Research paper on Is there a solution to food desert.

timer Asked: Oct 22nd, 2018
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Question description

Paper should be in APA format.

I have attached 4 sources and these 4 sources should be used in the paper. Besides, other sources should also be used (at least 1).

Food Deserts: People can make a difference

We live in the Dallas Fort Worth Metroplex. This sprawling urban area spans over 9, 200 square miles. Driving around the metroplex one can see the prevalence of many grocery stores. However, a closer look shows that the distribution of the amenities in not uniform. Some areas have multiple stores where residents have many options and can access quality nutritional resources, while other areas have none to a few. These areas are a referred to as food deserts.

Write a 1000-word research paper and position paper on whether individual and community effort are sufficient to address food deserts.


What are food deserts and what is the criteria based on which an area is designated a food desert?

Identify areas in Dallas that can are considered Food Deserts.

Discuss the median incomes and resources available to the individuals living in these areas

Discuss the health of individuals living in food deserts. What is the long term impact of lack of access to adequate nutrition?

How is the city of Dallas trying to address this concern?

How are other cities addressing this issue?


Yes, individual and community efforts can make a difference in alleviating the lack of adequate nutritional resources in food deserts.

Individual and community efforts are not an effective strategy in improving the availability of resources in food deserts.

Your opinion

Clearly state your position on whether community and individual efforts can or cannot improve availability of resources in food deserts




  1. (Purdue Owl) is an excellent source on APA format.
  2. Use a minimum of eight references. You may use more as necessary.Be sure to evaluate the validity of information from Internet sources.You must include at least four Primary Citations (written by the scientist and not a news summary).Sources such as the Mountain View College Library (, Nature (,PLoS Biology ( are worthwhile.Google Advanced Scholar Search and www.googlescholar.commay contain primary articles.
  3. Government agencies usually provide a good source of information such as
    1. NIH: National Institutes of Health (NIH),
  4. This is the link to DCCCD library database.
  5. The librarians are helpful and welcome students seeking help with their research papers.
  6. Do Not Use Wikipedia, other encyclopedias or only authentic scientific resources.News articles and TV reports may be used as a starting point but must be supported by credible scientific resources.
  7. Mountain View College resources such as the library, writing center and instructional support area are some excellent resources for students. Please utilize them.

How to Research your topic?

