| | Food Store Types, Availability, and Cost of Foods in a Rural EnvironmentAbstract ObjectiveTo characterize the built nutritional environment in terms of types and number of food stores, availability, and cost of selected food items in a rural area. DesignA cross-sectional survey of food stores conducted in 2004. Subjects/settingWe selected a rural county (population 91,582; 1,106 square miles). Food stores identified from a database were mapped and presence, location, and store type verified by ground-truthing. Stores were surveyed for availability and cost of selected foods. Main outcome measuresPrice and availability of a limited number of staple foods representing the main food groups. Statistical analyses performedAvailability comparisons used least square means models and price comparisons used t tests. ResultsOf 77 stores identified, 16% were supermarkets, 10% grocery stores, and 74% convenience stores. There were seven stores per 100 square miles and eight stores per 10,000 residents. Availability of more healthful foods was substantially higher at supermarkets and grocery stores. For instance, low-fat/nonfat milk, apples, high-fiber bread, eggs, and smoked turkey were available in 75% to 100% of supermarkets and groceries and at 4% to 29% of convenience stores. Foods that were available at both supermarkets and convenience stores tended to be substantially more expensive at convenience stores. The healthful version of a food was typically more expensive than the less healthful version. ConclusionsIn this rural environment, stores offering more healthful and lower-cost food selections were outnumbered by convenience stores offering lower availability of more healthful foods. Our findings underscore the challenges of shopping for healthful and inexpensive foods in rural areas. The newly revised Dietary Guidelines for Americans encourage healthful eating patterns and making “wiser food choices.” For instance, a daily intake of 4 ½ cups of fruits and vegetables is recommended, and at least half of the grain intake should come from whole-grain foods (1). While dietary behavior has traditionally been considered a function of individual choice, the ecologic perspective questions to what extent the nutritional environment may facilitate or present barriers to health-promoting individual behaviors (2). The importance of the neighborhood environment for health behaviors and health status is increasingly being recognized (3, 4, 5, 6). Disadvantaged neighborhoods, characterized by overcrowding, lower social class, high percent male unemployment, or households without cars, have been shown to be associated with a variety of poor health behaviors (7). While extensive research efforts are underway assessing how physical activity levels are impacted by physical environment (8), comparatively little attention has been paid to the nutritional environment and its influence on dietary behaviors and health outcomes. The absence of supermarkets and the availability of fast-food restaurants in a neighborhood has been related to a reduced likelihood of consuming sufficient amounts of fruits and vegetables (9, 10). As the distance to a supermarket increases, a lower quality of dietary intake was observed in lower-income pregnant women (11). In addition, the availability and display of health-promoting foods seems to impact food intake (12). While the vast majority of research in this area has been conducted in urban or suburban settings, very little is known about the nutritional environment in rural areas. In the United States, 20% of the population are rural residents (13). Rural areas are not only inherently sparsely populated and characterized by large distances, but also by high levels of poverty, low housing values, and low educational attainment of the resident population. These characteristics can be associated with lower geographic purchasing power, which can affect business decisions about the location and types of food stores and service places. The purpose of this study was to characterize the types and numbers of food stores and the availability and cost of certain food items in a rural South Carolina county. Methods  This study was conducted in Orangeburg County, South Carolina, a rural county with a total population of 91,582. Orangeburg, the largest city, has a population of 12,765 (13). We selected Orangeburg County because of its geographic proximity to Columbia, SC, and because it covers a large land area containing rural, mixed, and urban Census tracts. Data were collected in fall/winter 2004 as part of a broader pilot effort. Identification, Classification, and Mapping of Food Stores In order to compile a complete database listing of all food outlets in Orangeburg County, data