Practice & Policy Research Quarterly Consumer Nutrition ... PDF Library/S15030...Appendix 4:...

April 2015 Volume 3, Issue 1

Consumer Nutrition Environment Disparities in

Oklahoma County Supermarkets

Kristin N. Culver, MA, MSW, MPH

Christina M. Shay, PhD

Cynthia Harry, MS

Sheryl Magzamen, PhD

Oklahoma Department of Human Services

Office of Planning, Research and Statistics

The Practice and Policy Research Quarterly highlights program evaluation and research findings on

social and economic issues. It is designed to inform and provide policy and academic research

audiences with timely and high quality data and statistical, economic and social analyses.

If you have questions, comments, or suggestions regarding the report, please contact the Oklahoma

Department of Human Services, Office of Planning, Research and Statistics at 405-521-3552.

Oklahoma Department of Human Services

Office of Planning, Research and Statistics

P.O. Box 25352

Oklahoma City, Oklahoma 73125

Consumer Nutrition Environment Disparities in Oklahoma County Supermarkets

Kristin N. Culver, MA, MSW, MPH Oklahoma Department of Human Services

Christina M. Shay, PhD University of North Carolina

Cynthia Harry, MS Washington State Department of Health

Sheryl Magzamen, PhD Colorado State University

Kristin N. Culver, MA, MSW, MPH Oklahoma Department of Human Services

Kristin Culver, MA, MSW, MPH, is a Senior Researcher in the Office of Planning, Research, and

Statistics at the Oklahoma Department of Human Services. Her research interests broadly include

program evaluation, social determinants of health and health disparities, relationships between

theory and praxis, social justice, and the history of thought. In her previous role with the Oklahoma

City-County Health Department, she served as founding co-chair and director of Open Streets

OKC, which was named Best Public Initiative by the Urban Land Institute of Oklahoma in 2015.

Kristin also serves on the advisory board of the Salween Institute for Public Policy and is an adjunct

faculty member at Oklahoma City University.

Christina M. Shay, PhD University of North Carolina

Christina M. Shay, PhD, is a diabetes and cardiovascular epidemiologist with specialized doctoral

and post-doctoral training in the development, implementation, and data analysis in prospective,

observational population-based studies. She also has experience in metabolic, physical activity and

nutritional assessments in both the clinical and population settings. Dr. Shay is a Fellow of the

American Heart Association (AHA) and is an active scientific volunteer for the Council on Lifestyle

and Cardiometabolic Health. Dr. Shay is a member of several national AHA committees and has a

particular interest in advancing early career development activities for the AHA at a Council and

National level.

Cynthia Harry, MS Washington State Department of Health

Cynthia Harry, MS, is currently an Epidemiology Supervisor at the Washington State Department of

Health working on surveillance, grants and data projects. Prior to joining the Washington State

Department of Health, she worked for the Oklahoma City-County Health Department as the

Administrator over data and grant evaluations. Her interests have centered around chronic disease

and social determinants of health analysis. She received her Master of Science in Epidemiology from

the University of Oklahoma. Prior to receiving her masters, she worked in cell biology as a research

assistant on cone and rod receptor research after receiving a Bachelor of Science from Michigan

State University.

Sheryl Magzamen, PhD Colorado State University

Sheryl Magzamen, PhD, is an Assistant Professor of Epidemiology in the Department of

Environmental and Radiological Health Sciences at Colorado State University, and an Adjunct

Assistant Professor in the Department of Biostatistics and Epidemiology at the University of

Oklahoma Health Sciences Center. Her primary research focuses on understanding the relative

contribution of environmental exposures and social factors on chronic disease outcomes,

particularly in pediatrics populations. She teaches courses on applications of geographic information

systems (GIS) and health.

