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3 Department of Social and Behavioral Health, 4 Program for Research in Nutrition and Health Disparities, and 5 Program on GIS and Spatial Statistics, Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843-1266
* To whom correspondence should be addressed. E-mail: jrsharkey{at}srph.tamhsc.edu.
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ABSTRACT |
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 TOP ABSTRACT IntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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Introduction |
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TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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The availability of healthy foods in the home (e.g., fruits, vegetables, low-fat dairy products, grains, and foods low in total and saturated fat, cholesterol, sodium, and sugar) depends to a large extent on the potential spatial (i.e., geographic) access of a household to the food environment; that is, the number, type, size, and distance of food stores (FS)6 to the neighborhoods where people reside (5–17).
The inclusion of environmental approaches with health interventions requires an accurate determination of potential spatial access to FS, which relies on true identification of store types that make food available for consumer purchase and precise locational point data (18). Although ground-truth surveys of FS, which involve an in-person, neighborhood street canvass and enumeration of FS, may be the most accurate assessment of the food environment, the preponderance of published work on food access utilizes public records or commercially available business listings to identify select types of FS (11,14,15,17,19–24). Little is known about the degree to which the use of public data within a rural area may misrepresent the food environment through overstatement and/or understatement of FS present. Additionally, determination of locational points of public or commercially available data customarily uses a commercial vendor or software with a street database, which can result in greater positional errors and address inaccuracies, particularly in rural or poorer areas. (25,26). As part of ground truthing to pinpoint exact locations, locational point data are determined using Global Positioning System (GPS) technology, which is considered the gold standard for both urban and rural areas (26).
Researchers have shown that neighborhood disadvantages may underlie the spatial inequality that residents, especially minority populations in urban areas, confront with regard to increased obesity risk and access to healthful foods (12,14,19,23,27–30). Others have recognized the spatial inequality that persists between families in rural communities and families in urban areas (31,32). Without easy geographic access to supermarkets or full grocery stores, individuals either have to pay higher travel costs to reach a supermarket or are only able to shop at convenience or small grocery stores and pay higher prices for limited selections of food products (9,17,22).
Because little is known about spatial inequalities and potential spatial access to FS within rural areas, this study expands our understanding of potential spatial access to the rural food environment by 1) identifying and geocoding all FS in a 6-county rural region in Texas, using ground-truth surveys: direct observation and on-site GPS; 2) determining the distribution of network distances from the neighborhood center to the nearest supermarket, supermarket or grocery store of any size, convenience store, and discount store in all 101 neighborhoods; and 3) examining the relationship between neighborhood inequalities (e.g., socioeconomic deprivation and minority composition) and network distance to the nearest FS.
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Materials and Methods |
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Direct measurement of the rural food environment. Considering that direct observation (i.e., ground truthing) is the ideal methodology (15), an approach was developed for the BVFEP to directly observe, identify, and geocode (i.e., assign geographic coordinates to specific locations) all FS within the study area. On-site measurement of locational points was deemed necessary because existing road files that are often used to geocode locations from an address may be an inaccurate representation of current roads in rural areas. On-site measurement ensured accuracy and identification of 100% of the locations.
Observers were trained and ground-truth protocols were pretested that included the following components: 1) identify and classify an FS; 2) determine latitude and longitude for the specific location of each FS; 3) photograph the location; and 4) complete a "windshield survey" of the characteristics of each FS observed from the outside. All highways (Interstate, U.S., and State), farm-to-market roads, and city or town streets/roads within the study area were systematically driven. The geographic coordinates (latitude and longitude) for the specific location of each FS were determined by a camera-based GPS. Geographic position was measured in front of each FS with a Bluetooth Wide Area Augmentation System–enabled portable GPS receiver after at least 4 satellite signals were detected; the World Geodetic System 1984 datum was used. Locational data were then converted utilizing GPS-Photo Link into an Environmental Systems Research Institute-compatible shape file.