collection proceeded in the following steps. The Licensed Food Service Facilities Database, which is maintained for all of South Carolina by the South Carolina Department of Health and Environmental Control’s Office of Public Health Statistics and Information Services, Division of Biostatistics and Health GIS, lists all foodservice facilities that sell prepared foods and contains their address and contact information. The database was queried for code 206 (foodservice facilities), which covers food stores and foodservice facilities, such as restaurants and school cafeterias. In order to assist the fieldwork, addresses of all food outlets were mapped using ArcGIS software (version 8.3, copyright 1999-2002, ESRI, Redlands, CA) and 911-enhanced TIGER road files as the reference road network file. These maps were used to locate the stores. In a second step, project staff verified the list in Orangeburg County, specifically focusing on food stores, in numerous trips canvassing the entire county. This ground-truthing effort was critical to obtaining a complete and accurate enumeration of food stores. Any additional stores that we found (mostly convenience stores with gas stations) were added to the database and geocoded based on their full street address. Several listed stores were not found because of closure or inaccurate addresses. Store types were categorized as supermarkets, grocery stores, or convenience stores by the store managers who were shown a cue card with definitions for each store type (14). Supermarkets were defined as having >$2 million in gross sales annually. They typically belonged to a larger chain and offered a full range of foods. Grocery stores were defined as having <$2 million in annual sales. They typically had less varieties of food available, though still offering a large range of foods, and were generally smaller in size than supermarkets. Convenience stores were identified as stores with a very limited variety of foods. The vast majority of convenience stores had a gas station. In the few instances where store managers refused participation or could not self-identify, project staff classified stores based on their experience. Characterization of Food Stores We developed a food store survey instrument (available from the authors on request) based in part on an existing, shorter survey (15). Our survey assessed the availability and cost of a limited number of staple foods selected from the five main food groups (ie, fruits, vegetables, grains, milk, meat and beans and other products), and the presence of certain store amenities. For certain items, we ascertained both the more healthful version (ie, lower fat, higher fiber) and the less healthful version of the same food. Permission was obtained from each store manager prior to data collection. The first part of the survey was an interview with the manager consisting of questions on the square footage of the store, reasons for present location, and store type as classified by the store managers. Thereafter, project staff conducted a store walk-through to collect data on availability and prices of certain food items, such as fresh vegetables; low-fat/nonfat, reduced fat, and whole milk; regular and low-fat options for chicken and beef; canned tuna and salmon in water; eggs; packaged roasted or smoked turkey; and low-fiber (<2 g/slice) and high-fiber (>2 g/slice) loaves of bread. Information on pricing and availability of vegetables and fruits was restricted to cucumbers, tomatoes, oranges, and apples. The lowest available price of a particular item (per unit weight or per item) was recorded. In a few stores, we converted the price of apples, oranges, and tomatoes to a uniform unit, using the average gram weights per product listed in the US Department of Agriculture database (16). In addition, availability (but not price) was collected for canned tuna and salmon in oil, fresh and frozen seafood, cigarettes, beer, and wine. The store amenities section surveyed attributes such as hours of operation, presence of handicap parking, ramp and curb cuts, automatic doors, off-street parking, number of registers, and participation in Food Stamp and other assistance programs. A team of two investigators (K.E.W. and E.S.) conducted the assessments. One person obtained permission from the manager and conducted the interview, while the other conducted the food price and availability survey. Prior to the fieldwork, staff members were trained in a supermarket in Richland County by the study’s principal investigator (A.D.L.). Surveys were completed at supermarkets or grocery stores in 15 to 20 minutes and in about 5 to 10 minutes at convenience stores. After data for the pilot study had been collected, we utilized remaining