Health disparities research in rural populations is based on several common taxonomies identified by geography and population density. However, little is known about the implications of different rurality definitions on public health outcomes. To help illuminate the meaning of different rural designations often used in research, service delivery, or policy reports, this study will (1) review the different definitions of rurality and their purposes; (2) identify the overlap of various rural designations in an eight-county Brazos Valley region in Central Texas; (3) describe participant characteristic profiles based on distances traveled to obtain healthcare services; and (4) examine common profile characteristics associated with each designation. Data were analyzed from a random sample from 1,958 Texas adults participating in a community assessment. K-means cluster analysis was used to identify natural groupings of individuals based on distance traveled to obtain three healthcare services: medical care, dental care, and prescription medication pick-up. Significant variation in cluster representation and resident characteristics was observed by rural designation. Given widely used taxonomies for designating areas as rural (or provider shortage) in health-related research, this study highlights differences that could influence research results and subsequent program and policy development based on rural designation.
According to the United States Census, in 2010, there were approximately 60 million adults or 20% of the population living in rural areas [
Public health researchers, practitioners, and policy-makers often use underserved and rural interchangeably when referring to areas with low population density, but to what extent do these two terms carry similar meaning? Our current understanding of rurality and rural designations is based largely on research using one of several common taxonomies related to geography, population density, and distance to certain functions such as employment, food, and health services. While the existing research is informative, it is critical to acknowledge that the rural designations used were not developed specifically for public health research, understanding health disparities, and informing health policy, although they are routinely applied this way. Thus, these designations may not capture important aspects of rurality when applied this way. Defining “rural” for health research and policy development demands that we specify which dimensions of rurality are most relevant to a specific issue and select a taxonomy accordingly. Existing definitions for categorizing “rural” and “urban” often disregard significant cultural, demographic, and socioeconomic characteristics that have critical implications for health disparities research and policy [
Thus, the general purposes of this study were to review the different definitions of rurality and their purposes and examine common resident profile characteristics associated with each designation using an eight-county sample from Central Texas. Such an examination will add to the research about the consistency between and application of taxonomies used to define areas as rural or having a provider shortage, which has implications for research results and subsequent program and policy development based on rural designation (see specific study aims description later).
As indicated by the Office of Rural Health Policy [
The definition of nonmetropolitan applied by the National Center for Health Statistics largely follows the Census and OMB, as it deals with much of the same data, dividing nonmetropolitan counties into “micropolitan” and “noncore” [
Each of the current rural definitions was developed for a specific purpose; when used in other applications, each has its limitations. The Census Bureau definition is oversimplified, counting any area rural that is not urban. This false dichotomy misses a great deal of variation in important community characteristics outside large metro areas. The OMB definition and all the codes that use it as a basis for their stratification have the issue of over- and underbounding, from the use of counties as statistical areas. A county with a large urban region is classified as metropolitan, although it may also house extremely rural regions devoid of the services available in the metro region. Additionally, residents on the border of counties may have access to goods and services in the adjoining county that are closer to them than those within their county of residence [
In addition to measures of rurality, health research and policy commonly use several specific measures of access to health services to designate underserved areas. These designations are based on provider-to-population ratios, not necessarily population density and were developed to highlight gaps in service availability and delivery, equivalent to access to care. While the underserved designations apply equally to rural and urban populations, these designations most often coincide with rural areas, while metropolitan areas occasionally have smaller designated areas or subpopulations within them. Two designations are mostly used—the Health Professional Shortage Area (HPSA) and the Medically Underserved Area (MUA); both designations are managed by the US Health Resources and Services Administration and are used to allow certain communities to be eligible for specific types of funding to improve their access to services.
