Food insecurity, defined as inadequate access to healthy and affordable food, affects approximately 10.2 percent of the US population. In particular, food insecurity negatively affects health outcomes in the impacted populations. This paper discusses the association between convenience stores and food insecurity, including how access to food access impacts health factors/outcomes, specifically for New Jersey populations. The comparative study was conducted using the data on the number of convenience stores, food access, and health factors/outcomes in each NJ county. The findings reveal that low food access generally increases with the number of convenience stores in a county. However, the number of convenience stores exhibits a weaker relationship with health measures and no obvious pattern with health factors and outcomes. In addition, further study has to be done to disclose the causal relationship between different variables. Despite these limitations, the results from the survey can be used to determine the recipients of food assistance programs, such as the Healthy Food Financing Initiative (HFFI). Ultimately, this study yields a critical conclusion that the abundance of small food stores, such as convenience stores, often limits access to healthy foods and, consequently, can be a measure for food program intervention to be implemented.
Introduction
What if the only place to get fresh food in your neighborhood was the convenience store? This is the reality of people who do not have adequate access to supermarkets and are instead exposed to the abundance of small food stores that lack fresh food options. In this case, food insecurity is not just about hunger or a lack of food but about availability.
Food insecurity exists in every part of our globe, with varying forms and degrees. For example, residents in food deserts are considered to be food insecure because they do not have adequate access to healthy and affordable food. In New Jersey, for instance, 7.4 percent of individuals and 9 percent of children live under food insecurity1. Understanding food insecurity is essential because it impacts many aspects of people’s lives, including health outcomes and social connections. Thus, better insight into food insecurity helps us find solutions to fight food insecurity. There is abundant research on the causes and effects of food insecurity. Many papers examine the relationship between food insecurity and small food stores, including convenience stores. Although previous research has shown that “corner or convenience stores tend to offer fewer healthy options,” various health indicators have not been looked at2. Therefore, this paper found that their association will be better explained by examining multiple variables, such as how the number of convenience stores is related to the health factors/outcomes in a neighborhood.
This paper discusses the association between convenience stores and food insecurity, including how food access impacts health factors/outcomes, specifically for New Jersey populations. Based on the literature review, it can be proposed that a higher number of convenience stores in a neighborhood is correlated with heightened food insecurity.
Food Insecurity
Andersondefines food insecurity as “the limited or uncertain availability of nutritionally adequate and safe foods, or limited or uncertain ability to acquire acceptable foods in socially acceptable ways”3. Food insecurity appears to be different in different households with different demographics and economic statuses. Households with children, households with children headed by a single woman or a single man, households headed by Black, non-Hispanics, Hispanics, and low-income households with incomes below 185 percent of the poverty threshold especially reported higher rates of food insecurity than the national average4. Bown et al. suggest that food insecurity is about twice more prevalent among Black and Latino/a/x-headed households than white-headed households5. Even worse, in 2020, 21.7% of Black households were food insecure compared to only 7.1% of White households6. Economic factors also contribute to food insecurity as “states with lower wages, higher housing costs, and food prices, higher unemployment rates, and regressive tax policies” face more food insecurity6.
According to the US Department of Agriculture (USDA), 10.3 percent (10.5 million) of U.S. households were food insecure in 19957. This number spiked up to 14.6 percent (17 million) of U.S. households in 2008 when many people had difficulties maintaining their living standards due to The Great Recession. This number stayed high until 2012, with 14.5 percent (17.6 million) of food insecure U.S. households, and went down from 2013, with 8 percent (9.8 million) of U.S. households. In 2021, the most recent data, 10.2 percent (13.5 million) of U.S. households were food insecure. This number is similar to that of 1995.
The USDA measures food insecurity, based on the responses to 18 questions, of about 40,000 households in the Food Security Supplement of the Census Bureau’s Current Population Survey (CPS-FSS). The questions are about conditions and behaviors that help identify households having difficulty meeting basic food needs. Each question asks whether the respondent experienced the listed conditions or behaviors during the previous 12 months and specifies a lack of money or resources as the reason. The first ten questions are asked to all respondents; if children aged 0-17 are present in the household, an additional 8 questions are asked. The 18 survey questions used by USDA are listed in the table below. Households are classified as food insecure if they report 3 or more conditions that indicate food insecurity. They are classified as food-insecure children if they report 2 or more conditions that suggest food insecurity among the children in response to questions 11-18. Then, food-insecure households are further divided into 2 different categories: low food security and very low food security. Households are classified as having very low food security if they address 6 or more food-insecure conditions; households with children are classified as having very low food security if they address 5 or more food-insecure conditions among the children4.
