What Explains Differences in Availability of Community Health-Related Services for Seniors in the United States?

Abstract

Objective: This study analyzes the links between planning, the built environment, and availability of health-related community services across U.S. urban and rural communities. Method: We analyze the first national survey of health-related community services for seniors (2010 Maturing of America), covering 1,459 U.S. cities and counties. We tested the influence of morbidity (diabetes and obesity), city management, socioeconomic characteristics, planning and the built environment, metro status, and government finance. Results: Community health-related services are more common in places that plan for and involve seniors in planning processes. Places with higher need and government capacity also show higher levels. Service levels in rural communities are not lower after controlling for other population characteristics. Morbidity measures (diabetes and obesity) do not explain differences in service availability. Discussion: Policies promoting planning for aging and elder involvement in the planning process have the greatest impact on the level of community health-related services for seniors.

Keywords

health services planning elder engagement rural

Introduction

Municipalities across the United States are facing new demand for a broad set of health-related services to meet the needs of an aging population. By 2030, nearly one in five people will be more than 65 years old, and 88% of seniors hope to age in place in their own homes and communities (Keenan, 2010). The ability to age in place is enhanced by services provided at the municipal level to older adults—from exercise, to nutrition, to transportation and health care. Although some cities are adopting “age-friendly” planning in anticipation of the impending demographic shift (World Health Organization, 2007), less attention is given to the challenges faced by rural communities. Age-friendly communities have been defined as those with infrastructure and services that effectively accommodate the changing needs of older adults, both active and frail (Alley, Liebig, Pynoos, Banerjee, & Choi, 2007).

Creating age-friendly communities is made more challenging by high rates of obesity and diabetes among older people. These “twin epidemics” impose huge economic and human costs on society (American Diabetes Association, 2013; Lakdawalla, Goldman, & Shang, 2005). Among adults aged 65 and older, today more than one third are obese (Fakhouri, Ogden, Carroll, Kit, & Flegal, 2012). Nearly 27% have diabetes, compared with 11% of adults overall (Centers for Disease Control and Prevention, 2011), and rates are highest in rural communities (Befort, Nazir, & Perri, 2012). Obese elderly have a poorer quality of life, require far greater medical spending, and experience considerably higher prevalence of disability and disease (Lakdawalla et al., 2005). This includes diabetes, which itself is a leading cause of kidney disease, heart disease, blindness, and stroke, among other serious medical issues (Centers for Disease Control and Prevention, 2011). Medical expenses for people with diabetes are more than two times higher than for people without diabetes, and the risk for death among people with diabetes is about twice that of people of similar age but without diabetes (Centers for Disease Control and Prevention, 2011).

Numerous studies have demonstrated positive relationships between more accessible built environments and physical activity levels (Balfour & Kaplan, 2001; Berke, Koepsell, Moudon, Hoskins, & Larson, 2007; Clarke & George, 2005; Ewing, Schmid, Killingsworth, Zlot, & Raudenbush, 2003; Forsyth, Hearst, Oakes, & Schmitz, 2008; Fuzhong, Fisher, Brownson, & Bosworth, 2004; Giles-Corti, Bull, Knuiman, McCormack, & Van Niel, 2013; Hannon, Sawyer, & Allman, 2012; James et al., 2013). Features like retail and recreation density, walkability to these destinations, mixed land use, and street connectivity may encourage physical activity, improve safety perceptions, and further promote mobility for older adults (McCormack & Shiell, 2011; Yen, Thompson, Anderson, & Wong, 2014). Thus, planning and design create the leverage points, which can promote community health (Wells, Evans, & Yang, 2010). Using measurements of sprawl, Ewing, Meakins, Hamidi, and Nelson (2014) found compact planning design is negatively related to obesity, diabetes, and other chronic diseases at the county scale. For older adults, environmental infrastructure features were found to be significantly associated with obesity (Eisenstein et al., 2011). However, the positive effects of good environmental design on increasing physical activity and reducing obesity are more commonly studied on youth than seniors (Ding & Gebel, 2012). When the effects are examined for elders, the results are inconsistent (Van Cauwenberg et al., 2011). Case studies point to the importance of neighborhood design on physical activities among older adults (e.g., Portland, Oregon, by Michael, Green, & Farquhar, 2006). But meta-analyses of the literature find divergent results. For example, in a systematic review of 31 articles, Van Cauwenberg et al. (2011) found most of the studied environmental characteristics were not related to physical activities for older adults.

