Identification of Functional,Social,and Health Needs of Older Residents in Nonurban Settings: A Community Risk Assessment

Abstract

Objectives: We examined functional, social, and health needs and service delivery gaps among a geographically expansive and economically diverse population of older adults to identify service priorities. Methods: We conducted analyses based on 1280 respondents aged 60 years and older surveyed for an 8-county needs assessment. Results: We identified three distinct risk profiles using two-step cluster analysis. ANOVA and chi-square analyses revealed significant differences between risk profiles on a range of health and social service needs. Discussion: Identifying high-risk residents can support community providers’ efforts to help older adults age in place. Community-based organizations are well-positioned to respond early to social determinants of health needs. Targeting services and supports that reduce risk, extend independence, and prevent avoidable adverse healthcare situations to improve health and achieve health equity among historically underserved and hard-to-reach populations and prioritizing low-cost community interventions can reduce the need for high-cost interventions (e.g., emergency care and hospitalizations).

Keywords

rural aging social factors health services

Increased life expectancy and a rapidly growing aging population have the potential to place significant stress on both aging services organizations and healthcare systems. Although most individuals prefer to live independently in their homes, they may need regular evaluation and outside support to age in place safely. Aging in place enables older people to maintain independence, autonomy, and connection to social support and helps to avoid or prolong the need for high-cost institutional care (Wiles et al., 2012). Efforts to age in place have been hindered by older adults’ experiences with unmet or under-identified needs, specifically, inadequate informal assistance from family/friend caregivers (Davey et al., 2013; Kaye et al., 2010) or formal services provided by community health agencies (Casado et al., 2011; Pass et al., 2019). Individuals with advanced age, high level of disability, multiple difficulties with basic and instrumental activities of daily living (ADLs), cognitive impairment, and low socioeconomic status are more likely to experience unmet needs in their daily routine (Davey et al., 2013).

Compared to other industrialized counties, the United States (U.S.) spends a higher percentage of gross domestic product on care for older adults (Anderson & Hussey, 2000; Sawyer & Cox, 2018). A recent report from the Organization for Economic Cooperation and Development (OECD) revealed that the United States spends more on health care than any other country, spending nearly twice as much as the average OECD country (i.e., comprised 36 high-income member countries; Tikkanen & Abrams, 2020). Nonetheless, Americans experience worse health outcomes and lower life expectancy at birth than the OECD average. Compared to peer nations, the United States had the highest chronic disease burden and fewer physicians per capita (Tikkanen & Abrams, 2020). While some preventive measures (e.g., breast cancer screening and flu vaccination) in the United States outperform peer nations, preventable hospitalizations and avoidable deaths are highest in the United States.

In the United States, long-term services and supports (LTSS) are available through a patchwork system that assist individuals with functional limitations and chronic illnesses to perform daily ADLs and are attributed to individuals’ ability to live independently. Typically, this system includes support from family and friends and use of private resources to spend down for Medicaid eligibility. With the increasing out-of-pocket cost for LTSS (American Medical Association, 2018) and Medicaid being the primary LTSS payer for individuals who meet functional limitation and financial criteria (Reaves & Musumeci, 2015), there is a gap in supportive assistance services for individuals who require some level of assistance but are not yet Medicaid eligible. That is, the cost burden associated with LTSS leaves the oldest and sickest least likely to have sufficient income to afford LTSS (Johnson & Wang, 2019). Individuals who perceive a need for assistance but do not meet functional and financial eligibility criteria tend to be overlooked.

Nonmedical factors or social determinants of health (SDoH) like individuals’ behaviors, supports, and environment encountered outside the healthcare setting are directly and substantially related to community health outcomes (Hood et al., 2016). Thus, nonclinical community-based agencies that support individuals’ efforts to age in place also provide a unique contribution to the LTSS system. These agencies can accelerate health improvement through a range of strategies, such as increasing access to healthy foods, social interaction, adequate education, and affordable quality housing (Thornton et al., 2016). These efforts contribute to individuals’ health and well-being.

LTSS in Nonurban Settings

There are additional challenges with receiving adequate LTSS among underserved or hard-to-reach populations, including individuals living in rural and/or remote areas. Individuals residing in rural areas are disproportionately older, poorer, and less healthy (Hash et al., 2015) than individuals living in urban areas. Rural-dwelling individuals are also typically characterized as being independent, maintaining close relationships with family and friends (Cohen, 2013), and, thus, often hesitant to ask for or accept help from others (Carson & Mattingly, 2018). In rural regions of the United States, issues with accessing LTSS, continuity of care, and efficiency of healthcare delivery (Menec et al., 2015) can also exacerbate health disparities. Rural-dwelling individuals had a greater risk of chronic disease and disability (Coburn et al., 2016), were less likely to receive services and had increased risk of mortality (Hoffman & Holmes, 2017; Weaver & Roberto, 2019) than urban-dwelling individuals.

While there is no standard definition for what constitutes “rural,” government agencies have moved beyond the basic binary classification. For example, counties are designated as metropolitan (urban), micropolitan (rural), or neither (rural) by the Office of Management and Budget (Myers et al., 2016). However, the National Center for Health Statistics (NCHS) classifies counties using a 6-point urban–rural continuum indicating the most urban (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) to the most rural (micropolitan and noncore; Ingram & Franco, 2014). Going beyond the binary classification reveals nuances within the “rural” or “urban” categorization, which in turn can inform community-based strategies to meet the needs of older residents. This is particularly important because a disproportionate share of the aging population growth is occurring in nonurban areas (Lang, 2013). To meet the SDoH needs among underserved populations, including low-income older adults in nonurban areas, it is imperative to examine how the community and social environment influences individuals’ actual and perceived health.