Questions |
1. “We worried whether our food would run out before we got money to buy more.” Was that often, sometimes, or never true for you in the last 12 months? 2. “The food that we bought just didn’t last and we didn’t have money to get more.” Was that often, sometimes, or never true for you in the last 12 months? 3. “We couldn’t afford to eat balanced meals.” Was that often, sometimes, or never true for you in the last 12 months? 4. In the last 12 months, did you or other adults in the household ever cut the size of your meals or skip meals because there wasn’t enough money for food? (Yes/No) 5. (If yes to question 4) How often did this happen—almost every month, some months but not every month, or in only 1 or 2 months? 6. In the last 12 months, did you ever eat less than you felt you should because there wasn’t enough money for food? (Yes/No) 7. In the last 12 months, were you ever hungry, but didn’t eat, because there wasn’t enough money for food? (Yes/No) 8. In the last 12 months, did you lose weight because there wasn’t enough money for food? (Yes/No) 9. In the last 12 months did you or other adults in your household ever not eat for a whole day because there wasn’t enough money for food? (Yes/No) 10. (If yes to question 9) How often did this happen—almost every month, some months but not every month, or in only 1 or 2 months? (Questions 11-18 were asked only if the household included children age 0-17) 11. “We relied on only a few kinds of low-cost food to feed our children because we were running out of money to buy food.” Was that often, sometimes, or never true for you in the last 12 months? 12. “We couldn’t feed our children a balanced meal, because we couldn’t afford that.” Was that often, sometimes, or never true for you in the last 12 months? 13. “The children were not eating enough because we just couldn’t afford enough food.” Was that often, sometimes, or never true for you in the last 12 months? 14. In the last 12 months, did you ever cut the size of any of the children’s meals because there wasn’t enough money for food? (Yes/No) 15. In the last 12 months, were the children ever hungry but you just couldn’t afford more food? (Yes/No) 16. In the last 12 months, did any of the children ever skip a meal because there wasn’t enough money for food? (Yes/No) 17. (If yes to question 16) How often did this happen—almost every month, some months but not every month, or in only 1 or 2 months? 18. In the last 12 months did any of the children ever not eat for a whole day because there wasn’t enough money for food? (Yes/No) |
Food Desert
Food deserts encompass geographical areas that align with the USDA’s criteria for food insecurity. The USDA defines food deserts as “the areas that consist of large proportions of households with low incomes, inadequate access to transportation, and a limited number of food retailers providing fresh produce and healthy groceries for affordable prices”8. More specifically, a food desert is “a low-income tract with at least 500 people, or 33% of the population, living more than ½ mile (urban areas) or more than 10 miles (rural areas) from the nearest supermarket, super-center or large grocery store”9. Chenarides et. al (2021) says that more than 39 million people in the United States live in food deserts. These food deserts typically lack supermarkets or large grocery stores. Limited access to fresh foods exposes households living in food deserts to “a density of lower-quality food outlets, such as convenience stores”10.
Mageementions that the main factors of food deserts in the United States are race and poverty. Many food deserts are located in low-income neighborhoods with ethnic minorities 11. Because supermarkets tend to locate their stores in “wealthier, white” neighborhoods, low-income or minority areas are more likely to experience inadequate access to fresh food12.
For instance, only 8 percent of African Americans live in a census tract with a supermarket, while 31 percent of White Americans do. Also, lower-income neighborhoods have 30 percent more convenience stores than higher-income neighborhoods, with the lack of supermarkets. In addition to race and income, transportation is critical in determining food deserts. Residents without access to transportation and vehicles often fail to obtain fresh foods from grocery stores or supermarkets13.
Specifically, the New Jersey Economic Development Authority reported that about 1.3 million New Jersey residents lived in food deserts in 202214. The report specifies that various locations, such as north, central, and south Camden, Atlantic City, Newark, and Paterson, need help in obtaining fresh and healthy foods.