Studies specifically focused on rural aging are rare, and yet the proportion of seniors in the rural population is higher than in urban areas (Glasgow & Brown, 2012). For many rural communities with low density, achieving compact, walkable neighborhood design is not realistic or achievable. An array of services must complement the challenges or opportunities presented by the built environment in any community, but in rural and suburban places, an appropriate service overlay is especially important to overcome deficiencies in physical design (Warner, Homsy, & Morken, 2016).

Eisenstein et al. (2011) have pointed out that health services present new opportunities beyond physical activity interventions to help promote health for older adults. From 789 in-person interviews with adults aged 65 and above, they found access to services remained significant to reduce obesity after controlling for other variables. Seniors in neighborhood environments with less health service accessibility and greater barriers are likely to be overweight or obese, regardless of physical characteristics. Other empirical studies also show that community-based prevention programs can successfully prevent or decrease incidence of obesity, diabetes, and related conditions (Garcia, Boufford, & Finkelstein, 2009; Trust for America’s Health, 2009, 2012). However, rural communities usually provide lower levels of services overall (Warner, 2006) and for elders in particular (Morken and Warner, 2012; Warner & Morken, 2013).

Moreover, rural communities, especially poor areas, are more likely to report problems with access to health services (Auchincloss, van Nostrand, & Ronsaville, 2001). Mortality is also highest in remote rural communities where access to services is harder to achieve (Morton, 2004). The importance of health services to rural seniors is illustrated by Ziembroski and Breiding (2006), who found the health risks of rural residency only exist in the Southern region where service levels are historically lower, in contrast to the positive health effects of Midwestern rural residency. They found health services can offset the disparity of rural residence. Therefore, given increased aging in rural communities and the need to overcome the disadvantages of the rural built environment, more attention to community-related health services is required.

This article explores what U.S. municipalities are doing to respond to an aging population, giving special attention to rural communities. Using the first-ever national survey data on service delivery for seniors combined with additional data on health, socioeconomic, government, and environmental characteristics, we examine the factors of health condition, planning for elders, the built environment, local need, and government capacity that determine differences in community delivery of health-related services for seniors across the rural to urban spectrum.

Method

In 2010, the International City/County Management Association conducted a survey for the National Association of Area Agencies on Aging on senior-focused services offered by municipalities across the United States.¹ The Maturing of America (2010) survey was mailed to municipal governments with populations more than 2,500 across the United States.² We combine these data with data on morbidity and mortality from the Centers for Disease Control and the 2011-2012 Area Resource File, socioeconomic data from the U.S. 2010 Decennial Census, the 2006-2010 American Community Survey and 2010 County Business Patterns, and government finance data from the 2007 Census of Governments. Our final sample includes usable data for 1,418 local governments. The survey was addressed to the chief executive—the person with the broadest knowledge of service provision in the community and who would have access to staff members with more specific expertise. The survey asked about the local availability of a wide range of services to support older adults. Local government, nonprofit or for-profit entities, could provide these services (National Association of Area Agencies on Aging, 2013). For our analysis, we isolated the following 16 survey questions that focus on health-related services to create a health services index as our dependent variable:

Health care (five services—services that meet a range of needs, prescription programs, wellness programs, preventive screenings, and immunizations),

Nutrition (three services—congregate meals, home-delivered meals, and nutrition education),

Exercise (two services—exercise classes and parks and walking/biking trails),

Transportation (three services—transportation to and from health care services/appointments, sidewalks and street crossings that are safe and accessible for older pedestrians, and sidewalk systems linking residences and essential services),

Public safety/emergency (two services—elder abuse/neglect identification and prevention), and

Housing (one service—home modification programs).

We selected this subset of questions because it measures those services and programs that would most directly impact older persons’ physical health and well-being. We were especially interested in how health-related service delivery levels varied across communities. We differentiate communities according to Office of Management and Budget definitions of metro status: metropolitan communities and nonmetropolitan, which is divided into two groups—micropolitan (rural population centers) and non-Core-Based Statistical Area (CBSA; remote rural).³ Table 1 shows the distribution of the health-related services included in our index by metro status. We see that metropolitan and micropolitan communities show higher frequency of service delivery than non-CBSA communities, especially for mobility-related services (exercise, transportation). Micropolitan communities are service centers for surrounding rural communities. Non-CBSA municipalities rank significantly lower in wellness programs, parks and exercise classes, sidewalks, elder abuse prevention, and home modification. Community-based service delivery is especially important for these remote rural communities due to a higher proportion of seniors and the fact that these municipalities are not economically integrated with nearby metropolitan or micropolitan centers.