Study Purpose

To identify health needs and service delivery gaps among a geographically expansive and economically diverse 8-county region across Eastern Washington and North Central Idaho, a needs assessment was conducted to assist community-based agencies working to support aging residents living in rural-leaning counties according to the NCHS guidelines (i.e., small metropolitan, micropolitan, and noncore). The community survey was adapted from a previously implemented, federally funded standardized survey (see Bittinger, 2015). Using cross-sectional survey data, the purpose of this study was to answer the following research questions (RQs):

RQ1: How do older adults’ functional health needs, social needs, and health service use differ by county classification (i.e., small metropolitan, micropolitan, and noncore)?

RQ2: Are there distinguishable groups of older adults based on enabling and need-based characteristics?

RQ3: How do groups differ by sociodeomographic variables?

RQ4: How do groups differ on functional health needs, social needs, and health service use?

Methods

Data for this study are based on a community survey designed to evaluate individual and household characteristics and needs related to employment, transportation, financial circumstances, healthcare access, health conditions, food security, and housing stability. Selected households were drawn randomly from USPS records based on a sample frame of 8419 across this 8-county region, including hard to reach populations, which enhances the generalizability and representativeness of conclusions regarding collective needs. Both online and printed survey options were provided, and after a maximum of four contact attempts, 2487 households completed the survey, yielding a response rate of 32% (providing a calculated sample error of ±1%, indicating high reliability of population estimates (see Burley, 2019 for more information). One adult in each household was asked directly about income, employment, community engagement, health conditions, and other needs. On average, respondents were 59 years old (SD = 17; range 18–98) and the majority identified as white/Caucasian (n = 2237, 91.3%). More than half were female (57.4%) and nearly one in five respondents reported a household income less than 25,000 (n = 426; 18.2%), demonstrating financial need.

Sample

The study sample for the present analysis consisted of 1280 adults aged 60 years and older (51% of the overall random sample). We focused on respondents with a minimum age of 60 years because this is the typical minimum age of eligibility for home- and community-based services through the Older Americans Act (Administration on Aging, 2017).

Variables

Variables selected for this analysis were based on the literature related to aging in place and the healthcare utilization framework first proposed by Andersen (1995). The framework posits that predisposing characteristics (e.g., demographic characteristics, social structures, and health beliefs), enabling resources (e.g., personal/family and community), and need-based factors (both perceived and evaluated) influence health behavior and use of health services, which ultimately influence health outcomes (Andersen, 1995). Predisposing characteristics included sex (binary) and age (continuous). Enabling resources included income, education level, rurality, and living situation (continuous). Income was measured at five levels, from less than 25,000 to more than 100,000. Education level was measured by six categories including no response, less than high school, high school graduation/GRE, vocational/technical school, two-year degree, and four-year degree or more. We used NCHS county classification to reflect increasingly rural areas of residence (small metropolitan = 1, micropolitan = 2, and noncore = 3). Respondents indicated the number of persons in the household at specific ages, which we computed into a living situation variable, excluding respondent; this variable served as a proxy for potential caregiver. Respondents reported information at the individual-level, except for household-level income.

We included several functional health and social need-based factors in our analyses. Functional health needs included number of chronic conditions and number ADL limitations. Without the inclusion of a validated scale in the community needs assessment survey, we created a socioemotional engagement index as an indicator of social need. This was computed based on responses to individual-level frequency (never = 0, daily = 1, weekly = 2, monthly = 3, a few times a year = 4, and annually = 5) of engaging in nine specific socioemotional activities (range 0–45). The specific activities were going to a movie, sporting event, concert, or museum; volunteering for a community organization; participating in community events; attending religious services; following what local government is doing (through newspapers, TV, etc.); going to a library; going to a place to exercise; visiting public parks and trails; and taking time off other than scheduled days off. Higher scores indicated greater levels of socioemotional engagement.

Respondents also indicated household-level of need for SDoH services. We created a sum score to show the total number of reported SDoH needs (range 0–15). There was no validated scale included that captured SDoH needs, so we also created a SDoH needs index based on responses about the level of need (none = 0, slight = 1, moderate = 2, or strong = 3; range 0–45). Higher scores indicated greater need. The specific need categories included: mortgage/rental assistance, help with utility bills, legal help, getting enough food, affordable medical care, affordable dental care, mental health services/family counseling, drug/alcohol treatment and/or counseling, family violence advocacy/treatment/counseling, transportation, help finding a job/job training, access to library system, affordable housing, emergency housing, and caregiver support.

Respondents reported perceived quality of life in the community (QOL) and health service use at the individual or household level. Respondents were asked to rate overall QOL in their community, which was recoded to indicate poor/fair or excellent/good QOL. This single-item indicator has been included in various community needs assessments, including the Canada’s most extensive community-driven data project (Community Foundations of Canada, n.d.). As for health service use, respondents reported whether they experienced access issues with health care over the past 12 months, which we coded to indicate yes or no (binary). Specific aspects of healthcare access included being unable to get in to see a physician due to at least one reason (e.g., inability to pay for services, no transportation/too far, and no insurance), experiencing barrier(s) to see a primary care physician, having visited the emergency department (ED) for nonemergency care, or having experienced an unplanned hospitalization.

As secondary outcomes of interest (i.e., post hoc analyses), we examined specific SDoH-related social and health service needs (described above), as well as nutritional assistance received. Reported nutritional assistance included meals on wheels, senior center meals, SNAP benefits church meals, foodbank/pantry, or reliance on friends/family. We coded variables as a binary indicator of reported need/assistance.

Data Analysis

We conducted descriptive analyses to characterize the study sample. To understand differences by county classification (i.e., small metropolitan, micropolitan, and noncore) in functional health needs, social needs, and health service use, we conducted a series of one-way ANOVAs for continuous outcomes and chi-square analyses for categorical outcomes (RQ1).