Convenience Store
Convenience stores are crucial in discussions about food deserts. In this paper, a convenience store refers to a retailer with “a limited line of goods that generally includes milk, bread, soda, and snacks,” according to the North American Industry Classification System Association (NAICS Association)15. More specifically, in regards to this paper, a convenience store also means a small store with mainly pre-cooked foods (“ready-to-eat foods”), “staple groceries[,] and a limited supply of fresh food” 16. To distinguish convenience stores from supermarkets and other grocery retailers, it is important to highlight the differences between them. Supermarkets and other grocery retailers mostly stock “a general line of food, such as canned and frozen foods; fresh fruits and vegetables; and fresh and prepared meals, fish, and poultry (NAICS Association)15.” Also, for the sake of this research paper, gasoline stations with convenience stores are not considered convenience stores to eliminate outside factors, such as residents from other towns. Gasoline stations with convenience stores are defined differently from convenience stores, as they mainly “engage in retailing automotive fuels (e.g. gasoline, diesel fuel, gasohol, alternative fuels) in combination with a limited line of groceries (NAICS Association) (NAICS Association)15.” Examples of convenience stores in the data set include 7-Eleven, Circle K, Speedway, and Duane Reade Pharmacy.
Much research has been conducted on the association between small food stores, such as convenience stores, and food environments. According to the FAO’s Agriculture and Development Economics Division (ESA)17, food access is defined as “access by individuals to adequate resources (entitlements) for acquiring appropriate foods for a nutritious diet.” Many studies have found a positive relationship between the number of supermarkets and improved food access. Larson et al. suggest that people with better access to supermarkets with nutritious food options tend to have healthier food intakes, which means better food access18. However, small food stores, such as convenience stores, hurt households’ food access in a neighborhood. Peng & Kaza19 found that residents in areas with many convenience stores purchased fewer fruits than those with fewer convenience stores19. Their study shows that the number of convenience stores in neighborhoods was inversely related to “self-reported diet quality.” Because convenience stores mainly offer “prepared, high-calorie foods and little fresh produce at higher prices,” they do not improve households’ food access, as access should be achieved by healthy foods. Likewise, the entry of dollar stores into a food desert will not likely improve food access in a neighborhood10.
Based on their research, this paper investigates the relationship between New Jersey convenience stores, food access, and health factors/outcomes.
Health Outcomes of Food Insecurity
Food insecurity is closely related to adverse health outcomes for the impacted populations. One study found that food insecurity is associated with “increased risks of some congenital disabilities, anemia, lower nutrient intakes, cognitive problems, and aggression and anxiety”20.. Food insecurity also results in “higher risks of being hospitalized and poorer general health and with having asthma, behavioral problems, depression, suicide ideation, and worse oral health.”
Nagata et al. explain the association between food insecurity and mental health21. Food-insecure young adults were more likely to have mental health problems, including depression, anxiety or panic disorder, and suicidal ideation in the past 12 months. Food insecurity was also related to poorer sleep outcomes, including trouble falling and staying asleep. Moreover, the experience of being food insecure generates a feeling of perceived powerlessness, desperation, shame, and guilt, which may lead to anxiety and depressive symptoms. In addition, Nagata et al.found that a more significant proportion of their research populations reporting food insecurity had “poor health status, diabetes, hypertension, being ‘very overweight,’ obesity, and obstructive airway disease” compared with research populations reporting to be food secure22. Food insecurity often leads to the consumption of “cheaper, calorie-dense but nutrient-poor foods” and less consumption of fresh produce. In addition, food programs and monthly paychecks “may promote insulin resistance due to alternating periods of food access and food shortage.” The uncertainty of acquiring food may also lead to binge eating behaviors and obesity. These diets and eating patterns due to food insecurity result in poor health outcomes, including mental health and cardiometabolic disease.
Specifically in food deserts, residents are exposed to “energy-dense food (empty calorie food)” available at convenience stores and fast-food restaurants since there is a lack of fresh food outlets. One research found that a diet filled with processed foods “containing high contents of fat, sugar and sodium” often results in poor health outcomes compared to a diet with enough complex carbohydrates and fiber23. Because the major food supplies from small food stores are processed foods, residents in food deserts often display poorer health outcomes than those outside of food deserts. While previous studies address health outcomes resulting from food insecurity, there is not enough study regarding the number of convenience stores and associated health outcomes. Thus, this research aims to connect poor health outcomes to the abundance of convenience stores, possibly connected to lower food access.