Table 1.

Health Services Index Components by Metro Status.

Variable	Total	Metro-politan	NonMetMicro	NonMetNon-CBSA	Significant difference
Health care
Health care services that meet a range of needs	63.90%	63.30%^a	68.40%^a	62.40%^a
Prescription programs that meet a range of needs (other than Medicare Part D)	46.90%	46.70%^a	52.90%^a	42.80%^a
Wellness programs for older adults, including evidence-based programs such as chronic disease self-management	59.70%	60.80%^a	64.10%^a	51.60%^b	*
Preventive screenings, such as blood pressure, mammograms	68.80%	68.80%^a	70.40%^a	67.60%^a
Immunizations, such as flu shots	73.10%	72.90%^a	74.30%^a	72.80%^a
Nutrition
Congregate meals, such as lunch at a recreation or senior center	79.10%	78.00%^a	84.00%^a	79.60%^a
Nutrition education (e.g., heart-healthy diets, diabetes, food and drug interactions, preparing meals on a budget)	67.10%	67.80%^a	69.40%^a	62.80%^a
Meals delivered to homes	75.70%	75.80%^a	75.20%^a	75.60%^a
Exercise
Exercise classes tailored to specific health concerns, such as heart disease, arthritis, diabetes, falls	67.30%	70.40%^a	68.90%^a	54.00%^b	***
Local parks and other venues that have safe, easy-to-reach walking/biking trails	82.20%	84.70%^a	83.00%^a	72.00%^b	***
Transportation
Transportation to and from health care services/medical appointments	76.90%	77.00%^a	77.70%^a	76.00%^a
Sidewalks and street crossings that are safe and accessible for older pedestrians (e.g., flashing walk signs, sidewalk bump outs)	68.10%	70.70%^a	69.40%^a	57.20%^b	***
Sidewalk system linking residences and essential services	55.60%	60.10%^a	54.40%^a	39.60%^b	***
Public safety/emergency programs
Elder abuse/neglect identification/detection	62.10%	65.00%^a	56.80%^b	55.20%^b	**
Elder abuse/neglect prevention	56.70%	60.60%^a	51.00%^b	46.40%^b	***
Housing
Services for modification of existing homes of older adults (e.g., building a ramp for easier access or modifying showers)	61.60%	63.90%^a	60.70%^a	53.20%^b	**
No. responding	1,418	962	206	250

Source. Maturing of America Survey, 2010, U.S. cities and counties. n = 1,418.

Note. Scheffé test of difference between metropolitan groups. Superscript letters a and b represent the ranked groups based on the mean difference test. NonMet includes micropolitan and remote rural, nonCBSA. CBSA = Core-Based Statistical Area.

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

Independent Variables

Health

Morbidity is measured using obesity and diabetes rates among adults, drawn from the Centers for Disease Control and Prevention (2009; see Table 2). We choose these two measures of morbidity because they are the most closely linked to the health-related services we measure (which relate to nutrition and physical activity). These conditions have special implications for seniors, who have high rates of both obesity and diabetes. Baby boomers have the highest obesity rate of any age group, portending even higher obesity rates among seniors in coming decades (Sommers, 2008). Significantly more rural adults are obese (40%) than are urban adults (33%; Befort et al., 2012), and the highest diabetes and obesity rates are concentrated mostly in the south and southeastern United States (Barker, Kirtland, Gregg, Geiss, & Thompson, 2011). Places with more obesity and diabetes may have greater need for services, given the poorer quality of life and health outcomes obese and diabetic elderly people often experience. We test whether communities with greater morbidity have more health-related community services.

Table 2.

Summary Statistics for Variables.