If using Goggle to research this topic selectlinks ending in .gov or .edu

| Forum Forum News If you are ever at a loss to support a flagging conversation, introduce the subject of eating. Leigh Hunt, poet (1784–1859) White House Proposes Healthy Food Financing Initiative The Obama administration announced in February a $400 million initiative it hopes will lure retailers of healthy foods into the socalled food deserts of America. The program, proposed as part of the fiscal year 2011 budget, aims to boost public health by eliminating urban and rural food deserts within 7 years. The term “food desert” refers to areas that, although often served by fast food restaurants and convenience stores, lack easy access to affordable fruits, vegetables, whole grains, low-fat milk, and other foods that make up the full range of a healthy diet. About 23.5 million people—including 6.5 million children—live in low-income areas that are more than 1 mile from a supermarket, according to the June 2009 report Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences by the U.S. Department of Agriculture (USDA). Fast Food Grocer White Black Hispanic Asian In a study of Chicago food deserts, black residents had to travel farther than other racial groups to reach a grocery store but not a fast food restaurant. Distance shown in tenths of a mile. Adapted from Mari Gallagher Research & Consulting Group. 2006. Examining the impact of food deserts on public health in Chicago. A 156 The new Healthy Food Financing Initiative (HFFI) would be administered jointly by the Departments of Health and Human Services, Agriculture, and the Treasury, and would dovetail with Michelle Obama’s recently announced “Let’s Move” campaign to end childhood obesity within a generation. It would emphasize provision of fresh produce. And although the primary goal is nutrition, it would also seek to “create jobs and economic development, and establish market opportunities for farmers and ranchers,” said agriculture secretary Tom Vilsack at the program’s announcement. That is what a Pennsylvania program, a model for the HFFI, has done, says Ann Wright, the USDA deputy undersecretary for marketing and regulatory programs. Launched in 2004, the Pennsylvania Fresh Food Financing Initiative has opened about 80 stores ranging from small mom-and-pop grocers to large full-service supermarkets, providing food for around 400,000 people, says John Weidman, deputy executive director of The Food Trust (TFT), 1 of 3 nonprofits managing the program. The program also has provided jobs for 4,800 people, he says. The public health problem these programs address is obvious but imprecisely defined. Many of the highly processed, fat- and sugarrich foods sold at convenience stores and fast food restaurants are implicated in cardio­ vascular disease, diabetes, and cancer. Several studies have reported that easy access to healthy foods and limited access to convenience stores is associated with healthier eating and reduced obesity, according to a review by Nicole Larson et al. in the January 2009 issue of the American Journal of Preventive Medicine. However, the actual health toll from living in a food desert environment has not been tabulated in a peer-reviewed study. Moreover, the only 2 studies that examined diets before and after grocery stores were installed in food deserts—rather than comparing neighborhoods with grocery stores to similar neighborhoods without—are not encouraging, says Steven Haider, an associate professor of economics at Michigan State University. Neil Wrigley et al. wrote in volume 35, issue 1 (2003) of Built Environment that people consumed an extra half a serving of fruit and/or vegetables daily, while Steve Cummins et al. reported no change in the Winter 2005 issue of Planning Healthy Towns and Cities. And global nutrition professor Barry Popkin of the University of North Carolina at Chapel Hill says a January 2009 workshop he chaired at the Institute of Medicine on the public health effects of food deserts “could find no evidence that adding new retail stores to depressed areas changed what people consumed.” volume Meanwhile, Amy Lanou, an assistant professor of health and wellness at the University of North Carolina at Asheville, argues that education, a demand-side measure, is needed to maximize the benefits of the HFFI. (Demandside measures are measures that boost peoples’ desire to purchase healthy food. Their converse, supply-side measures, are those, such as grocery stores, that make it more available.) Wright says the USDA has been promoting better nutrition in schools through agency programs and support for local efforts to bring healthy food into the school cafeteria. But while school lunches must meet specific nutritional requirements, Lanou says schools “can sell almost anything à la carte,” giving kids the option of eating unhealthily if they can afford it. Mari Gallagher, whose eponymous research and consulting company has studied food deserts extensively in Chicago and other cities, says it’s not enough to live near healthy food outlets. People who are time-stressed—working multiple jobs, for instance, or commuting on several different transit lines—will travel half a mile for junk food rather than a mile for healthy food, she says. If access to affordable wholesome food alone does not alter eating habits, perhaps other factors will. In the 8 March 2010 issue of Archives of Internal Medicine, Popkin showed that localized hikes in fast food prices over a 20-year period tracked with reduced risk of obesity and diabetes in affected communities. Another study finds cultural sensitivity is important in promoting healthy eating. “Our qualitative research in New York City suggests that Hispanic immigrants conceptualize ‘healthy foods’ more in terms of freshness and local origin than in terms of nutritional content,” says Andrew Rundle, an associate professor of epidemiology at the Mailman School of Public Health, Columbia University. This is consistent, he says, with initial findings in Hispanic neighbor­hoods that access to farmers’ markets was a better predictor of produce consumption than access to supermarkets. Gallagher warns that determining which locales have the greatest need for subsidies, as well as keeping politics from affecting the flow of money, will be challenging. It is important to “make sure we’re armed with neutral data that directs the flow of resources,” she says. Nonetheless, she says, “I’m very thrilled [about the proposed initiative]. We think this is needed. We encourage the administration to disburse these funds with the best data and methods so we get the highest public health return for the investment.” David C. Holzman writes on science, medicine, energy, economics, and cars from Lexington and Wellfleet, MA. His work has appeared in Smithsonian, The Atlantic Monthly, and the Journal of the National Cancer Institute. 118 | number 4 | April 2010 • Environmental Health Perspectives Joseph Tart/ EHP diet and nutrition Forum marine and coastal science Satellite image: NASA SeaWiFS Project; inset: Biodisc / Visuals Unlimited; Inc. Will Ocean Acidification Erode the Base of the Food Web? Acidification of the world’s oceans is already damaging coral reefs and could produce other unexpected chemical and biological consequences. Princeton University researchers now report that at low pH, phytoplankton take up less iron, a key nutrient needed for photo­synthesis and growth. The results, reported in the 5 February 2010 issue of Science, suggest ocean acidification could have a profound impact on these tiny one-celled plants, which reside at the bottom of the food web and support commercially important fisheries. Seawater becomes more acidic when atmospheric carbon dioxide (CO 2) absorbed by the water is converted into carbonic acid. The acidity of oceans is changing very rapidly. The hydrogen ion concentration of surface ocean water (a reflection of pH) is now about 30% higher than it was 200 years ago, according to William Sunda, a research chemist at the National Oceanic and Atmospheric Association in Beaufort, North Carolina, while atmospheric concentrations of CO2 have risen by about 38%. Most of the research focus has been on how ocean acidification negatively impacts marine creatures, such as mollusks and corals, that form shells or exoskeletons from calcium carbonate [EHP 116:A292–A299 (2008)]. Little attention has been paid to how increasing acidity changes the chemistry and biological availability of essential nutrients such as iron. In the current study, Dalin Shi, Francois M. M. Morel, and colleagues at Princeton University measured the uptake of iron in Thalassiosira weissflogii, Thalassiosira oceanica, Phaeodactylum tricornutum, and Emiliana huxleyi. As the researchers lowered the pH of model laboratory culture media from 8.6 to 7.7, they observed a significant decrease in the rate of iron uptake by all species. A similar trend occurred when laboratory phytoplankton were placed in natural seawater collected off the New Jersey coast and the open ocean near Bermuda. The average iron uptake rate decreased by 10–20% between the highest- and lowest-pH conditions in natural seawater. “The average pH of Environmental Health Perspectives • volume Ocean color remote sensing of chlorophyll concentrations off the U.S. East Coast reflects swirling fields of phytoplankton (inset). These microorganisms form the base of the marine food web. ocean water today is 8.08,” says Shi, a graduate student in oceanography. Much of the iron in ocean water is strongly bound to natural organic chelators, such as siderophores, which bind and release iron in different ways. The research team examined the effect on iron uptake of 3 chemically different model chelators—the synthetic chelator ethylenediaminetetraacetic acid (EDTA) and two siderophores, desferriferrioxamine B (DFB) and azoto­c helin. As the pH dropped, iron availability was dramatically reduced by EDTA and moderately reduced by DFB, but was unchanged by azotochelin. Little is known about how marine ligands bind and release iron in seawater. The model chelator findings “show in principle that lowering pH can decrease iron present for biological use, depending on the chelator. And the results with natural seawater show this also occurs with natural chelators,” says Sunda. One conceivable consequence of limited iron due to ocean acidification could be a decline in phytoplankton populations, resulting in reduced fish harvests for human consumption, according to Morel, a professor of geosciences. “But this is all speculation,” he cautions. “The only thing we documented is a decrease in the bioavail­a bility of dissolved iron in four laboratory organisms.” Phytoplankton species perform almost all marine photosynthesis, a biochemical process that requires iron to convert CO2 from air into 118 | number 4 | April 2010 organic matter and oxygen. Some of this organic matter sinks, carrying carbon into the deep oceans; calculations by Josep D. Canadell and colleagues in the 20 November 2007 issue of Proceedings of the National Academy of Sciences estimate this “carbon pump” has absorbed about a quarter of the CO2 emitted by human activities. A decrease in iron availability through ocean acidification could restrict this carbon pump, resulting in an increase in atmospheric CO2 , notes Sunda. On the other hand, marine organisms may evolve their own nutri­t ional coping strategies. For instance, Sunda and colleagues, recently discovered that members of the bac­terial genus Marinobacter, which live in close contact with phytoplankton that cause harmful algal blooms, produce a novel siderophore that tightly binds iron in the dark. But when exposed to sunlight, the siderophore breaks down and releases an unbound form of iron that the phytoplankton readily take up to drive photosynthesis. The relationship is mutually beneficial; when the Marinobacter and phytoplankton species are grown separately, both grow poorly compared with when they grow together. “Nature is incredibly clever when it comes to obtaining scarce nutrients,” says Sunda, whose work was described in the 6 October 2009 issue of the Proceedings of the National Academy of Sciences. Carol Potera, based in Montana, has written for EHP since 1996. She also writes for Microbe, Genetic Engineering News, and the American Journal of Nursing. A 157 Forum CHildren’s health Secondhand Smoke Exposure May Alter Fetal Blood Pressure Programming Foodborne Illness Costs: No Small Potatoes for the United States The Produce Safety Project of The Pew Charitable Trusts estimated in March 2010 that foodborne illnesses cost the United States $152 billion each year and each citizen an average of $1,850 per case. The report, available at, based its estimate on medical costs as well as costs due to lost life expectancy, pain and suffering, and functional disability. The CDC estimates more than 76 million new cases of foodborne illness resulting in 5,000 deaths and 325,000 hospitalizations occur each year in the United States. an inclination of 60º over 5 seconds and held in that position for 1 minute. “As the body becomes more upright, the heart rate should rise temporarily, and different blood vessels should constrict to increase the blood pressure and ensure enough blood gets to the brain,” explains first author Gary Cohen, a senior research scientist in the Department of Women’s and Children’s Health at the Karolinska Institute, Stockholm. Sure enough, that is what the authors observed for the nonexposed infants, with peak values becoming somewhat higher between 1 week and 1 year as expected. TSCA Information Now Free Online The EPA announced 15 March 2010 it will now provide free online access to the Toxic Substances Control Act Chemical Substance Inventory, which provides data on thousands of industrial chemicals. Until now, this consolidated set of information was available only by purchase. The move is part of the agency’s stated priority of making chemical information more accessible to the public and follows a January announcement that the EPA is seeking to reduce some confidentiality claims on the identity of chemicals (read more about chemical confidentiality on p. A168 of this issue). The inventory is available at oppt/newchems/pubs/invntory.htm. AL AK AR AZ CA CO CT DE FL GA HI ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY DC National Comparison of Annual Health-Related Costs of Foodborne Illness Source: A 158 volume Paint and contaminated dust are major sources of lead exposure in U.S. children. New Lead Paint Rule Takes Effect Effective 10 April 2010 all renovations of housing constructed before 1978 and of child-occupied facilities (such as schools) must be performed by certified renovators using specific lead-safe work practices. “Renovation” is defined broadly under the EPA rule to include window repair, weatherization, and modification of painted doors. The new regulation goes beyond earlier tenant notification stipulations by 118 | number 4 | April 2010 • Environmental Health Perspectives © Jeff Albertson/CORBIS The Beat | by Erin E. Dooley Prenatal exposure to maternal smoking may have contributed to blood pressure abnormalities observed in infants. Gary Cohen Babies born to mothers who smoke cigarettes may be at risk for abnormal blood pressure and heart rate control at birth, suggests research published in the March 2010 issue of Hypertension. The results of the new study further hint that this control may become worse as exposure to secondhand smoke continues, perhaps increasing the risk of developing hypertension in later life. The study compared the heart rate and blood pressure control of 19 infants born to non­ smokers with those of 17 infants whose mothers reported smoking an average of 15 cigarettes per day before and after giving birth. The resting blood pressure of the infants in both groups followed essentially the same developmental trend over the first year of life, although the smoke-exposed infants had higher diastolic blood pressure at age 3 months. The resting heart rate of both groups also was similar and followed the same trend up to age 3 months. But by 1 year the resting heart rate of the smoke-exposed infants averaged 20% slower than that of their unexposed counterparts. The researchers also monitored changes in heart rate and blood pressure over a span of 40 beats as the infants, sleeping soundly on tilt tables, were raised from a supine position to Forum The exposed infants showed a similar trend over time. However, between 3 months and 1 year their responses became exaggerated, with their heart rate rising faster (and increasing by an average of 11.5% instead of the 6.5% seen in the nonexposed babies) before falling more quickly. Their diastolic blood pressure followed suit. When the nonexposed infants were tilted and maintained upright, sustained rises in systolic, diastolic, and mean blood pressure of 2–3% were seen at age 1 week, rising to 8–10% by 1 year as expected. In contrast, in the exposed infants the increases in blood pressure were nearly double at age 1 week but failed to increase over time. “Thus, the newborns of smokers hyperreact to positional change, but by the time they are one year old and want to stand up they are underreacting; their routine blood pressure compensation systems just don’t work properly,” says Cohen. “It would appear that neither their heart rate nor sympathetic constrictor tone [impulses from the sympathetic nervous system that help control blood vessel constriction] are properly ‘programmed’ even at birth, with things getting worse over time.” This programming problem could lie in an overly strong sympathetic tone caused by exposure to some compound in cigarette smoke in the womb and after birth, the researchers say. This might slowly increase vascular resistance, leading to the increased diastolic blood pressure seen at rest at 3 months, and the eventual loss of sympathetic reactivity. The authors further hypothesize that the fall in heart rate observed in exposed infants at age 1 year was an attempt to restore some kind of equilibrium. Unfortunately, this reprogramming solution appears to hinder proper positional blood pressure control, “and there is evidence this could increase the chances of hypertension later on,” explains Cohen. requiring that renovators post warning signs at the remodeling site to inform workers and occupants of lead hazards. Contractors also must now follow lead dust containment and waste management procedures. The EPA provides more information for contractors, certification trainers, homeowners, and landlords at Shutterstock UNEP Offers E-Waste Predictions, Guidance The world’s stockpile of e-waste—discarded computers, mobile phones, and other electronic devices—is growing by an estimated 40 million tons per year with little sign of stopping. In Recycling—From E-Waste to Resources, released 22 February 2010, UNEP estimates the number of discarded computers shipped to some developing countries could increase by as much as 500% by 2020. The informal recycling of electronics is a lucrative but highly hazardous cottage industry in many developing countries. The UNEP report therefore offers guidance for countries to build successful and safer e-waste management systems. Environmental Health Perspectives • volume In adults, cardiovascular pathophysiology can involve chronic sympathetic overactivity leading to increased blood pressure. The authors suggest something similar may be happening in the children they studied. “[Whether these observations can be] explained by alterations in central sympathetic outflow requires further investigation as this was not directly assessed in this study,” remarks James Fisher, a lecturer in exercise physiology in the School of Sport and Exercise Sciences, University of Birmingham who was not involved in the study. “As is often the case with good research, we are left with more questions than answers. Is the altered cardiovascular reactivity specific to postural stress, or is it more generalized? What is the biological significance of the magnitude of the alteration in cardiovascular reactivity? How “The newborns of smokers hyperreact to positional change, but by the time they are one year old and want to stand up they are underreacting; their routine blood pressure compensation systems just don’t work properly.” permanent is the ‘reprogramming,’ and is it reversible if smoke exposure is withdrawn?” Although interesting, the study is rather small. “I would like to see confirmation in a larger study,” says Mark Caulfield, director of the William Harvey Research Institute at Barts and The London School of Medicine and Dentistry, “with formal proof of cigarette consumption status in each of the study groups before drawing a firm conclusion.” Adrian Burton is a biologist living in Spain who also writes regularly for The Lancet Oncology, The Lancet Neurology, and Frontiers in Ecology and the Environment. Review of Environmental Factors in Malaria’s Spread A review by Luis Fernando Chaves and Constantianus Koenraadt in the March 2010 Quarterly Review of Biology assesses the factors contributing to increases in malaria cases worldwide. The researchers report that climate change, human migration, and land-use changes all are causing malaria to spread into highland areas of East Africa, Indonesia, Afghanistan, and elsewhere. They systematically show how climate affects multiple biological components of malaria transmission and highlight the need for research to better understand the transmission dynamics of this disease and how to sustainably control or eliminate it. New Jersey Harbor. Their findings, reported in the March–April 2010 Journal of Environmental Quality, show stormwater runoff was the main pathway, contributing about half the harbor’s PAH load, and atmospheric deposition was an important contributor of smaller PAH compounds. The results suggest that minimizing the flow of PAHs into waterways may require tweaking stormwater management plans to control runoff. PAHs: Pathways to Waterways Polycyclic aromatic hydrocarbons (PAHs), chemicals released during combustion of biomass and fossil fuels, are ubiquitous in the environment. Lisa Rodenburg and colleagues undertook a 4-year study to identify the primary routes by which PAHs end up in New York/ 118 | number 4 | April 2010 Stormwater runoff is a major route by which PAHs enter waterways. A 159