Methods
Data
To examine the accessibility of food to NJ populations, data on food access was obtained from the Food Access Research Atlas of the Department of Agriculture Economic Research Service (USDA ERS). The Food Access Research Atlas offers information on food access for individuals and neighborhoods by considering accessibility indicators to healthy food sources. These individual-level resources may affect accessibility and neighborhood-level indicators of resources. The data uses census tracts,“small, relatively permanent statistical subdivisions of a county or statistically equivalent entity” that normally have a population size between 1,200 and 8,00024. Urban census tracts are “densely developed territory and encompass residential, commercial, and other non-residential urban land uses”24. The data on census tracts was used to identify areas with low access to healthy food. The number of census tracts and urban census tracts for each county in New Jersey was found using the information on the Food Access Research Atlas. Urban census tracts were identified to differentiate them from rural census tracts, as in the food access measures by the Food Research Atlas. The number of low access tracts at ½-mile and 1-mile demarcations to the nearest supermarket for urban areas and 10-mile and 20-mile demarcations to the nearest supermarket for rural areas was found using the Food Access Research Atlas information.
Information on the number of convenience stores in each county in NJ was collected from ScrapeHero’s data set, “Top Convenience Stores in New Jersey.” This data set presents the locations of 751 convenience stores across New Jersey as of June 27, 2023.
Data on health factors and outcomes in each county in NJ were obtained from New Jersey Data (2019) by County Health Rankings & Roadmaps (CHR&R) of the University of Wisconsin Population Health Institute25. CHR&R demonstrates different elements that affect health outcomes and provides health rankings for counties in New Jersey. Only specific measures were used in this study, including various health factors and outcomes. This study looks at only diabetes prevalence, the percentage of populations aged 20 and older diagnosed with diabetes. 4 measures of health factors — food environment index, limited access to healthy foods, median household income, and residential segregation — were used because they are the most direct measures influencing food accessibility. The food environment index represents the “index of factors that contribute to a healthy food environment, from 0 (worst) to 10 (best).” It considers the proximity to nutritious foods and income simultaneously. Limited access to healthy foods refers to the percentage of low-income populations who do not live close to a grocery store. Median household income is a social & economic health factor that looks at “the income where half of households in a county earn more and half of households earn less.” Lastly, residential segregation represents the “index of dissimilarity where higher values indicate greater residential segregation” between Black and white/non-white and white county residents” Zero refers to “complete integration” of 2 groups, and 100 refers to “complete segregation” between 2 groups.
Method
This paper examines the relationship between the number of convenience stores, food access, and health factors/outcomes in New Jersey. The locations (addresses) of convenience stores were used to determine the number of stores in each county. Then, the Food Access Research Atlas was used to identify the degree of food access in NJ counties and each county’s number of low-access census tracts in NJ. Lastly, the County Health Rankings & Roadmaps (CHR&R) was used to compare the health outcomes of each county in NJ.
Data for each county was listed in ascending order with the least to the most number of convenience stores in order to distinguish any patterns regarding the association among convenience stores, food access, and health measures. To easily compare these data with one another, the top half counties with the number of convenience stores were named Group A, and the bottom half counties with the number of convenience stores were named Group B. Since there is an odd number of counties (21), 1 county in the middle (Hudson County) was excluded from both Group A and Group B.
Results
Table 2 shows the number of convenience stores in each county in New Jersey. The top half counties with the highest number of convenience stores, or Group A, are Ocean County, Bergen County, Monmouth County, Middlesex County, Burlington County, Camden Count, Union County, Atlantic County, Essex County, and Mercer County. These counties, in common, have relatively higher populations than other counties in NJ. The bottom half of counties with the number of convenience stores, or Group B, are Hunterdon County, Salem County, Warren County, Sussex County, Somerset County, Cumberland County, Cape May County, Passaic County, Gloucester County, and Morris County. These counties, in common, have relatively lower populations compared to other counties in NJ. In addition, Figure 1 shows that counties in each group are mainly located near each other. Group A counties are mostly located on the right side of NJ, while Group B counties are mostly located on the left side of NJ.