Variables	M (or % “yes”)	SD	By metro status
Variables	M (or % “yes”)	SD	Metro-politan	NonMetMicro	NonMetNon-CBSA	Significant difference
Health service index^a (index 0-16)	10.6	4.8	10.9¹	10.8¹	9.7²	**
Health
Obesity rates among adults (20 and above) 2009^b (%)	28.0	4.0	27.1³	29.4²	30.4¹	***
Diabetes rates among adults (20 and above) 2009^b (%)	9.3	2.1	8.9³	9.8²	10.4¹	***
3-year mortality rate, age 65+, 2004-2006^c (%)	4.4	0.8	4.3³	4.5²	4.8¹	***
Doctors per thousand 2010^c	0.6	0.3	68.3¹	53.1²	46.6²	***
SNAP benefit (food stamp) per capita recipient estimate 2008^d (%)	9.9	5.7	8.8²	12.0¹	12.0¹	***
Environment and planning
Density (people/square mile)^e	1,696.7	2,461.4	2,130.1¹	904.0²	682.2²	***
Single-family homes in housing stock^f (%)	72.6	13.7	72.9¹	69.7²	73.7¹	**
Planning for elders index^a (index 0-14)	6.6	3.7	7.0¹	6.2²	5.5²	***
Engage elders in planning^a (1 = yes)	62.0	48.6	66.0¹	55.8²	51.6²	***
Socioeconomic characteristics
Population^e	55,119.6	148,125.5	69,276.1¹	24,458.3²	25,910.1²	***
Ln population^e	9.8	1.3	10.0¹	9.6²	9.1³	***
Population more than 65 years old^e (%)	14.9	5.6	14.0³	15.2²	18.0¹	***
Population change in 65+ years old 2000-2010^g (%)	24.9	59.2	32.0¹	16.2²	4.9²	***
Population 65+ years living alone^f (%)	29.0	8.1	28.0²	31.0¹	31.2¹	***
Poverty rate 65+ years living independently^f (%)	9.0	5.7	7.9³	10.3²	12.1¹	***
Gini coefficient^f	0.4	0.1	0.4²	0.4¹	0.4¹	***
White^e (%)	80.8	17.7	80.0²	83.4¹	81.4¹	*
Per capita income^f (US$)	27,510.6	11,863.0	30,309.1¹	21,368.3²	21,803.2²	***
Ln per capita income^f (US$)	10.2	0.4	10.2¹	9.9²	10.0²	***
Educational attainment (high school plus)^f (%)	86.5	8.6	88.0¹	83.7²	82.8²	***
Government characteristics
Federal aid per capita^h (US$)	47.8	231.2	32.1²	69.0¹	90.4¹	***
State aid per capita^h (US$)	303.5	447.8	261.6²	272.9²	489.9¹	***
Total expenditures per capita^h (US$)	1,763.2	1,763.4	1,670.5²	1,652.5²	2,211.0¹	***
Council manager government^a (1 = yes)	56.8	49.5	62.0¹	55.3¹	38.4²	***
	No.			%
Metro status
Metropolitan (reference)	962			67.8
Micropolitan (1 = yes rural)	206			14.5
Non-CBSA (1 = remote rural)	250			17.6
	n = 1,418			n = 1,418

Note. Scheffé test of difference between metropolitan groups. Superscript numbers 1, 2, and 3 represent the ranked groups based on the mean difference test. CBSA = Core-Based Statistical Area; SNAP = Supplemental Nutritional Assistance Program.

National Association of Area Agencies on Aging, Maturing of America Survey, 2010.

Centers for Disease Control and Prevention: Division of Diabetes Surveillance System, 2009.

Area Resource File, 2011-2012 Release, U.S. Department of Health and Human Services.

Supplemental Nutrition Assistance Program Quality Control Data, 2008, U.S. Department of Agriculture.

U.S. Census, 2010.

American Community Survey, 2006-2010.

U.S. Census, 2000, 2010.

U.S. Census of Governments, 2007.

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

We also look at mortality, using death rates among those 65 years old and older (2004-2006), as well as doctors per thousand (2010), and Supplemental Nutritional Assistance Program (SNAP) benefit (food stamp) recipients from the U.S. Department of Health and Human Services’ Area Resource File. We hypothesize that communities with higher death rates, more SNAP recipients, and fewer doctors will have fewer services.

Environment and planning

The built environment—such as housing, streets, buildings, parks, businesses, and other such structures that are part of daily life—greatly affects the level of independence of older persons (Lawton, 1977; World Health Organization, 2002). As an aging person becomes frail, the impact of environmental factors on that individual becomes greater (Lawton, 1986). In environments less conducive to aging in place, more services will be required. Rural communities that lack density and that have a large percentage of older single-family housing may be less prepared to support seniors who wish to age in place. We include population density and percent single-family homes in housing stock (drawn from the American Community Survey, 2006-2010) to distinguish these communities. Our models test whether these communities provide more health-related services.