Following the completion of FS identification through ground truthing, a public listing of names and addresses of all FS was acquired from local/area telephone directories, Internet telephone directories, and a list of Current Food Establishment Group Firms from the Texas Department of Agriculture. The list of 208 FS identified through ground-truth methods was compared with the public listing of 169 FS for the study area. Roads were again driven, looking specifically for the 37 (22%) FS that appeared in public data but were not identified during ground truthing. At the completion of this "revisit," an additional 5 FS (3 convenience stores, 1 discount store, and 1 specialty FS) were identified and GPS locational points were collected.
Potential spatial access of FS. Distance measures from a predefined area (e.g., CBG or census tract), primarily in urban-based studies, use the geographic center or centroid to represent individuals living in the center of the neighborhood or area (12,14,28). Because CBG in rural areas are generally much larger than CBG in urban areas, the geographic center may not accurately represent the population center. Therefore, we chose the population-weighted centroid in a CBG to represent the population center of the CBG (41). Using the SF-3 that provides the total population for census blocks within CBG, the population-weighted centroid for each CBG was calculated using the ArcGIS Desktop tool Mean Center (Version 9.2, Environmental Systems Research Institute). This tool constructs the CBG mean center based on the mean-weighted x and y values of the block population centroids. The network distance along the road network to the nearest FS was calculated between paired point data (the population-weighted CBG centroid and nearest corresponding FS within the study area). Network distance was calculated with ESRI's Network Analysis extension in ArcInfo 9.2, which computed the distance along the road network to the geographic position measured in front of each FS. Separate network distances were calculated to the nearest large supermarket, supermarket/grocery store regardless of size, convenience store, and discount store.
Neighborhood socioeconomic deprivation. The socioeconomic measures of 7 CBG were extracted from the SF-3 for the study area, which represented neighborhood unemployment (persons age 16 y and older in the labor force who were unemployed and actively seeking work), poverty (persons with incomes below the federal poverty line), low education attainment (persons age 25 y and older, with less than a 10th-grade education), household crowding (occupied households with more than 1 person per room), public assistance (households receiving public assistance), vehicle availability (occupied housing with no vehicle available), and telephone service (occupied housing with no telephone service).
Using established procedures (42–46), CBG data from the 6 rural counties were merged and a factor analysis, using the iterated principal factor method (Release 8, 2003, Stata Statistical Software), was constructed to reduce the number of linear combinations and to identify an overall index of neighborhood socioeconomic deprivation. There was 1 factor (eigenvalue 3.2) that was identified and provided item loadings (in parenthesis), which were used to weight each variable's contribution to the deprivation summary score (42,44,45): unemployment (0.43), poverty (0.90), education (0.61), crowding (0.69), public assistance (0.56), vehicle (0.81), and telephone (0.58). The internal consistency of this measure was good (Chronbach's 
= 0.82). The area-level deprivation index was standardized by dividing the index by the square of the eigenvalue (42,47). Based on the distribution of deprivation scores, a 3-category neighborhood socioeconomic deprivation variable was constructed: low deprivation (LD, highest overall socioeconomics and lowest quartile of deprivation scores), medium deprivation (MD, middle 2 quartiles), and high deprivation (HD, lowest overall socioeconomics and highest quartile of deprivation scores). CBG measures of socioeconomic position meaningfully summarized important aspects of the specified area's socioeconomic conditions and provided data that can be compared over time and across regions (48).
Statistical analysis. All statistical analyses were performed using Stata Statistical Software Release 8; P < 0.05 was considered statistically significant. Distances from the population-weighted centroid of each CBG to the nearest FS (supermarket, supermarket or full-line grocery store, convenience store, and discount store) were calculated. The ground-truthed (GT) method for identification of FS was compared with public lists (PL) of FS by comparing frequencies and testing for equalities in mean, median, and distribution of distance measures, using Student's t test and Wilcoxon matched-pairs signed-ranks test (46). Because geographic centroids are commonly used in urban areas, equality of means, medians, and distributions of calculations between population-weighted centroids and geographic centroids were compared using the tests above (12,14).