County | Number of Convenience Stores |
Hunterdon County | 6 |
Salem County | 6 |
Warren County | 8 |
Sussex County | 9 |
Somerset County | 12 |
Cumberland County | 14 |
Cape May County | 17 |
Passaic County | 19 |
Gloucester County | 25 |
Morris County | 25 |
Hudson County | 29 |
Mercer County | 29 |
Essex County | 33 |
Atlantic County | 38 |
Union County | 51 |
Camden County | 58 |
Burlington County | 62 |
Middlesex County | 76 |
Monmouth County | 77 |
Bergen County | 78 |
Ocean County | 79 |
Table 3 shows the number of census tracts, the number of low access tracts at 1 mile for urban areas or 10 miles for rural areas in each county, and the number of convenience stores. From Figure 2 below, we can see that the number of low-access tracts generally increases as the number of convenience stores in a county increases. Hunterdon County, with the lowest number of convenience stores (6), has the third to last lowest number of low-access tracts (9). Similarly, Ocean County, with the highest number of convenience stores (79), has the highest number of low-access tracts (87). The mean percentage of low-access tracts in Group A counties is 39.8%. The mean percentage of low-access tracts in Group B counties is 47.36%, being 7.56% higher than that of Group A counties. However, Figure 2 also depicts some exceptions to the general trend of the graph. For instance, Somerset County has a relatively high number of low-access tracts, producing a small peak in the graph. A t-test was performed between Group A and Group B’s number of census tracts (df=18) to compare the means of the groups, and the p-value was 0.0000022. An additional t-test was performed between the number of low-access tracts at 1 mile between Group A and Group B to compare the means (df=18), and the p-value obtained was 0.002475. This test shows that there is significant statistical evidence that the groups have a different number of low-access tracts in convenience stores.
County | Number of Convenience Stores | Number of Census Tracts | Number of low access tracts at 1 mile for urban areas or 10 miles for rural areas |
Hunterdon County (B) | 6 | 26 | 9 |
Salem County (B) | 6 | 24 | 8 |
Warren County (B) | 8 | 23 | 11 |
Sussex County (B) | 9 | 41 | 19 |
Somerset County (B) | 12 | 68 | 40 |
Cumberland County (B) | 14 | 33 | 14 |
Cape May County (B) | 17 | 32 | 18 |
Passaic County (B) | 19 | 100 | 30 |
Gloucester County (B) | 25 | 63 | 41 |
Morris County (B) | 25 | 100 | 59 |
Hudson County | 29 | 165 | 3 |
Mercer County (A) | 29 | 77 | 32 |
Essex County (A) | 33 | 210 | 28 |
Atlantic County (A) | 38 | 69 | 27 |
Union County (A) | 51 | 108 | 27 |
Camden County (A) | 58 | 127 | 47 |
Burlington County (A) | 62 | 114 | 64 |
Middlesex County (A) | 76 | 175 | 75 |
Monmouth County (A) | 77 | 143 | 71 |
Bergen County (A) | 78 | 179 | 41 |
Ocean County (A) | 79 | 125 | 87 |
Table 4a contains the number of convenience stores and data about health measures for each county. In terms of health behaviors and health measures, percentages of diabetic populations show a difference between the 2 groups of counties. The mean percentage of diabetes prevalence for the Group A counties is 9.8%, while that for Group B counties is 9.1%. Group A counties have a mean percentage of 0.7% higher diabetes prevalence than Group B counties. This means that residents in Group A counties are likely to have a higher chance of diabetes than those in Group B counties. To understand if there is a significant difference in the higher diabetes prevalence, a t-test was performed from Group A and Group B (df=18), and the p-value obtained was 0.1301. In terms of food environments, percentages of limited access to healthy food show differences between 2 groups of counties. The mean percentage of limited access to healthy food in Group A counties is 4.4%, while that for Group B counties is 5.4%. Group B counties have a mean percentage of 1% higher limited access to healthy food than Group A counties. This means that residents in Group B counties are more likely to have restrictions to obtain healthy foods due to low income or distance to a grocery store. Moreover, the mean score of the food environment index for Group A counties is 8.42 while that for Group B counties is 8.56. The mean score for Group A is 0.14 lower than that of Group B counties, which indicates that Group B counties are less likely to face barriers in obtaining healthy food than Group A counties. In terms of social & economic factors of health measures, residential segregation non-white/white shows differences between 2 groups of counties. The mean score of Group A counties is 42.8, while that of Group B counties is 36.8. A t-test was performed to show that the mean of group A would be larger than group B (t=1.6357) and the p-value is 0.0595 (df=18). Group B have a mean score of 6 higher degrees of residential segregation for non-white and white than Group A counties. This means that white and non-white residents of Group B counties are more likely to be segregated than those of Group A counties.
However, the degree of residential segregation for black and white households shows a reverse relationship in the 2 groups of counties, unlike previous measures. The mean score of Group A is 55.1, while that of Group B is 48.2. Group A counties have a mean score of 6.9 higher degree of residential segregation for black and white than Group B counties. Moreover, the overall ranks for health factors and outcomes do not show any correlations with the number of convenience stores.