The primary care Health Professional Shortage Area designation is based on the ratio of primary care providers to the population. If the ratio is less than 1 : 3 per 500 then the region is designated as a shortage area; the mental health and dental Health Professional Shortage Area designations have their own unique provider ratios. Once a Health Professional Shortage Area is identified, it is scored based on poverty line, Infant Health Index, and distance to the healthcare. Regions that do not automatically qualify for Health Professional Shortage Area status can appeal or be granted such status if other significant health needs are demonstrated. The Health Professional Shortage Area system varies from either the Urban Influence Codes or Rural Urban Continuum Codes in that it is not entirely county based. Because the criteria for Health Professional Shortage Area designation are calculated as a ratio, population density itself is not considered. A shortage area can be geographic, a population group, or a facility, with each area defined by unique guidelines. The Health Professional Shortage Area designation brings with it eligibility to receive certain federal benefits, training, and loan repayment [
Medically Underserved Areas use Index of Medical Underservice (IMU) which measures infant mortality, percent of population below the poverty line, percent of the population 65 or older, and the number of primary care providers per 1,000 people. Like the Health Professional Shortage Area, the Medically Underserved Area designation is not based solely on population density and can be given to regions that do not exactly fit the criteria if they can document extraordinary barriers to health services and the designation is advocated by state health officials [
Clearly, although the different taxonomies for defining rurality share certain characteristics, they each hinge on specific aspects of ruralness while not including others. The designations of underserved are based on relatively objective measures, and the vast majority of rural communities fall into one or more of the underserved categories. Extensive research indicates that rural health disparities are pervasive—particularly around access to care. This raises the question, to what degree do the different designations of rural and underserved coincide and offer similar conclusions when applied to the same research question regarding access to care? Given the previous descriptions of rural designations provided previously, the remainder of the paper will utilize data collected through an eight-county health assessment to identify the extent to which various designations of rurality and medically underserved overlap in an eight-county region of Central Texas that includes variation in population density and other characteristics; examine the key characteristics of the cluster profiles generated based on distance traveled to obtain health services; and describe unique and common participant and cluster profile characteristics by association with each of the 4 rural designation classifications.
Data were collected in 2010 as part of a regional eight-county health assessment of the Brazos Valley in Central Texas. The survey was conducted by the Center for Community Health Development at the Texas A&M School of Rural Public Health and was intended to assist local communities in identifying and prioritizing health problems. Results of this survey, conducted approximately every 4 years, are used by the Brazos Valley Health Partnership as part of their planning for community health action. The assessment utilized random-digit dialing to obtain a population-based sample of the noninstitutionalized civilian population. Sampling was stratified by county to ensure adequate representation of counties in the region.
Further randomization within each household was achieved using the next-birthday method. That is, when making recruiting phone calls, investigators asked to speak with the adult resident present in the household who had the birthday that would next occur. That resident was then informed of the survey purpose and recruited to participate in the assessment. Of those reached by phone, 51.9% agreed to participate and received a paper survey by mail. Two reminder postcards were sent at 2-week intervals following mail-out of the survey packet. Of those who were sent surveys, 62.1% (
Participants were surveyed using a mailed community assessment instrument that asked questions about the respondent’s health, lifestyle behaviors, health care access, neighborhood factors, and personal characteristics. The instrument included Likert-type scales, checklists, and close-ended and open-ended response formats. Participants took approximately 45 minutes to complete the questionnaire.
Rurality and medically underserved designations were used as the dependent variables for this study (i.e., Urban Influence Codes, Rural Urban Commuting Area, National Center for Health Statistics Urban-Rural Classification (NCHS), Medically Underserved Area, Health Professional Shortage Area, Frontier). All 8 counties were classified using the criteria specific to each of the designations separately. Counties were coded into dichotomous categories for each designation based on the respective categorization recommendation (i.e., metropolitan/rural; Medically Underserved Area/not Medically Underserved Area; Health Professional Shortage Area/not Health Professional Shortage Area; frontier/not frontier). County-level assignments were identical for Urban Influence Codes, Rural Urban Commuting Area codes, and National Center for Health Statistics Urban-Rural Classification codes, thus these designations were combined as 1 category for study analyses (i.e., metro, nonmetro). Medically Underserved Areas, Health Professional Shortage Areas, and Frontier were categorized as either “not designated” or “designated.” All dichotomized designations were commonly coded as 0 and 1, whereas the score of 1 consistently refers to the rural or underserved designation (i.e., nonmetro, medically underserved areas, health provider shortage areas, frontier areas).
The survey gathered self-reported distance driven as a measure for access to healthcare. Regarding distance traveled, Fortney and colleagues noted that GIS-based systems can accurately assess access to healthcare [
Participants were asked to self-report the distance they traveled from their home to obtain healthcare services. Specifically, participants were asked how many miles they traveled from home to (1) their medical care facility, (2) their dental care facility, and (3) retrieve prescription medications. These variables were scored continuously and used in the clustering process described later.