Next, we conducted two-step cluster analysis, which is an exploratory tool to identify natural groupings with a dataset. The two-step cluster analysis procedure handles categorical and continuous variables and maximizes distance between clusters (heterogeneity) while rewarding parsimonious, simple groupings (Sarstedt & Mooi, 2019). The analysis yielded three clusters based on enabling (living situation), functional health need (ADLs), and social need (socioemotional engagement index) variables (RQ2). According to Andersen (1995) healthcare utilization framework, need is a predominant factor associated with health outcomes. We considered living situation as a proxy indicator for having a possible caregiver, thereby highlighting another potential context of need. Thus, the particular measures that inform cluster development were derived from minimally intrusive questions. Using minimally intrusive questions is critical, especially when working with individuals living in rural areas, as there are unique privacy and confidentiality concerns (Carson & Mattingly, 2018). Although using information about income would be preferable, individuals are more likely to provide invalid responses (e.g., “do not know” or “prefer not to answer”) to questions about income (Bee & Mitchell, 2017). We selected these need-based measures because residents are more likely to provide forthcoming information about their current situation. Furthermore, we intentionally included individual-level variables to inform risk profiles rather than environmental context (i.e., rural classification) in an effort to identify high risk or vulnerable populations in need of targeted intervention, regardless of geographic setting.

Based on group membership from cluster analysis, we conducted a series of one-way ANOVA for continuous outcomes and chi-square analyses for categorical outcomes to examine how groups differed by sociodemographic variables (RQ3) and level of functional health needs, social needs, and health service use (RQ4). We used IBM SPSS Statistics version 23 to conduct analyses. This study was determined as exempt by the institutional review board.

Results

Sample Characteristics

Overall, our sample of 1280 respondents ranged in age from 60 to 98 (M = 71.0). The majority of them were white, female, retired, and reported at least some college education. The majority of respondents lived with at least one person, with nearly one-third of respondents living alone. Most individuals owned their place of residence, which for the majority of respondents was a house. Respondents generally perceived their housing situation as very stable and secure. Overall, perceived QOL in the community was good or excellent. It was most common to experience delayed or canceled dental procedures, unplanned hospitalizations, or nonemergent ED care. See Table 1.

Table 1.

Descriptive Characteristics of Community Residents 60 Years and Older (N = 1280).

Variable	M (SD)
Age, years	71.0 (7.6)
# of ADL limitations (range 0–5)	0.5 (1.1)
# of chronic conditions (range 0–8)	1.3 (1.3)
# of SDoH needs (range 0–15)	1.2 (2.0)
SDoH needs index (range 0–45)	2.4 (4.2)
Socioemotional engagement index (range 0–45)	16.1 (7.5)
Years living in community (range 0–98)	33.1 (22.2)
Living situation1 (range 0–3)	0.8 (.6)

	n (%)
Sex
Female	717 (56.2)
Race
White	1227 (95.9)
Education level
≤High school/GED	344 (26.9)
Some college/2-year degree	406 (31.7)
≥4-year degree	469 (36.6)
Income
Less than $25,000	228 (17.8)
$25,000–$50,000	381 (29.8)
$50,000–$75,000	274 (21.4)
$75,000–$100,000	186 (14.5)
>$100,000	211 (16.5)
Employment status
Full time	176 (13.8)
Part-time/seeking	53 (4.1)
Retired	1017 (79.5)
Rural–urban classification (NCHS)
Small metropolitan	671 (52.4)
Micropolitan	356 (27.8)
Noncore	253 (19.8)
Housing type
House	1041 (81.3)
Multiplex2	92 (7.2)
Manufactured home	122 (9.5)
Other (e.g., with friends)	16 (1.3)
Housing status
Own	1121 (87.6)
Rent	147 (11.5)
Housing stability
Very stable/secure	965 (75.4)
Fairly stable/secure	263 (20.5)
Unstable/insecure	26 (2.0)
Not sure	19 (1.5)
Insurance coverage
Private, Medicare, or Medicaid	1248 (97.5)
Quality of life of community
Good/excellent	1064 (83.1)
Very poor/fair	182 (14.2)
Healthcare situations
Delay/cancelation of dental procedure	197 (15.4)
Delay filling a prescription	90 (7.0)
No access to preventive care	54 (4.2)
Barriers to see a physician	267 (20.9)
Nonemergent ED visit	140 (10.9)
Unplanned hospitalization	150 (11.7)

Note. Total N (%) may not add up due to missing data; ED = emergency department; ADL = activities of daily living; NCHS = National Center for Health Statistics; SDoH = social determinants of health.

Respondents were asked how many other people they lived with.

Apartment, condo, or multiplex living situation.

Although all respondents resided in rural-leaning counties, we conducted a series of analyses to identify differences by county classification (i.e., small metropolitan, micropolitan, and noncore) in functional health needs, social needs, and health service use to answer our first RQ. Individuals in small metropolitan counties were older than individuals in micropolitan or noncore counties (F = 6.043 and p = .002). There were differences in the level of education (X² = 40.01 and p < .001), with individuals in noncore counties were more likely to indicate their highest level of education as high school/GED or less than individuals in micropolitan or small metropolitan counties. Similarly, there were differences by income (X² = 45.63, p < .001) indicating that individuals in noncore counties were more likely to have an income of less than $25,000 (and less likely to report higher income brackets) than individuals in small metropolitan or micropolitan counties. As for the socioemotional engagement index, individuals in noncore counties had lower socioemotional engagement than individuals in small metropolitan and micropolitan counties (F = 7.61, p ≤ .002). Individuals in small metropolitan counties had more functional health conditions than individuals in micropolitan counties (F = 4.529, p = .011). Individuals in small metropolitan counties were more likely to access nonemergency ER care than individuals in micropolitan and noncore counties (X² = 13.79, p = .001). However, individuals in noncore counties were more likely to indicate being unable to access preventative care than individuals in small metropolitan areas (X² = 6.57, p = .037) (Full results are available upon request from the first author.)