County | Health Factors Rank | Health Outcomes Rank | Diabete Prevalence (% Diabetic) | Food Environment Index | Limited Access to Healthy Foods (% Limited Access) | Residential Segregation – black/white | Residential Segregation – non-white/white |
Hunterdon County | 1 | 2 | 7 | 9.4 | 2 | 57 | 30 |
Salem County | 20 | 19 | 12 | 8.1 | 3 | 52 | 46 |
Warren County | 10 | 14 | 9 | 8.6 | 6 | 36 | 32 |
Sussex County | 7 | 7 | 8 | 8.9 | 6 | 49 | 32 |
Somerset County | 3 | 3 | 9 | 9.2 | 3 | 59 | 35 |
Cumberland County | 21 | 21 | 11 | 7.5 | 10 | 28 | 28 |
Cape May County | 14 | 15 | 9 | 7.7 | 9 | 58 | 51 |
Passaic County | 18 | 12 | 8 | 8.8 | 2 | 63 | 49 |
Gloucester County | 13 | 16 | 10 | 8.1 | 9 | 36 | 31 |
Morris County | 2 | 1 | 8 | 9.3 | 4 | 44 | 34 |
Hudson County | 16 | 11 | 8 | 8.8 | 0 | 59 | 35 |
Mercer County | 9 | 13 | 9 | 8.4 | 4 | 55 | 39 |
Essex County | 17 | 17 | 10 | 7.6 | 1 | 75 | 61 |
Atlantic County | 19 | 18 | 12 | 7.6 | 8 | 58 | 48 |
Union County | 12 | 8 | 9 | 8.9 | 1 | 56 | 45 |
Camden County | 15 | 20 | 11 | 8.1 | 5 | 56 | 48 |
Burlington County | 8 | 10 | 10 | 8.4 | 6 | 51 | 40 |
Middlesex County | 6 | 5 | 10 | 8.9 | 4 | 37 | 36 |
Monmouth County | 5 | 6 | 9 | 8.8 | 5 | 59 | 42 |
Bergen County | 4 | 4 | 8 | 9.3 | 1 | 57 | 35 |
Ocean County | 11 | 9 | 10 | 8.2 | 9 | 47 | 34 |
Table 4b shows the correlation matrix among different pairs of variables in Table 4a. For instance, health factors rank and outcome have a high positive correlation (0.897), implying that counties with better health factors tend to have better health outcomes. Furthermore, the food environment index strongly correlates negatively (-0.818) with diabetes prevalence. This suggests that the lower the food environment index is, which means having an unhealthy food environment, the more likely it is for residents to get diabetes. The food environment index and health outcomes rank also have a correlation coefficient of -0.895, implying that the healthier the county’s food environment, the higher it is ranked for health outcomes. From these correlation coefficients, the food environment index has the most direct correlation with the health of residents. Because the food environment index measures the proximity to grocery stores or supermarkets that provide healthy food options, convenience stores are not likely to be included in the calculation. Therefore, these correlation coefficients suggest that the lack of nutritious food in a close proximity has a negative correlation with residents’ health.
Number of Convenience Stores | Health Factors Rank | Health Outcomes Rank | Diabete Prevalence (% Diabetic) | Food Environment Index | Limited Access to Healthy Foods (% Limited Access) | Residential Segregation – black/white | Residential Segregation – non-white/white | |
Number of Convenience Stores | 1 | -0.216 | -0.225 | 0.049 | 0.081 | -0.027 | 0.070 | 0.093 |
Health Factors Rank | -0.216 | 1 | 0.897 | 0.672 | -0.798 | 0.258 | 0.035 | 0.468 |
Health Outcomes Rank | -0.225 | 0.897 | 1 | 0.760 | -0.895 | 0.421 | -0.081 | 0.406 |
Diabete Prevalence (% Diabetic) | 0.049 | 0.672 | 0.760 | 1 | -0.818 | 0.528 | -0.191 | 0.353 |
Food Environment Index | 0.081 | -0.798 | -0.895 | -0.818 | 1 | -0.603 | 0.059 | -0.447 |
Limited Access to Healthy Foods (% Limited Access) | -0.027 | 0.258 | 0.421 | 0.528 | -0.603 | 1 | -0.584 | -0.220 |
Residential Segregation – black/white | 0.070 | 0.035 | -0.081 | -0.191 | 0.059 | -0.584 | 1 | 0.715 |
Residential Segregation – non-white/white | 0.093 | 0.468 | 0.406 | 0.353 | -0.447 | -0.220 | 0.715 | 1 |
Table 5 shows the median household incomes and the number of convenience stores for each county. The mean number of median household incomes of Group A counties is \$77,429 while the mean number of median household incomes of Group B counties is \$83,389.10. Group B counties have a mean number of \$5,960.10 higher median household incomes than Group A counties.