Body mass index (BMI) was calculated from participants’ self-reported height (in feet and inches) and weight (in pounds), which were converted to meters and kilograms, respectively. BMI levels were calculated by dividing weight by height and rounded to the nearest tenth [
Personal characteristics of the participants included age, collected as a continuous variable then recoded into meaningful age groups (i.e., 18 to 40 years, 41 to 64 years, 65+ years), sex (i.e., male, female), race/ethnicity (i.e., non-Hispanic white, African American, Hispanic), and highest education level achieved (i.e., less than high school, graduate high school, and more than high school).
Consistent with the research questions of our study, we selected variables for cluster analysis that were indicative of the ability of respondents to reach healthcare services. Respondents were asked how far they traveled (in number of miles) to get medical care, dental care, and prescriptions. We only considered respondents who answered these questions in the context of starting these travels from their home (
The standard deviation among the three variables chosen for cluster analysis was substantial. Most respondents indicated traveling only a short distance, but other respondents indicated traveling substantial distances. To avoid biasing the study toward respondents living relatively close to medical services, we used a log transformation of the distance variables to achieve a normalized distribution of the variables. This process allowed for a better representation of differences between respondents and led to more cohesive clusters.
Based on variable selection for clustering as well as the log transformation, the number of observations was reduced based on missing data (
K-means clustering was selected as the clustering method since it is uniquely designed for nonhierarchical data partitioning. Because the underlying variables of the cluster analysis (distance) were expressed in mileage, Euclidean distance was used as the proximity function because of its utility in continuous variable analysis [
Determining the number of clusters to extract was a key consideration. We created 10 different cluster models to examine the change in both the within-group and between-group sum of squared errors (SSE) as additional clusters were added. Based on guidance from established literature [
We assessed the proportional overlap of study participants residing within each designation using Kendall tau rank correlation coefficients and a series of chi-squared tests. Personal characteristics, health indicators, distance traveled to healthcare services, and rurality designations were described by cluster profiles. Differences between cluster profiles were identified using chi-squared for categorical variables and tests and analyses of variance (
After reviewing the various definitions of rurality, an initial aim was to identify the extent to which various designations of rurality and medically underserved overlap in an eight-county region of Central Texas that includes variation in population density, and other characteristics. As illustrated in Figure
Rural designation by county.
Designation comparisons by participant residence are provided in Table
Distribution comparison by rural designation.
MUA | HPSA | Frontier | ||||
---|---|---|---|---|---|---|
Not designated |
Designated |
Not designated |
Designated |
Not designated |
Designated |
|
UIC | ||||||
Metro ( |
100.0% | 30.7% | 54.6% | 49.8% | 54.7% | 46.1% |
Nonmetro ( |
0.0% | 69.3% | 45.4% | 50.2% | 45.3% | 53.9% |
|
|
|
||||
|
|
|
||||
| ||||||
MUA | ||||||
Not designated | 54.6% | 0.0% | 41.6% | 0.0% | ||
Designated | 45.4% | 100.0% | 58.4% | 100.0% | ||
|
|
|||||
|
|
|||||
| ||||||
HPSA | ||||||
Not designated | 76.2% | 0.0% | ||||
Designated | 23.8% | 100.0% | ||||
|
||||||
|
UIC: Urban Influence Codes.
MUA: Medically Underserved Area.
HPSA: Health Professional Shortage Area.
Another study aim was to examine the key characteristics of the cluster profiles generated based on distance traveled to obtain health services. Table
Sample characteristics by cluster.