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Table of Contents Acknowledgements 1

Executive Summary. 2

Introduction 3

Review of the Literature 4

Community Nutrition Environment Research 4

Consumer Nutrition Environment Research 5

Limitations of Current Research 6

Study Purpose 8

The Community Nutrition Environment and Health of Oklahoma City and County 8

Methods 9

NEMS-S Instrument 9

Table 1. NEMS-S Measure 11

The Oklahoma County Wellness Score 12

Figure 1. Oklahoma County Wellness Score by ZIP Code 13

ZIP Code Selection 13

Supermarket Selection 14

Rater Training 14

Data Analysis Methods 14

Results 15

Table 2. NEMS-S Scores 15

Table 3. Differences in NEMS-S Scores between High and Low Wellness Scoring ZIP Codes 15

Figure 2. Mean NEMS-S Score by ZIP Code 16

Discussion 17

Figure 3. Racial/Ethnic Minority Density by ZIP Code 17

Figure 4. Density of Households with Zero Personal Vehicles by ZIP Code 18

Limitations 18

Policy Implications 19

Appendix 1: NEMS-S Survey 21

Appendix 2: Scoring System for NEMS Store Measures 33

Appendix 3: NEMS-S Scores 34

Appendix 4: Correlation Between Composite NEMS-S Score & Median Household Income 36

Appendix 5: Correlation Between Consumer Nutrition Environment Scores & Percent Minority in ZIP Code 37

Appendix 6: Correlation Between Consumer Nutrition Environment Scores & Percent Zero Vehicle in ZIP Code 38

References 39

Acknowledgements

This report was made possible by the contributions of numerous organizations and individuals. The

authors would like to thank the Oklahoma City-County Health Department and the City of

Oklahoma City Planning Department for providing data that was utilized in this research. In

addition, the authors are grateful to the Oklahoma Tobacco Settlement Endowment Trust for

supporting this research by allowing its inclusion among the Communities of Excellence in

Nutrition and Physical Activity grant activities in Oklahoma County. The authors would also like to

thank Oklahoma Idea Network of Biomedical Research Excellence (OK-INBRE) summer scholar

Kelly Stephens for her invaluable assistance with data collection. Finally, the authors are grateful to

Connie Schlittler, Naneida Lazarte-Alcala, Tosha Robinson, Nancy Kelly, and Eva Rohlman with

the Oklahoma Department of Human Services for their guidance and assistance with the

preparation of this report.

This project was supported by the National Institute of General Medical Sciences of the National

Institutes of Health through Grant Number 8P20GM103447.

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Executive Summary

The purpose of this Practice and Policy Research Quarterly is to inform stakeholders about the

relationship between health outcomes across Oklahoma County and the availability, quality, and

affordability of healthy food options in local supermarkets. The authors are hopeful that this

information may be of use to local agencies and organizations as they work to achieve the mission of

the Oklahoma Department of Human Services, which is to improve the quality of life of vulnerable

Oklahomans by increasing people’s ability to lead safer, healthier, more independent and productive

lives.

The State of Oklahoma ranks last in the nation in fruit and vegetable consumption and is

characterized by high rates of obesity and diet-related diseases (Oklahoma State Department of

Health, 2011). In 2010, the Oklahoma City-County Health Department reported that almost two-

thirds of the population of Oklahoma County lives in food deserts, with only 36% of the population

living within a “reasonable walking distance” of a grocery store (Meyers, 2010). Not only do the

majority of Oklahoma County residents have to travel considerable distances to purchase groceries,

Oklahoma City ranks 81st out of 100 metropolitan areas in public transit coverage, and last in public

transit ridership among all U.S. metropolitan areas (Tomer, 2011; Walker, 2013).

Some of the most influential public health organizations in the world, including the Centers

for Disease Control and Prevention, the Institute of Medicine, the International Obesity Task Force,

and the World Health Organization, promote environmental interventions as the most effective

strategies for changing dietary intake and weight status among populations (Gloria & Steinhardt,

2010). Not only are environmental strategies more cost-effective and impact larger numbers of

people than strategies that focus on individual behavior change, their results are also more likely to

have an enduring effect on behavior because they have the potential to be incorporated into policies,

structures, and social norms (Larson & Story, 2009, p. S56).

The purpose of the investigation detailed in this report was to quantify associations between

the food environments within supermarkets and community wellness in Oklahoma County. The

results of the analysis indicate that disparities in access to healthy groceries are associated with less

favorable regional indicators of health and socioeconomic status. These findings highlight the need

for interventions and policies that ensure equitable access to healthy foods across all sectors and

demographic groups within Oklahoma County. Promoting healthy food availability in

neighborhoods with poorer health statuses may be a successful strategy for improving health

outcomes.