resources to evaluate the inter-rater reliability of our survey instrument in a small sample of supermarkets located conveniently in urban Richland County. Ten supermarkets were chosen. Each store was assessed independently on the same day by two persons. We chose supermarkets because of greater variety of products. Our staff achieved 100% agreement with respect to the availability criterion for all surveyed food items. With respect to the cost criterion of food items, assessment of the price of milk (one gallon: low-fat/nonfat, reduced fat, whole milk) was highly reliable (r=1). Reliability for most meats, high-fiber bread, and cucumber prices was good, ranging from 0.80 to 0.98. Reliability was moderate for bacon, eggs, low-fiber bread, oranges, and tomatoes (r=0.40 to 0.77). The assessment of prices of apples was unsatisfactory at 0.20, because of incomplete specification of packaging types. For future work, a more detailed training protocol particularly regarding prices of bulk items will need to be developed. Statistical Analyses Data were recorded and analyzed using Statistical Analysis Software (version 8, 2001, SAS Institute, Inc, Cary, NC). Categorical variables were created for the store amenity variables. Availability of food items was compared between store types using least square means models. t tests were used to compare prices of food items by store type. Results Orangeburg County is located southeast of Columbia in the Midlands region of South Carolina. It covers 1,106 square miles and 20 Census tracts (Table 1). The majority of Orangeburg’s population is African American (61%) and 67% live in a rural area, as defined by the United States Census Bureau. Other important socioeconomic and demographic characteristics are shown in Table 1. We identified a total of 77 food stores in Orangeburg County, including 12 supermarkets (16%), eight grocery stores (10%), and 57 (74%) convenience stores. The total number of food stores relative to land area in Orangeburg County was 7.0 per 100 square miles. For supermarkets, this was 1.1 per 100 square miles. The per-capita density was 8.4 per 10,000 individuals for food stores and 1.3 per 10,000 individuals for supermarkets, respectively. There are 10 predominately rural Census tracts (covering 896 square miles), four mixed tracts, and six predominately urban tracts in Orangeburg County. Forty percent of rural tracts had at least one supermarket, compared to 67% of urban tracts. Sixty percent of the rural tracts contained at least one grocery store, while none of the urban tracts housed a grocery store. Forty-nine percent of all convenience stores were located in the rural tracts and 30% were located in mixed tracts, while only 21% of convenience stores were located in urban areas. Availability of Foods and Store Amenities Availability results of the food store survey are shown in Table 2. The food store survey was conducted in 75 stores, as the managers of one supermarket and one convenience store refused to participate. The vast majority of surveyed food items was available in all supermarkets. Exceptions included tuna in oil, fresh seafood, lean ground beef, and packaged smoked turkey. Ten of 21 food items surveyed were similarly available in supermarkets and grocery stores. For some items, availability in grocery stores was substantially lower than in supermarkets (eg, lean and high-fat ground beef; skinless, boneless chicken breast and drumsticks; and frozen seafood). Convenience stores had a very limited range of food items, and often stocked none or only a few of the items surveyed. Consequently, statistically significant differences between supermarkets and convenience stores were observed in the availability of all but one food item (low-fiber bread). Similar differences were found between grocery stores compared to convenience stores (low-fiber bread and canned tuna in oil were the exceptions). Furthermore, convenience stores were more likely to stock the less healthful version of a given food item (eg, low-fiber breads [86% availability] vs high-fiber bread [4%], whole milk [68%] vs reduced-fat milk [30%] vs low-fat or nonfat milk [2%]). Overall, the variety of produce was highest at supermarkets (P<0.0001) followed by grocery stores and then convenience stores. Across all stores, fruits and vegetables were available in 28% of all stores, and high-fiber bread in 27% of stores. | a Statistically significant (P<0.05) differences between supermarkets and convenience stores. bStatistically significant (P<0.05) differences between grocery stores and convenience stores. cStatistically significant (P<0.05) differences between supermarket and grocery stores. |