Cluster Membership

We used cluster analysis to answer our second RQ. Cluster analysis distinguished three distinct groups of residents with similar characteristics in relation to ADL limitations, living situation, and level of socioemotional engagement (cluster size ranged from 164 to 769). Cluster quality was .6, which is considered good quality, reflecting reasonable partitioning of data (Kaufman & Rousseeuw, 1990). As shown in Table 2, the three key variables distinguished three significantly different risk profiles (all at p < .001). It is common practice to label clusters (Farber et al., 2011) with descriptive labels that capture the essence of each groups’ phenomenon. Group members in Cluster 1 (13%) were resource poor/functionally vulnerable (i.e., high risk). They had significant functional limitations (as determined by ADL limitations) and were commonly living with others (i.e., a potential caregiver), which would help offset their limitations. However, this group was also characterized by low socioemotional engagement.

Table 2.

Sociodemographic Characteristics that Distinguish Cluster Membership.

Variable	Resource poor/functionally vulnerable	Income constrained/maintaining independence	Adequate resources/functionally healthy	F value	p value
	(N = 164)	(N = 347)	(N = 769)
	M (SD)	M (SD)	M (SD)
Living situation1 (range 0–3)	0.8 (0.7)	0 (0.0)	1.14 (0.4)	997.89	<.001
Socioemotional engagement index (range 0–45)	11.5 (6.5)	15.7 (7.8)	17.3 (7.2)	42.77	<.001
# of ADL limitations (range 0–5)	2.8 (1.2)	0.2 (0.5)	0.1 (0.3)	1830.19	<.001

Note. Each cluster is significantly different from the other clusters by each variable; ADL = activities of daily living.

Respondents were asked how many other people they lived with.

Group members in Cluster 2 (27%) were income constrained/maintaining independence (i.e., moderate risk). They had few functional limitations, but were living entirely alone, with no potential caregiver and reported modest socioemotional engagement.

Group members in Cluster 3 (60%) were considered adequate resources/functionally healthy (i.e. low risk). This group had the fewest functional limitations, were typically living with others (i.e., a potential caregiver), and reported high socioemotional engagement.

Next, we considered how each risk profile differed by sociodemographic variables (i.e., third RQ). Resource poor/functionally vulnerable and income constrained/maintaining independence members were significantly older than their adequate resources/functionally healthy counterparts. Income constrained/maintaining independence members were more likely to be female than their resource poor/functionally vulnerable and adequate resources/functionally healthy counterparts. Income level was varied across groups, but resource poor/functionally vulnerable members were significantly more likely to report <$25,000 than adequate resources/functionally healthy members. Resource poor/functionally vulnerable members were more likely to have Medicaid and Medicare than their income constrained/maintaining independence and adequate resources/functionally healthy counterparts; adequate resources/functionally healthy members were significantly more likely to have coverage through private employers than the other risk profiles. Regarding current housing situations, resource poor/functionally vulnerable and income constrained/maintaining independence members were less likely to be living in a house and more likely to be living in a multiplex living situation, manufactured home, or other housing situation than their adequate resources/functionally healthy counterparts. Although there were differences in characteristics between persons by county classification, rural–urban classification (NCHS) was not significantly associated with group membership. See Table 3.

Table 3.

Group Differences in Sociodemographic Variables.

Variable	Resource poor/functionally vulnerable	Income constrained/maintaining independence	Adequate resources/functionally healthy	F value	p value
	(N = 164)	(N = 347)	(N = 769)
	M (SD)	M (SD)	M (SD)
Age, years (range 60–98)	72.9 (8.7)^a	72.5 (8.2)^a	69.9 (6.8)^b	20.87	<.001

	n (%)	n (%)	n (%)	X ²	p value
Sex				15.76	<.001
Female	92 (55.8)^a,b	224 (65.1)^b	402 (52.3)^a
Income				203.18	<.001
< $25,000	56 (34.1)^a	114 (32.9)^a	58 (7.5)^b
$25,000–$50,000	49 (29.9)^a.b	131 (37.8)^b	201 (26.1)^a
$50,000–$75,000	23 (14.0)^a	54 (15.6)^a	197 (25.6)^b
$75,000–$100,000	13 (8.9)^a	25 (7.2)^a	148 (19.2)^b
> $100,000	23 (14.0)^a	23 (6.6)^b	165 (21.5)^a
Education level				20.47	<.001
≤High school/GED	57 (38.0)^a	107 (33.1)^a	180 (24.1)^b
Some college/2-year degree	52 (34.7)^a	99 (30.7)^a	255 (34.2)^a
≥4-year degree	41 (27.3)^a	117 (36.2)^a,b	311 (41.7)^b
Insurance coverage
Medicaid	30 (18.3)^a	32 (9.2)^b	81 (10.5)^b	10.03	.007
Medicare	141 (86.0)^a	267 (76.9)^b	597 (77.6)^b	6.27	.043
Private insurance	79 (48.2)^a	169 (48.7)^a	499 (64.9)^b	33.81	<.001
Rural–urban classification (NCHS)				2.23	.694
Small metropolitan	82 (50.0)	191 (55.0)	398 (51.8)
Micropolitan	46 (28.0)	88 (25.4)	222 (28.9)
Noncore	36 (22.0)	68 (19.6)	149 (19.4)
Current housing				84.97	<.001
House	107 (66.0)^a	248 (72.1)^a	686 (89.7)^b
Multiplex1	23 (14.2)^a	45 (13.1)^a	24 (3.1)^b
Manufactured home	28 (17.3)^a	42 (12.2)^a	52 (6.8)^b
Other (e.g., with friends)	4 (2.5)^a	9 (2.6)^a	3 (<1)^b

Note. ^a significantly different than ^b; NCHS = National Center for Health Statistics.

Apartment, condo, or multiplex living situation.

We examined how groups differ on their social needs, health, and health service use to answer our fourth RQ. We found that groups differed significantly on the number of chronic conditions, number of SDoH needs, and the SDoH needs index. The resource poor/functionally vulnerable group indicated the greatest number of health/social needs and number of chronic conditions and has the highest need index compared to the adequate resources/functionally healthy and income constrained/maintaining independence groups.