Tests for trend were estimated across categories of increasing deprivation and minority composition using nptrend, which performs the nonparametric test for trend across ordered groups (49). Finally, multivariable regression models were individually fitted, using robust (White-corrected) SE, to determine the relationship of neighborhood measures (neighborhood deprivation, minority composition, interaction between neighborhood deprivation and minority composition, and population density) to the network distance to the nearest supermarket, supermarket/grocery store, convenience store, and discount store. The robust command in Stata corrects SE for heteroscedasticity of unknown form. Afterward, stratified models were estimated separately for each category of population density.
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Results |
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TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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Comparing the use of GT FS with PL FS, mean, median, and distributions of distances from the same neighborhoods were different (P < 0.001). For example, the median distance to the nearest supermarket was 14.9 km using GT and 22.0 km using PL; to the nearest supermarket or grocery store, the distance was 8.4 km (GT) and 14.5 km (PL). In almost 34% (n = 34) of the neighborhoods, distance to the nearest supermarket was overestimated from 10.5–70.1 km when using FS identified from PL compared with GT; the distance to the nearest supermarket or grocery store was overestimated in 34 CBG (2.0–70.1 km); and the distance to the nearest convenience store was overestimated in 12.9% (n = 13) of CBG (range 1–24.2 km).
Neighborhood characteristics. The distribution of socioeconomic characteristics, minority composition, population, land area, and population density (persons/km2) was estimated across the 101 rural CBG (Table 1). The distribution of neighborhood population density (persons/km2) was used to determine 3 categories of population density and serve as an indicator of rurality (Fig. 1): low (lowest quartile of population density and highest degree of rurality), medium (middle 2 quartiles of population density), and high (highest quartile of population density and lowest degree of rurality). In data not shown, the proportion of each of the 7 socioeconomic characteristics present in the CBG increased with greater overall neighborhood socioeconomic deprivation, proportion of minority residents, and population density (P < 0.001). For example, the median percentage of occupied households with no vehicle available increased with greater neighborhood deprivation (LD, 3.4%; MD, 6.8%; and HD, 14.9%), greater percentage of minority residents [low minority composition (LM), 3.8%; medium minority composition (MM), 7.2%; and high minority composition (HM), 13.4%], and increased population density (low, 5.4%; medium, 6.3%; high, 11.8%).
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24 km one way from the nearest supermarket, 17.7 km from the nearest supermarket or full-line grocery, or 7.6 km from the nearest convenience store (data not shown). A map of the study area was produced to include 3 layers of data that were based on SF-3 and GPS for FS: neighborhood socioeconomic deprivation, neighborhood minority composition, and location for supermarkets and grocery stores (Fig. 4).
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Discussion |
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TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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Not only did the most socioeconomically deprived neighborhoods have the best spatial access to all 4 types of FS, but within HD neighborhoods, spatial access was increasingly better for neighborhoods that also had a greater percentage of African American and Hispanic residents (Table 2). This is contrary to published reports of urban areas (14,30). Interestingly, not only were there no HD neighborhoods that had <15% minority residents, but among the 26 neighborhoods with LM, the distance to the nearest FS, regardless of type, was greater in the 12 MD neighborhoods than in the 12 LD neighborhoods. Even after controlling for socioeconomic deprivation, minority composition, and population density, the results of the multivariable regression models extended our knowledge by identifying the coexistence of high socioeconomic deprivation and HM as an independent correlate of better spatial access to the nearest supermarket, grocery store, and discount store. These results persisted in analyses stratified by level of population density.
Compared with previous research that used publicly or commercially available lists of FS (11,14,17,20–24,30), we identified all FS in 6 rural counties using ground truthing and then geocoded all locations on-site using a portable GPS. Evaluation of PL to identify FS in this study area showed that exclusive reliance on PL would misrepresent FS in both directions; that is, PL would include FS that did not exist (18.9%) and omit FS that did exist (35.7%). In fact, 26% of supermarket/grocery stores, 36% of convenience stores, and 20% of discount stores were identified through GT methods only. In addition to affecting the accurate enumeration of the food environment, reliance on PL provided distance measures that significantly misrepresented spatial access for all types of FS when compared with GT methods. To our knowledge, this same level of evaluation of FS identification has not been reported for urban studies in the United States. (12,14,17,30,50).