Cluster 1 |
Cluster 2 |
Cluster 3 |
Cluster 4 |
Cluster 5 |
Total |
|
| |
---|---|---|---|---|---|---|---|---|
Age | 57.796 | <0.001 | ||||||
18–40 years | 7.4% | 14.4% | 14.3% | 13.1% | 10.5% | 12.4% | ||
41–64 years | 30.9% | 37.6% | 49.1% | 49.5% | 52.3% | 46.2% | ||
65+ years | 61.7% | 48.0% | 36.6% | 37.4% | 37.3% | 41.4% | ||
Sex | 4.374 | 0.358 | ||||||
Male | 29.3% | 34.2% | 28.0% | 30.4% | 28.6% | 29.9% | ||
Female | 70.7% | 65.8% | 72.0% | 69.6% | 71.4% | 70.1% | ||
Education | 13.737 | 0.089 | ||||||
Less than high school | 7.0% | 5.2% | 5.3% | 6.2% | 3.4% | 5.2% | ||
High school graduate | 33.7% | 31.9% | 40.0% | 39.7% | 38.8% | 37.6% | ||
More than high school | 59.4% | 62.9% | 54.6% | 54.1% | 57.8% | 57.2% | ||
Race/ethnicity | 33.592 | <0.001 | ||||||
Non-Hispanic white | 79.8% | 74.9% | 76.7% | 84.2% | 87.1% | 81.1% | ||
African American | 12.0% | 11.4% | 10.5% | 6.0% | 6.1% | 8.6% | ||
Hispanic | 8.2% | 13.7% | 12.8% | 9.9% | 6.9% | 10.3% | ||
Body mass index | 13.557 | 0.094 | ||||||
Normal weight | 28.0% | 30.7% | 27.7% | 33.9% | 28.2% | 29.9% | ||
Overweight | 41.8% | 35.8% | 34.6% | 35.7% | 33.3% | 35.4% | ||
Obese | 30.2% | 33.4% | 37.8% | 30.4% | 38.6% | 34.7% | ||
Number of chronic conditions | 1.42 (±1.08) | 1.32 (±1.17) | 1.32 (±1.10) | 1.28 (±1.11) | 1.30 (±1.15) | 1.32 (±1.13) | 0.536 | 0.709 |
Distance traveled to (in miles) | ||||||||
Medical care facility | 3.59 (±6.29) | 3.33 (±1.96) | 14.32 (±14.96) | 23.29 (±17.05) | 45.01 (±26.70) | 21.41 (±23.51) | 388.041 | <0.001 |
Dental care facility | 1.24 (±0.58) | 6.39 (±11.01) | 14.26 (±17.42) | 15.62 (±8.00) | 42.70 (±25.89) | 19.27 (±22.13) | 371.269 | <0.001 |
Retrieve prescription medications | 1.24 (±0.83) | 2.50 (±1.69) | 4.43 (±2.27) | 14.27 (±8.26) | 26.45 (±17.75) | 11.81 (±13.94) | 472.017 | <0.001 |
Total | 6.07 (±6.14) | 12.21 (±10.31) | 33.02 (±21.35) | 53.18 (±17.59) | 114.15 (±46.52) | 52.49 (±48.13) | 1011.251 | <0.001 |
UIC | ||||||||
Metro | 12.5% | 25.5% | 23.9% | 20.2% | 17.9% | 52.6% | 158.623 | <0.001 |
Nonmetro | 6.4% | 9.2% | 22.0% | 28.0% | 34.5% | 47.4% | ||
MUA | ||||||||
Not designated | 17.1% | 37.0% | 30.5% | 14.1% | 1.3% | 31.6% | 510.874 | <0.001 |
Designated | 6.1% | 8.9% | 19.5% | 28.5% | 37.0% | 68.4% | ||
HPSA | ||||||||
Not designated | 13.2% | 25.9% | 28.1% | 20.4% | 12.4% | 57.9% | 365.209 | <0.001 |
Designated | 4.7% | 6.5% | 16.0% | 28.7% | 44.0% | 42.1% | ||
Frontier | ||||||||
Not designated | 11.9% | 21.8% | 26.1% | 22.9% | 17.3% | 75.9% | 290.413 | <0.001 |
Designated | 2.3% | 5.1% | 13.2% | 27.0% | 52.4% | 24.1% |
UIC: Urban Influence Codes.
MUA: Medically Underserved Area.
HPSA: Health Professional Shortage Area.