Resource poor/functionally vulnerable respondents were significantly more likely to delay or cancel a dental procedure due to inability to pay, delay filling a prescription, experience barriers to seeing a physician, use the ED for nonemergent care, and experience an unplanned hospital visit because usual care was unavailable than the other respondent groups. Resource poor/functionally vulnerable respondents were significantly less likely to have access to preventive care than income constrained/maintaining independence respondents, too. As for QOL in the community, the majority of respondents reported excellent/good QOL. However, resource poor/functionally vulnerable respondents were less likely to report excellent/good QOL than their adequate resources/functionally healthy counterparts. See Table 4.

Table 4.

Group Differences in Functional Health Needs, Social Needs, and Health Service Use.

Variable	Resource poor/functionally vulnerable	Income constrained/maintaining independence	Adequate resource/functionally healthy	F value	p value
	(N = 164)	(N = 347)	(N = 769)
	M (SD)	M (SD)	M (SD)
# of chronic conditions (range 0–8)	2.3 (1.4)^a	1.2 (1.1)^b	1.1 (1.2)^b	73.90	<.001
# of SDoH needs (range 0–15)	2.6 (2.7)^a	1.1 (1.7)^b	1.1 (1.78)^b	47.02	<.001
SDoH needs index (range 0–45)	5.0 (6.1)^a	1.9 (3.4)^b	2.0 (3.9)^b	38.65	<.001

	n (%)	n (%)	n (%)	X ²	p value
Delay/cancelation of dental procedure¹	59 (36.0)^a	41 (11.8)^b	97 (12.6)^b	61.33	<.001
Delay filling a prescription¹	26 (15.9)^a	17 (4.9)^b	47 (6.1)^b	22.94	<.001
No access to preventive care¹	13 (7.9)^a	10 (2.9)^b	31 (4.0)^a,b	7.18	.028
Barriers to seeing a physician¹	62 (37.8)^a	71 (20.4)^b	134 (17.4)^b	34.05	<.001
Nonemergent ED visit¹	35 (21.3)^a	26 (7.5)^b	79 (10.3)^b	22.80	<.001
Unplanned hospitalization1	33 (20.1)^a	30 (8.6)^b	87 (11.3)^b	14.48	.001
QOL in community (excellent/good)	120 (75.5)^a	282 (84.2)^a,b	662 (88.0)^b	17.14	<.001

Note. ^a significantly different than ^b; SDoH = social determinants of health; ED = emergency department; QOL = quality of life.

Occurrence of health situations within the last year.

To understand group differences further, we conducted chi-square analyses to evaluate the extent of specific social and health needs and nutritional assistance received (see Table 5). In general, resource poor/functionally vulnerable respondents were significantly more likely to report using nutritional assistance and needing all types of social and health services (p ≤ .01) than income constrained/maintaining independence and adequate resources/functionally healthy respondents. For several social needs (affordable housing, mortgage/rent assistance, utility bill assistance, and transportation) and nutritional assistance (foodbank pantry and friends/family reliance), all three groups differed significantly from each other, distinguishing low, moderate, and high-risk profiles for needing specific services.

Table 5.

Group Differences in SDoH Needs and Nutritional Assistance.

Service type	Resource poor/functionally vulnerable	Income constrained/maintaining independence	Adequate resources/functionally healthy	X ²	p value
	(N = 164)	(N = 347)	(N = 769)
	n (%)	n (%)	n (%)
Social needs
Affordable housing	25 (17.4)^a	22 (7.2)^b	30 (4.1)^c	34.52	<.001
Mortgage/rent assistance	27 (3.2)^a	25 (7.9)^b	28 (3.9)^c	42.42	<.001
Utility bill assistance	40 (27.2)^a	32 (10.2)^b	40 (5.5)^c	67.78	<.001
Transportation	40 (27.2)^a	21 (6.8)^b	23 (3.2)^c	106.58	<.001
Library access	10 (7.1)^a	18 (5.9)^a	47 (6.5)^a	0.27	.873
Legal assistance	28 (20.1)^a	25 (8.1)^b	65 (9.0)^b	17.69	<.001
Job search/training	12 (8.6)^a	7 (2.3)^b	31 (4.3)^b	9.27	.010
Health needs
Affordable medical care	63 (44.4)^a	62 (20.3)^b	167 (23.0)^b	33.65	<.001
Affordable dental care	80 (53.3)^a	91 (29.0)^b	222 (30.5)^b	32.51	<.001
Enough food	31 (21.2)^a	22 (7.1)^b	34 (4.7)^b	49.17	<.001
Caregiver support	38 (26.6)^a	19 (6.3)^b	38 (5.3)^b	73.76	<.001
Mental health/counseling	24 (16.9)^a	11 (3.6)^b	52 (7.2)^b	25.15	<.001
Nutritional assistance
Church meals	11 (7.2)^a	7 (2.2)^b	19 (2.5)^b	10.69	.005
Foodbank/pantry	33 (21.0)^a	23 (6.9)^b	25 (3.3)^c	67.14	<.001
Friends/family reliance	24 (15.9)^a	22 (6.8)^b	27 (3.6)^c	34.65	<.001
Meals on wheels	8 (5.2)^a	5 (1.5)^b	8 (1.1)^b	13.09	.001
Senior center meals	16 (10.5)^a	32 (9.6)^a	33 (4.4)^b	15.04	.001
SNAP benefits	11 (7.2)^a	9 (2.7)^b	11 (1.5)^b	17.09	<.001

Note. ^a significantly different than ^b; percentage is based on unique sample size due to missing data, which varied for each service, ranging from 1162 to 1244 out of a total 1280 respondents. SDoH = social determinants of health.