County | Number of Convenience Stores | Median Household Income ($) |
Cumberland (B) | 14 | 51786 |
Atlantic (A) | 38 | 59309 |
Essex (A) | 33 | 60284 |
Salem (B) | 6 | 61322 |
Passaic (B) | 19 | 63127 |
Cape May (B) | 17 | 64450 |
Hudson | 29 | 65673 |
Camden (A) | 58 | 65817 |
Ocean (A) | 79 | 70493 |
Union (A) | 51 | 76830 |
Mercer (A) | 29 | 78161 |
Warren (B) | 8 | 79633 |
Gloucester (B) | 25 | 84639 |
Middlesex (A) | 76 | 85187 |
Burlington (A) | 62 | 86777 |
Sussex (B) | 9 | 89744 |
Bergen (A) | 78 | 93805 |
Monmouth (A) | 77 | 97627 |
Somerset (B) | 12 | 111838 |
Hunterdon (B) | 6 | 113083 |
Morris (B) | 25 | 114269 |
Analysis & Discussion
The results show that the number of convenience stores is related to food access and health measures to a certain extent. While the number of low-access tracts generally increases with the number of convenience stores in a county, there are a few exceptions. While the first 4 counties in Group B mostly have fewer low access tracts compared to other Group B counties, Somerset County, with the fifth lowest number of convenience stores, shows a spike in low-access tracts. Similarly, Hudson County has the lowest number of low-access tracts even though it has the eleventh lowest number of convenience stores in New Jersey. However, most counties still show a positive pattern between the number of convenience stores and the number of low-access tracts. This suggests that the prevalence of convenience stores can generally negatively affect access to supermarkets and fresh food. However, public health researchers must perform more research to determine the causal relationship between the two factors.
The number of convenience stores shows a less clear relationship with health measures than food access. While Group A and Group B counties show a difference in the number of health measures, counties as a whole do not show a distinctive pattern between convenience stores and health measures. However, the findings between Group A and Group B counties are still worth mentioning. Group B counties generally display better health factors and outcomes compared to Group A counties. Health factors, such as food environment index and residential segregation for black and white, and health outcomes, such as diabetes prevalence, have better measures for Group B counties. This suggests that the prevalence of convenience stores contributes to negative health measures of populations in a county. Specifically, diabetes prevalence can be related back to Nagata et al., which describes the relationship between food insecurity and more frequent health issues26. Therefore, it can be reasonably inferred that higher diabetes prevalence in Group A counties is associated with lower food access, which might have been caused by the abundance of convenience stores. Still, more research has to be done to examine the relationship between convenience stores and other health measures that are not considered in this paper. Also, a larger trend between convenience stores and health measures in all countries has to be examined in further studies.
However, health factors and outcome rankings do not show any pattern regarding the number of convenience stores in each county. This may be explained by various factors considered in deciding health rankings: these measures include alcohol & drug use, sexual activity, and air & water quality. Because health rankings take a wide range of measures into account, they may not reflect health outcomes precisely due to the absence of fresh or healthy food.
Even though these health measures may not reflect the degree of being food insecure perfectly, it can still be used in determining where to implement food assistance programs, such as Healthy Food Financing Initiative (HFFI). HFFI is a food program established by the US Department of Health and Human Services and provides one-time financing for the opening of full-service supermarkets in food deserts27. HFFI currently uses the USDA’s interactive map that shows low-access tracts to serve Underserved Areas28. This research’s findings suggest that, in addition to food access, the number of convenience stores and health measures in a neighborhood can be used to determine the eligibility of HFFI funds. Since the abundance of convenience stores is likely to negatively affect food access and health outcomes, it can be used as one measure to identify a neighborhood in need of new fresh food outlets funded by HFFI. Looking at the abundance of convenience stores also helps HFFI’s goal: increase the availability of healthy foods, which cannot readily be acquired in small food stores, such as convenience stores.