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Introduction

Over the course of the last few decades, obesity rates have risen sharply in the United States

(Ferdinand, Sen, Rahurkar, Engler, & Menachemi, 2012). The health consequences of obesity and

sedentary lifestyles are now estimated to result in over 300,000 premature deaths each year

(Ferdinand et al., 2012). Researchers and medical professionals have long recognized that “what and

how much people eat defines to a large extent their health,” with a growing body of evidence linking

obesity and other chronic diseases to dietary intake (Larson & Story, 2009, p. S56; Morland, Wing, &

Roux, 2002). However, the mounting economic and public health impacts of diet-related chronic

disease have inspired recent interest in determining the causal factors that drive the daily dietary

decisions made by Americans.

In the past, explanations of eating behavior have focused primarily on biological,

physiological, and psychological influences (Drewnowski & Specter, 2004). More recently, however,

a growing number of public health researchers have shifted the focus of their inquiry toward the

environment, recognizing that as with other major public health issues such as tobacco use and

infectious disease prevention, success at the population level depends on the identification and

modification of environmental factors (Larson & Story, 2009; Lytle, 2009).

Globally, some of the most influential public health organizations, including the Centers for

Disease Control and Prevention, the Institute of Medicine, the International Obesity Task Force,

and the World Health Organization, assert that environmental interventions are the most effective

strategies for changing dietary intake and weight status among populations (Gloria & Steinhardt,

2010). Not only are environmental strategies more cost-effective and impact larger numbers of

people than strategies that focus on individual behavior change, their results are also more likely to

have an enduring effect on behavior because they have the potential to be incorporated into policies,

structures, and social norms (Larson & Story, 2009, p. S56).

Of the many environmental factors associated with obesity and chronic disease, the

accessibility of healthy food has emerged as an issue of primary concern. Research related to the

accessibility of healthy food can be divided into two basic categories according to the focus of

investigation: community nutrition environment research, which analyzes the “number, type, location, and

accessibility” of food outlets, and consumer nutrition environment research, which focuses on factors that

influence consumers once they access food outlets, such as the “availability, cost, and quality of

healthful food choices” (Glanz, Sallis, Saelens, & Frank, 2007, p. 282).

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Review of the Literature

Community Nutrition Environment Research

Much of the interest in community nutrition environment research centers around a scant but

rapidly expanding body of research linking so-called “food deserts” (i.e., geographical regions with

low access to healthy food, typically quantified in terms of convenient access to a supermarket) with

eating behavior and obesity in the United States (Larson & Story, 2009; Gloria & Steinhardt, 2010).

While some discrepancies do exist, most research has found positive relationships between

improved access to supermarkets, healthier diets, and lower obesity rates (Ahern, Brown, & Dukas,

2011; Bader, Purciel, Yousefzadeh, & Neckerman, 2010; Glanz et al., 2007; Gloria & Steinhardt,

2010; Hosler, Rajulu, Fredrick, and Ronsani, 2008; Jilcott, Keyserling, Crawford, McGuirt, &

Ammerman, 2011; Larson & Story, 2009; Larson, Story, & Nelson, 2009; Morland, Wing, & Roux,

2006; Treuhaft & Karpyn, 2010).

In 2009, the U.S. Department of Agriculture reported that nearly 24 million people do not

have access to a supermarket within one mile of their homes (Treuhaft & Karpyn, 2010). One study

of over 3,000 U.S. metropolitan counties across all 50 states found lower obesity rates in areas with

higher supermarket density (Jilcott et al., 2011). A nationwide study of middle-aged and elder adults

found that living in a census tract with at least one supermarket is associated with meeting

recommended guidelines for fruit and vegetable consumption, as well as lower obesity prevalence

(Morland, Wing, & Roux, 2002; Morland et al., 2006).

Unsurprisingly, the relationship between supermarket access, healthy dietary intake, and

lower obesity rates appears to be mediated by indicators of socioeconomic status such as race,

ethnicity, income, and access to a personal vehicle (Larson et al., 2009; Morland & Evenson, 2008).