J Community Health (2016) 41:910–923 DOI 10.1007/s10900-016-0171-0 ORIGINAL PAPER Analyzing the Role of Community and Individual Factors in Food Insecurity: Identifying Diverse Barriers Across Clustered Community Members Becca B. R. Jablonski1 • Dawn Thilmany McFadden1 • Ashley Colpaart1 Published online: 24 February 2016  Springer Science+Business Media New York 2016 Abstract This paper uses the results from a community food security assessment survey of 684 residents and three focus groups in Pueblo County, Colorado to examine the question: what community and individual factors contribute to or alleviate food insecurity, and are these factors consistent throughout a sub-county population. Importantly, we use a technique called cluster analysis to endogenously determine the key factors pertinent to food access and fruit and vegetable consumption. Our results show significant heterogeneity among sub-population clusters in terms of the community and individual factors that would make it easier to get access to fruits and vegetables. We find two distinct clusters of food insecure populations: the first was significantly less likely to identify increased access to fruits and vegetables proximate to where they live or work as a way to improve their household’s healthy food consumption despite being significantly less likely to utilize a personal vehicle to get to the store; the second group did not report significant challenges with access, rather with affordability. We conclude that though interventions focused on improving the local food retail environment may be important for some subsamples of the food insecure population, it is unclear that proximity to a store with healthy food will support enhanced food security for all. We recommend that future research recognizes that determinants of food insecurity may vary within county or zip code level regions, and that multiple interventions that target sub-population clusters may elicit & Becca B. R. Jablonski 1 Department of Agricultural and Resource Economics, Colorado State University, B337 Clark Building, Fort Collins, CO 80523-1172, USA 123 better improvements in access to and consumption of fruits and vegetables. Keywords Food security  Food insecurity  Community interventions  Cluster analysis  Fruit and vegetable consumption Introduction Measuring US food insecurity and its causes has spurred a rich and multi-disciplinary literature. Defined by the U.S Department of Agriculture (USDA) as, ‘‘access by all people at all times to enough food for an active, healthy life [1],’’ (4) its prevalence rate was relatively stable at 10–12 % until 2008, when concomitant with the Great Recession, it grew to almost 15 % nationwide [2]. Though many economic indicators signal an end to the Great Recession, national food insecurity incidence rates remain above 14 %, raising questions about the efficacy of traditional interventions, including how households or communities of need are identified, and what strategies are pursued to support increased access to and consumption of healthy food [3]. Since 1997, the USDA Food and Nutrition Service (FNS) has provided standardized estimates of food security at the state and national level [4]. Though the USDA does not provide sub-state level estimates, Feeding America, the country’s largest domestic hunger-relief organization, generates county-level estimates to fill this gap [5]. These standardized approaches to the measurement of food security have attracted substantial attention in the literature, as well as with policymakers, funders, and nonprofit organizations. Although food security is primarily defined by a household’s stated need to compromise the food offerings or alter eating patterns, it may also suggest that J Community Health (2016) 41:910–923 cheaper food options are chosen because of budget constraints. In USDA Economic Research Service estimates of food security, one of the key questions is on the ability of households to offer well-balanced meals [6]. The USDA/ HHS and CDC both report that, among dietary guidelines, fruits and vegetables are most commonly below recommended intake levels [7, 8]. So, a focus on compromised fresh produce choices is commonly integrated into food security studies, especially those funded by public health agencies and non-profits. The challenge with these data’s emphasis on identifying specific geographies (i.e., the state, county, or neighborhood) as food secure or insecure is that sub-regional variations or delineations that define populations by nongeographic identifiers may be obscured. Identifying this within-region heterogeneity can be important in eliciting more targeted and appropriate interventions [5, 9–13]. This paper uses the results from a community food security assessment survey of 684 residents and three indepth focus groups in Pueblo County, Colorado (CO) to examine the question: what community and individual factors contribute to or alleviate food insecurity, and are these factors consistent throughout a sub-county population. An additional contribution of this research is to use a clustering approach to examine the responses, which allows the data to endogenously determine the key factors pertinent to food access and fruit and vegetable consumption. Previous Literature and Key Study Variables The need to better understand the heterogeneity of food insecurity and access within communities is well-documented in recent literature. Authors involved in generating Feeding America’s estimates note reliance on county-level data available from the Current Population Survey, American Community Survey, and Bureau of Labor Statistics, cannot elucidate questions about heterogeneity within county populations, or reflect local-level efforts to reduce food insecurity [5]. Likewise, researchers caution that using a specific geography may disregard segments of food insecure populations [11, 13]. Carter et al.’s review of the literature found that none of the studies control for the potential for disadvantaged areas to confound relationships between the place factors and food insecurity [9]. Similarly, Harris et al. [10] motivated their research by stating that, despite a large literature on food insecurity, very little is known about its distribution at the community level. Community and Individual Factors One innovation of this study’s methodology is to divide potential factors related to food insecurity into two broad 911 categories: community and individual factors; the review of literature on these factors is summarized here. Community Factors: Food Retail Access Within the US context, interventions to support improved food security outcomes are framed in terms of improving access to healthy foods [14]. Wilde et al. [15] classify three dominant approaches to assess the environment (low-income low-access, low-vehicle low-access, and relative distance), each of which includes varying assumptions about the relationships between poverty, vehicle access, population density, and proximity to supermarkets. Research, however, is mixed on the relationship between food security and adequacy of the food retail environment. The Food Acquisition and Purchase Survey (FoodAPS) data provide strong evidence that households, including those that are low-income, do not do their primary food shopping at the closest available store. Rather, there are a variety of other factors, including price, quality, and selection, that affect where households do their primary food shopping. Interestingly, this holds true even for people who walk, bike, or use other transit to get to the store [1]. Concerns about well-balanced diets among the food insecure led to fruit and vegetable consumption being integrated as a key indicator in many food security studies [7, 8], including connection between the availability of supermarkets, healthier eating outcomes, and lower food prices [9, 16]. However, Kyureghian et al. [11] suggest that the densities of supermarkets and other retail outlets do not have significant effects on household fruit and vegetable purchases. Dean and Sharkey found that the relationship between the food retail environment, fruit and vegetable purchases, and subsequent intake differs when considering urban and rural settings [17]. Community Factors: Transportation There is a substantial literature on the relationship between access to transportation and food security; travel modes and distances may be important if food access depends on proximity to food retailers and/or access to a vehicle. Preliminary FoodAPS data show that the majority of households (88 %) use their own vehicle to get to the store where they do their main grocery shopping, but this share increases to 91 % if looking only at food secure households, and decreases to 70 % if looking only at food insecure households [1]. Carney found that physical proximity of outlets was the main determinant in household decisions, as those without adequate transportation were often reliant on walking for food shopping trips [18]. 123 912 Community Factors: Community Food Assistance There is very limited evidence that the utilization of community food assistance programs reduces food insecurity (note that these are distinct from government sponsored programs like SNAP). Loopstra and Tarasuk [19] found that ‘‘among families who used food banks, there was no evidence that food bank use alleviated food insecurity’’ (508), which was consistent with an earlier study by Tarasuk and Beaton [20]. Community Factors: Locally Grown Food Access There is a small body of literature that examines the relationship between access to ‘‘locally grown’’ food and food security. Taylor and Lovell hypothesize that home gardens in developed countries contribute to food security by increasing the production and sharing of food, thus increasing overall daily consumption of fruit and vegetables [21]. Kortright and Wakefield [22] conclude that growing food contributes to enhanced food security at all income levels. Carney et al. [23] evaluated 163 household members who participated in a community garden program and found the frequency of both children and adult vegetable intake increased. Individual Factors: Cost There is broad agreement in the literature about the inverse relationship of food insecurity and household income [12]. Guo [24] concludes that household assets have a significant association with food security in both the total population and among low-income households. Similarly, Gundersen et al. [25] find that perhaps the most important factor is the resources available to a household. Using representative data from the Canadian Community Health Survey, Olabiyi and McIntyre [26] find that food insecurity was higher among single-parent households and those with greater household size. Similarly, Harris et al. [10] find that fewer adults and the presence of children in the household predicted lower rates of household food security. Several authors note that the relationship between food cost and food insecurity deserves additional attention. In part this is driven by disparities in percentage of income spent on food budgets: US households in the middle income quintile spent 13.1 % of their incomes on food, but the lowest income households spent 36.2 % [27]. Gregory and Coleman-Jensen [28] find that the average effect of food prices on the probability of food insecurity is positive and significant for households participating in the SNAP. Carney and Hendrickson et al. [18, 29] reported similar results: increases in food prices during the economic 123 J Community Health (2016) 41:910–923 recession translated to less robust food budgets and dietary consequences for low-income households. Individual Factors: Time and Education There is also literature exploring the relationship between time, level of education, and food security. Beatty et al. find a significant relationship between time spent on foodrelated activities and food insecurity, and further conclude that low education, low food knowledge, and low healthy eating self-efficacy are associated with food insecurity [30]. Likewise, Davis and You argue that a better understanding of home food production may explain shortcomings in current nutrition programs since time is a more important factor in achieving nutritional targets than money [31]. Whereas for some of these factors there is broad agreement on the relationship (i.e., increased income leads to improved food security), for other factors, more research is needed (i.e., the relationship between the availability of food assistance programs or nearby retail food stores and food security). Methodology This study shares a community-driven process to explore food insecurity in one region of Colorado. According to the USDA’s Food Environment Atlas, Pueblo County has the highest percentage of the population in the state that is low income and also has low access to stores (1780 households, 67,049 people, 2010) [27]. Additionally, it is one of the poorest counties in Colorado. Almost 1 of 5 residents (19.1 %) are below the poverty line (compared to 13.2 % in Colorado, 2009–2013), with 45 % of students eligible for the free lunch program (2006). In the 2015 County Health Rankings, Pueblo County ranks 57 out of 60 for health behaviors (e.g., adult obesity, food environment index, physical inactivity), and 56 out of 60 for socioeconomic factors (e.g., income inequality, education, unemployment, children in single-parent households) [32]. Demographically, Pueblo’s high percentage of the population that is Hispanic or Latino, 42.3 % compared to 21 % of Colorado, stands out [33]. In 2013, the Health Disparities Program at the Pueblo City-County Public Health Department (PCCHD) initiated a food security assessment with a health disparities grant from the Colorado Department of Public Health & Environment in partnership with Colorado State University and WPM Consulting, LLC. The project team developed, implemented, and documented this food system assessment, based on guidance from a community-recruited Advisory Council consisting of 17 members organizations. J Community Health (2016) 41:910–923 Data Collection The research team conducted surveys with 684 residents between March and June of 2013. The survey protocol was approved by Colorado State University’s Institutional Review Board in March 2013 (ID# 13-4142H). Survey questions were framed based on food security literature, initial discussions with the Advisory Council and themes that emerged from a focus group with stakeholders. The food security questions were based on the six-item food security scale, developed by researchers at the National Center for Health Statistics. A variety of outreach methods were used to reach and disseminate the survey to county residents, including: social media, radio, newspaper, flyers in public areas, and city and county government website postings. In addition, hardcopy surveys were distributed in-person at county emergency food pantries, CSU Extension Cooking Matters classes, the Care and Share Food Bank, the County’s Department of Social Services, one local hospital, numerous faith-based organizations, and classes given in the Pueblo County Women, Infant, and Children program. Referral sampling from community partners and stakeholders was used to reach targeted groups that use free and reduced food assistance, a demographic that was otherwise under-sampled in the initial surveys collected by the research team. Summary statistics of the socio-demographic information from the survey are reported in Table 1. Although referral sampling is a form of non-probability sampling, the sample demographics are quite similar to the population of Pueblo. Differences do appear in the percent of female and Hispanic respondents. The research team asked for survey respondents to be the primary household food shopper, which likely explains a skew towards female respondents (80 %) compared to the 51 % female sample expected in Pueblo County. In contrast, the sample is slightly underrepresentative of the Hispanic population; 34 % in our sample reported being Hispanic or Latino compared to the 42 % Hispanic share of population expected in the Pueblo region given US Census estimates [33]. In an effort to better familiarize the reader with the study region, we present Tables 2 and 3. Table 2 shows how some key demographics in Pueblo County, including how measures of food security, vary by race. Table 3 illustrates how one of the food security questions, ‘‘how frequently the respondent compromises their family’s food choices,’’ varies by key demographics. Some patterns emerge, such as larger households being more likely to have periods of insecurity, nonwhites having larger households, and subsequently, nonwhites being food insecure a bit more frequently. 913 In addition, the research team conducted three focus groups in June 2013 that included five randomly-selected individuals (for a total of 15 people). Qualitative responses provide additional context to the differing challenges with access to and consumption of healthy food. Factor and Cluster Analysis to Identify Pockets among the Food Insecure The literature review suggests broad-sweeping recommendations with respect to food insecurity are likely to be flawed since such a diverse set and mix of factors may be underlying the food choices and constraints faced by households. For these reasons, we chose to use factor and cluster analysis methods to explore how a variety of similarly-constrained groups may see food insecurity factors similarly to one another, but in a distinctly different manner than other parts of the Pueblo community. Given that we wanted to make sure to present respondents with the full range of potential community or individual factors that might contribute to food insecurity, our survey includes over one hundred possible variables. This large set of variables, however, encompasses too many dimensions to allow us to delineate concise themes and make useful recommendations. To narrow down to the most relevant issues, we first use factor analysis to identify clustering variables. Factor analysis allows us to confirm and understand the variability across people and the key variables that are most relevant to explain behavior [34]. This study’s factor analysis focused on the variables summarized in Table 4. These variables had high factor loadings among our respondents; the internal reliabilities of factors exceeded the minimum criterion of 0.60 generally used with this method [35, 36]. Based on the results of the factor analysis, we narrowed to a subset of 31 variables to statistically formulate and create segmented household profiles based on purchasing patterns, preferences, and food security measures. Beyond those groups of variables with high factor loadings, some demographics were also maintained as a means to describe cluster populations using metrics that commonly define a community. Using these key factors as attributes to define groups, a k-means clustering technique was used to segment respondents into subgroups such that individuals within a subgroup share similar behaviors relative to other subgroups, but factors and choices between the subgroups differ. In other words, respondents are grouped such that there is relative homogeneity within subgroups, yet the heterogeneity between subgroups suggests different approaches are needed to address food insecurity and challenges. 123 914 J Community Health (2016) 41:910–923 Table 1 Summary of descriptive statistics (n = 684) Variable Description (Coding) M SD Age In years 43.82 14.64 Gender 1 if female; 0 if male 0.80 0.40 Household size Actual no. in household, range: 1–5 or more 3.01 1.28 Members of household under age 18 Actual no. in household, range: 0–3 or more 1.99 1.10 Hispanic or Latino 1 if yes; 0 if otherwise 0.34 0.47 Education Less than high school graduate 3% N/A High school graduate/GED 14 % N/A Some college, no degree 28 % N/A Associate’s degree 18 % N/A Bachelor’s degree 23 % N/A Graduate or professional degree 14 % N/A 16 % N/A $10,000–$14,999 10 % N/A $15,000–$24,999 10 % N/A $25,000–$34,999 $35,000–$49,999 15 % 17 % N/A N/A $50,000–$74,999 12 % N/A $75,000–$99,999 10 % N/A $100,000–$149,000 6% N/A [$150,000 4% N/A \10,000 Household income N/A not applicable Table 2 Selected demographic variables, by race (n = 684) Household reported race Frequency respondent compromises on healthy food items based on budget concerns (1 always, 5 never) Frequency unable to feed your household (1 = always, 5 = never) Household size Gender (0 = male, 1 = female) Education (see Table 1 categories) Income (see Table 1 categories) White (n = 368)) 3.35 3.70 2.92 0.80 4.20 5.43 Nonwhite (n = 316) 3.24 3.66 3.13 0.92 3.41 4.62 Full sample 3.30 3.68 3.01 1.86 3.85 5.05 Table 3 Responses to ‘‘how frequently the respondent compromises their family’s food choices’’ by key demographics Frequency respondent compromises on healthy food items based on budget concerns Household size Gender (0 = male, 1 = female) Education (see Table 1 Income (see Table 1 categories) categories) Always (n = 108) 3.10 0.85 3.91 5.28 More than half the time (n = 124) 3.23 0.85 3.80 5.15 Half of the time (n = 106) 3.15 0.81 3.63 4.71 Less than half the time (n = 147) 2.86 0.88 3.87 4.69 Never (n = 199) 2.88 0.87 3.94 5.33 Full sample 3.01 1.86 3.85 5.05 123 J Community Health (2016) 41:910–923 915 Table 4 Community and individual variables used to determine clusters Questions Community and individual variables Where do you get most of the foods that your family eats? Fast food restaurants Other restaurants Work place and public cafeterias Senior centers Food assistance program Meal delivery program Large chain grocery stores Wholesale store Convenience store Natural food store Independent local food store Direct from meat processor or ranch Online purchases I grow make or hunt my food How do you get to the places where you buy or receive fruits or vegetables? Bike Walk Shuttle or taxi In someone else’s car Personal car Bus It is delivered to me What makes it challenging to get fruits and vegetable? Ease of access Ability to carry what I buy Store hours Cost Physical limitations Amount of time available Fruits and vegetables are not available where I get food Not applicable I do not eat fruits and vegetables Not applicable I have no challenges After testing for significant difference in variables across cluster groups, they are labeled as suggested by key attributes. Then, comparisons across clusters are used to explore the diversity of food access perceptions and challenges. Results We divide our cluster results into two sections. First, we define and name the clusters, based on each cluster subgroup’s responses. Second, we present individual and community variables, differentiating by cluster, and draw inferences on what factors may affect, alter or constrain the purchasing and consumption of fruits and vegetables of each cluster group. Finally, we use qualitative data from the focus groups to substantiate these results and better inform our inferences as applicable. Cluster Descriptive Statistics and Levels of Food Security Commonly shared demographic and descriptive statistics, by cluster, are presented in Table 5. Table 6 provides more detail on food security, fruit and vegetable consumption, and where households purchase most of their food, by cluster. Table 7 shows the differences in modes of transportation to fruit and vegetable purchase source among clusters. Though the significant differences between clusters are presented using pairwise comparisons, we additionally conducted f-tests for each of the clusters on two variables key to our research question: how often they 123 916 J Community Health (2016) 41:910–923 Table 5 Key demographics, by cluster n # In household 2.80c (.09) # In household under 18 Gender (0 = male, 1 = female) Age (years) Avg. income per household member ($) Hispanic/ Latino (0 = no, 1 = yes) Education (1 = less than high school grad, 6 = grad or professional degree) 1.79c (.08) 0.81 (.03) 45.98 (1.10) 19,160.92 (1643.00) 0.37 (0.04) 3.89 (0.11) 0.81 (0.05) 47.06 (2.07) 16,652.62 (2464.92) 0.33 (.06) 3.80 (0.20) (0.04) 45.31 (1.59) 24,765.10 (2,967.04) 0.35 (0.04) 3.99 (0.13) 173 Food engaged and secure 54 Away from home