 1.74]
Available resources
  Caregiver burden (ZBI)1.16 [0.71, 1.92]1.22 [0.73, 2.05]0.74* [0.55, 0.99]0.72 [0.51, 1.03]
  Average number of days between MVP visits0.80 [0.41, 1.56]0.92 [0.70, 1.23]
  Deviance statistic
  χ219.2115.9815.7918.9812.3911.63

Demographic Characteristics of Older Veterans Using a MVP at Baseline

Demographic CharacteristicMean (SD) (Range)
Age (years)79.1 (9.4) (55 to 95)
Number of days between MVP visits10.5 (3.4) (6 to 27)
Cognitive functioninga12.5 (6.9) (1 to 28)
Physical functioningb16.8 (2.6) (6 to 18)
Caregiver burdenc7.3 (3.0) (0 to 16)
Depressive symptomsd2.9 (2.9) (0 to 13)
Authors

Dr. Bowen is Associate Professor and Associate Dean of Research, Dr. Gaynor is Assistant Professor, Dr. Phillips is Associate Professor and Jeanne K. Buxbaum Chair of Nursing Science, School of Nursing, and Dr. Orsega-Smith is Associate Professor, Department of Behavioral Health and Nutrition School of Nursing, University of Delaware, Newark, Delaware; Dr. Lavery is Assistant Professor, Graduate Social Work Department, West Chester University, West Chester, Pennsylvania; Dr. Mitchell is Assistant Professor and Faculty Co-Leader of the Jefferson Health Mentors Program, College of Nursing, Thomas Jefferson University, Philadelphia, Pennsylvania; and Dr. Basehore is Research Program Coordinator, Coatesville Veterans Affairs Medical Center, Coatesville, Pennsylvania. Dr. Bowen is also Research Health Scientist, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania.

The authors have disclosed no potential conflicts of interest, financial or otherwise. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the U.S. government.

The authors acknowledge Karen Elechko, MSN, RN, who developed and implemented the Mobile Veterans Program (MVP) that is the subject of this work. The MVP was created with the support of the VA Office of Geriatrics and Extended Care and the Coatesville VA Medical Center.

Address correspondence to Beatrice Gaynor, PhD, APRN, FNP-C, Assistant Professor, School of Nursing, University of Delaware, Tower at STAR, 100 Discovery Boulevard, Newark, DE 19713; e-mail: bgaynor@udel.edu.

Received: June 19, 2019
Accepted: December 03, 2019

10.3928/00989134-20200313-01

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