Limitations
First, not all convenience stores in New Jersey are listed in the data from ScrapeHero. Since it only offers information on the top 751 convenience stores, it does not reflect other convenience stores that may not generate measurable profits. The number of convenience stores in each county may be smaller than it is. Second, this paper does not account for differences between counties. However, the assumption made is that the separation of counties already normalizes the number of convenience stores by area, population, and density. The income disparity is explored in the section about median household income. Third, Although this paper does not consider confounding variables, such as socioeconomic status, transportation options, and community resources, they still impact the equation, so additional research is needed. Fourth, Figure 2 shows some irregularities in the low access tracts pattern, including Somerset, Morris, Hudson, Union, and Bergen County. A possible explanation for the higher number of low access tracts in Morris county is that it has a seventh large land size, increasing the sample size. Similarly, Union County’s small land size (20th out of 21) may have contributed to a fewer number of low access tracts due to smaller sample size. A possible reason Hudson county has a smaller number of low access tracts is the lack of data collected. Additionally, Bergen County has the fifth highest median income, which may have contributed to its fewer number of low access tracts. Fifth, this research lacks an analysis of the types of populations, such as gender or race. This study does not consider differences in demographics when comparing data from one county to another. So, this study does not generate conclusions for specific populations in each county. Lastly, health measures may not directly result from convenience stores’ prevalence since there can be pre-existing conditions, such as genetics, in health outcomes. Because this study does not identify pre-existing health conditions, the effect of convenience stores on health outcomes can be more minor than measured in this study.
Despite these limitations, this study’s findings draw a concrete conclusion that the abundance of convenience stores can negatively affect food access and health measures to a certain extent. Future research that considers the possible number of convenience stores and a direct relationship between the abundance of convenience stores and their impact on health measures on a more specific demographic may be needed. Such research will be beneficial in informing the public and policymakers about possible health outcomes resulting from a neighborhood’s environment.
Appendix
Group | County | Number of Convenience Stores | % of low-access tracts | (%) of diabetes prevalence | % of limited access to healthy food | Food Environment Index | Residential segregation (non-white/white) | Residential segregation (black/white) | Median household income ($) |
Group B | Hunterdon County | 6 | 34.6% | 7% | 2% | 9.4 | 30 | 57 | 113083 |
Salem County | 6 | 33.3% | 12% | 3% | 8.1 | 46 | 52 | 61322 | |
Warren County | 8 | 47.8% | 9% | 6% | 8.6 | 32 | 36 | 79633 | |
Sussex County | 9 | 46.3% | 8% | 6% | 8.9 | 32 | 49 | 89744 | |
Somerset County | 12 | 58.8% | 9% | 3% | 9.2 | 35 | 59 | 111838 | |
Cumberland County | 14 | 42.4% | 11% | 10% | 7.5 | 28 | 28 | 51786 | |
Cape May County | 17 | 56.3% | 9% | 9% | 7.7 | 51 | 58 | 64450 | |
Passaic County | 19 | 30.0% | 8% | 2% | 8.8 | 49 | 63 | 63127 | |
Gloucester County | 25 | 59.0% | 8% | 9% | 8.1 | 31 | 36 | 84639 | |
Morris County | 25 | 65.1% | 10% | 4% | 9.3 | 34 | 44 | 114269 | |
Hudson County | 29 | ||||||||
Group A | Mercer County | 29 | 41.6% | 9% | 4% | 8.4 | 39 | 55 | 78161 |
Essex County | 33 | 13.3% | 10% | 1% | 7.6 | 61 | 75 | 60284 | |
Atlantic County | 38 | 39.1% | 12% | 8% | 7.6 | 48 | 58 | 59309 | |
Union County | 51 | 25.0% | 9% | 1% | 8.9 | 45 | 56 | 76830 | |
Camden County | 58 | 37.0% | 11% | 5% | 8.1 | 48 | 56 | 65817 | |
Burlington County | 62 | 56.1% | 10% | 6% | 8.4 | 40 | 51 | 86777 | |
Middlesex County | 76 | 42.9% | 10% | 4% | 8.9 | 36 | 37 | 85187 | |
Monmouth County | 77 | 50.0% | 9% | 5% | 8.8 | 42 | 59 | 97627 | |
Bergen County | 78 | 22.9% | 8% | 1% | 9.3 | 35 | 57 | 93805 | |
Ocean County | 79 | 69.6% | 10% | 9% | 8.2 | 34 | 47 | 70493 |
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