Planning is especially important for aging because the issue is complicated and cuts across numerous domains. However, many rural communities lack planning capacity, especially for aging, and Table 2 shows both planning for elders and elder engagement are significantly lower in rural communities (micropolitan and non-CBSA). The World Health Organization’s (WHO; 2002) Active Ageing: A Policy Framework strongly emphasizes community engagement and civic participation of seniors. All cities engaged in WHO’s (2007) Global Age-Friendly Cities hold extensive discussions with their older residents as a foundation for the work to come. Studies that give attention to rural elders have pointed to the importance of empowerment and social engagement of elders on both individual and community health (Glasgow & Berry, 2013, Walsh & O’Shea, 2008).

We measure planning and participation using a Planning Index based on the Maturing of America survey. The Planning Index is based on seven planning items measured in the survey: “a strategic plan that specifically reflects the needs and potential contributions of older adults”; “a comprehensive assessment of the needs of older adults (e.g., health, transportation, housing, education)”; “a process that solicits input from older adults to identify their needs”; “a master plan or land use plan that embodies the vision, which is then reflected in zoning and subdivision ordinances”; “zoning requirements that support ‘complete street’ design, enabling safe access for all users”; “zoning requirements that support aging in place and active lifestyles for older adults (e.g., higher density, mixed-use development, and amenities)”; and “building codes that incorporate universal design in new construction.” Each planning action was scored as follows: two points if a plan is in place, one point if the plan is under discussion, and no points if there is no plan in place or under discussion. We hypothesize that places that engage in more planning and participation will provide more services.

Socioeconomic characteristics

We hypothesize that larger communities and communities that have more seniors in the population will provide more health-related services for seniors. Using data from the Census of Population (2000 and 2010) and the American Community Survey (2006-2010), we include population aged 65 years and older as well as growth in senior population (65 and above), as these communities might recognize the need to develop services and plan for seniors. Additional measures of need include population 65 and above living alone and poverty rate of seniors aged 65 living independently. Seniors are considered the “deserving poor” (Isaacs, 2009; Katz, 2013), so we expect that communities with higher poverty rates among seniors will provide more services. But we recognize there may be competing demands between seniors and other community needs. Thus, we also include measures of heterogeneity—the Gini coefficient of income inequality, per capita income, and percent White. We expect communities with greater inequality will not provide more services to elders, but those with higher educational attainment and more income will provide more services. We include percent population with high school diploma or more drawn from the American Community Survey, 2006-2010. Educational attainment and per capita income are highest in metro core communities. Remote rural (non-CBSA) communities rank highest on all measures of need (low income, high elder poverty, larger proportion of elders, and elders living alone).

Government characteristics

We expect communities with higher total expenditures and those that receive more federal and state aid will provide more services. Fiscal measures are drawn from the 2007 Census of Government Finance. We also control for council manager governments as we expect governments with professional management will be more likely to provide services (Hefetz, Warner, & Vigoda-Gadot, 2014; Nelson & Svara, 2012). Intergovernmental aid and professional management may be especially important in rural communities, which lack internal resources. We expect service provision will be higher in communities with more resources.

Metro status

Finally, we control for metro status. Key differences in planning and health services, local senior need as well as government spending are shown in Table 2 as well. Metropolitan and micropolitan communities have a high degree of social and economic integration with neighboring communities. This facilitates service sharing and positive spillovers, which result in higher levels of service availability than communities in non-CBSA areas. Analysis of the data shows that non-CBSA communities in our sample have significantly higher percentages of elders (18%) in their populations than metropolitan and micropolitan communities (14% and 15%, respectively). Planning for elders scores are significantly lower in rural communities (5.5 for non-CBSA and 6.2 for micropolitan) than in metropolitan communities (7.0). Elder engagement is also lower in rural communities (52% in non-CBSA and 56% in micropolitan, compared with 66% in metropolitan communities). Remote rural communities also have lower capacity; only 38% of non-CBSA have council managers, whereas 55% of micropolitan and 62% of metropolitan governments have managers. Rural communities have higher need for elder services due to a significantly higher proportion of elders (18% in non-CBSA, 15% in micropolitan, and 14% in metropolitan). Elder poverty follows a similar ranking, highest in remote rural communities (12%), next highest in micropolitan communities (10%), and lowest in metropolitan communities (8%). State aid per capita is significantly higher in non-CBSA communities (US$490) than metropolitan or micropolitan communities (US$262 and US$273, respectively), and expenditures per capita are as well (US$2,211 for non-CBSA vs. US$1,671 and US$1,653 for metropolitan and micropolitan, respectively). These differences reflect the higher cost of service delivery in sparse rural areas due to lack of economies of scale (Bel & Warner, 2015; Warner, 2006), but are they enough to stimulate higher service provision in remote rural areas?