With respect to store amenities, supermarkets typically had the easiest access, with 100% of stores having off-street and handicap parking and automatic doors and 82% having ramp and curb cuts (Table 3). Convenience stores tended to have the least accessibility, with 98% having off-street parking, 35% having handicap parking, 51% having ramp and curb cuts, and only one convenience store surveyed had an automatic door. All supermarkets accepted food stamps, as did the majority of grocery stores (63%). In contrast, only one of 56 convenience stores accepted food stamps. Cost of Foods Differences in food prices between store types, shown in Table 4, were most pronounced between supermarkets and convenience stores, but not very pronounced when comparing supermarkets to grocery stores. Of 13 food items that were available at all three store types, nine were substantially more expensive at convenience stores than at supermarkets, including low-fat/nonfat and whole milk, low-fiber bread, canned tuna and salmon, eggs, packaged smoked turkey and bacon, and apples. Differences in prices for reduced-fat milk and high-fiber bread pointed in the same direction, but did not reach statistical significance. The price of oranges and tomatoes, which were available at only one of the 56 convenience stores, were intermediate between supermarkets and grocery stores. | a This table describes the average price of the surveyed food items. Given the differences in availability of food items, sample sizes vary within the table. If no standard deviation is listed, only one store stocked the food item. bStatistically significant (P<0.05) differences between supermarkets and grocery stores. cStatistically significant (P<0.05) differences between supermarkets and convenience stores. dStatistically significant (P<0.05) differences between grocery stores and convenience stores. eThe price of apples was identical in both stores. |
Average differences in prices of select items between convenience stores and supermarkets were as follows: $0.56 for packaged bacon, $0.94 for packaged smoked/roasted turkey, $0.29 for eggs, $0.88 for canned salmon in water, $0.45 for canned tuna in water, $0.88 for low-fiber bread, $0.58 for high-fiber bread, $0.42 for whole milk, $0.17 for reduced-fat milk, and $0.54 for low-fat/nonfat milk. When comparing grocery stores to convenience stores, prices of seven of the 13 items were substantially higher at convenience stores. Furthermore, more-healthful versions of food items were typically more expensive than the corresponding less-healthful version, with the exception of milk. The difference in price between high-fiber and low-fiber bread ranged from $0.23 in convenience stores to $0.61 in grocery stores, between low-fat beef and high-fat beef, from $0.80 in grocery stores to $0.94 in supermarkets, and between chicken breasts and drumsticks, from $1.64 in grocery stores to $2.29 in supermarkets. However, for milk, the price differences were much less pronounced, but the more-healthful versions were cheaper. The low-fat/nonfat milk was, on average, 3 cents (supermarkets) to 20 cents (grocery stores) less expensive than reduced-fat milk, which cost, on average, 3 cents less than whole milk in supermarkets. Discussion A small body of literature has recently emerged demonstrating that in the United States, neighborhoods differ markedly with respect to the number and types of food stores (17). Moore and Diez-Roux (17) have studied three communities, including parts of Baltimore, MD (242 square miles), Manhattan and the Bronx, NY (26 square miles), and Forsyth County, North Carolina (410 square miles). The percentage of stores that were supermarkets was much higher in predominantly white areas, and percentage of stores that were grocery stores was higher in predominantly minority areas. Morland and colleagues (10) studied four areas, including Jackson City, MS (107 square miles), Washington County, Maryland (458 square miles), Minneapolis, MN (91 square miles), and Forsyth County, NC (410 square miles), with a total study area 1,066 square miles. They reported a substantially higher prevalence of supermarkets in wealthier neighborhoods and a higher prevalence of convenience stores in medium wealthy neighborhoods. A study contrasting two areas with high vs low-percent African-American residents (56 vs 19 square miles) within the Los Angeles, CA, metropolitan area (75 square miles), found that supermarkets were much less frequent in the high-minority area (18). In the most impoverished neighborhoods in Detroit, MI, there was a substantial positive relationship between proportion of African-American residents and distance to the nearest supermarket (19). Our study adds to this literature by characterizing a rural community in South Carolina, a state that