Most research indicates that the diets of marginalized socioeconomic groups tend to be more energy

dense, containing fewer whole grains, fruits, vegetables, low-fat milk products, and lean meats

(Larson & Story, 2009). Fruit and vegetable consumption is especially low among low-income

groups with high rates of obesity, with the relatively high cost of produce a commonly cited cause of

this disparity (Jilcott et al., 2011). Interestingly, the relationship between proximity to supermarkets

and fruit and vegetable consumption was demonstrated to be stronger among Black than white

residents; each additional supermarket in a given census tract is associated with a 32% increase in

meeting fruit and vegetable intake guidelines among Black residents compared to an 11% increase

among white residents (Morland et al., 2002). The white residents of the locations studied had three

times greater access to private transportation than Black residents of similar areas, suggesting that

proximity may be a less important factor for white Americans when selecting where to shop for

groceries (Morland et al., 2002).

In addition, there are documented racial and ethnic disparities in access to supermarkets,

which often force residents to travel out of their neighborhoods to buy groceries or shop at

convenience stores that typically stock few, if any, healthy food options and charge higher prices

than supermarkets (Bader et al., 2010; Giang et al., 2008; Glanz et al., 2007; Kelly, Flood, &

Yeatman, 2011; Larson et al., 2009). Overall, the number of supermarkets in predominantly white

census tracts is five times greater than predominantly racial-ethnic minority tracts (Morland et al.,

2002). In fact, only 8% of Black Americans live in a census tract that contains a supermarket, but the

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diets of those who do are healthier overall. In census tracts that contain at least one supermarket, the

proportion of Black Americans meeting dietary recommendations for total fat is 25% higher than in

census tracts with no supermarkets (Morland et al., 2002).

Whereas most research investigating “food deserts” has found significant relationships

between proximity to supermarkets and health outcomes, several studies have yielded mixed results.

Two studies found unexpected relationships between Wal-Mart supercenters and obesity, with the

addition of one Wal-Mart per 1,000 residents being associated with an increase, rather than decrease,

in obesity prevalence (Jilcott et al., 2011). Jilcott et al. (2011) suggest that these unexpected outcomes

may be related to lower food prices or higher rates of bulk purchases at superstores, or it may be

that supercenters locate in areas that already have high rates of obesity. The first known longitudinal

study investigating proximity to supermarkets and fruit and vegetable consumption found no

association with the exception of a positive correlation among low-income men (Boone-Heinonen

et al., 2011; Gustafson, Hankins, & Jilcott, 2012). The authors of the longitudinal study subsequently

concluded that supermarket availability and diet are unrelated, and they suggested that the

contradictory findings of prior studies likely reflected “unmeasured respondent characteristics

related to both diet behaviors and selection of certain types of neighborhoods or placement of

supermarkets in areas with the greatest demand” (Boone-Heinonen et al., 2011, p. 1166).

Consumer Nutrition Environment Research

Although the only longitudinal community nutrition environment study conducted in the

U.S. to date found no relationship between proximity to supermarkets and dietary intake, it should

be noted that along with most food environment research, this study did not take into account the

potential impact of the consumer nutrition environment (Gustafson et al., 2012). Nonetheless, it stands to

reason that the availability of healthy food within food outlets is a critical factor in understanding the

relationship between the environment and diet. Kelly et al. (2011) argue, “[M]easures of actual food

and beverage products provide a more discrete indicator of the local food environment and are

likely to have a greater impact on food purchasing decisions than the spatial availability of food

outlets alone” (p. 1285). In other words, not only are measures of the consumer nutrition

environment more direct indicators of the overall food environment within a given area, the

consumer nutrition environment may also have a greater impact on food purchasing decisions than

the proximity of food outlets within a given area (Kelly et al., 2011). Accordingly, growing interest in

the impact of the consumer nutrition environment on health is evidenced by a dramatic increase in

the number of publications on the topic over the last few years. Between 2000 and 2003, only four

articles addressing the consumer nutrition environment were published, whereas 35 articles were

published between 2008 and 2011 (Gustafson et al., 2012).

In general, studies of the environments encountered by consumers inside food outlets

demonstrate a relationship between the availability of foods and dietary intake (Glanz et al., 2007).