Results

Regression Models

We ran ordinary least squares (OLS) regression models to assess what factors help explain higher levels of health-related services for elders (see Table 3).⁴ Regarding our health variables, none proved significant as drivers for services. As pervasive as obesity and diabetes have become in recent years, they have not yet triggered a service response. Preventive services are new terrain for many municipalities, which may be struggling to find appropriate interventions and services for public health issues that largely overlap with personal lifestyle choices and cultural norms. In addition, many existing efforts to curb obesity target children and youth, not elders (Garcia et al., 2009). Mortality rates (age 65 and above) were not significant drivers in the models, either. Communities with more doctors per thousand were more likely to provide health-related services, as expected. SNAP recipient rates were not significant.

Table 3.

Health-Related Service Availability in U.S. Municipalities: Results of OLS Regression.

Variables	Coefficient	p > \|z\|
Health
Obesity rates (2009)	0.156	.971
Diabetes rates (2009)	−15.561	.097
3-year mortality rate, age 65+ (2004-2006)	3.615	.845
Doctors per capita (2010)	0.887*	.041
Food stamp/SNAP per capita recipient estimate (2008)	−1.519	.588
Metro status
Metropolitan	Reference	Reference
Micropolitan	0.667	.052
Non-CBSA	0.183	.599
Environment and planning
Density (people/square mile)	6.76^e-05	.210
Single-family homes in housing stock (%)	0.015	.154
Planning index (index 0-14)	0.127***	.000
Engage elders in planning (1 = yes)	3.310***	.000
Socioeconomic characteristics
Population	1.108***	.000
Population more than 65 years old (%)	0.090***	.001
Population change in 65+ years old, 2000-2010^c (%)	−0.002	.363
Population 65+ years living alone (%)	0.037*	.024
Poverty rate 65+ years living independently (%)	−0.010	.713
Gini coefficient	−0.315	.913
White (%)	0.003	.724
Per capita income (US$)	−1.813**	.002
Education attainment (high school plus; %)	0.043*	.050
Government characteristics
Federal aid per capita (US$)	3.887^e-04	.457
State aid per capita (US$)	6.776^e-04*	.031
Total expenditures per capita (US$)	1.713^e-04*	.038
Council manager government (1 = yes)	0.549*	.018
Constant	7.993	.127
Model statistics
R²	.3051
Adjusted R²	.2931
n	1,418

Note. OLS = ordinary least squares; SNAP = Supplemental Nutritional Assistance Program; CBSA = Core-Based Statistical Area.

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

Of our built environment variables, none emerged as significant drivers for service levels. Improvements to the built environment can be difficult and take time, making it a more complicated variable to measure, even in places where change is happening. Research also cautions that the relationship between built environment, attitudinal factors, and physical activity levels is a complex one tied in part to individual and cultural characteristics (Forsyth et al., 2008; Rodríguez, Khattak, & Evenson, 2006) and recommends that interventions aimed at decreasing obesity and sedentary lifestyle should be culturally appropriate (Barker et al., 2011).

Service levels are higher in communities that plan for elders and engage elders in the planning process. Local governments that have planning measures focused on the elderly have more services available than those that do not have any of these plans, zoning or building code provisions in place. Elder engagement in the planning process also is associated with higher health-related service levels. Communities that engage elders in planning processes have on average three more services available for seniors. These results suggest that engaging older adults in planning and decision-making processes may have a positive impact on level of service delivery, but causal analysis cannot be determined with a cross-sectional study.