carries a disproportionate burden of nutrition-related health conditions, having the third highest prevalence of diabetes and 15th highest prevalence of obesity nationwide (20). Rural residents of every racial/ethnic group are at higher risk of obesity than urban whites (21). The highest rates of hypertension have been reported in rural African Americans (22). With decreasing levels of urbanization, prevalence of sedentary leisure time increases markedly (23), rural residents being substantially less likely to meet recommendations regarding physical activity (24). Seventy-five percent of rural African-American adults do not meet dietary recommendations for fruit and vegetable intake (25). In a Southern rural population, residents generally exhibited poorer adherence to recommendations than national averages (26). In particular, African-American adults generally consumed less-optimal diets than white adults (27). In this context, our findings may be pointing toward one set of underlying societal determinants of these marked rural health disparities. The land area covered in this study, a total of 1,106 square miles, reflects the distances that rural populations face when considering their grocery shopping options. There were roughly 0.07 food stores per square mile in Orangeburg County, compared to 1 per square mile (North Carolina), 3 per square mile (Maryland), and 67 per square mile (New York) (17). On a per-capita basis, we identified 8.4 food stores per 10,000 residents and 1.3 supermarkets per 10,000 residents, which is comparable to the 8 to 10 food stores per 10,000 residents reported by Moore and Diez-Roux from mostly urban environments (17). Food stores in the latter study included supermarkets; groceries; and convenience stores; plus specialty stores such as meat and fish markets; fruit and vegetable markets; bakeries; and natural food stores. While the original database obtained from the South Carolina Department of Health and Environmental Control contained a few specialized food stores in Orangeburg County, the ground-truthing effort could not verify them. However, the distribution of food stores in Orangeburg County was heavily weighted toward convenience stores (74%) vs larger food stores (16% supermarkets, 10% grocery stores). In contrast, urban environments seem to have a higher proportion of grocery stores or supermarkets, ranging from 36% to 57% and, conversely, a lower percentage of convenience stores (which includes stores with gas stations) ranging from 8% to 41% (17). It can be argued that the mere number or presence of certain types of food stores might not be indicative of the types and quality of the foods sold (ie, availability of healthful food choices). Our study, however, provides evidence that there are marked differences in the availability of selected food items between supermarkets, grocery stores, and convenience stores. In this rural county, more healthful food options were generally available in all supermarkets; however, availability was lower in grocery stores and, as expected, lowest in convenience stores. Sloane and colleagues (18) reported that only 49% of 261 stores surveyed in their Los Angeles target area sold fruits and vegetables, and 42% sold whole-grain bread. A survey conducted in two urban Californian cities found that while most stores stocked healthier substitute items, the availability was more sporadic, varieties more limited, and nutritional quality noticeably lower in smaller, independent grocery stores (28). In rural Orangeburg County, only 28% of the stores sold any of the key fruits or vegetables we surveyed, and 27% sold high-fiber bread. Both cost and availability of foods have been shown to influence dietary behaviors (29, 30). We found marked differences in the costs of foods between the different store types, with food purchased at convenience stores with few exceptions being more expensive than at either supermarkets or grocery stores. Furthermore, more-healthful food items were typically more expensive than the corresponding less-healthful item. Two studies conducted in the United Kingdom have shown similar findings (31, 32), indicating that a healthful food basket is, on average, substantially more expensive than the corresponding less-healthful selection of foods. Likewise, a healthier market basket was roughly 16% to 22% more expensive than the corresponding regular market basket in a recent survey of Los Angeles and Sacramento stores (28). These studies, like ours, suggest that availability of healthful food choices is much greater in wealthier areas. Furthermore, in a review, Kaufman and colleagues (33) conclude that in areas with limited food store choices, such as low-income areas, households may well face