In what was one of the first studies of its kind investigating the relationship between the consumer

nutrition environment and diet, Cheadle et al. (1991) found significant relationships at the ZIP code

and community level between the availability of a variety of foods in supermarkets and the

healthfulness of individual diets. Larson et al. (2009) found that of five studies addressing the

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availability of healthful foods and diet, four found a positive relationship between availability of

healthful foods and intake or home availability of the same foods. The availability of red meat,

reduced-fat milk, and low-fat foods in food outlets was found to be significantly associated with the

consumption of the same foods among local residents (Larson et al., 2009). Similarly, other findings

have indicated a positive relationship between the availability of unhealthy foods and their

consumption (Gloria & Steinhardt, 2010).

As with investigations of the community nutrition environment, consumer nutrition

environment research reveals significant disparities related to race, ethnicity, and socioeconomic

status (Glanz et al., 2007; Gustafson et al., 2012; Larson et al., 2009; Morland & Evenson, 2008).

The findings of at least six recent studies conducted in the U.S. indicate lower availability of healthy

foods, including fresh produce, lean meats, low-fat dairy products, and high-fiber bread, in low-

income and/or high-minority neighborhoods (Andreyeva, Blumenthal, Schwartz, Long, & Brownell,

2008; Baker, Schootman, Barnidge, & Kelly, 2006; Franco et al., 2008; Franco, Diez-Roux, Glass,

Caballero, & Brancati, 2008; Glanz et al., 2007; Gustafson et al., 2012; Horowitz et al., 2004; Leone,

et al., 2011; Larson et al., 2009). Two studies found lower proportions of stores carrying fresh or

frozen produce in predominantly Black neighborhoods, and one study found no difference in the

availability of fruits and vegetables based on neighborhood racial and socioeconomic characteristics,

but found the perceived quality of available fruits and vegetables to be significantly lower in

predominantly Black, low-socioeconomic neighborhoods than racially-heterogeneous, middle-

socioeconomic neighborhoods (Gustafson et al, 2012; Zenk, Schulz, Israel, James, Bao, and Wilson,

2006). Another study conducted in Los Angeles found that Body Mass Index (BMI) was higher

among individuals who shopped for groceries in economically disadvantaged neighborhoods

(Inagami, Cohen, Finch, & Asch, 2006).

The majority of community and consumer nutrition environment research findings are

consistent with the growing body of evidence that indicates residential segregation by income, race,

and ethnicity contribute to health disparities in the U.S. It has long been established that there is an

inverse relationship between socioeconomic status and the incidence or mortality rates of many

health outcomes, including low birth weight and chronic disease (Larson & Story, 2009; Zhang,

Cook, Jarman, & Lisboa, 2011). With regard to obesity and type 2 diabetes, Drewnowski and

Spencer (2004) assert that these rates “follow a socioeconomic gradient, such that the burden of

disease falls disproportionately on people with limited resources, racial-ethnic minorities, and the

poor” (p.6). There is a substantially higher prevalence of obesity among Blacks, Hispanics, and

people living in poverty, who are also at greater risk for type 2 diabetes, cardiovascular disease,

osteoporosis, and some forms of cancer (Jilcott et al., 2011; Ferdinand et al., 2012; Larson & Story,

2009; Treuhaft & Karpyn, 2010).

Limitations of Current Research

There are a number of limitations to nutrition environment research. Due to the nature of

the research questions involved, all but a few studies related to the nutrition environment are based

on observation and cross-sectional analysis. Consequently, in most cases the causality of the

relationships identified cannot be assumed (Jilcott et al., 2011). Self-selection bias may influence

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outcomes, since individuals are not randomly selected into neighborhoods but instead exercise

varying degrees of choice in determining where to live. Over time, both the individual and his or her

neighborhood affect each other, a reality that may result in confounded research outcomes (Lytle,

2009). Similarly, supermarkets and other food outlets locate based, at least partly, on demand

(Boone-Heinonen et al., 2011).