price conscious fruit and vegetable eaters 3.15 (0.18) 2.12 (0.16) 115 Food secure with inconvenient access to fruits and vegetables 2.74h (0.12) 1.87 (0.10) 299 Compromised consumers 3.24ch (0.07) 2.11c (0.07) 0.88hj (0.02) 42.02 (0.84) 17,011.16 (1,161.49) 0.34 (0.03) 3.74 (0.08) 43 Single and food insecure 2.86 (0.23) 2.13 (0.19) 0.6j (0.08) 38.72 (2.52) 22,367.89 (5,452.24) 0.19 (0.06) 4.10 (0.21) 0.67 h Standard errors in parentheses. Superscripts denote statistical differences in means, where tests were based on pairwise comparisons. The codes defined in the footnote below indicate significant differences between the clusters of respondents analyzed in this study and are the findings from combinations of pairwise tests a j Cluster 1&2, b Clusters 1&3, c Clusters 1&4, d Clusters 1&5, e Clusters 2&3, f Clusters 2&4, g Clusters 2&5, h Clusters 3&4, i Clusters 3&5, Clusters 4&5 compromise their food choices; and how often they are unable to feed their household. The f-tests present additional, strong evidence that Pueblo citizens are effectively grouped into segments with similar perceptions (see Table 8). Cluster 1 (173 individuals, or 25 % of the sample), labeled Food engaged and secure, is the second largest subgroup. This group self-reports as the most food secure (4.38), and least likely to compromise healthy food choices because of budget concerns (4.12) (see table for detail of ranges). With high levels of access to their own personal vehicles (1.20) these households rarely use other forms of transportation. One reason for this group’s label is because they are more interested in enhancing local food infrastructure (farmers’ markets, 2.40, producer or farm stands, 3.08) to make it easier to access fruits and vegetables, and less likely to purchase food away from home. This cluster also self-reported the second highest daily consumption rate of fruits and vegetables compared to the other groups (3.92/day). This cluster has the smallest average number of household members under 18 (1.79) and the highest percentage of Hispanic and Latino respondents (0.37). Given their self-reported food security, it was a bit surprising they report one of the lowest average per capita incomes ($19,160). Cluster 2 (54 individuals, 8 % of the sample), labeled Away from home price conscious fruit and vegetable eaters, most commonly eat food away from home 123 and is most likely to get some food assistance from food banks, food pantries, or churches (3.26), as well as through senior centers or meal delivery programs (7.52). The cluster label indicates that individuals report the largest average number of fruit and vegetable servings per day (4.17/day) and are fairly food secure (3.56). Still, they are very price sensitive—they are the second most likely group to compromise on healthy food items because of budget concerns (3.11), are most likely to shop at wholesale stores (3.17), and have the lowest per capita income ($16,652). This group has the second largest number in its household (3.15), as well as the second largest number in household under 18 (2.12), and is largely comprised of female respondents (0.81). Cluster 3 (115 individuals, 17 % of the sample), labeled Food secure with inconvenient access to fruits and vegetables, is highly food secure (3.97), and has the second highest daily consumption of fruits and vegetables (3.74/day). However, on average this group gets its fruits and vegetables from sources a further distance away (3.85), and yet is slightly less likely to use a personal car to buy fruits and vegetables (1.29). They are also least likely to get food from food assistance programs (3.89). This cluster has the smallest average number in its household (2.74), which may explain why they have the highest per capita income ($24,765), and supports their perception of strong food security. This subgroup is comprised of the second lowest share of females (0.67), as well as the 4.12acd (0.09) 3.11a (0.21) 3.74hi (0.12) 2.78ch (0.08) 2.70di (0.23) 4.38abcd (0.08) 3.56a (0.19) 3.97bhi (0.13) 3.26ch (0.08) 3.19di (0.22) Food engaged and secure Away from home price conscious fruit and vegetable eaters Food secure with inconvenient access to fruits and vegetables Compromised consumers Single and food insecure 173 54 115 299 43 g (0.19) 3.09dgij (0.21) 3.85j (0.09) 3.82i (0.13) 4.17 3.92d (0.11) # Fruit and vegetable servings per day 3.05i (0.23) 3.42 (0.08) 3.85i (0.15) 3.41 (0.19) 3.51 (0.10) How far do you live from where you get most of your fruits and vegetables (1 = closest, 7 = farthest) 10.81 g (0.22) 10.84f (0.07) 10.84e (0.13) 8.96aefg (0.30) 10.75a (0.12) Fast food, restaurant, work place, public cafeteria (1 = most common, 12 = least common) h (0.05) 7.88 (0.83) 7.99fh (0.01) 7.87 7.52f (0.18) 7.91 (0.03) Senior center, meal delivery (1 = most common, 8 = least common) 3.86 g (0.09) 3.87f (0.03) 3.89e (0.04) 3.26efg (0.16) 3.72a (0.06) Food assistance, food bank, food pantry, church (1 = most common, 4 = least common) a Cluster 1&2, b Clusters 1&3, c Clusters 1&4, d Clusters 1&5, e Clusters 2&3, f Clusters 2&4, g Clusters 2&5, h Clusters 3&4, i Clusters 3&5, j Clusters 4&5 Standard errors in parentheses. Superscripts denote statistical differences in means, where tests were based on pairwise comparisons. The codes below were used for different combinations of pairwise tests Frequency compromise on healthy food items b/c of budget concerns (1 = always, 5 = never) Frequency unable to feed your household (1 = always, 5 = never) n Table 6 Food security, fruit and vegetable consumption, and most frequent place households get food, by cluster J Community Health (2016) 41:910–923 917 123 918 J Community Health (2016) 41:910–923 Table 7 Usual modes of transportation to the places where they buy/receive fruits and vegetables, by cluster n Bus 173 Food engaged and secure 54 Away from home price conscious fruit and vegetable eaters 115 Shuttle/taxi 3.99d (0.01) cd (0.00) 3.99 cd Bike (0.01) 3.94d (0.03) In someone else’s car 3.68d (0.05) 3.84 Personal car cd (0.04) g (0.08) 1.20d (0.06) 4.00efg (0.00) 3.98e (0.02) 3.96 (0.04) Food secure with inconvenient access to fruits and vegetables 3.98i (0.01) 3.92e (0.04) 3.81e (0.06) 3.90i (0.04) 3.56i (0.08) 3.69i (0.07) 1.29i (0.08) 299 Compromised consumers 3.99j (0.01) 3.98cfj (0.01) 3.92c (0.02) 3.95j (0.02) 3.48j (0.05) 3.66cj (0.04) 1.13j (0.03) 43 Single and food insecure 2.07dgij (0.11) 3.70dgj (0.13) 3.91d (0.04) 4.00dij (0.00) 1.19dgij (0.06) 3.00dgij (0.19) 3.60dgij (0.12) 3.61 g Walk (0.00) 4.00 g 4.00 It is delivered to me (0.10) 3.74 1.06 g (0.06) Standard errors in parentheses. Superscripts denote statistical differences in means, where tests were based on pairwise comparisons. The codes below were used for different combinations of pairwise tests a j Cluster 1&2, Clusters 4&5 b Clusters 1&3, c Clusters 1&4, dClusters 1&5, eClusters 2&3, fClusters 2&4, gClusters 2&5, hClusters 3&4, iClusters 3&5, Table 8 F test results for selected variables Frequency unable to feed your household Frequency compromise on healthy food items b/c of budget concerns Food engaged and secure 2.33*** 14136.24*** Away from home price conscious fruit and vegetable eaters 1.03 Food secure with inconvenient access to fruits and vegetables 1.24* 5.54*** Compromised consumers 148.37*** 2.84*** Single and food insecure 2.66*** 1.58* 1.44* Asterisks indicate significance at: *a = 0.1; **a = 0.05; ***a = 0.01 smallest number of household members under 18 (though insignificant). Cluster 4, (299 individuals, 44 % of sample), labeled Compromised consumers, is the largest subgroup. This is the second most food insecure cluster (3.26), and the second most likely to compromise on choosing healthy food due to budget concerns (2.78). Compared to other clusters, this group is most likely to walk to access fruits and vegetables (3.66), yet they do have access to a personal vehicle (1.13) and are not using other forms of transportation. This cluster is generally not using food assistance programs (3.87), despite reporting interest in accessing more affordable fruits and vegetables. This subgroup also has the largest average number in their household (3.24), with relatively low income per household member (although insignificant), the highest percentage of females (0.88) and 123 lowest average educational attainment (3.74, although insignificant). Cluster 5 (43 individuals, 6 %), labeled Single and food insecure, is most frequently unable to feed their household (3.19) and commonly compromise on choosing healthy food items because of budget concerns (2.70). They also consume fewer average servings of vegetables a day than any other subgroup (3.09/day). On average, this is the cluster with the highest proportion of male respondents (0.6), has an average number of people in its household (2.86), but the highest number in household under 18 (2.13), suggesting a higher prevalence of single parents. They are more likely than other subgroups to use a variety of transportation methods including: bus (2.07), shuttle or taxi (3.70), and someone else’s car (1.19), and are statistically less likely to use a personal vehicle (3.60). And J Community Health (2016) 41:910–923 919 perhaps, as a result, they live closer to where they get their fruits and vegetables than any other cluster (3.05). Individual and Community Variables that would Change Purchasing Patterns by Cluster The clusters are perhaps even more informative when one considers how those diverse households may view strategies that could impact their choices and lessen their perceived food insecurity. We see both evidence of heterogeneity and homogeneity among household groups, and consider qualitative focus group discussion to enrich the data. There are four statistically significant differences in community factors that households report might make it easier to get more fruits and vegetables (Table 9). As one would expect, the Single and food insecure cluster, the group of households least likely to use a personal vehicle to get fruits and vegetables, was the most likely to support increasing bus routes or shuttle services to places that sell fruits and vegetables (3.74) or would welcome ‘‘more grocery stores near where I live or work’’ (3.53). One focus group attendee illustrated the importance of this with the remark that ‘‘they are thinking about cutting bus service on Saturday. That eliminates one of our grocery shopping days. There’s no service at all on Sunday. Period.’’ For the majority of respondents, however, transportation was not an issue. To the point of addressing the retail food environment, one cluster, Food secure with inconvenient access to fruits and vegetables, was significantly more likely to prioritize more grocery stores near ‘‘where I live or work’’ (3.55). Yet the other clusters–including Single and food insecure, the most food insecure cluster—did not think having more convenient access to fruits and vegetables would be that important. One focus group participant remarked, ‘‘I would rather go to Double J once per month and buy all of the meat that I would need for the month and buy the rest of my groceries at Walmart because the quality is better.’’ This finding that the majority of households do not shop at the nearest store is directly in line with Ver Ploeg et al.’s [1] findings using the USDA FoodAPS data. Accordingly, transportation and nearby markets are a concern for some, but suggesting they may be a broadly impactful strategy to address food insecurity would be misguided. The Single and food insecure cluster was most likely to want increased offerings of produce in these food assistance programs (3.07). One focus group participant stated, ‘‘I think what we need to look at this type of food that is distributed. Having access to healthier options at food Table 9 Community and individual factors that would make it easier to get access to fruit and vegetables, by cluster n Community factors Individual factors More farmers’ markets More producer or farm stands Bus routes or shuttle service to places that sell them More grocery stores near where I live/work More provided at my food bank/food pantry/meal delivery program 3.91 173 Food engaged and secure 2.40 (0.10) 3.08d (0.08) 3.99d (0.01) 3.84b (0.05) 54 Away from home price conscious fruit and vegetable eaters 2.48 (0.17) 3.39 (0.14) 3.87 (0.07) 3.76 (0.11) 115 Food secure with inconvenient access to fruits and vegetables 2.58 (0.12) 3.37 (0.09) 3.93 (0.04) 299 Compromised consumers Single and food insecure 2.44 (0.07) 3.20 (0.07) 2.93 (0.19) 3.56d (0.15) 43 More affordable to me More time to prepare/cook them cd (0.03) 2.75acd (0.11) 3.06 (0.09) g (0.06) 1.85aef (0.17) 2.98 (0.16) 3.55b (0.09) 3.81i (0.07) 2.61ehi (0.13) 3.07 (0.12) 3.97 (0.02) 3.73 (0.04) 3.75cj (0.05) 1.35cfhij (0.05) 3.27 (0.06) 3.74d (0.10) 3.53 (0.14) 3.07dgij (0.20) 1.91dij (0.20) 3.23 (0.16) 3.91 * 1 = yes, would make it easier, 4 = no, would not make it easier Standard errors in parentheses. Superscripts denote statistical differences in means, where tests were based on pairwise comparisons. The codes below were used for different combinations of pairwise tests a j Cluster 1&2, b Clusters 1&3, c Clusters 1&4, d Clusters 1&5, e Clusters 2&3, f Clusters 2&4, g Clusters 2&5, h Clusters 3&4, i Clusters 3&5, Clusters 4&5 123 920 pantries is going to improve the health of the population because they are going to access that.’’ But, the other four clusters did not prioritize this strategy, even though the Away from home price conscious fruit and vegetable eaters were more likely to access that source of produce. One focus group participant remarked, ‘‘You are ashamed because the system makes you feel ashamed,’’ indicating the stigma associated with community food assistance programs as one of the barriers to utilization. The most food secure cluster, Food engaged and secure, was the most likely to want more producer or farm stands to support improved fruit and vegetable consumption (3.08). One focus group participant stated, ‘‘There is a couple that comes and sells produce out of a truck… quality is great, and it is cheaper than the grocery market, but it is only available a couple of months out of the year.’’ However, the least food secure group, Single and food insecure, was the least likely to support more producer or farm stands (3.56). Subsequently, focus group participants described concerns about distance as perceived challenges with farm stands. One focus group participant stated, ‘‘A lot of people like to buy their food from the…farms directly…but it’s really far to get there.’’ The community factor questions also revealed pockets of homogeneity among the subgroups: every subgroup gave ‘more farmers’ markets’ the highest marks as a method to facilitate their access to more fruits and vegetables. Focus group remarks about farmers’ markets also included positive perceptions toward direct channels with comments like, ‘‘getting local foods is very important because they are fresher and have more nutrients.’’ And, ‘‘…food from the farmers’ market…is fresher and cheaper.’’ Still, price matters. The individual factor that all of the clusters consistently ranked ‘if they were more affordable’ as the most important method to get more fruits and vegetables. Despite the relative homogeneity of this response, it is the only individual factor with statistically significant differences between the clusters, and not surprisingly, the Food engaged and secure group being least likely, the Single and food insecure were the second least likely, followed by Away from home price conscious fruit and vegetable eaters (1.85), with Compromised consumers reporting increased affordability as most important (1.35). One focus group participant, for example, said, ‘‘Affordability would be the top of the list for most people.’’ ‘More time to prepare and cook fruits and vegetables’ was collectively ranked the second most important personal factor and focus group participant stated, ‘‘For me, it would probably be having the time to fix what I like the most. I work again, and it is kind of hard to go home and roast a chicken in 30–45 min.’’ But, time was not as 123 J Community Health (2016) 41:910–923 important to any of the clusters as affordability, and there are no statistically significant differences. Discussion and Future Research This research examines community and individual factors that may contribute to or alleviate food insecurity. Using responses and cluster results from a community food security assessment that included a survey of 684 residents and focus groups, we find that there are significant differences in the determinants of food insecurity within subgroups of the population. Based on our results, we conclude that, though interventions focused on addressing the local food retail environment may be important for some subsamples of the food insecure population, it is not clear that proximity to a store with healthy food will support enhanced food security for all. In fact, the most food insecure cluster, Single and food insecure, was significantly less likely to identify increased access to fruits and vegetables near ‘‘where I live or work’’ as a way to improve their household’s consumption of these healthy foods – even though this group was also significantly less likely to use a personal vehicle to go to the store. Further, the second most food insecure cluster, Compromised consumers, did not report significant challenges with access to fruits and vegetables, rather, making fruits and vegetables more affordable was significantly more likely to make it easier for them to consume these goods. For ease of exposition, Table 10 provides a summary of key findings. Our findings align with the recently released data from the USDA FoodAPS survey, both of which provide substantial evidence that there are a variety of factors other than proximity that affect where households shops. This research shows that price is by far the most important of those factors: all respondents cited reduced cost as the primary factor that would support increased access to and consumption of fruits and vegetables. Accordingly, interventions that focus on getting more healthy foods into geographies identified as inadequate food retail access may not result in the intended outcomes for some segments of food insecure populations. This is particularly true as it is not clear that increasing access to healthy food at these local establishments—e.g., corner or neighborhood stores—will result in cost effective options for food insecure residents. In our study, though improving the local retail environment was desired in the eyes of the Food secure with inconvenient access to fruits and vegetables cluster, increased access to reliable, convenient, and cost effective transportation was reported to be significantly more important for the most food insecure cluster, Single and - ‘?’ significantly more likely than other clusters, ‘-’ significantly less likely than other clusters - ? Single and food insecure ? - ? ? ? - ? - - - - ? - - - - In someone else’s car - ? ? ? Personal car Bus Fast food, restaurant, work place, public cafeteria Food assistance, food bank, food pantry, church Usual mode of transportation to where household buys/receives fruits and vegetables: Where household gets food: Compromised consumers ? - # Fruit and vegetable servings per day - - Frequency compromise on healthy food items b/c of budget concerns Food secure with inconvenient access to fruits and vegetables Away from home price conscious fruit and vegetable eaters Food engaged and secure Frequently unable to feed your household Table 10 Summary of key findings ? - - More provided at my food bank/food pantry/meal delivery program ? ? - ? - More affordable to me Individual factors that would make it easier to get access to fruits and vegetables: J Community Health (2016) 41:910–923 921 123 922 food insecure. Given the latter is a more susceptible population, too narrow a focus on the retail environment would be problematic and minimize the chances for a meaningful food security outcome. Based on our results, we recommend that future research recognizes that determinants of food insecurity may vary and that multiple interventions that target sub-population clusters within a geography (regardless of its definition) may elicit better improvements in access to and consumption of healthy food. As is the case with so many significant societal challenges, a portfolio of policy and program options should be considered, along with metrics that are meaningful to the target populations of those interventions. Implications for the Pueblo Community The assessment uncovered both challenges and opportunities including untapped potential to feed more local residents, as well as data on the current realities of Pueblo’s residents who currently lack consistent access to healthy foods. Accordingly, after several presentations of results to Pueblo County stakeholders, the Food Assessment Advisory Council made the decision to transition to a Food Action Council under the Health Disparities Mid-Level Obesity Stakeholder group in order to provide long-term sustainability for the group. From the broader assessment’s recommendations on consumers and markets, as well as findings from the survey clustering, there is evidence that both education (on growing your own produce) and increasing outreach and awareness about current direct market outlets would benefit many Pueblo residents. Given the widespread popularity of farmers’ markets as a potential community solution, and how strong and well-established federal food assistance programs are in the region, Pueblo County immediately began enacting policies in support of EBT access at farmers’ markets, the use of Women, Infants, and Children (WIC) vouchers at markets or CSAs, and the Senior Farmers’ Market Nutrition Program that will effectively lower the cost of buying at some outlets. Moreover, they are actively (and successfully) exploring partnerships to establish an incentive program that would enhance the dollar amount of SNAP benefits redeemed at farmers’ markets. Pueblo is also assessing the option to host regular farmers’ markets at District 60 and District 70 schools that are in low retail food environment census tracts, allowing residents to experience increased access to safe, healthy foods each week without needing to construct a full-service grocery store. However, there were limitations to our study and its ability to drive changes that would benefit some of the food insecure. Although some of the most food insecure did see 123 J Community Health (2016) 41:910–923 transportation barriers and time as a challenge, few of the recommended steps by Pueblo’s Advisory Council address those issues, perhaps because of the inability to engage those that lead transportation planning in the region. In a more general sense, this community-driven study provides a rich context to explore those perceptions among the food insecure that are consistently held, and contrast them with those factors, determinants, and strategies that are viewed quite differently among households. Our hope is that this moves the field forward to consider the multiplicity of factors, individual and community-based, that may be of importance to future programming and policies. Acknowledgments The authors wish to thank the Pueblo CityCounty Health Department and Wendy Peters Moschetti at WPM Consulting LLC for supporting the research design and data collection. Funding for the data collection was provided by a grant from the Colorado Department of Public Health and Environment’s Office of Health Disparities. Colorado State University Extension and the Colorado Agricultural Experiment Station provided research support for this project. Compliance with Ethical Standards Conflict of interest of interest. The authors declare that they have no conflict References 1. Ver Ploeg, M., Mancino, L., Todd, J.E., Clay, D.M., & Scharadin, B. (2015). Where do Americans usually shop for food and how do they travel to get there? Initial findings from the national household food acquisition and purchase survey. EIB-138. Washington, D.C.: US Department of Agriculture, Economic Research Service. 