The final study aim was to describe unique and common participant and cluster profile characteristics by association with each of the 4 rural designation classifications. Table
Factors associated with rurality designation (
UIC | MUA | HPSA | Frontier | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
95% CI | 95% CI | 95% CI | 95% CI | |||||||||||||
OR |
|
Lower | Upper | OR |
|
Lower | Upper | OR |
|
Lower | Upper | OR |
|
Lower | Upper | |
Age: 18–40 years | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
Age: 41–64 years | 1.23 | 0.284 | 0.84 | 1.79 | 1.23 | 0.353 | 0.80 | 1.90 | 0.66 | 0.046 | 0.44 | 0.99 | 0.67 | 0.119 | 0.41 | 1.11 |
Age: 65+ years | 1.35 | 0.009 | 1.08 | 1.68 | 1.69 | <0.001 | 1.30 | 2.22 | 0.84 | 0.144 | 0.66 | 1.06 | 0.81 | 0.140 | 0.62 | 1.07 |
Male | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
Female | 1.18 | 0.143 | 0.95 | 1.47 | 1.12 | 0.405 | 0.86 | 1.45 | 0.88 | 0.294 | 0.70 | 1.12 | 0.94 | 0.665 | 0.72 | 1.23 |
Education: less than high School | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
Education: high school graduate | 1.28 | 0.353 | 0.76 | 2.15 | 1.44 | 0.228 | 0.80 | 2.60 | 1.04 | 0.889 | 0.59 | 1.83 | 1.33 | 0.406 | 0.68 | 2.60 |
Education: more than high School | 1.16 | 0.156 | 0.94 | 1.43 | 1.45 | 0.005 | 1.12 | 1.88 | 1.55 | <0.001 | 1.24 | 1.93 | 1.66 | <0.001 | 1.29 | 2.13 |
Non-Hispanic white | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
African American | 2.64 | <0.001 | 1.77 | 3.95 | 3.71 | <0.001 | 2.38 | 5.77 | 1.60 | 0.030 | 1.05 | 2.45 | 2.19 | 0.008 | 1.23 | 3.91 |
Hispanic | 1.43 | 0.162 | 0.87 | 2.37 | 2.14 | 0.006 | 1.24 | 3.70 | 1.39 | 0.229 | 0.81 | 2.38 | 2.49 | 0.010 | 1.25 | 4.97 |
BMI: normal weight | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
BMI: overweight | 1.00 | 0.998 | 0.77 | 1.30 | 1.18 | 0.302 | 0.86 | 1.61 | 1.39 | 0.019 | 1.06 | 1.84 | 1.24 | 0.188 | 0.90 | 1.72 |
BMI: obese | 1.09 | 0.499 | 0.85 | 1.39 | 1.17 | 0.283 | 0.88 | 1.57 | 1.33 | 0.031 | 1.03 | 1.73 | 1.58 | 0.003 | 1.17 | 2.13 |
Number of chronic conditions | 1.04 | 0.412 | 0.94 | 1.15 | 1.04 | 0.557 | 0.92 | 1.17 | 1.03 | 0.529 | 0.93 | 1.15 | 1.08 | 0.225 | 0.96 | 1.21 |
Health services travel: Cluster 1 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
Health services travel: Cluster 2 | 0.48 | <0.001 | 0.36 | 0.63 | 0.03 | <0.001 | 0.01 | 0.05 | 0.17 | <0.001 | 0.13 | 0.23 | 0.16 | <0.001 | 0.11 | 0.22 |
Health services travel: Cluster 3 | 0.70 | 0.012 | 0.53 | 0.93 | 0.08 | <0.001 | 0.04 | 0.17 | 0.39 | <0.001 | 0.30 | 0.52 | 0.36 | <0.001 | 0.27 | 0.48 |
Health services travel: Cluster 4 | 0.28 | <0.001 | 0.19 | 0.41 | 0.02 | <0.001 | 0.01 | 0.03 | 0.10 | <0.001 | 0.07 | 0.16 | 0.06 | <0.001 | 0.03 | 0.11 |
Health services travel: Cluster 5 | 0.21 | <0.001 | 0.15 | 0.28 | 0.01 | <0.001 | 0.01 | 0.02 | 0.07 | <0.001 | 0.05 | 0.11 | 0.07 | <0.001 | 0.04 | 0.11 |
| ||||||||||||||||
*Referent group: metro designation | *Referent group: non-underserved area | *Referent group: nonshortage area | *Referent group: nonfrontier designation | |||||||||||||
Nagelkerke |
Nagelkerke |
Nagelkerke |
Nagelkerke |
UIC: Urban Influence Codes.
MUA: Medically Underserved Area.
HPSA: Health Professional Shortage Area.
While the complexities of rurality-related designations have been identified and compared by other researchers [
The current study offers valuable insights for disparities research that examines relationships relevant to rural areas and access to care. Given that the taxonomies for designating certain areas as rural or as provider shortage areas are widely used in public health research, the results of this study highlight discrepancies among conclusions that may be drawn based upon the application of the different definitions of rurality when applied to health data. These discrepancies point to four important conclusions.