In addition, people choose where to purchase groceries based on a variety of factors, and it

is likely that the impact of proximity differs among individuals (Morland & Evenson, 2008). Boone-

Heinonen et al. (2011) note, “[D]iet decisions may be influenced by more proximate food resources

for low-income individuals, who may have limited transportation options, and for fast food

restaurants, which may involve more impulsive trips” (p. 1162). Indeed, the impact of transportation

on food access cannot be ignored, especially with regard to the relationship between physical

distance and travel burden (Bader et al., 2010). Lytle (2009) posits, “Examining the influence of the

environment on individuals’ food choices may benefit from a realization that, across populations or

communities, the physical environment, the social environment, and personal choice may have

differential influences on the foods that people choose to eat” (p. 11).

Moreover, because obtaining individual level data is an onerous process, ecological-level

measures of socioeconomic status are typically used, thereby placing individual-level inferences at

risk of the ecological fallacy (Zhang et al., 2011). It cannot be assumed that correlations between

nutrition environment outcomes and population-level demographic variables are the same at the

level of the individual. Of the studies that do gather individual-level data, many rely on self-report

measures, which are prone to measurement error (Larson & Story, 2009). In addition, because the

field of nutrition environment research is still in its infancy, there are a wide variety of measures,

operational definitions, and study designs currently being utilized (Shier et al., 2012). This variation

among studies makes meta-analysis difficult. Nevertheless, as Greenland (2001) acknowledges,

“[E]cologic data are worth examining, as demonstrated by careful ecologic analyses and by methods

that combine individual and ecologic data. Furthermore, it is important to remember that the

possibility of bias does not demonstrate the presence of bias” (p. 1348).

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Study Purpose

The Community Nutrition Environment and Health of Oklahoma City and County

The State of Oklahoma ranks last in the nation in fruit and vegetable consumption and the

entire state is characterized by high rates of obesity and diet-related diseases (Oklahoma State

Department of Health, 2011). Almost 15% of Oklahoma households qualify as food insecure,

meaning that they “had difficulty at some time during the year providing enough food for all their

members due to lack of resources” (Coleman-Jensen, Nord, Andrews, & Carlson, 2012, p. v). In

addition, the percentage of Oklahoma households that have very low food security (7%), defined by

one or more household members being forced to reduce or interrupt their eating patterns multiple

times during the course of a year due to lack of money or other resources, is higher than the national

average of 5.7%. In 2010, the Oklahoma City-County Health Department reported that almost two-

thirds of the population of Oklahoma County lives in food deserts, with only 36% of the population

living within half a mile of a grocery store, as one-half mile is a common measure of proximity in

food desert research because it is considered a “reasonable walking distance” (Meyers, 2010).

Not only do the majority of Oklahoma County residents have to travel considerable

distances to purchase groceries, it should also be noted that Oklahoma City ranks 81st out of 100

metropolitan areas in public transit coverage, and last in public transit ridership among all U.S.

metropolitan areas (Tomer, 2011; Walker, 2013). Only 69.1% of the city’s metropolitan residents

and 42.3% of its suburban residents have easy access to public transit (Tomer, 2011). Low

supermarket density and poor public transit infrastructure disproportionately impact the

economically disadvantaged. Of the more than 25,000 households that do not have access to a

personal vehicle (i.e., “zero-vehicle households”), nearly three quarters are low-income (Tomer,

2011).

Although findings from previous research provide insight into the community nutrition

environment in Oklahoma County and the overall health of its residents, assessments of the

consumer nutrition environment are needed in order to have a more comprehensive understanding

of the relationship between food accessibility and health in Oklahoma County. The purpose of this

study is to analyze the consumer nutrition environment in supermarkets across 51 ZIP codes in

Oklahoma County.

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Methods

This study utilizes primary and secondary data sources to conduct an observational, cross-

sectional, ZIP code-level analysis of the relationship between the consumer nutrition environment,

health outcomes, and indicators of race, ethnicity, and socioeconomic status in Oklahoma County.

The consumer nutrition environments within 56 supermarkets across 51 ZIP codes were assessed

using the Nutrition Environment Measures Survey in Stores (NEMS-S) (Glanz et al., 2007).