In addition, despite the ubiquitous use of a geographic centroid as the neighborhood center, population-weighted centroids may be a more accurate depiction of the population center of a rural CBG. A comparison of geographic and population-weighted centroids revealed that the use of a geographic centroid would have significantly overestimated distance and misrepresented potential spatial access when compared with a population-weighted centroid. This is critically important when considering that selection of the method for identification of FS and the choice of centroid (geographic vs. population-weighted) are the 2 points used to construct distance and access measures from a neighborhood to the food environment.
It is especially important to identify the challenges faced by rural residents to the achievement and maintenance of good nutritional health. This study contributes to a greater understanding of part of the challenge: the potential spatial access to different types of FS. The next step will be to better understand the barriers and facilitators for utilization of specific types of FS. Research on the prevention of overweight and obesity has started to recognize that the food choices people make may have more to do with household, neighborhood, and community contexts than with individual psychosocial factors (51–56). In particular, potential spatial access (availability and distance) to supermarkets, which are larger and where consumers usually have greater selection and lower cost for healthful food options than full-line grocery or convenience stores, may provide barriers or facilitators to the actual use of healthy and affordable food resources (9,17,55–57). However, little attention has been paid to the food environment in rural areas, where the prevalence of obesity is higher and where households face considerable geographic and economic challenges (15,58).
Our findings further confirm that rural residents have overall low potential access to FS (50). This is of particular concern, given that greater distance from a supermarket has been associated with the lowest diet quality (23). Spatial inequality experienced by rural families, especially those who are low-income, may further be exacerbated by mobility and time constraints: namely, time spent commuting to work, lack of or limited access to transportation, or not being able to afford the cost of transportation (1,9,50,59).
Limitations to this study also warrant mention. First, the use of administrative-defined areas, such as CBG or census tracts, for a neighborhood may not be consistent with the perceptions of residents (60). Future work is planned to triangulate objective and subjective measures of FS access. Second, measurements of distance to the nearest FS may not be the actual experience of people who choose for a variety of reasons to shop at a store other than the one closest to them. Decisions on where to shop may be influenced by opening hours, standard of service, familial preferences, and established relationships, to name a few (60). Third, data do not capture food purchasing and acquisition patterns, such as who goes shopping, frequency of food shopping, day and time of the typical big shopping trip for food, location, type of items, resources, transportation and route, mobility strategies, and time spent (9,13,61,62). Finally, because we only explored 1 rural region of Texas, we are unable to generalize results beyond this area.
Despite these limitations, this study furthers our knowledge about the rural food environment and the distances households must navigate to purchase needed food. For many residents who lived in neighborhoods that were considered to have better potential access to the food environment, they still had to travel at least 4 km one way to the nearest supermarket or grocery store. For many of these residents, food shopping may be especially challenging; there is no regular public transportation and many households do not have access to a vehicle. All of these factors, along with transportation-related expenses, pose added problems for households in the more isolated rural areas.
Large numbers of an increasingly diverse U.S. population are living in rural areas with a greater burden of disease, increased economic constraints, and greater spatial inequality for access to healthful food (31,32,63). Thus, greater attention must be directed toward the availability and utilization of food resources in rural areas. To foster creative and effective community-based approaches to meeting dietary needs, prospective research that identifies the household, neighborhood, and community barriers and facilitators to healthful food choices needs to be conducted.
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FOOTNOTES |
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2 J. R. Sharkey and S. Horel, no conflicts of interest.
6 Abbreviations used: BVFEP, Brazos Valley Food Environment Project; CBG, census block group; FS, food store; GPS, Global Positioning System; GT, ground-truthed; HD, high deprivation; HM, high minority composition; LD, low deprivation; LM, low minority composition; MD, medium deprivation; MM, medium minority composition; PL, public lists; SF-3, 2000 U.S. Census Summary File 3; UC, urban clusters.
Manuscript received 22 August 2007. Initial review completed 18 September 2007. Revision accepted 12 December 2007.
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