Discussion

To understand community-driven efforts to support aging in place across eight rural-leaning counties, we conducted analyses on survey data from respondents who were aged 60 years and older. Overall, the sample was predominately white, older, female, and college educated. The majority of respondents were retired and reported a stable/secure housing situation and excellent/good QOL in the community. The overall study sample was a relatively healthy population, reporting few functional health limitations, about one chronic condition, one SDoH need, and less than one ADL limitation. Although individuals may underreport their income (e.g., Bee & Mitchell, 2017), one in five respondents reported a household income less than 25,000. This is nearly double the national poverty rate for adults between the ages of 18–64 years and 65 years and older (Semega et al., 2019). Regarding living situation, the average respondent lived with 0.8 other people; according to the Pew Research Center, the average person who is 60 years and older lives with 2.1 people (Ausubel, 2020). Similarly, we know that the likelihood of living alone increases with age, and among our sample of respondents aged 60 years and older, more than one-third lived alone; this is higher than the national average (26%; Ausubel, 2020). These findings demonstrate the cumulative disadvantage of individuals who were resource poor, whether financially, socially, or both, which increases risk of experiencing adverse healthcare situations. This also raises concern about the risks associated with having unmet care needs (Pass et al., 2019; Weaver & Roberto, 2019) and experiencing social isolation; the deleterious effects of loneliness (Hawkley et al., 2019) include decline in functional status (Shankar et al., 2017).

Moving beyond the average, cluster analysis revealed three distinct subpopulations of residents with low, moderate, or high risk for having functional, social, and health needs. Aptly named, the resource poor/functionally vulnerable group was comprised individuals reporting the most serious financial constraints and functional challenges. Their likelihood of experiencing various health situations was greater than that of individuals in the income constrained/maintaining independence or adequate resources/functionally healthy groups. More specifically, the resource poor/functionally vulnerable group was significantly more likely to report having functional limitations and social and health needs, and they were more likely to report using nutritional assistance. All three groups differed significantly from each other for several social needs (i.e., affordable housing, mortgage/rent assistance, utility bill assistance, and transportation) and regarding nutritional assistance (i.e., foodbank pantry and friends/family reliance), demonstrating clear differentiation in risk profiles.

Likelihood of experiencing interruptions in health services also differed by risk profile. Resource poor/functionally vulnerable respondents were more likely to delay or cancel a dental procedure, delay filling a prescription, experience barriers to seeing a physician, visit the ED for a nonemergent issue, and have an unplanned hospital visit because usual care was not available than members of the other groups. In general, few respondents (4.2%) reported that they were unable to access to preventive care; however, there was a significant difference between resource poor/functionally vulnerable and income constrained/maintaining independence respondents. Helping this group access regular, preventive care would be another cost saving opportunity (Musich et al., 2016). Overall, the majority of respondents reported good/excellent QOL in the community, still a significant difference was found between resource poor/functionally vulnerable respondents less likely to report good/excellent QOL than adequate resources/functionally healthy members.

We derive two key implications from these findings. First, it is essential to look beyond averages in the total population to understand subpopulations at risk of adverse healthcare experiences while aging in place. For example, the overall sample reported much better outcomes on various health indicators (e.g., number of chronic conditions, ADL limitations, and SDoH needs; severity of SDoH needs index; and socioemotional engagement index) than the resource poor/functionally vulnerable group in this study. There were significant differences by group membership in functional limitations, SDoH needs, use of nutritional assistance, and risk of adverse healthcare experiences including barriers to accessing primary care, ED visit for nonemergent issues, and unplanned hospitalizations. When considering household economic circumstances, resource poor/functionally vulnerable respondents were significantly more likely to report being in a lower income bracket, more likely to have Medicaid, and less likely to have private insurance than their healthier counterparts. Medicaid is the primary payer for LTSS for individuals who meet functional and financial criteria (Reaves & Musumeci, 2015). However, older adults proactively seeking help for ongoing care needs may not qualify for Medicaid-funded LTSS if their functional and financial needs are not dire enough. In one study, individuals who met financial, but not functional, eligibility standards for assistance were at an increased risk of mortality (Weaver & Roberto, 2019). Thus, ensuring services and supports are available for individuals who are proactive in addressing care needs before they become severe (i.e., anticipating future care needs) is an important cost saving measure.

The second implication considers the importance of addressing SDoH as a clinical prevention measure and the suitability for community-based organizations to serve in a preventative role. Individuals with fewer resources and household supports (i.e., resource poor/functionally vulnerable) were more likely to report costly and acute care encounters than their more fortunate counterparts. The resource poor/functionally vulnerable group was also more likely to experience SDoH needs and rely on nutritional assistance, such as food banks. Community-based service organizations are well-positioned to identify and address both social and emerging health needs through regular engagement with community residents. Findings point to the value of identifying residents at risk or near risk, so community agencies and healthcare providers can engage with older adults early to prevent avoidable health concerns and support efforts to age in place.

Future Directions

To prevent or delay adverse health outcomes, it may be more cost-effective to encourage community-based efforts from social service agencies, rather than solely relying on clinical interventions. This study demonstrates the value of identifying distinct subpopulations of older adults that experience greater health and social risks. However, it is not feasible or sustainable to ask individuals to answer a 49-item survey to assess risk, like the survey from which the data come. Rather, it would be more appropriate to distill the survey down to a brief screening assessment and implement at various sites in the community. Based on study findings, we suggest developing a three-factor short screener to pilot in conjunction with community-based agencies. A screener tool would allow nonclinicians to quickly capture informative data (i.e., living situation, ADL limitations and extent of socioemotional engagement) to distinguish subpopulations of risk. Adding questions about Medicaid and Medicare insurance coverage could also serve as a proxy to identify low income, older, or health-compromised individuals. Senior centers, libraries, and churches may be an appropriate screening site for older adults seeking connection and support.