higher food prices. Several reports now link the availability of foods offered in stores to individual dietary behaviors. Both presence and type of food outlets (eg, absence of supermarkets and presence of fast-food restaurants) and their distance to a person’s residence (eg, distance to supermarkets) have been associated with dietary patterns or specific food group intake (9, 10, 11). In addition, the availability and display of health-promoting foods seems to have a positive impact on healthful food intake (12, 34). In addition, evidence is emerging that the food store environment is related to disease risk. In four, mostly urban, communities in the United States, Census tracts with a supermarket had a 9% lower prevalence of overweight and a 24% lower prevalence of obesity compared to Census tracts without supermarkets (35). These substantial effects persisted after taking into account factors such as other types of food stores and foodservice places, age, race, sex, education, and income. As expected, obesity was more common in areas with small corner grocery stores and convenience stores. More broadly, socioeconomic characteristics of the neighborhood have been linked to dietary habits of youth and adults alike, independent of individual income (36, 37). This has been shown for a variety of foods, including fruits, vegetables, salad, fish, and meats, including hamburgers and hot dogs, but also french fries and chips. There are a number of limitations of our study, pertaining to both identification of potential food outlets in Orangeburg County, and to the design of the food store survey. While we believe our study achieved a high level of completeness with respect to identifying food stores, we did not include pharmacies or drug stores that sell packaged foods. However, because these stores, at best, offer a level of food quality similar to convenience stores, our estimates regarding the convenience stores are likely conservative. Furthermore, our data do not include farmers’ markets or roadside fruit or vegetable stands, given that data collection occurred in the winter months. Our food store survey instrument was developed specifically for our study objective and may not be directly comparable to other instruments developed in the past (18) or more recently (38). The list of food items surveyed was very limited, intended to identify a few staple foods from the five main food groups. We did not ascertain specific varieties of fresh fruit or vegetables, other forms of the same foods, such as canned or frozen vegetables, or information on the type or number of specific brands. Because of the pilot nature of our study, it was not designed at the level of sophistication of formal food or market basket analyses (28, 33). We carefully trained our project staff and assessed the reliability of our instrument, albeit in a very small substudy. Our instrument shows excellent inter-rater reliability with respect to the availability criterion for all food items. The cost component was also moderately to highly reliable for most foods, while training specifications and the survey protocol for apples, for instance, clearly need to be improved in future efforts. We did not validate the instrument. Finally, our study does not include food purchasing data or sales data on individual food items, so we have no information on the extent to which availability of certain foods may be a function of consumer demand or lack thereof. Conclusions Our work adds to a growing body of evidence suggesting that rural populations face great disparities in terms of many health outcomes and health behaviors. Although the Dietary Guidelines for Americans (1) are intended for all US residents, our study suggests that rural residents may be at a marked disadvantage when it comes to meeting these guidelines. In our study area, only one fourth of all food stores supported the specific healthful dietary choices surveyed. Stores offering more-healthful and lower-cost food selections were outnumbered by convenience stores offering lower availability of healthful foods. Research is needed to evaluate to what extent the large distances and transportation challenges faced by rural communities impact decisions to shop in more-healthful and lower-cost food environments. This project was funded by a seed grant from the Center for Research in Nutrition and Health Disparities at the Arnold School of Public Health, University of South Carolina. We would like to thank Guang Zhao, PhD, and Jared Shoultz, South Carolina Department of Health and Environmental Control for access to their GIS-based food service database; Dwayne Porter, PhD, Arnold School of Public Health and Baruch Institute, University of South Carolina, and Kevin Remington, University of South Carolina Center for GIS and Remote Sensing, for support of GIS-related aspects of this study; and Akiko Hosler, PhD, University at Albany, the Albany Prevention Research Center, and the New York State Department of Health for sharing their food store survey instrument. References 1. 1US Department of Health and Human Services and US Department of Agriculture. Dietary Guidelines for Americans. 6th ed.. Washington, DC: US Government Printing Office; 2005;. 2. 2Susser M, Susser E. Choosing a future for epidemiology: II. From black box to Chinese boxes and eco-epidemiology. Am J Public Health. 1996;86:674–677. MEDLINE |