2. US Department of Agriculture Economic Research Service (USDA ERS). (2015). 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J Community Health (2013) 38:1182–1187 DOI 10.1007/s10900-013-9731-8 ORIGINAL PAPER Food Insecure Families: Description of Access and Barriers to Food from one Pediatric Primary Care Center Tori L. DeMartini • Andrew F. Beck Robert S. Kahn • Melissa D. Klein • Published online: 14 July 2013 Ó Springer Science+Business Media New York 2013 Abstract Despite evidence that food insecurity negatively impacts child health, health care providers play little role in addressing the issue. To inform potential primary care interventions, we sought to assess a range of challenges faced by food insecure (FI) families coming to an urban, pediatric primary care setting. A cross-sectional study was performed at a hospital-based, urban, academic pediatric primary care clinic that serves as a medical home for approximately 15,000 patients with 35,000 annual visits. Subjects included a convenience sample of caregivers of children presenting for either well child or ill care over a 4 months period in 2012. A self-administered survey assessed household food security status, shopping habits, transportation access, budgeting priorities, and perceptions about nutrition access in one’s community. Bivariate analyses between food security status and these characteristics were performed using Chi square statistics or Fisher’s exact test. The survey was completed by 199 caregivers. Approximately 33 % of families were FI; 93 % received food-related governmental assistance. FI families T. L. DeMartini (&)  R. S. Kahn Division of General and Community Pediatrics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, ML 7035, 3333 Burnet Avenue, Cincinnati, OH 45229, USA e-mail: R. S. Kahn e-mail: A. F. Beck  M. D. Klein Division of General and Community Pediatrics and Division of Hospital Medicine, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, ML 7035, 3333 Burnet Avenue, Cincinnati, OH 45229, USA e-mail: M. D. Klein e-mail: 123 were more likely to obtain food from a corner/convenience store, utilize food banks, require transportation other than a household car, and prioritize paying bills before purchasing food. FI families perceived less access to healthy, affordable foods within their community. Thus, FI families may face unique barriers to accessing food. Knowledge of these barriers could allow clinicians to tailor in-clinic screening and create family-centered interventions. Keywords Food insecurity  Access  Underserved populations  Pediatric primary care Abbreviations FI Food insecure USDA US Department of Agriculture WIC Special Supplement Nutrition Program for Women Infants Children SNAP Supplemental Nutrition Assistance Program NSLP National School Lunch Program FS Food secure PPC Pediatric primary care IQR Interquartile range Introduction Food security is defined by the US Department of Agriculture (USDA) as ‘‘access by all people at all times to enough food for an active, healthy life.’’[1] In 2011, 20.6 % of households with children in the United States experienced food insecurity at some point in the preceding year [2]. Childhood food insecurity has been associated with negative health and development outcomes including more frequent common illnesses [3], iron-deficiency anemia [4], overweight or obese status [5, 6], increased J Community Health (2013) 38:1182–1187 likelihood of hospitalization [7], increased risk of developmental delays [8], and lower physical and psychosocial functioning [9]. Food insecurity is primarily driven by financial constraints, and although participation in food benefit programs such as the Supplemental Nutritional Assistance Program (SNAP), Special Supplemental Nutrition Program for Women, Infants and Children (WIC), and National School Lunch Program (NSLP) provides some relief, studies have shown that participation does not eradicate the problem [10–12.] While financial constraints directly affect food purchasing power, related constraints and barriers may also affect access to affordable, high-quality food. As a result, food insecure (FI) families often rely on inexpensive, energy dense foods and have lower consumption of fresh fruits and vegetables overall [13–15.] Few studies have assessed the different challenges faced by FI and food secure (FS) families in providing adequate nutrition and none, to our knowledge, have examined them in the context of a pediatric primary care (PPC) encounter. This study aimed to compare characteristics of FI and FS populations and describe barriers to accessing healthy food for FI families within one PPC center. Specifically, we sought to better understand where FI families obtain food, modes of transportation used to access food, budgeting priorities, and perceptions about the food environment in one’s community. A better understanding of such characteristics could help to improve strategies to identify FI families and create or modify interventions in a way that is more tailored to the specific barrier or need that is faced. Methods Study Design and Data Collection A cross-sectional study was performed at the PPC Center, a hospital-based, urban, academic PPC clinic that serves as a medical home for approximately 15,000 patients with 35,000 visits annually. A convenience sample of caregivers of children present for either well child or ill care completed a self-administered survey over a 4 months period in 2012. Participation was voluntary, and all English-speaking caregivers were eligible. The study was limited to Englishspeaking caregivers; \3 % of the clinic’s population was excluded as a result. Patient demographic information including age, gender, race, and insurance status was extracted from the electronic medical record, Epic (Epic Systems Corporation, Verona, Wisconsin). The study was reviewed by the Cincinnati Children’s Hospital Medical Center Institutional Review Board and determined to be exempt. 1183 Survey Development A 43-question survey was developed to assess food security status, access to food stores, and perceived barriers to obtaining food for one’s household. Initial survey questions addressed demographic information. Caregivers completing the survey were queried on their relationship to the patient being seen, their age, the number of children and adults living within their household, their educational attainment, and whether their child or household received governmental benefits through SNAP, WIC, or NSLP. The survey included 11 questions from the USDA’s Food Security Core-Module Questionnaire, including the standard 6-item indicator set for classifying households by food-security-status-level [1.] Consistent with the USDA’s classification criteria using the standard 6-item indicator set, we classified a household as having ‘‘low’’ food security if the respondent answered in the affirmative to 2–4 of the 6 questions, while households with affirmative answers to 5–6 of the questions were classified as having ‘‘very low’’ food security. Households with ‘‘low’’ and ‘‘very low’’ food security were grouped together and were defined as FI. The next section of the survey focused on food shopping habits and barriers to accessing food using a set of questions developed de novo. Participants were asked where they obtained their household’s food; response options included supermarket chain, wholesale chain, small neighborhood grocery store, corner/convenience store, food bank/pantry, or other. They were first asked to select all places from which they obtain food and then to select the one place from which they get the majority of their household’s food. Participants’ reasons for choosing a particular food store were assessed and options included proximity to home, accessible to the bus line, convenience, low cost, wide variety, or other. Survey respondents were then asked about the mode of transportation used to get to their chosen food store with possible answers including household car, a car borrowed from family/friend/neighbor, a ride from family/friend/neighbor, the bus, walking, a taxi, or other. They were also asked the amount of time required to travel to the food store, and how frequently they go to the store for food. To gauge participants’ level of support and connectedness and coping strategies they were asked whether they had family and/or friends that they could count on to feed their family for a day. Participants were also asked about their household budgeting strategies and priorities (i.e., places where they spend their money first, such as on food, rent, utilities, etc.). The survey’s final section addressed the participant’s perceptions about the food environment within their community using previously developed questions [16]. The food environment was assessed through availability of 123 1184 fresh and canned fruits and vegetables, and perceived hunger within the community. Potential barriers to getting desired food included transportation, number of stores, affordable healthy choices, and crime. Each question in this section used a 5-point Likert-based response scale where 1 indicated strong disagreement and 5 strong agreement. Analysis J Community Health (2013) 38:1182–1187 Table 1 Demographic information for children and caregivers (N = 199) Characteristic N % Mother 171 85.9 Father 14 7.0 Other 14 7.0 Less than high school degree 25 12.8 High school degree or equivalent Some college 65 69 33.3 35.4 College degree 36 18.5 112 56.3 87 43.7 Relationship to child Parent education Descriptive statistics were used to report baseline sample characteristics as well as prevalence of USDA-defined household food insecurity. Bivariate analysis of food security status and food shopping habits, barriers to accessing food, and respondent’s perception of their community’s food environment were performed. Associations were assessed using Chi square statistics or Fisher’s exact test. Analyses were performed using SAS statistical software (version 9.3, Cary, NC). Survey data was captured using Research Electronic Data Capture (REDCap), a secure, web-based application [17]. Results Child gender Male Female Child race/ethnicity Black or African American 147 73.9 43 21.6 9 4.5 Public 178 89.4 Private 12 6.0 9 4.5 Supplemental Nutrition Assistance Program (SNAP) Women Infants Children (WIC) 146 118 73.4 59.3 National School Lunch Program (NSLP) 115 58.4 66 33.2 133 66.8 White or caucasian Other Medical insurance Self-pay/unknown Supplemental food benefit program participation A total of 247 caregivers were approached and 200 (81 %) completed the survey. One caregiver completed two surveys during two separate clinic visits, and the second survey was excluded from our analysis making the total number of completed surveys used for analysis 199. The median caregiver age was 27 years [interquartile range (IQR) 23–32]. The children of the caregivers were 56 % male, 74 % African American, and 22 % Caucasian with a median patient age of 2.1 years (IQR 0.6–6.2) (Table 1). Eighty-nine percent of children were publicly insured. A total of 73 % received SNAP, 59 % received WIC, and 58 % received NSLP. Children for whom surveys were completed were demographically equivalent to the patient population cared for at the PPC clinic. Thirty-three percent of caregivers lived in households classified as FI. Caregivers in FI households were significantly more likely to report that there was at least 1 day in the past 30 days when their household did not have enough food to make a meal and did not have money or governmental benefits to buy food (Table 2). Additionally, caregivers in FI households were more likely to report, compared to those in FS households, that at some point in the last year their children were not eating enough food (53 vs. 8 %, p \ 0.0001) and that some went an entire day without eating (6 vs. 0 %, p = 0.01) because there was not enough money to buy food. They were also significantly more likely to rely on low-cost food to feed their children (69 vs. 14 %, p \ 0.0001). 123 Food security status Food insecure Food secure Food shopping habits and transportation modes differed significantly between FI and FS households. While the majority of caregivers (97 %) reported primarily shopping for food at a supermarket, FI households, compared to FS households, were more likely to buy some portion of the household’s food from a convenience/corner store and get food from a food bank four or more times per year (Table 3). Additionally, caregivers from FI households were significantly more likely to use transportation other than a household car to get to the supermarket and report that transportation was a barrier to eating healthy. Although it did not reach statistical significance, shoppers in FI households were more likely to travel more than 15 min to the food store (p = 0.14). Purchasing food was not always the first priority for FI household budgets; when money was limited, paying rent/ mortgage, utilities, transportation costs, and phone service were often of higher priority (Table 3). Budgeting concerns involving food purchases were much more common in FI households compared to FS households (70 vs. 18 %, J Community Health (2013) 38:1182–1187 1185 Table 2 Child-related food insecurity characteristics Food insecure N = 66 On at least 1 day in the past 30 days, our household did not have food to make a meal or money, SNAP or WIC to get food 53 In the past 12 months, my child was not eating enough because I/we couldn’t afford enough food 21 In the past 12 months, my child did not eat for a whole day because there wasn’t enough money for food 6 In the past 12 months, we relied on only a few kinds of lowcost food to feed the children because we were running out of money to buy food 69 Food secure N = 133 8 P-value Table 3 Differences in characteristics of food insecure households compared to food secure households Food Insecure N = 66 (%) \0.0001 Food Secure N = 133 (%) P-value Shopping habits 0 \0.0001 Get food from convenience/ corner store 20 9 0.03 Get food from food bank C4 times/year 46 12 \0.0001 Do not use household car to get to food store 48 20 \0.0001 Transportation is a barrier to eating healthy 19 6 0.006 Travel [15 min to food store 18 11 0.14 Transportation 0 14 0.01 \0.0001 SNAP Supplemental Nutrition Assistance Program, WIC Special Supplement Nutrition Program For Women Infants Children Strategies Budgeting priority Rent/mortgage 50 16 \0.0001 Utilities 32 11 0.0004 Transportation 24 8 0.001 9 3 0.06 18 70 \0.0001 35 16 0.0003 44 13 \0.0001 Healthy food choices in my community are not affordable 37 20 0.01 People in my community are going hungry 48 40 0.45 Phone bill p \ 0.0001). Twenty-five percent of caregivers in FI households, compared to 4 % in FS households, reported that there was nobody that they could count on to feed their family for a day if they ran out of food (p \ 0.0001). Perceptions of the food environment in one’s community varied between caregivers of FI and FS households. A significantly higher percentage of caregivers in FI households did not believe there were enough food stores, did not feel that they are able to get the types of food they wanted, and felt that healthy food choices were not affordable in their community (Table 3). Respondents from both FI and FS households believed people in their community were going hungry. Discussion Food insecurity is widespread in the United States despite its known detrimental impact on child health and development. One-third of households in our PPC clinic were FI, higher than the national average, despite the fact that 93 % of the entire population received at least one form of governmental assistance related to food (SNAP, WIC, NSLP). A better understanding of barriers and challenges faced by FI families could help to inform and tailor screening and intervention practices in PPC clinics such as our own. Caregivers in FI households face many challenges trying to provide nutritious food for their family. While the vast majority of caregivers (97 %) primarily purchased food from a supermarket, FI families were more likely to utilize other sources of food such as convenience/corner stores and food Do not have budgeting concerns Perceptions There are not enough food stores in my community I am not able to get the types of food I want in my community banks. Convenience stores may be more readily available to residents in urban neighborhoods than supermarkets [18– 20], but they typically have higher prices [20] and do not routinely stock healthy foods such as fresh fruits and vegetables [19–23]. Increasing the variety of fruits and vegetables available at a convenience store increased the odds that FI and low-income customers actually purchased such fresh produce [24]. Recognizing where families obtain food and acquiring knowledge about the quality of food available for purchase could be an opportunity for both in-clinic assessments as well as community-level advocacy. Transportation was a barrier to obtaining food for FI households. Our FI families were less likely than FS families to use a household car to get to the food store; instead they relied on public transportation, transportation from nonhousehold members, taxis or walking. Families who depend on public transportation or others’ cars, either by borrowing or getting a ride, have less flexibility in choosing when and where they obtain food and the quantity and types of food they 123 1186 purchase during a shopping trip [11, 25]. Without a household car, families may need to time shopping trips, either based on availability of a ride or the timing of public transportation, which may be an inconvenience. Although one may schedule a more convenient shopping trip by paying for a taxi, the expense of such a trip likely makes it impractical for most families. Walking to the store may be a convenient option for those who live close to the food store but limits the amount one may purchase. The limited flexibility associated with transportation difficulties can be problematic for a family trying to maximize their food purchasing power by shopping at stores with less expensive, high-quality food. Often, the store with the best prices or most variety is not the one closest to home, and with inflexible transportation, a family may be forced to make sub-optimal food purchasing choices [25]. For example, a shopper who is dependent on someone else’s car or a taxi may make infrequent trips to the store and be forced to buy in bulk; this pattern, while likely less expensive may limit the amount of fresh produce purchased to avoid food spoiling. Alternatively, someone who relies on public transportation or walking may shop more frequently due to limited carrying capacity; this shopper may be more likely to buy fresh produce but is also likely to pay higher unit costs [25] and spend more money overall. Knowledge of where families shop and their mode of transportation could therefore be relevant to in-clinic guidance. This may also represent an area in which pediatricians can serve as advocates, influencing neighborhood development and planning. FI households must prioritize spending to make ends meet; other bills may take priority over buying food. In our study, rent/mortgage and utilities were the most common bills prioritized over purchasing food. Others have shown that the odds of food insecurity increase for families with housing costs [30 % of their income [26]. For some, food spending may fall below the cost of a basic nutritious diet as housing costs rise [27]. Additionally, as utility costs vary with the season, so do food expenditures for low-income households. Low-income families reduce their food expenditures by approximately the same amount that they increase fuel expenditures during cold-weather months, the ‘‘heat or eat’’ phenomenon [28]. It is important for clinicians to consider such variations in spending and screen accordingly, maximizing the opportunity to connect families with pertinent resources. Caregivers in FI households have different perceptions of their own community than those in FS households. FI caregivers were twice as likely to report not having enough food stores in their community and three times more likely to report that they could not get the food they want in their community. Interestingly, it is debatable whether better physical access to food can alleviate food insecurity for families with resource constraints [29]. Kirkpatrick and 123 J Community Health (2013) 38:1182–1187 Tarasuk found no association between families living within 2 km of a supermarket and whether they were FI. Overall, our survey respondents recognized that hunger was an issue within their community, a perception that did not differ significantly between FI and FS respondents. There were limitations to our study. First, this was a convenience sample of English-speaking caregivers. Given a similar demographic profile to the clinic population as a whole, and given that non-English speakers make up\3 % of our clinic population, we do not expect it to have greatly impacted our results. Second, this survey was conducted at a single clinic site with a modest sample size, making it less generalizable. Third, given its cross-sectional design, we cannot conclude that the barriers identified caused the food insecurity. Finally, this study demonstrates reported food shopping habits and not actual purchases. Therefore, we cannot make conclusions on the nutritional quality of food actually procured. As we better understand how FI and FS households differ, it is imperative for clinicians to enhance screening practices to both identify food insecurity and elicit specific barriers. Clinicians then need to have on hand clinic- and community-based resources aimed at improving food access. They may benefit from strengthened partnerships with and/or knowledge of community organizations poised to intervene (e.g., food banks, neighborhood-based farmers’ markets, community gardens, etc.) [30–32]. Clinicians could also engage in community-level advocacy aimed at enhancing convenient and effective public transportation that may benefit their patient population. Conclusions Families in our urban, underserved PPC clinic were more likely to be FI than the national average despite the vast majority receiving food-related public benefits. FI households in this urban PPC population face a unique set of barriers to accessing nutritious food that include limited access to transportation, financial constraints related to paying bills, and perceived impediments within their community’s food environment. Knowledge of such barriers could allow clinicians to improve screening and create family-centered interventions in ways that more effectively meet individual patient and family needs. Acknowledgments The authors would like to thank Angela Howald for her help with data collection. 