There are some limitations to address in future iterations of this type of research. Regarding study design, a random sample of households received the survey and one individual responded. Questions asked about both individual- and household-level need, making it difficult to attribute need to the individual or the household. To account for this, we excluded responses from individuals younger than 60 years and assumed that household-level need captured individual-level need. The survey included several nonvalidated scales that we used to generate indices (i.e., socioemotional engagement and SDoH needs) foundation to our study. The community survey was not representative of the United States population of older adults in terms of race and ethnicity, thus limiting generalizability of findings. However, findings are relevant for underserved aging populations in nonurban areas and offer insight about identifying high-risk populations and targeting appropriate, accessible, and affordable services and supports. Ensuring racial and ethnic representation is necessary to determine whether findings are replicable and to further understand cultural nuances (e.g., expectations for family support/structure). This study represents a point in time recall of events, rather than longitudinal accounting of risk factors. Findings suggest associations, not causal relationships between need, service use, and adverse healthcare situations. We cannot state with certainty the direction of relationships uncovered. Thus, future research is warranted to examine the complex interplay among individuals’ health and SDoH needs, attitudes about, access to, use of community-based services, and long-term outcomes; this would illuminate potential barriers to using existing local support services and investigate residents’ readiness and willingness to accept help.

Conclusion

Identifying and comparing subpopulations of older adults reinforces the importance of looking beyond average health outcomes to design and deliver individualized services and supports that address SDoH needs, which affect a wide range of health risks and outcomes. We identified three risk profiles (adequate resources/functionally healthy, income constrained/maintaining independence, and resource poor/functionally vulnerable) with significantly different likelihood of experiencing adverse healthcare situations. These classifications can aid program planners and service providers in how they target and tailor more intensive services for older adults aging in place. Since community-based organizations are well positioned to intervene early with approaches that target social, economic, and environmental factors (Fraze et al., 2016; Marmot et al., 2008), efforts will promote health equity and contribute to individuals’ ability to age in place safely in their home and community. That is, low-cost community interventions (i.e., assessing residents for services in regularly attended locations) should be emphasized to extend the ability of older residents to live independently and reduce the need for high-cost interventions (e.g., emergency care and hospitalizations). Researchers need to collaborate with healthcare and social service providers to implement rigorous, evidence-based research and assess the effectiveness of interventions and programs supporting community living to demonstrate the system-wide “return on investment.” Health expenditures for individuals aged 55 years and older accounted for 56% of the total health spending in 2016 (Sawyer & Claxton, 2019), while older adults comprise only 29% of the total population. Thus, from societal and economic perspectives, it is vital that community service organizations and health providers work collaboratively to target services and supports that reduce risk, extend independence, reduce costs, and prevent avoidable adverse healthcare situations.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research of this article: This work was supported by the Lewis-Clark Valley Healthcare Foundation, with additional financial support from Idaho Community Foundation, Avista Foundation, and Premera Social Impact.

ORCID iD

Raven H. Weaver

References

Administration on Aging (2017). Administration on aging. Administration for Community Living. https://acl.gov/about-acl/administration-aging

American Medical Association (2018). AMA adopts policy to address costs of long-term services, supports. https://www.ama-assn.org/press-center/press-releases/ama-adopts-policy-address-costs-long-term-services-supports

Anderson

G. F.

Hussey

P. S.

(2000). Population aging: A comparison among industrialized countries. Health Affairs, 19(3), 191-203. doi:10.1377/hlthaff.19.3.191

Andersen

R. M.

(1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36, 1-10.

Ausubel

(2020). Older people are more likely to live alone in the U.S. than elsewhere in the world. Pew Research Center. https://www.pewresearch.org/fact-tank/2020/03/10/older-people-are-more-likely-to-live-alone-in-the-u-s-than-elsewhere-in-the-world/

Bee

Mitchell

(2017). Do older Americans have more income than we think? U.S. Census Bureau. https://www.census.gov/content/dam/Census/library/working-papers/2017/demo/SEHSD-WP2017-39.pdf.

Bittinger

(2015). Health and social service need in Whitman County: 2015 community needs assessment results. General Report. https://whitmanunited.org/uploads/editor/files/WhtmanCountyNeedsAssessment2015.pdf

Burley

(2019). A region-wide community needs and opportunity assessment community data indicators and strategic philanthropic plan for the Lewis-Clark Valley Healthcare Foundation. Innovia Foundation. https://inlandnorthwestinsights.org/wp-content/uploads/sites/34/2019/12/FinalNAReport-DEC2019.pdf

Carson

J. A.

Mattingly

M. J.

(2018). “We’re all sitting at the same table”: Challenges and strengths in service delivery in two rural New England counties. Social Service Review, 92(3), 401-431. doi:10.1086/699212

10.

Casado

B. L.

van Vulpen

K. S.

Davis

S. L.

(2011). Unmet needs for home and community-based services among frail older Americans and their caregivers. Journal of Aging and Health, 23, 529-553. doi:10.1177/0898264310387132

11.

Coburn

A. F.

Griffin

Thayer

Croll

Ziller

E. C.

(2016). Are rural older adults benefiting from increased state spending on Medicaid home and community-based services. Maine Rural Health Research Center: Research & Policy Brief, 65, 1-10. http://muskie.usm.maine.edu/Publications/rural/Medicaid-Home-Community-Based-Services-Rural.pdf

12.

Cohen

A. L.

(2013). Family assistance and autonomy in the lives of rural elders. Marriage & Family Review, 49, 491-503. doi:10.1080/01494929.2013.771130

13.

Community Foundations of Canada. (n.d.). VitalSigns. https://www.communityfoundations.ca/initiatives/vital-signs/

14.

Davey

Takagi

Sundström

Malmberg

(2013). (In)formal support and unmet needs in the National Long-Term Care survey. Journal of Comparative Family Studies, 44, 437-453. doi:10.3138/jcfs.44.4.437

15.

Farber

Shinkle

Lynott

Fox-Grage

Harrell

(2011). Aging in place: A state survey of livability policies and practices. Retrieved from http://assets.aarp.org/rgcenter/ppi/liv-com/aging-in-place-2011-full.pdf

16.