CrossRef
3. 3Berkman LF, Kawachi I. Social Epidemiology. New York, NY: Oxford University Press; 2000;. 4. 4Kawachi I, Berkman LF. Neighborhoods and Health. New York, NY: Oxford University Press; 2003;. 5. 5Glanz K, Sallis JF, Saelens BE, Frank LD. Healthy nutrition environments: Concepts and measures. Am J Health Promot. 2005;19:330–333. MEDLINE 6. 6Popkin BM, Duffey K, Gordon-Larsen P. Environmental influences on food choice, physical activity and energy balance. Physiol Behav. 2005;86:603–613. MEDLINE |
CrossRef
7. 7Ellaway A, Macintyre S. Does where you live predict health related behaviours?: A case study in Glasgow. Health Bull (Edinb). 1996;54:443–446. MEDLINE 8. 8Booth SL, Sallis JF, Ritenbaugh C, Hill JO, Birch LL, Frank LD, et al. Environmental and societal factors affect food choice and physical activity: Rationale, influences, and leverage points. Nutr Rev. 2001;59(3 Pt 2):S21–S39. MEDLINE |
CrossRef
9. 9Edmonds J, Baranowski T, Baranowski J, Cullen KW, Myres D. Ecological and socioeconomic correlates of fruit, juice, and vegetable consumption among African-American boys. Prev Med. 2001;32:476–481. MEDLINE |
CrossRef
10. 10Morland K, Wing S, Diez-Roux A, Poole C. Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med. 2002;22:23–29. Abstract | Full Text |
Full-Text PDF (76 KB)
|
CrossRef
11. 11Laraia BA, Siega-Riz AM, Kaufman JS, Jones SJ. Proximity of supermarkets is positively associated with diet quality index for pregnancy. Prev Med. 2004;39:869–875. MEDLINE |
CrossRef
12. 12Cheadle A, Psaty BM, Curry S, Wagner E, Diehr P, Koepsell T, et al. Community-level comparisons between the grocery store environment and individual dietary practices. Prev Med. 1991;20:250–261. MEDLINE |
CrossRef
13. 13US Census Bureau. American Fact Finder, Census 2000. Available at: http://factfinder.census.gov/. Accessed April 11, 2006. 14. 14Cohen B. Community Food Security Assessment Toolkit: A report from the Economic Research Service of United States Department of Agriculture. Electronic Publication from the Food Assistance & Nutrition Research Program; 2002;. 15. 15Hosler AS, Varadarajulu D, Ronsani AE, Fredrick BL, Fisher BD. Low-fat milk and high-fiber bread availability in food stores in urban and rural communities. J Public Health Manag Pract. 2006;12:556–562. MEDLINE 16. 16United States Department of Agriculture. Nutrient Data Laboratory. USDA: Agricultural Research Service. Available at: http://www.nal.usda.gov/fnic/foodcomp/search/. Accessed March 28, 2005. 17. 17Moore LV, Diez-Roux AV. Associations of neighborhood characteristics with the location and type of food stores. Am J Public Health. 2006;96:325–331. MEDLINE |
CrossRef
18. 18Sloane DC, Diamant AL, Lewis LB, Yancey AK, Flynn G, Nascimento LM, et al. Improving the nutritional resource environment for healthy living through community-based participatory research. J Gen Intern Med. 2003;18:568–575. MEDLINE |
CrossRef
19. 19Zenk SN, Schulz AJ, Israel BA, James SA, Bao S, Wilson ML. Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit. Am J Public Health. 2005;95:660–667. MEDLINE |
CrossRef
20. 20Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003;289:76–79. MEDLINE |
CrossRef
21. 21Patterson PD, Moore C, Probst JC, Shinogle JA. Obesity and physical inactivity in rural America. J Rural Health. 2004;20(2):. 22. 22Patterson PD, Moore CG, Probst JC, Samuels ME. Hypertension, diabetes, cholesterol, weight, and weight control activities among non-metro minority adults. Report #3, December, 2002. South Carolina Rural Health Research Center, Arnold School of Public Health, University of South Carolina, Columbia, SC. Available at: http://rhr.sph.sc.edu/index6.html#Report3. Accessed January 15, 2003. 23. 23Eberhardt MS, Pamuk ER. The importance of place of residence: Examining health in rural and nonrural areas. Am J Public Health. 2004;94:1682–1686. MEDLINE |
CrossRef
24. 24Parks SE, Housemann RA, Brownson RC. Differential correlates of physical activity in urban and rural adults of various socioeconomic backgrounds in the United States. J Epidemiol Community Health. 2003;57:29–35.