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The Journal of nutrition, 129(2), 517S–520S. 1187 16. Hendrickson, D. S. C., & Eikenberry, N. (2006). Fruit and vegetable access in four low-income food deserts communities in Minnesota. Agriculture and Human Values, 23, 371–383. 17. Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic data capture (REDCap)– a metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381. 18. Powell, L. M., Slater, S., Mirtcheva, D., Bao, Y., & Chaloupka, F. J. (2007). Food store availability and neighborhood characteristics in the United States. Preventive Medicine, 44(3), 189–195. 19. Sloane, D. C., Diamant, A. L., Lewis, L. B., et al. (2003). Improving the nutritional resource environment for healthy living through community-based participatory research. Journal of General Internal Medicine, 18(7), 568–575. 20. Chung, C., & Myers, S. L. (1999). Do the poor pay more for food? An analysis of grocery store availability and food price disparities. Journal of consumer affairs, 33(2), 276–296. 21. Andreyeva, T., Middleton, A. E., Long, M. W., Luedicke, J., & Schwartz, M. B. (2011). Food retailer practices, attitudes and beliefs about the supply of healthy foods. Public health nutrition, 14(6), 1024–1031. 22. O’Malley, K., Gustat, J., Rice, J., Johnson, C. C. (2013). Feasibility of increasing access to healthy foods in neighborhood corner stores. Journal of community health. Epub 2013/04/03. 23. Sharkey, J. R., Dean, W. R., & Nalty, C. (2012). Convenience stores and the marketing of foods and beverages through product assortment. American Journal of Preventive Medicine, 43(3 Suppl 2), S109–S115. 24. Martin, K. S., Havens, E., Boyle, K. E., et al. (2012). If you stock it, will they buy it? Healthy food availability and customer purchasing behaviour within corner stores in Hartford, CT. USA. Public health nutrition, 15(10), 1973–1978. 25. Clifton, K. (2004). Mobility strategies and food shopping fo lowincome families: A case study. Journal of Planning Education and Research, 23(402), 402–413. 26. Kirkpatrick, S. I., & Tarasuk, V. (2011). Housing circumstances are associated with household food access among low-income urban families. Journal of urban health, 88(2), 284–296. 27. Kirkpatrick, S. I., & Tarasuk, V. (2007). Adequacy of food spending is related to housing expenditures among lower-income Canadian households. Public health nutrition, 10(12), 1464–1473. 28. Bhattacharya, J., DeLeire, T., Haider, S., & Currie, J. (2003). Heat or eat? Cold-weather shocks and nutrition in poor American families. American Journal of Public Health, 93(7), 1149–1154. 29. Kirkpatrick, S. I., & Tarasuk, V. (2010). Assessing the relevance of neighbourhood characteristics to the household food security of low-income Toronto families. Public health nutrition, 13(7), 1139–1148. 30. Jones, P., & Bhatia, R. (2011). Supporting equitable food systems through food assistance at farmers’ markets. American Journal of Public Health, 101(5), 781–783. 31. George, D. R., Kraschnewski, J. L., & Rovniak, L. S. (2011). Public health potential of farmers’ markets on medical center campuses: A case study from Penn State Milton S. Hershey medical center. American Journal of Public Health, 101(12), 2226–2232. 32. Carney, P. A., Hamada, J. L., Rdesinski, R., et al. (2012). Impact of a community gardening project on vegetable intake, food security and family relationships: A community-based participatory research study. Journal of Community Health, 37(4), 874–881. 123 Copyright of Journal of Community Health is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
RESEARCH AND PRACTICE T h e S u p p le m e n ta l N u tr itio n A s s is ta n c e P ro g ra m , F o o d In s e c u r ity , D ie ta r y Q u a lity , a n d O b e s ity A m o n g U S A d u lts I B in h T. N gu ye n , P hD , K e re m S h u v a l, PhD, Farryl B e rtm a n n , PhD , a n d A m y L Y a ro c h , PhD Food insecurity, broadly defined as having limited access to adequate food,1 is associated with increased stress levels and reduced overall well-being.2 In addition, food insecurity has been shown to diminish dietary quality and affect nutritional intake and has been associ­ ated with chronic morbidity (e.g., type 2 di­ abetes, hypertension) and weight gain.1,3' 5 In 2012, approximately 14.5% of US households (17.6 million households) experienced food insecurity, of whom 5.7% (7.0 million house­ holds) experienced very low food security (i.e., reduction in food intake).6 The Supple­ mental Nutrition Assistance Program (SNAP), formerly known as food stamps, is the largest government assistance program in the United States and seeks to alleviate food insecurity in US households.7 SNAP has the potential to mitigate the adverse effects of food insecurity on health outcomes not only through attenu­ ating food insecurity but also by enhancing the dietary quality of its participants.8'9 Although cross-sectional studies have found no significant differences in food insecurity levels between SNAP participants and nonpar­ ticipants,10'11 in a longitudinal study, Nord observed a 28% reduction in the odds for very low food security among households that remained on SNAP throughout the year rela­ tive to those who left before the last 30 days of the year.12 In addition, studies by Leung and Villamor13 and W ebb et al.14 found that in­ Objectives. W e exam in e d w h e th e r S u p p le m e n ta l N u tritio n A ssistance P ro­ g ra m (SNAP) p a rtic ip a tio n ch a n g e s a s s o c ia tio n s b e tw e e n fo o d in s e c u rity , d ie ta ry q u a lity , and w e ig h t a m o n g US a d u lts. Methods. W e analyzed a d u lt d ie ta ry Intake data (n = 8333) fro m th e 2003 to 2010 N atio na l Health and N u tritio n E xa m in atio n S urvey. B ivariate and m u ltiv a ri­ able m e th o d s assessed associatio ns o f SNAP p a rtic ip a tio n and 4 levels o f fo o d se c u rity w ith d ie t and w e ig h t. M easures o f d ie ta ry q u a lity w e re th e H ealthy Eating Index 2010, to ta l ca lo ric Intake, e m p ty calories, and so lid fa t; w e ig h t m easures w ere b o d y m ass in d e x (BM I), o v e rw e ig h t, and obesity. Results. SNAP p a rticip a n ts w ith m a rg in a l fo o d se cu rity had lo w e r BMI (1.83 kg /m 2; P< .01 ) and lo w e r p ro b a b ility o f o b e s ity (9 percentage p oints; P < .05). SNAP p a rticip a n ts w ith m a rg in a l (3.46 p o in ts; P < .01 ), lo w (1.98 p o in ts; P< .05), and v e ry lo w (3.84 p o in ts; P< .01 ) fo o d se cu rity had b etter diets, as illu stra te d by th e H ealthy Eating Index. A sso cia tio n s betw een SNAP p a rtic ip a tio n and im ­ p ro ved d ie t and w e ig h t w ere stro n g e r a m o ng W h ite s th an Blacks and H ispanics. Conclusions. O u r re se a rch h ig h lig h ts th e ro le o f S N A P In h e lp in g in ­ d iv id u a ls w h o are a t ris k fo r fo o d in s e c u rity to o b ta in a h e a lth ie r d ie t and b e tte r w e ig h t s ta tu s . {Am J Public Health. 2 0 1 5 ;1 0 5 :1 4 5 3 -1 4 5 9 . d o i: 10.2105/ A J P H .201 5.3 0 25 80 ) and weight status warrant further investigation to inform SNAP programming, policy, and from an in-home questionnaire, as well as obtaining dietary data and medical and phys­ outreach to ultimately improve the health and iological measurements and performing labo­ well-being of SNAP participants. W e explored ratory tests and a physical examination in these relationships in data from the National mobile examination centers.18 Health and Nutrition Examination Survey We focused on participants with family income less than 200% of the federal poverty level in (NHANES) over multiple years. W e aimed to determine mitigating effects SNAP participa­ 2003 to 2010 to reduce residual confounding in tion might have on the association of food the sample, especially between the high-income insecurity with dietary quality and obesity food security group and other groups. W e did not among a nationally representative sample of limit our sample to SNAP-eligible participants dependent of food insecurity, SNAP participa­ tion is associated with the increased likelihood of obesity, and other studies have observed lower dietary quality specifically among SNAP US adults. with incomes lower than 130% of the federal participants.15,16 K reider et al. used partial identification bounding methods to take into account the endogenous selection and misreporting of SNAP enrollment and found that SNAP reduced the prevalence of food insecu­ rity, poor genera] health, and obesity among children.17 The NHANES is a multistage, cross-sectional, nationally representative survey conducted by SNAP.6'9 Thus our sample consisted of 8333 the National Center for Health Statistics to had completed day 1 dietaiy interviews. Thus, the interrelationships among SNAP participation, food insecurity, dietary quality, poverty level because we aimed at capturing both M ETHODS the marginal food security and food insecurity population, many of whom are not eligible for nonpregnant adults aged 20 years and older who explore the health and nutritional status of US children and adults.18 Our participants came M easu res from 4 waves: 2 0 0 3 to 2004, 2 0 0 5 to 2006, 2 0 0 7 to 2 0 0 8 , and 2 0 0 9 to 2010. The W e determined participation in the SNAP program by an affirmative response to the NHANES collects information on demographic question, “In the last 12 months, did [you, or and socioeconomic characteristics and health any member of your household] receive food July 2 0 1 5 , Vol 10 5, No. 7 | American Journal o f Public Health Nguyen et al. \ Peer Reviewed | Research and Practice | 1 4 5 3 RESEARCH AND PR AC TICE stamp benefits?”18 We derived 4 levels of food security from responses to the NHANES Food Security Survey Module questionnaires, details of which are available online.20 Households with high food security reported no food access problems or limitations; households with mar­ ginal food security may have had anxiety over food sufficiency or shortage of food in the house; households with low food security generally reported reduced quality, variety, or desirability of diet without changes in diet or food intake; and households with very low food security generally reported multiple indications of disrupted eating patterns and reduced food intake. We considered households in the high and marginal categories to be food secure. Our key outcome variables were (1) diet (the Healthy Eating Index 2010 [HEI-2010]21 and intake of added sugar, solid fat, empty calories, and total calories) and (2) weight (body mass index [BMI], defined as weight in kilograms divided by the square of height in meters; overweight; and obesity). From the first-day dietary recall data (24 hours), we computed HEI-2010 as well as other dietary indicators, such as empty calorie, solid fat, and addedsugar intake, with National Cancer Institute methodology.22 We used HEI-2010, a tool that aims to determine compliance with the 2010 Dietary Guidelines for Americans, to assess overall dietary quality.21 We calculated BMI with the standard formula and objectively measured height and weight. We used World Health Organization criteria to categorize par­ ticipants’ BMI as underweight (< 18.5 kg/m2), normal weight (18.5—< 25 kg/m2), overweight (> 2 5 -< 30 kg/m2), or obese (> 30 kg/m2).22 S ta tis tic a l A nalysis We used the first-day 24-hour dietary recall data to document participants’ sociodemo­ graphic characteristics by participation status in the SNAP program. We also examined the differences in HEI-2010 score and intake of added sugar, solid fat, empty calories, and total calories among those with full food security versus all others (i.e., participants with mar­ ginal, low, and very low food security). In addition, we examined the differences in per­ centage of underweight, overweight, and obe­ sity and in BMI between these 2 groups. To examine the combined effect of SNAP participation and food security, we estimated an ordinary least squares model with the in­ teraction coefficient of SNAP and food security. Our formula was ( 1) Y ij = a ¡j + bSNAPj + yFoodlnsecj + aSNAPj x Foodlnsecj + SyXy + e,y Where T,y, the dependent variables, denoted outcomes of individual i in household a ¡ywas the intercept; b was a parameter estimate for the baseline difference between SNAP partici­ pants and SNAP nonparticipants; and y was an estimate for the difference between 4 levels of food security. The main parameter of interest, a, was an estimate of the cross-level interac­ tions of a household’s SNAP status and food security. Other control covariates (Xy) were age; gender; race/ethnidty (non-Hispanic White, non-Hispanic Black, Hispanic, other); education ( college); marital status (married, never married, divorced or separated, widowed); poverty-to-income ratio; Women, Infants, and Children program par­ ticipation in the past year; health insurance status (insured or not); employment status (employed or not); whether the survey was completed on a weekday or weekend24; and interview wave (2003-2004, 2 0 0 5 -2 0 0 6 , 2007-2 0 0 8 , 2009-2010). We conducted all statistical analyses with STATA version 1325 and accounted for the NHANES complex, multistage probability sam­ pling design of households and individuals to enable nationally representative estimates.26 Because individuals in the mobile examination centers sample provided the dietary recall data, we used the centers’ sample weights (provided by NHANES) in all analyses. We computed HEI-2010 scores with SAS software version 9.3.27 R ESU LTS The characteristics of the study population are shown in Table 1. The study sample consisted of 8333 adults. Participants had a mean age of 45.5 years; 55.4% were women, 55.9% were non-Hispanic Whites, 16.4% were non-Hispanic Blacks, 21.9% were Hispanics, 51.1% were married, 64.5% had health insurance, and 49.2% were employed. Food security was high in 59.1% 1 4 5 4 I Research and Practice | Peer Reviewed | Nguyen et al. of respondents’ households, marginal in 13.2%, low in 17.2%, and very low in 10.5%. The bivariate relationship of food security status to dietary quality and weight status is presented in Table 2. Participants with any level of food insecurity had a significantly lower HEI-2010 score than those with full food security (43.7 vs 46.6), higher intake of added sugar (22.0 vs 18.7 teaspoons), and higher intake of empty calories (787.9 vs 731.5 kilocalories; P< .05 for all). Furthermore, in­ dividuals living in households without food security had significantly higher BMIs and were likelier to be obese than those with food security (38.4% vs 33.7%; P <.01). How­ ever, we observed no significant differences in solid fat consumption or the probability of being underweight. The association of SNAP participation and food security status with dietary quality and weight status among low-income respondents is presented in Table 3. SNAP participants had a poorer nutrient profile (lower HEI-2010 score, higher consumption of added sugar, solid fat, and empty calories) than nonpartidpants. HEI-2010 scores were lowest among participants who reported living in households with very low food security (2.59 points lower than in the reference group, partidpants with high food security), followed by those with marginal (-2.27 points), and low (-1.63 points) food security. Table 3 also shows the interaction between SNAP partidpation and food security (i.e., whether SNAP partidpation may change the assodations between food insecurity, dietary quality, and weight status among US adults). Partidpation in SNAP was associated with higher HEI-2010 scores (better nutrient profile) among individ­ uals in households with marginal (+3.46 points), low (+1.98 points), and very low (+3.84 points) food security than among re­ spondents with corresponding food insecurity who did not receive SNAP benefits. For participants with low food security, partici­ pating in SNAP was only associated with lower added-sugar (-3.88 teaspoons) and empty calorie (—67.56 kcal) intake. Although SNAP participants and respondents experi­ encing food insecurity each independently had a higher BMI and higher probability of being obese, the combined association of SNAP participation and food insecurity American Journal of Public Health | July 2 0 1 5 , Vol 10 5, No. 7 RESEARCH AND PRACTICE TABLE 1-Summary Statistics of Low- TABLE 1-C o n tin u ed Income Adults: National Health and Nutrition Examination Survey, 2 0 0 3 - Wave 2010 Variable Full Sample (n * 8333) Women, % 55.4 Age, y, mean 45.5 SNAP participation, % 27.3 Household food security,3 % Full 59.1 Marginal 13.2 Low 17.2 Very low 10.5 Race/ethnlcity, % Non-Hispanic White 55.9 Non-Hispanic Black 16.4 Hispanic 21.9 Other 5.9 Marital status, % Married Widowed 51.1 24.1 23.0 3 (2007-2008) 26.2 4 (2009-2010) 26.7 Note. FPL = federal poverty level; SNAP'Supplemen­ tal Nutrition Assistance Program; WIC = Women, Infants, and Children program. Results take survey weights into account. Respondents were aged 20 years or older and had family income under 200% of the FPL. Respondents from households with children younger than 18 years were asked 18 questions from the US Food Security Survey Module; respondents from households without children were asked 10 questions. The food insecurity variable, with 4 response levels, was derived from affirmative responses. Household full food security »zero affirmative responses; marginal food security* 1-2 affirmative responses; low food security * 3 -5 affirmative responses for households without children 3 -7 affirmative responses for households with children; very low food security* 6 -1 0 affirmative responses for households without children and 8 -1 8 affirmative responses for house­ holds with children. 9.6 Divorced/separated 17.1 Never married 22.2 Education, % < high school 35.7 High school 28.3 Some college 26.6 > college 1 (2003-2004) 2 (2005-2006) 9.4 Health insurance, % 64.5 Currently employed, % 49.2 Received WIC benefits in 21.1 past year, % Poverty-to-income ratio, FPL, % 0 -5 0 12.3 5 1 -10 0 27.1 10 1-1 30 20.4 13 1-2 00 40.2 Household size, mean 3.3 Survey on weekend, % 39.0 Continued appeared to decrease BMI across all 3 food-insecure groups and reduce the likelihood of obesity among participants with marginal food security (9 percentage points). Table 4 presents the associations of SNAP participation and food security with dietary quality and weight status, stratified by race/ ethnicity. These results indicated that SNAP participation had limited effect on dietary quality and weight status among food-insecure non-Hispanic Black adults. By contrast, SNAP participation among food-insecure non-Hispanic Whites was associated with a higher HEI-2010 score for respondents with marginal (+5.29 points), low (+3.92 points), and very low (+4.83 points) food security as well as with lower overall BMI among participants with marginal (-2.59 kg/m2) and very low (-2.03 kg/m2) food security. Among Hispanic adults, SNAP participation was related to lower added-sugar consumption (-3.15 teaspoons) lower BMI (-1.54 kg/m2), and lower likeli­ hood of obesity (-12 percentage points) among the marginal food security group. DISCUSSION We analyzed nationally representative data to determine whether SNAP participation modified the associations between food inse­ curity and individuals’ dietary quality and weight. Consistent with the literature, we found that food insecurity and SNAP participation, independently, were associated with lower di­ etary quality and a higher prevalence of obesity among adults.15,16 In addition, we augmented July 2 0 1 5 , Vol 1 0 5 , No. 7 | American Journal o f Public Health previous research with our finding that SNAP participation among those who do not have full food security might protect against a less healthful diet and obesity. Specifically, we found that the interaction between SNAP par­ ticipation and marginal, low, or very low food security was associated with higher dietary quality and lower BMI. The interaction be­ tween SNAP participation and food insecurity was significantly associated with a lower likeli­ hood of obesity only among the marginal food security group. This result aligns with Hanson et al., who studied the interaction between food insecurity, marital status, and body weight and found that food insecurity was related to a greater likelihood of obesity among married women with marginal food security.28 Because the recession of 2007 to 2009 was associated with a record high rate of job loss, low rate of reemployment, and substantial earnings losses,29 the population of persons temporarily experiencing marginal food secu­ rity is expected to grow.30 SNAP could play a prominent role in ensuring that this popula­ tion has the necessary resources to obtain a nutritionally adequate diet during difficult times.31 On the other hand, it is important to understand the reasons some participants are still unable to consume healthy food, whether it is because of inadequate SNAP benefit, insuf­ ficient time to shop for and prepare nutritious meals, or lack of nutrition knowledge and budgeting skills. Our results also showed that adults with­ out full food security had a higher intake of total calories, added sugar, and empty calo­ ries than those with full food security. Re­ search shows that food insecurity, often a cyclic phenomenon, is associated with preferences for energy-dense foods, because adults who anticipate future food scarcity often overconsume when food is available.32 Moreover, food-insecure persons, who are often low income, may be hesitant to pur­ chase nutrient-rich foods such as fruits and vegetables, which cost more per calorie than energy-dense foods with minimal nutritional values.33-35 Those who experience food in­ security may also not have the means to travel to buy food frequently and may opt to purchase nonperishable or canned products or energy-dense foods that are less healthy, yet less costly.36 Nguyen et ai. | Peer Reviewed ¡ Research and Practice ¡ 14 55 RESEARCH AND PR AC TICE TABLE 2 -F o o d Security, D ie ta ry Q uality, and W e ig h t S ta tu s Am ong US Low -Incom e Adults: N a tio n al H e a lth and N u tritio n E xam ination Survey, 2 0 0 3 -2 0 1 0 Pa Food Insecurity (n = 36 88), No. or % Full Food Security (n = 46 4 5 ), No. or % Full Sample (n = 83 3 3 ), No. or % Dietary quality Healthy Eating Index 20 10, to ta l score 46.6 45.4 43.7 < .0 0 1 < .0 0 1 20.1 18.7 2 2 .0 Solid fat, g 39 9.5 398.1 4 0 1 .5 .768 Empty calories, kcal 75 4.6 73 1.5 787.9 < .0 0 1 Total calories, kcal 2 1 28.8 2 1 03.0 2 1 66.2 .084 28.8 28.5 29.2 < .0 0 1 2.3 2.3 2.4 .607 Overweight ( < 2 5 -< 30 k g /m 2) 31.6 32.6 30.2 .178 Obese ( > 30 kg /m 2) 35.6 33.7 38 .4 < .0 0 1 Added sugar, teaspoons W eight (BMI) Continuous Underweight ( < 18.5 kg /m 2) Note. BMI = body mass index. Results take survey weights into account. Not full food security group includes people living in households w ith marginal food