Our socioeconomic variables show that service levels are higher in larger communities and in communities with higher educational attainment. This reflects both size and sophistication in community response. Service levels also are a response to population-based measures of need (e.g., percent population 65 and above living independently, and percent population 65 and above living alone are significant drivers). Other need-based population measures, including population change in those 65 and older, proved insignificant as drivers for service delivery. Similarly, none of our poverty or income inequality measures are significant. However, communities with higher per capita income have lower service provision, contrary to expectations.⁵ Our dependent variable includes both government and market-provided services. Although some of the health-related services are means tested, most are not. Our finding that higher income communities provide fewer services suggests markets are slow to respond to rising demand for health-related services for elders.

Regarding government characteristics, we find state aid is a significant driver, but federal aid is not. This may be because much federal funding is channeled through the states, and state legislators generally support services for seniors. Places with higher total expenditure per capita show higher service provision. Municipalities with the council manager form of government also provide more services, suggesting a role for professional management in generating a response to emerging service needs. While political leaders may respond to voter pressure, professional managers generally show higher levels of service innovation (Hefetz et al., 2014; Nelson & Svara, 2012).

Metro status shows some surprising results. We had expected remote rural communities to provide fewer services. However, we find municipalities in non-CBSA counties and micropolitan counties do not provide fewer services than their metropolitan counterparts, after controlling for other factors. Higher need is matched with higher expenditures and higher state aid in non-CBSA and micropolitan municipalities, and this may help explain why we do not find a difference between rural and metropolitan communities after controlling for these factors.

Discussion

Limitations and Strengths

The Maturing of America survey is the only national survey of its type. It has broad coverage by metro status and includes a wide range of services relevant to public health. It also includes specific measures of community-level planning processes related to aging. However, as a cross-sectional sample, it suffers from the standard limitations of cross-sectional research. Furthermore, morbidity data available at the municipal level (in the Area Resource File) are for the entire population, not just seniors. Thus, we are unable to directly tie service levels for seniors to morbidity levels in the senior population. We selected diabetes and obesity as our morbidity measures because these diseases are especially high among elders and they are most likely to be effected by the preventive health-related services in our analysis.

One advantage of cross-sectional data is the ability to look across space at differential community responses, especially differences in rural and urban communities, which we find to not be significant after controlling for other community characteristics. Although service availability may be higher due to spillover effects in communities located in metropolitan or micropolitan areas, elders needing these community services likely face mobility and transportation constraints that may limit their access to regionally available services. Although our measures of geographic and built environment differences are rough proxies of actual conditions, they show that communities with less favorable built environments are not providing more services to make up for this lack. Changes to the design of the physical and built environment occur slowly over time, and thus, the service overlay is particularly important.

Conclusion

Despite research showing increasing linkages between planning and health (Wells et al., 2010), direct connections at the community level are still being developed. Municipalities must find ways to meet the needs of an aging population. Today’s and tomorrow’s seniors face dangerous rates of obesity and diabetes, complicating their needs and the services they require. Obesity is preventable, and diabetes is primarily tied to lifestyle, so both can be averted or delayed through community-based prevention programs that help with weight loss and increasing physical activity (Garcia et al., 2009; Trust for America’s Health, 2009), especially for older adults (Knowler et al., 2002; Tuomilehto et al., 2001).

Our findings contribute to the growing body of literature on effective planning for the aging population (Kerr, Rosenberg, & Frank, 2012). Our models show that planning for seniors and engaging seniors in that planning are powerful drivers for increasing health-related service delivery to older adults. Cities and states that have taken an early and comprehensive approach to preventing obesity have made notable progress in addressing this epidemic (Trust for America’s Health, 2012). Although our models show planning is critical in generating a health-related service delivery response, we find no connection between morbidity and mortality and provision of community-based health services. This suggests more education for planners and local government leaders is needed to make the link between services and health. Such professional education may help communities with the greatest need improve and expand health-related services. Planning has a key role to play in helping communities make this connection. Our models find that, after controlling for other factors, rural communities are not lagging as far behind in this effort as descriptive statistics suggest. Our models also show a way forward, as addressing the lower rates of planning and elder engagement in rural communities, especially remote rural areas, may help communities increase the availability of health-related community services for elders.

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, authorship, and/or publication of this article: Analysis was funded in part by U.S. Department of Agriculture, National Institute for Food and Agriculture Grant No. 2011-68006-30793. The survey was funded by MetLife Foundation and conducted by the National Association of Area Agencies on Aging and the International City/County Management Association.

Notes

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