CrossRef
25. 25McClelland JW, Demark-Wahnefried W, Mustian RD, Cowan AT, Campbell MK. Fruit and vegetable consumption of rural African Americans: Baseline survey results of the Black Churches United for Better Health 5 A Day Project. Nutr Cancer. 1998;30:148–157. MEDLINE |
CrossRef
26. 26Champagne CM, Bogle ML, Karge WH. Using national dietary data to measure dietary changes. Public Health Nutr. 2002;5:985–989. MEDLINE |
CrossRef
27. 27Champagne CM, Bogle ML, McGee BB, Yadrick K, Allen HR, Kramer TR, et al. Dietary intake in the lower Mississippi delta region: Results from the Foods of our Delta Study. J Am Diet Assoc. 2004;104:199–207. Abstract | Full Text |
Full-Text PDF (361 KB)
|
CrossRef
28. 28Jetter KM, Cassady DL. The availability and cost of healthier food items (University of California, Agricultural Issues Center). AIC Issues Brief. 2005;29:1–6. 29. 29Glanz K, Basil M, Maibach E, Goldberg J, Snyder D. Why Americans eat what they do: Taste, nutrition, cost, convenience, and weight control concerns as influences on food consumption. J Am Diet Assoc. 1998;98:1118–1126. Abstract | Full Text |
Full-Text PDF (1027 KB)
|
CrossRef
30. 30Wechsler H, Basch CE, Zybert P, Lantigua R, Shea S. The availability of low-fat milk in an inner-city Latino community: Implications for nutrition education. Am J Public Health. 1995;85:1690–1692. MEDLINE |
CrossRef
31. 31Sooman A, Macintyre S, Anderson A. Scotland’s health: A more difficult challenge for some? (The price and availability of healthy foods in socially contrasting localities in the West of Scotland). Health Bull. 1993;51:276–284. 32. 32Mooney C. Cost and availability of healthy food choices in a London health district. J Human Nutr Diet. 1990;3:111–120. 33. 33Kaufman PR, MacDonald JM, Lutz SM, Smallwood DM. Do the Poor Pay More for Food? (Item Selection and Price Differences Affect Low-Income Household Food Costs). In: Agricultural Economic Report #759. Washington, DC: Food and Rural Economics Division, Economic Research Service, US Department of Agriculture; 1997;p. 1–27. 34. 34Cheadle A, Psaty BM, Curry S, Wagner E, Diehr P, Koepsell T, et al. Can measures of the grocery store environment be used to track community-level dietary changes?. Prev Med. 1993;22:361–372. MEDLINE |
CrossRef
35. 35Morland K, Diez-Roux AV, Wing S. Supermarkets, other food stores, and obesity: The Atherosclerosis Risk in Communities study. Am J Prev Med. 2006;30:333–339. Abstract | Full Text |
Full-Text PDF (95 KB)
|
CrossRef
36. 36Diez-Roux AV, Nieto FJ, Caulfield L, Tyroler HA, Watson RL, Szklo M. Neighbourhood differences in diet: The Atherosclerosis Risk in Communities (ARIC) Study. J Epidemiol Community Health. 1999;53:55–63.
CrossRef
37. 37Lee RE, Cubbin C. Neighborhood context and youth cardiovascular health behaviors. Am J Public Health. 2002;92:428–436. MEDLINE |
CrossRef
38. 38Nutrition Environments Measures Study. Rollins School of Public Health, Emory University; Atlanta, GA. Available at: http://www.sph.emory.edu/NEMS/in2.htm. Accessed April 11, 2005. A. D. Liese is an associate professor, K. E. Weis is a doctoral candidate, E. Smith is a recent masters graduate, and A. Lawson is a professor, Department of Epidemiology and Biostatistics; D. Pluto is associate director, Prevention Research Center and research assistant professor, Department of Health Promotion, Education, and Behavior; all at the Arnold School of Public Health, University of South Carolina, Columbia.  Address correspondence to: Angela D. Liese, PhD, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 800 Sumter St, Columbia, SC 29208.
PII: S0002-8223(07)01622-7 doi:10.1016/j.jada.2007.08.012 © 2007 American Dietetic Association. Published by Elsevier Inc. All rights reserved. | |
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