security, low food security and very low food security. ’ Difference between fu ll food security and any category o f food insecurity derived from S tudent t test. Our subgroup analysis revealed that SNAP might affect racial/ethnic groups differentially: interactions between SNAP participation and weight status only in households with marginal food insecurity benefited dietary quality and weight status among Whites (all food insecurity tial modification of the association of food in­ groups) to a much greater extent than among Blacks. Among Hispanics, SNAP participation was associated with improved diet quality and food security. One possible explanation of SNAP’s differen­ security to dietary intake and weight status is neighborhood disparities in access to healthy food .37 Although low-income Whites tend to live in neighborhoods with other socioeconomic groups, low-income Blacks and Hispanics often live in segregated neighborhoods, especially in inner cities.38 Studies have found that residents of mixed-race or solely Black neighborhoods (regardless of income) are less likely than those in predominantly White communities to have access to healthy food choices,3 9 even if they have SNAP benefits.37 Many studies have TABLE 3 -M u ltiv a r ia b le Regression Analysis on A ssociations of S u p p le m e n ta l N u tritio n A ssistan ce Program P a rtic ip atio n and Food Insecurity W ith D ie ta ry Q uality and W e ig h t S ta tu s Am ong US Low -Incom e Adults: N a tio n al H e a lth and N utritio n E xam ination Survey, 2 0 0 3 - 2 0 1 0 Dietary Quality SNAP Weight Status Healthy Eating Index Added Sugar Solid Fat Empty Calories BMI (continuous; Overweight Obese (n - 8174), (n - 8 3 3 3 ), b (SE) (n - 8 3 3 3 ), b (SE) (n = 83 3 3 ), b (SE) (n = 83 33), b (SE) n - 81 7 4 ), b (SE) (n - 81 7 4 ), b (SE) b (SE) -3 .1 8 * * (0 .53) 2 .5 3 * * (0 .74) 18.65 (11.70 ) 5 3 .3 4 * * (19.70 ) 2 .1 0 ” (0 .28) -0 .0 3 (0.02) 0 .1 2 * * (0.02) Household food security Marginal -2 .2 7 * * (0 .55) 0 .4 8 (0.77) -1 4 .3 1 (12.10 ) -1 5 .7 9 (20.36 ) 0 .6 3 * (0 .29) -0 .0 1 (0.02) 0 .0 4 * (0.02) Low -1 .6 3 * * (0 .53) 2 .3 5 * * (0.73) -3 .1 9 (1 1.52 ) 17 .44 (19.39 ) 0 .4 7 (0.28) -0 .0 3 (0 .02) 0 .0 4 * (0.02) Very low -2 .5 9 * * (0.67) 4 .9 4 * * (0.94) 13.31 (1 4.77) 1 0 2 .4 8 ** (2 4.87) 1 .0 2 ” (0.35) -0 .0 2 (0 .02) 0 .0 5 * (0.02) 3 .4 6 * * (0.99) -1 .4 2 (1.37) 15 .84 (2 1.64) 0 .5 4 (3 6.43) - 1 .8 3 ” (0.52) 0 .0 0 (0 .03) -0 .0 9 * (0.03) 1 .9 8 * (0.88) - 3 .8 8 * * (1.22) -2 1 .5 0 (1 9.36) -6 7 .5 6 * (3 2.58) -0 .9 8 * (0.46) 0 .0 0 (0 .03) -0 .0 5 (0.03) 3 .8 4 * * (1.04) -2 .9 9 * (1.44) -1 1 .5 9 (2 2.76 ) -6 5 .2 4 (3 8.32) -1 .1 7 * (0.55) 0.0 2 (0 .04) -0 .0 6 (0.04) SNAP x marginal food security SNAP x low food security SNAP x very low food security Note. BMI - body mass index; FPL - federal poverty level; SNAP - Supplem ental N utrition Assistance Program; WIC « Women, Infants, and Children program. Respondents were aged 20 years or older and had fam ily incom e under 200% o f the FPL. Values are coefficients derived from ordinary least squares regressions. Dependent variables were healthy eating index (m axim um score - 1 0 0 ) , added sugar (teaspoons), solid fa t (grams), empty calories (kilocalories), BMI (continuous value), overweight (dummy variable), and obesity (dummy variable). These dependent variables were regressed on variables indica ting SNAP participation, households’ food security categories (dum m y variables), and SNAP participation interacted w ith food security categories. Control variables were age, ra ce/eth nicity, incom e (via poverty incom e ratio groups), m arital status, education, insurance status, WIC pa rticipation, and em ploym ent status. Results take survey weights into account. * P < .05; ” P < .0 1 . 1 4 5 6 I Research and Practice ¡ Peer Reviewed | Nguyen et al. American Journal o f Public Health | July 2 0 1 5 , Vol 1 0 5 , No. 7 RESEARCH A ND PR AC TICE TABLE 4 -M u ltiv a r ia b le Regression Analysis on R a c ia l/E th n ic D iffe ren c e s in A ssociations o f S u p p le m e n ta l N utritio n A ssistance Program P a rtic ip a tio n and Food Insecurity W ith D ie ta ry Q uality and W eigh t S ta tu s Am ong US Low -Incom e Adults: N a tio n al H e a lth and N utrition E xam ination Survey, 2 0 0 3 - 2 0 1 0 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Dietary Q uality_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ H ealthy Eating Added Sugar, Solid Fat, Empty Calories, Index, b (SE) b (SE) b (SE) b (SE) W eight Status B M I, b (SE) Overweight, Obese, b (SE) b (SE) N on-H ispanic Blacks (n - 1 7 6 8 ) SNAP x m arginal food security - 0 .2 9 (1 .7 9 ) - 2 .8 5 (2 .5 0 ) - 2 2 .0 8 ( 4 1 .8 1 ) - 3 3 .4 1 ( 6 9 .7 8 ) - 0 .6 9 (1 .1 5 ) 0 .0 3 (0 .0 6 ) - 0 .0 6 (0 .0 7 ) SNAP x x low food security 0 .0 0 (1 .6 7 ) - 2 .1 3 (2 .3 3 ) 2 2 .6 1 ( 3 8 .8 5 ) - 1 6 .6 9 ( 6 4 .8 4 ) - 0 .5 5 (1 .0 7 ) 0 .0 5 (0 .0 6 ) - 0 .0 8 (0 .0 6 ) very low food security 3 .0 4 (1 .9 0 ) - 2 .4 5 (2 .6 5 ) - 1 .4 8 ( 4 4 .3 4 ) - 9 .8 6 ( 7 4 .0 1 ) - 0 .3 9 (1 .2 2 ) 0 .0 7 (0 .0 7 ) - 0 .0 6 (0 .0 7 ) - 0 .0 8 (0 .0 6 ) SNAP N o n-H ispanic W hites (n = 3 3 9 8 ) SNAP SNAP SNAP x x x m arginal food security low food security very low foo d security 5 . 2 9 * * (1 .7 1 ) 1 .5 5 (2 .5 5 ) 2 9 .1 5 ( 3 7 .5 4 ) 7 4 .6 2 ( 6 4 .8 9 ) - 2 . 5 9 * * (0 .8 8 ) - 0 .0 6 (0 .0 6 ) 3 .9 2 * (1 .5 3 ) - 7 . 9 3 * * (2 .2 9 ) - 3 4 .3 8 ( 3 3 .6 9 ) ■ 1 4 3 .9 0 * ( 5 8 .2 3 ) - 1 .0 7 (0 .7 9 ) - 0 .0 7 (0 .0 5 ) - 0 .0 0 (0 .0 5 ) 4 . 8 3 * * (1 .7 6 ) - 5 .0 1 (2 .6 3 ) - 3 7 .6 3 (3 8 .7 3 ) - 1 2 7 .0 6 ( 6 6 .9 4 ) - 2 . 0 3 * (0 .9 1 ) 0 .0 4 (0 .0 6 ) - 0 .0 9 (0 .0 6 ) H ispanics (n - 2 8 0 6 ) SNAP SNAP SNAP x x x m arginal food security 2 .5 5 (1 .6 0 ) - 3 .1 5 (1 .7 3 ) 4 8 .4 5 ( 3 3 .1 6 ) - 3 4 .1 2 ( 5 0 .8 1 ) - 1 . 5 4 * (0 .7 2 ) 0 .1 4 * (0 .0 6 ) - 0 . 1 2 * (0 .0 6 ) low food security 1 .5 3 (1 .4 2 ) - 1 .2 6 (1 .5 3 ) - 3 6 .1 3 (2 9 .4 8 ) - 3 8 .2 7 ( 4 5 .1 7 ) - 0 .6 4 (0 .6 4 ) 0 .0 6 (0 .0 5 ) - 0 .0 5 (0 .0 5 ) very low food security 1 .4 3 (1 .7 8 ) - 1 .3 1 (1 .9 3 ) 3 .2 5 ( 3 7 .0 1 ) - 5 2 .4 4 ( 5 6 .7 1 ) - 0 .2 4 (0 .8 0 ) - 0 .0 1 (0 .0 7 ) - 0 .0 1 (0 .0 6 ) N ote. FPL = fed eral poverty level; SNAP = S upplem ental N utrition Assistance Program; W IC - W om en, Infants, and Children program . R espondents w ere aged 2 0 years or o ld er and had fam ily incom e un d er 2 0 0 % o f th e FPL. Values are coefficients derived from ordinary least squares regressions. D ep en den t variables w ere healthy eatin g index (m axim um score - 1 0 0 ) , add ed sugar (teaspoons), solid fa t (gram s), em pty calories (kilocalories), BMI (continuous valu e ), overweight (dum m y v aria b le), and obesity (dum m y v aria b le). These dep e n de n t variables w ere regressed on variables indicating SNAP p artic ip atio n , households’ food security categories (dum m y variab les), and SNAP particip atio n interacted with food security categories. Control variables were age, ra ce /eth n icity , incom e (via poverty incom e ratio groups), m arital status, edu c atio n , insurance status, W IC partic ip atio n , and em ploym ent status. Results ta k e survey weights into account. * P < .0 5 ; * * / > < . 0 1 . assessed food deserts (low-income areas with limited access to fresh, healthy, and affordable food )4 0 '41 and policies, such as the Healthy Food Financing Initiative, that have been de­ veloped to address these inequities.42 Fur­ thermore, dietary intake disparities may turn into discrepancies in the incidence and man­ agement of obesity, hypertension, diabetes, and other diet-sensitive chronic diseases.4,32 Thus, further research is needed to better understand why and how government nutrition assistance programs such as SNAP can affect varying in meeting the challenge of consuming a health­ ful diet on a limited budget 46,47 In addition, SNAP-Ed interventions have begun addressing represent longer-term dietary intake pat­ te rn s 4 9 " 51 In addition, we could not establish a causa] relationship because of the cross- environmental factors affecting dietary intake, such as providing access to more healthful foods (e.g., fruits and vegetables) in local comer or convenience stores, which are more prevalent in sectional nature of the data. Furthermore, we could not control for self-selection into SNAP, which could have been affected by such un­ observed factors as personal preferences and underlying health conditions. Therefore, our identification strategy, similar to many studies in the literature ,11 did not permit us to identify the causal effects of SNAP participation on health behaviors and outcomes. food-insecure populations differently and to assess the possible solutions. low-income neighborhoods.48 Our findings are consistent with the litera­ ture suggesting that racial/ethnic groups may differ in taking advantage of government as­ sistance programs, so interventions encourag­ ing increasing utilization of SNAP and SNAP-Ed and incorporating promotion of more healthful The Department of Agriculture has continued to make improvements to the SNAP-Education food consumption should be tailored to ethnic minority subgroups. These programs will help (SNAP-Ed) program, specifically to enhance the quality of SNAP participants’ diets.16'43,44 The main aim of SNAP-Ed is to improve the likeli­ hood that SNAP participants will make healthier not only food-insecure populations, but also the marginally food secure, to achieve a more healthful diet and consequently improve health outcomes and well-being. food choices within a limited budget 43 Research shows that nutrition education programs can lead to healthier food choices among lowincome households participating in SNAP,45 and thus SNAP-Ed could aid SNAP participants L im ita tio n s As a self-reported dietary recall data set, NHANES may be prone to overestimation of portion size and dietary intake and may not July 2 0 1 5 , Vol 10 5, No. 7 | American Journal o f Public Health Our results were derived from self-reported food security and SNAP participation status, which may reflect an individual’s own percep­ tion rather than the actual situation, and the reported numbers were subject to possible measurement error (e.g., misreporting or misclassification bias).52 For example, weight sta­ tus may influence reported food insecurity: obese individuals may be more likely to report food insecurity because of their habits and perceptions about food consumption .28,53 However, although our measure of food in­ security relied on self-report,617 it adhered to Nguyen et al. | Peer Reviewed | Research and Practice | 1457 RESEARCH AND PR AC TICE the Department of Agriculture classification, which is regarded as the gold standard.3,6 Future research should aim to enhance un­ derstanding of the interrelationship of SNAP and food insecurity with health outcomes and account for both selection bias and measure­ ment error, as in a few works on related topics.17,54 Conclusions We found that among the food-insecure population, SNAP participation appears to buffer against poor dietary quality and obesity, particularly among non-Hispanic Whites and marginally food-secure Hispanics. Most impor­ tant, our research highlights the role that SNAP may play in helping individuals who are at risk for food insecurity to obtain a healthful diet and better weight status. SNAP, food insecurity, obesity, dietary pat­ terns, food availability and access, and other factors should be considered together rather than separately, because these factors may interact in a complex relationship. ■ About the Authors Birth T. Nguyen and Kerem Shuval are with the Intramural Research Department, Economic and Health Policy Re­ search Program, American Cancer Society, Atlanta, GA. Farryl Bertmann is a public health nutrition and dietetics research consultant, South Hero, VT. Amy L. Yaroch is with the Gretchen Swanson Center for Nutrition, Omaha, NE. Correspondence should be sent to Binh T. Nguyen, PhD, American Cancer Society, 2 50 William St, Atlanta, GA 30303 (e-mail: Reprints can be ordered at by clicking the ‘Reprints" link. This article was accepted January 15, 2015. Contributors B. T. Nguyen conceptualized and designed the study, with contributions from the other authors, analyzed the data, and drafted the article. All authors interpreted the data and reviewed and revised the article. Human Participant Protection No protocol approval was required because the study used de-identified, publicly available data. References 1. US Dept of Agriculture, Economic Research Service. Definitions of food security. 2014. Available at: http:// .aspx#. U7VjvPldUSV. Accessed July 3, 2014. 2. Holben DH; American Dietetic Association. Position of the American Dietetic Association: food insecurity in the United States. J Am Diet Assoc. 2010;110(9): 13681377. 3. Coleman-Jensen A, Nord M, Andrews M, Carlson S. Household food security in the United States in 2010. 2011. Available at: or Accessed July 3, 2014. 4. Seligman HK, Laraia BA, Kushel MB. Food in­ security is associated with chronic disease among lowincome NHANES participants./ Nutr. 2010; 140(2): 304-310. 5. Laraia BA. Food insecurity and chronic disease. Adv Nutr. 2013;4(2):203-212. 6. Coleman-Jensen A, Nord M, Singh A. Household Food Security in the United States in 2012. Washington, DC: US Dept of Agriculture, Economic Research Service; 2013. ERR-155. 7. US Dept of Agriculture. Supplemental Nutrition Assistance Program (SNAP). 2013. Available at: http:// Accessed July 10, 2014. 8. Larson NI, Story MT. Food insecurity and weight status among U.S. children and families: a review of the literature. Am J Prev Med. 2 0 1 1;40(2): 166-173. 9. Berkowitz SA, Seligman HK, Choudhry NK. Treat or eat: food insecurity, cost-related medication underuse, and unmet needs. Am J Med. 2014;127(4):303-310.e3. 10. Gundersen C, Oliveira V. The food stamp program and food insufficiency. Am J AgrEcon. 2001 ;83(4):875887. 11. Dinour LM, Bergen D, Yeh M-C. The food insecurity-obesity paradox: a review of the literature and the role food stamps may play. J Am Diet Assoc. 2007 ; 107(11 ): 1952—1961. 12. Nord M. How much does the Supplemental Nutri­ tion Assistance Program alleviate food insecurity? Evi­ dence from recent programme leavers. Public Health Nutr. 2 0 12;15(5):811-817. 13. Leung CW, Villamor E. Is participation in food and income assistance programmes associated with obesity in California adults? Results from a state-wide survey. Public Health Nutr. 2 0 1 1;14(4):645-652. 14. Webb AL, Schiff A, Currivan D, Villamor E. Food Stamp Program participation but not food insecurity is associated with higher adult BMI in Massachusetts resi­ dents living in low-income neighbourhoods. Public Health Nutr. 2008;11(12):1248-1255. 15. Leung CW, Ding EL, Catalano PJ, Villamor E, Rimm EB, Willett WC. Dietary intake and dietary quality of low-income adults in the Supplemental Nutrition Assis­ tance Program. Am J Clin Nutr. 2012;96(5):977-988. 20. US Dept of Agriculture. Food security in the U.S.— survey tools. 2014. Available at: http://www.ers.usda. gov/topics/food-nutrition-assistance/food-security-inthe-us/survey-tools.aspx#.U7qhOPldUSV. Accessed July 7, 2014. 21. National Cancer Institute. Applied research: cancer control and population sciences. Healthy Eating Index. 2012. Available at: tools/hei. Accessed February 4, 2014. 22. Guenther PM, Casavale KO, Kirkpatrick SI, et al. Update of the Healthy Eating Index: HEI-2010. J Acad Nutr Diet. 2013;113(4):569-580. 23. World Health Organization. Global database on body mass index. 2014. Available at: http://apps.who. int/bmi/index.jsp?introPage=intro_3.html. Accessed July 7, 2014. 24. Haines PS, Hama MY, Guilkey DK, Popkin BM. Weekend eating in the United States is linked with greater energy, fat, and alcohol intake. Obes Res. 2003; 11(8):945-949. 25. Stata, Version 13 [computer program]. College Station, TX: StataCorp LP; 2013. 26. Centers for Disease Control and Prevention. Na­ tional Health and Nutrition Examination Survey. Over­ view of NHANES survey design and weights. Available at: orientation/sample_design/index.htm. Accessed July 7, 2014. 27. SAS, Version 9.3 [computer program]. Cary, NC: SAS Institute Inc; 2011. 28. Hanson KL, Sobal J, Frongillo EA. Gender and marital status clarify associations between food insecurity and body weight. J Nutr. 2007;137(6):1460-1465. 29. Färber HS. Job Loss in the Great Recession: Historical Perspective From the Displaced Workers Survey, 1 9 8 4 2010. Cambridge, MA: National Bureau of Economic Research; 2011. Working paper 17040. 30. Ganong P, Liebman JB. The Decline, Rebound, and Further Rise in SNAP Enrollment: Disentangling Business Cycle Fluctuations and Policy Changes. Cambridge, MA: National Bureau of Economic Research; 2013. Working paper 19363. 31. Schmidt L, Shore-Sheppard L, Watson T. The effect of safety net programs on food insecurity. University of Kentucky Center for Poverty Research discussion paper series, DP2012-12. Available at: Publications/DP2012-12.pdf. Accessed July 1, 2014. 32. Seligman HK, Schillinger D. Hunger and socioeco­ nomic disparities in chronic disease. N EnglJ Med 2010; 363(l):6-9. 16. Nguyen BT, Shuval K, Njike VY, Katz DL. The Supplemental Nutrition Assistance Program and dietary quality among U.S. adults: findings from a nationally representative survey. Mayo Clin Proc. 2014;89(9): 1211-1219. 33. Monsivais P, Drewnowski A. The rising cost of lowenergy-density foods. J Am Diet Assoc. 2007; 107(12): 2071-2076. 17. Kreider B, Pepper JV, Gundersen C, Jolliffe D. Identifying the effects of SNAP (food stamps) on child health outcomes when participation is endogenous and misreported./Am Stat Assoc. 2012;107(499):958-975. 34. Maillot M, Darmon N, Darmon M, Lafay L, Drewnowski A. Nutrient-dense food groups have high energy costs: an econometric approach to nutrient pro­ filing. J Nutr. 2007; 137(7): 1815-1820. 18. Centers for Disease Control and Prevention Na­ tional Center for Health Statistics. National Health and Nutrition Examination Survey. 2013. Available at: Accessed May 12, 2013. 35. Carlson A, Frazäo E. Are healthy foods really more expensive? It depends on how you measure the price. 2012. US Dept of Agriculture, Economic Research Service. Economic information bulletin 96. Available at: 1_.pdf. Accessed August 8, 2014. 19. Gundersen C, Kreider B, Pepper J. The economics of food insecurity in the United States. Appl Econ Perspect Pol. 2011 ;33(3):281-303. 14 5 8 j Research and Practice | Peer Reviewed | Nguyen et al. 36. Coveney J, O’Dwyer LA. Effects of mobility and location on food access. Health Place. 2009; 15(1):45—55. American Journal o f Public Health | July 2 0 1 5 , Vol 10 5, No. 7 RESEARCH A ND PR AC TICE 37. Larson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the U.S. Am JPrevMed. 2009;36(1):74-81. 38. Galvez MP, Morland K, Raines C, et al. Race and food store availability in an inner-city neighbourhood. Public Health Nutr. 2008; 1 1(6):624-631. 39. Baker EA, Schootman M, Bamidge E, Kelly C. The role of race and poverty in access to foods that enable individuals to adhere to dietary guidelines. Prev Chronic Dis. 2006;3(3):A76. 53. Kaiser LL, Townsend MS, Melgar-Quiñonez HR, Fujii ML, Crawford PB. Choice of instrument influences relations between food insecurity and obesity in Latino women. Am J Clin Nutr. 2004;80(5): 1372-1378. 54. Gundersen C, Kreider B. Bounding the effects of food insecurity on children’s health outcomes. J Health Econ. 2009;28(5):971-983. 40. US Dept of Agriculture, Agricultural Marketing Service. Food deserts. 2014. Available at: http://apps. Accessed February 6, 2014. 41. Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: a review of food deserts literature. Health Place. 2010;16(5):876884. 42. Administration for Children and Families, Office of Community Services. Healthy Food Financing Initiative. 2011. Available at: ocs/resource/healthy-food-financing-initiative-O. Accessed August 5, 2014. 43. US Dept of Agriculture. Supplemental Nutrition Assistance Program education guidance: Nutrition Education and Obesity Prevention Grant Program. Available at: FinalFY2015SNAP-EdGuidance.pdf. Accessed August 9, 2014. 44. Guthrie JF, Frazào E, Andrews M, Smallwood D. Improving food choices—can food stamps do more? Amber Waves. 2007;5(2):22-28. 45. Long V, Cates S, Blitstein J, et al. Supplemental Nutrition Assistance Program Education and Evaluation Study (Wave II). Washington, DC: Dept of Agriculture, Food and Nutrition Service; 2013. 46. McLaughlin C, Tarasuk V, Kreiger N. An examina­ tion of at-home food preparation activity among lowincome, food-insecure women. J Am Diet Assoc. 2003; 103(11):. 47. Rose D. Food Stamps, the Thrifty Food Plan, and meal preparation: the importance of the time dimension for US nutrition policy. J Nutr Educ Behav. 2007;39(4): 226-232. Chronic Disease, Epidemiology, and Control, Third Edition B y P atrick L. R em ington, M D , M PH; Ross C. Brownson, Ph.D; an d M a rk V. Wegner, M D , M P H “This book is an indispensable tool for the practitioner in charge of de­ veloping and implementing chronic disease programs. It describes the latest developments in the science of chronic diseases, and puts at your fingertips solid, evidence-based strategies that can be put to use to com­ bat the growing chronic disease epidemic." 48. US Dept of Agriculture. SNAP-ED strategies and interventions: an obesity prevention toolkit for states. Available at: Accessed December 9, 2014. 49. Faggiano F, Vineis P, Cravanzola D, et al. Validation of a method for the estimation of food portion size. Epidemiology. 1992;3(4):379-382. 50. Mertz W, Tsui JC, Judd J, et al. What are people really eating? The relation between intake derived from estimated diet records and intake determined to maintain body weight. Am J Clin Nutr. 1991;54(2):291-295. 51. Briefel RR, Sempos CT, McDewell MA, Chien S, Alaimo K. Dietary methods research in the Third Na­ tional Health and Nutrition Examination Survey: under­ reporting of energy intake. A m ] Clin Nutr. 1997; 65(4 suppl): 1203S-1209S. — Victor D. Sutton, PhD, MPPA Director, Office o f Preventive Health — Mississippi Department o f Health and President, National Association o f Chronic Disease Directors The third edition o f Chronic Disease, Epidemiology, and Control pro ­ vides the reader with up-to-date inform ation about chronic diseases. Leading scholars and practitioners have consolidated inform ation from countless research studies—providing clear and concise inform ation about chronic disease causes, their consequences, the groups at highest risk, and effective m ethods o f prevention. This book provides all the clues about ways to reduce the burden of chronic diseases, conditions, and risk factors. ©APHA PRESS ORDER TODAY! 52. Gundersen C, Kreider B. Food stamps and food insecurity what can be learned in the presence of nonclassical measurement error? J Hum Resour. 2008; 43(2):352-382. July 2 0 1 5 , Vol 10 5, No. 7 | American Journal o f Public Health imprint o, NMERicNN public »..LT» .«oc,.T,o« g 5 9 pages, softcover, 2 0 1 0 , ISBN 9 7 8 ‘ 0 ‘8 7 5 5 3 -1 9 2 -2 ORDER ONLINE: ww w E-MAILAPHA@PBD.COM TEL: 888.320.APHA FAX: 888.361.APHA Nguyen et al. | Peer Reviewed | Research and Practice | 1 4 5 9 Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