Fraze

Lewis

V. A.

Rodriguez

H. P.

Fisher

E. S.

(2016). Housing, transportation, and food: How ACOs seek to improve population health by addressing nonmedical needs of patients. Health Affairs, 35, 2109-2115. doi:10.1377/hlthaff.2016.0727

17.

Hash

K. M.

Jurkowski

E. T.

Krout

J. A.

(Eds). (2015). Aging in rural places: Policies, programs, and professional practice. Springer Publishing Company.

18.

Hawkley

L. C.

Wroblewski

Kaiser

Luhmann

Schumm

L. P.

(2019). Are US older adults getting lonelier? Age, period, and cohort differences. Psychology and Aging, 34(8), 1144-1157. doi:10.1037/pag0000365

19.

Hoffman

Holmes

(2017). Regional differences in rural and urban mortality trendsNC Rural Health Research Program. https://www.shepscenter.unc.edu/wp-content/uploads/dlm_uploads/2017/08/Regional-Differences-in-Urban-and-Rural-Mortality-Trends.pdf

20.

Hood

C. M.

Gennuso

K. P.

Swain

G. R.

Catlin

B. B.

(2016). County health rankings: Relationships between determinant factors and health outcomes. American Journal of Preventive Medicine, 50, 129-135. doi:10.1016/j.amepre.2015.08.024

21.

Ingram

D. D.

Franco

S. J.

(2014). 2013 NCHS urban-rural classification scheme for counties. Vital and Health Statistics. Series 2, Data Evaluation and Methods Research, 166, 1-73. https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdf

22.

Johnson

R. W.

Wang

C. X.

(2019). The financial burden of paid home care on older adults: Oldest and sickest are least likely to have enough income. Health Affairs, 38(6), 994-1002. doi:10.1377/hlthaff.2019.00025

23.

Kaufman

Rousseeuw

P. J.

(1990). Finding groups in data: An introduction to cluster analysis. John Wiley & Sons.

24.

Kaye

H. S.

Harrington

LaPlante

M. P.

(2010). Long-term care: Who gets it, who provides it, who pays, and how much? Health Affairs, 29, 11-21. doi:10.1377/hlthaff.2009.0535

25.

Lang

S. S.

(2013). Graying of rural America has policy implications. Cornell Chronicle. http://news.cornell.edu/stories/2013/07/graying-rural-america-has-policy-implications

26.

Marmot

Friel

Bell

Houweling

T. A.

Taylor

, & the Commission on Social Determinants of Health.(2008). Closing the gap in a generation: Health equity through action on the social determinants of health. Lancet, 372(9650), 1661-1669. doi:10.1016/S0140-6736(08)61690-6

27.

Menec

Bell

Novek

Minnigaleeva

G. A.

Morales

Ouma

Parodi

J. F.

Winterton

(2015). Making rural and remote communities more age-friendly: Experts’ perspectives on issues, challenges, and priorities. Journal of Aging & Social Policy, 27, 173-191. doi:10.1080/08959420.2014.995044

28.

Musich

Wang

Hawkins

Klemes

(2016). The impact of personalized preventive care on heatlh care quality, utilization, and expenditures. Population Health Management, 19(6), 389-397. doi:10.1089/pop.2015.0171

29.

Myers

Greiman

von Reichert

Seekins

(2016). Rural matters: The geography of disability in rural America. The University of Montana Rural Institute for Inclusive Communities. http://rtc.ruralinstitute.umt.edu/research-findings/geography/#omb

30.

Pass

L. E.

Kennelty

Carter

B. L.

(2019). Self-identified barriers to rural mental health services in Iowa by older adults with multiple comorbidities: Qualitative interview study. BMJ Open, 9(11), Article. e029976. doi:10.1136/bmjopen-2019-029976

31.

Reaves

E.L.

Musumeci

(2015). Medicaid and long-term services and supports: A primer. Kaiser Family Foundation. https://www.kff.org/medicaid/report/medicaid-and-long-term-services-and-supports-a-primer/

32.

Sarstedt

Mooi

(2019). Cluster analysis. In A concise guide to market research (pp. 301-354). Springer.

33.

Sawyer

Claxton

(2019). How do health expenditures vary across the population? Peterson-KFF Health System Tracker. https://www.healthsystemtracker.org/chart-collection/health-expenditures-vary-across-population/#item-start

34.

Sawyer

Cox

(2018). How does health spending in the U.S. compare to other countries? Peterson-KFF Health System Tracker. https://www.healthsystemtracker.org/chart-collection/health-spending-u-s-compare-countries/#item-start

35.

Semega

Kollar

Creamer

Mohanty

(2019). Income and poverty in the United States: 2018. United States Census Bureau. https://www.census.gov/library/publications/2019/demo/p60-266.html

36.

Shankar

McMunn

Demakakos

Hamer

Steptoe

(2017). Social isolation and loneliness: Prospective associations with functional status in older adults. Health Psychology, 36(2), 179-187. doi:10.1037/hea0000437

37.

Thornton

Glover

Cené

Glik

Henderson

Williams

(2016). Evaluating strategies for reducing health disparities by addressing the social determinants of health. Health Affairs, 35, 1416-1423. doi:10.1377/hlthaff.2015.1357

38.

Tikkanen

Abrams

M. K.

(2020). U.S. health care from a global perspective, 2019: Higher spending, worse outcomes? The Commonwealth Fund. doi:10.26099/7avy-fc29

39.

Weaver

R. H.

Roberto

K. A.

(2019). Effects of long-term services and supports on survival of poor, highly vulnerable older adults. The Gerontologist, 17(59), 936-946. doi:10.1093/geront/gny040

40.

Wiles

J. L.

Leibing

Guberman

Reeve

Allen

R. E. S.

(2012). The meaning of “aging in place” to older people. The Gerontologist, 52, 357-366. doi:10.1093/geront/gnr098