Andrew Myers, Lillie Greiman, Christiane von Reichert, Tom Seekins
Introduction and Background
Rural landscapes dominate American geography. Depending on the definitions used, rural areas account for 72% to 97% of the total landmass of the United States. However, only a minority of Americans live in rural areas (approximately 15-19% again depending on the definition). Yet, people living in rural areas represent a higher percentage of people who are unemployed, living in poverty, are elderly, and experience a disability. Further, it has been well documented that individuals with disabilities living in rural areas face unique challenges in acquiring services and supports. For example, rural residents typically rely on services that are more informal and less specialized; must travel farther and pay more for those services; and tend to receive lower quality services than their urban counterparts (Whitener, Weber, & Duncan, 2001; Dabson & Weber, 2008).
Ideally, services, programs and policies are based on up-to-date information about the people and places on the ground (locally, regionally, nationally). While Census data about more urbanized areas have been available in contiguous years, data about rural America have been limited. This is particularly relevant for disability data as from 2000 to 2013 when there was no new disability data available for rural areas. This knowledge gap has impacted our ability to understand or track changes in the needs of people with disabilities living in rural communities. As a result, policy makers and program planners have been left in the dark while advocacy efforts highlighting the needs of people with disabilities in rural America have been hampered. There is an urgent need to update analyses of the geographic distribution of people with disabilities living in rural America to provide data necessary to improve policy and program development.
The American Community Survey (ACS) replaced the U.S. Census’s long-form sample of the population’s characteristics in 2005. Historically, the long-form provided detailed data about rural America, including disability, every decade. However, questions about disability were not included in the new ACS until 2008. While new data about more urban areas is available annually, it takes five years of data collection to accumulate a sufficient sample to conduct the same analyses for more rural areas because the ACS only samples 2.5% of the population every year. The ACS collects data on demographic variables such as age, sex, race, ethnicity, income, employment, disability, education, transportation, household size, housing tenure, and utilities.
The ACS does not directly measure disability. Instead, it uses questions related to difficulty and functional impairment to identify individuals who may experience a disability. Not all age groups are included in every question. For example, only people aged 15 years and older are included in the independent living difficulty question. The ACS asks six questions related to functional limitation:
- Hearing difficulty: deaf or having serious difficulty hearing (all ages)
- Vision difficulty: blind or having serious difficulty seeing, even when wearing glasses (all ages)
- Cognitive difficulty: because of a physical, mental, or emotional problem, having difficulty remembering, concentrating, or making decisions (ages 5+)
- Ambulatory difficulty: having serious difficulty walking or climbing stairs (ages 5+)
- Self-care difficulty: having difficulty bathing or dressing (ages 5+)
- Independent living difficulty: because of a physical, mental, or emotional problem, having difficulty doing errands alone such as visiting a doctor’s office or shopping (ages 18+)
Limitations of ACS
Though the ACS is a rich and comprehensive data source it is not without limitations. Rural researchers, in particular, are acutely aware of these limitations. The continuous nature of ACS data collection and the resulting sample size impacts the availability and validity of the data, particularly for rural areas. ACS data is aggregated as it is collected and then released in 1 year, 3 year and 5 year estimates. For urban areas (geographies with populations of 65,000 more) data is available across all estimate groups. However, for rural areas throughout the United States (geographies with populations of 20,000 or fewer), data must be aggregated across 5 years in order to collect a suitable sample size to produce reliable estimates. This means that even though the ACS oversamples rural areas, researchers can still only use 5 year estimates for rural counties. Even with data collected across 5 years the sample sizes for some subgroups (i.e. disability) at some geographies (i.e. counties) remain very small resulting in less reliable population estimates with large margins of error. The larger the margin of error the less reliable the estimate. In fact, for some variables, at some levels, the margins of error can be the same size or larger than the estimate itself, compromising the accuracy of the estimate.
An accurate, precise, and standardized definition of rural is necessary to develop effective policies and deliver meaningful services. Such a task is difficult because there are multiple definitions used by various governmental agencies and non-government (Cromartie & Bucholtz, 2008; Enders, Seekins, Brandt 2005). In this report, we use the Office of Management and Budget (OMB, 2013) county classification, which classifies metropolitan counties (“urban”) and nonmetropolitan counties (“rural”). Nonmetropolitan (rural) counties can be further split into two categories: micropolitan and noncore counties.
OMB County Types:
- Metropolitan counties include at least one urban core of 50,000 or more people. (Nearby, lower population counties with close commuting ties may also be part of a metropolitan area.)
- Micropolitan counties are nonmetropolitan counties with an urban core of 10,000 to 50,000 people.
- Noncore counties are nonmetropolitan counties with an urban core population of less than 10,000.
Disability demographics across America
The maps and chart below explore the 2010-2014 ACS 5-year (Table S1810) disability estimates by county type (OMB county classifications). The ACS asks a set of disability indicator questions to determine disability, if a respondent can answer “yes” to any disability question they are classified as having a disability.
Chart 1. Disability Rates by Metro Status.
The chart above shows the rate of disability across county types and are for all age groups. The rate of disability across the nation is 12.4%. However, when looking at just the rates of disability in metropolitan counties, the most urban county type, we see that number drop slightly to 11.7%. As counties get more rural the rate of disability increases to 15.5% in micropolitan counties and to 17.7% in noncore counties (the most rural counties).
Clearly, disability matters for rural America, but why is this? In the past policy makers and researchers have speculated that these higher rates could be due to the older population of rural counties. Indeed, the national rate of individuals aged 65 years and older is 13.6% of the population but this number increases to 15.7% in micropolitan counties and 18% in noncore counties, mirroring disability rates. Explore these data further in the map below and the links that follow.
Map 1. Disability Rates Across America.
This map of the United States shows general rates of disability by county. Rates tend to be highest in the South Eastern United States (particularly in Appalachia), and lowest in the Northern Great Plains and Rocky Mountain West (both very rural regions of the United States).
Click on the links below to explore more data about disability demographics across the United States:
This work is part of the Geography of Disability Project.
Suggested Citation: Myers, A., Greiman, L., von Reichert, C., and Seekins, T. (July, 2016). Rural Matters: The Geography of Disability in Rural America. Missoula, MT: The University of Montana Rural Institute for Inclusive Communities. http://bit.ly/2aiGImY
Funding: © 2016 RTC:Rural. Our research is supported by grant #90RT50250100 from the National Institute on Disability, Independent Living, and Rehabilitation Research within the Administration for Community Living, Department of Health and Human Services. The opinions expressed reflect those of the author and are not necessarily those of the funding agency.