First, various aspects of a community factor into whether it is considered rural or underserved, which transcends its geography, population density, or distance to services. The current designations focus on a few key characteristics but cannot fully capture all of the aspects that collectively define rurality or access to care. The designations of rural and medically underserved examined in this study, while useful for specific purposes, lack key components to make the meanings they convey interchangeable. Rural designations focus on geography and population density, while underserved designations focus on provider to population ratios, infant mortality, percent of population below poverty level, and percent of population over 65 years of age. As seen in this study, the same area can be classified in multiple ways depending upon the designation criteria applied. Of the eight counties examined, only two were consistently designated regardless of the criteria used. More often, we found significant variation in whether counties were designated as rural or frontier, as Health Professional Shortage Areas, or Medically Underserved Areas. The application of different designations results in differing descriptions for the same county; this reinforces the conclusion that it is difficult to generalize health risks and health disparities among rural populations, even within the same geographic region.
Second, it can be argued that there are no “rural” designations that are designed specifically for applications in health research. Consequently, public health researchers are forced to utilize taxonomies developed by agencies focused on other issues when parceling out segments of the population to strengthen our understanding of health issues and disparities. Although this practice is very common, it is not without problems. The issues with using other disciplines’ designations in population health data are typically acknowledged in one sentence in the limitations section of a research article and not further explored. The current study adds to what we know about those limitations that may need to be addressed more thoroughly.
Third, repeating the same study with the same data but applying different definitions of rural and medically underserved may result in very different conclusions. As illustrated in the current study, applying six different criteria yielded four divergent models of rurality and underserved-ness for the study region, with only two counties consistently classified across all 4 models. When analyzed together, both rurality and underserved designations are likely to coincide when defining only two types of communities: metropolitan and frontier. Metropolitan communities have a large population density, shorter distances to care, and are less likely to be designated as Health Professional Shortage Areas or Medically Underserved Areas. On the other hand, frontier communities are nonmetropolitan communities with small population densities and longer distances to care and are likely to also be designated as Health Professional Shortage Areas and Medically Underserved Areas. However, how do the designations of rurality and medically underserved define those areas that are neither metropolitan nor frontier? Based on current designations, these communities are defined by what they are not rather than what they are. These are the areas in which applying different designations to the same data yields different conclusions. Thus, generalizing to the rural communities is much more difficult that to urban and frontier communities.
Finally and perhaps most importantly, given that research employing these various definitions of rurality is used to inform health policy that ultimately affects people residing in rural areas, we must be cognizant of the implications this may have. Rural populations often face challenges in the implementation of policies that are developed for urban communities with greater population and resource concentration. When researchers specifically examining rural populations recommend policy based on conclusions of research using one taxonomy or another, it is critical to know the assumptions and limitations of that designation, as well as how using a different established designation may change those conclusions. These differences may greatly affect the resulting policy development.
This study also has policy implications. While the designations included in this study inconsistently overlap, it is important to recognize that various funding agencies as well as governmental and community-based organizations utilize these different calculations and computations to target populations in need of resources, programs, and services. As such, the designation used to identify target populations may impact decisions about the types of intervention strategies selected to address health issues in that area or ways in which success of the efforts is measured/determined. This study highlights the importance of researchers and community-based organizations to investigate designation categories in which relevant and allied agencies are used to allocate funding or services, so they may better understand the missions of these organizations and their basis for prioritizing certain designations over others. Researchers and organizations can subsequently align their efforts and proposals to ensure compatibility and avoid the potential for service gaps associated with area misclassification.
Although this study contributes with an important perspective to the existing literature, several limitations must be acknowledged. The primary limitation of this study is that the population survey was conducted in eight contiguous counties in Central Texas; thus, the data only represent one region, and data from other regions may indicate a variety of other discrepancies. Additional research should replicate this cluster analysis to determine what those might be. In addition, while the sample size was sufficient for the analysis, a greater sample with more variability would undoubtedly strengthen the conclusions drawn. Finally, the sample did not include a comparison area of a mega-urban population; it is unclear what those data may have suggested related to the application of Health Professional Shortage Area and Medically Underserved Area criteria and access to care measures. Future research should aim to incorporate such populations into their samples.
In conclusion, it is important to highlight that definitions of rurality were not developed with health factors in mind, thus they were not created specifically to be used in health research. When applying them without acknowledging their original purpose and thus recognizing the subsequent limitations, misleading conclusions may be drawn from certain types of data analysis. However, it is not our contention that any of these taxonomies be abandoned in health studies. Rather, these designations should be used with caution, and researchers and policymakers must respect differences across various categorizations and acknowledge their limitations in application and interpretation.
The research presented in this paper was supported, in part, by Cooperative Agreement no. 1U48 DP001924 from the Centers for Disease Control and Prevention through the National Center for Chronic Disease Prevention. The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.