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Solutions to End the Food Deserts and
The Contribution Individuals and Communities can make to Help


Solutions to End the Food Deserts and
The Contribution Individuals and Communities can make to Help
Food deserts are areas with low access to affordable high quality whole foods and
fresh foods such as fruits and vegetables. For an area to be designated as a food desert, the
distance between the neighborhood and the closest grocery or food market within a certain
radius. On an economic level, one needs to evaluate the median income in such a
neighborhood, and the relative cost of acquiring such healthy and whole foods (Jiao et al.
2012). In Dallas some areas identified as food deserts include; Far East Dallas, Euless,
Pleasant Grove, Oak Cliff, Wolf Creek, Fair Park and parts of South and West Dallas. In
Dallas, about 19% of the population is living below poverty line with an income of less than
$25,000 household income per year (Albert, Manon & Waldoks, 2015), and these are
majority of the people in these food deserts.
Food deserts have negative health impacts on the people living in these areas. People
who live far from food stores have higher rates of obesity, and the health complications
associated with the disease. It is imperative to note that while these places have a shortage of
fresh healthy foods, there is a high supply of quick and junk foods, which they eat. Over a
long period, their immune systems become weak, and most of them will die due to poor
nutrition (David, Adrian & Carol, 2010). The City of Dallas tries to solve this problem by
encouraging opening of grocery and fresh food stores across such neighborhoods. Many
cities across the world are adopting this strategy, coupled with encouraging and educating
people in these neighborhoods to embrace healthy eating.




Pros of individual and community effort to deal with food deserts
People are part of these communities, and they are the ones at risk if they do not
observe quality nutrition habits. At the end of the day, even without involving people from
these communities, at some level whether in growing, logistics, supplying and others there
must be human contribution even if it is from without the given community. Since most of
them are from low-income households, with low levels of employment, these people would
benefit from the jobs such initiatives would bring (Becca, Dawn & Ashley, 2016).
Making the food available is just but a part of the solution, the hardest part is to
convince such people on the need to change their diet. In food deserts a food culture is not
lacking, the problem is the unhealthy nature of the culture in place. Changing it would require
communal effort, and people are the best channels of passing such information. Prominent
members of such communities such as leaders, community nurses, and teachers among others
need to sensitize the community on the importance of maintaining a proper diet (Brinkley,
Raj & Horst, 2017). Some people might even lack basic knowledge on the effects of the type
of diets they keep, but with community members informing them, and if they start to see
results around them, most of them will change.
To ensure such measures and healthy diets are in place, there are some places such as
schools, hospi...

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