NEMS-S Instrument

The NEMS-S is a measure of the consumer nutrition environment, and both the survey tool

and rater training are available free of charge through the University of Pennsylvania

(www.med.upenn.edu/nems). The NEMS-S, which is a popular tool in the emerging field of

consumer nutrition environment research, assesses the availability, quality, and price of healthy

options within food outlets (see Table 1 & Appendix 1).The survey assesses a variety of food

products commonly purchased in the U.S., including milk, fruits, vegetables, ground beef, hot dogs,

frozen dinners, bread, baked goods, baked chips, and beverages. With the exception of the portions

of the survey dedicated to fruits and vegetables, the measure is designed to gather data on “regular”

and “healthier” food options for the purposes of comparison (e.g., regular potato chips vs. baked

potato chips). Whenever possible, data on “regular” and “healthier” options are gathered using items

within the same brand.

Milk. The NEMS-S assesses the availability, price, and shelf space occupied by pints, quarts,

half gallons, and gallons of skim, 1%, and whole milk. If no 1% low-fat milk is available, price

information is recorded for a quart and half-gallon of 2% milk. The store brand of milk is the

preferred reference brand for assessment. If a store does not have its own brand, whichever brand

of milk that occupies the most shelf space is assessed as an alternative. If different brands of milk

occupy equal amounts of shelf space, whichever brand is closest to the beginning of the alphabet is

selected for assessment.

Fruits. The NEMS-S assesses the availability, price, and quality of 10 top-selling fruits in the

United States. These fruits were selected using data from the Produce for Better Health Foundation

and the U.S. Department of Agriculture (USDA) Economic Research Service (Glanz, Clawson,

Young, & Carvalho, 2008a). Regarding fruits that are often available in multiple varieties, the

measure specifies a particular variety of fruit (e.g., Gala apples) to be assessed, but also includes a

blank space for the rater to write the name of an alternative variety in the event that the preferred

variety is unavailable. The rater indicates the availability of each fruit by marking “yes” or “no.”

Information on price is recorded per piece or per pound. If a fruit is on sale, only the regular price is

recorded. The rater indicates the quality of each fruit by marking “acceptable” or “unacceptable.”

“Acceptable” fruits are those that the rater judges to be “of peak condition, top quality, good color,

fresh, firm, and clean” (Glanz, Clawson, Young, & Carvalho, 2008b, p. 5). “Unacceptable” fruits are

those that the rater judges to be “bruised, old looking, mushy, dry, overripe,” or to have “dark

sunken spots in irregular patches or cracked or broken surfaces, signs of shriveling, mold, or

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excessive softening” (Glanz et al., 2008b, p. 5) This rating is based on the majority (>50%) of fruits

of a particular type.

Vegetables. The NEMS-S assesses the availability, price, and quality of 10 top-selling

vegetables in the United States. These vegetables were selected using data from the Produce for

Better Health Foundation and the USDA Economic Research Service, but potatoes were excluded

because of their relatively high energy density (Glanz et al., 2008a). When necessary, the measure

specifies a particular variety of vegetable (e.g., green bell peppers) to be assessed, but also includes a

blank space for the rater to write the name of an alternative variety in the event that the preferred

variety is unavailable. The rater indicates the availability of each vegetable by marking “yes” or “no.”

Information on price is recorded per piece or per pound. If a vegetable is on sale, only the regular

price is recorded. The rater indicates the quality of each vegetable by marking “acceptable” or

“unacceptable.” The standard for acceptability is the same as the standard for fruit, and this rating is

based on the majority (>50%) of vegetables of a particular type.

Ground beef. The NEMS-S assesses the availability and price of standard and lean ground

beef. Standard ground beef is defined as 80% lean and 20% fat. Lean ground beef is defined as at

least 90% lean and at most 10% fat. The rater indicates the availability of each type of meat by

marking “yes” or “no” and records the regular price per pound. If ground beef is on sale, only the

regular price is recorded. 90% lean, 10% fat is the preferred type of lean ground beef, but ground

beef containing less fat is acceptable as an alternative if the preferred type is unavailable (a blank is

provided to record the specific lean/fat content). If no lean ground beef is available, ground turkey

may be assessed.

Hot dogs. The NEMS-S assesses the availability and price of regular and reduced-fat

wieners. Oscar Meyer is the preferred reference brand, but other brands may be assessed if Oscar

Meyer is not available (blank spaces are provided to record alternative brands). If Oscar Meyer 98%

fat-free wieners are not available, light, fat-free, or turkey wieners may be assessed as alternatives.

The rater indicates the availability of each type of hot dogs by marking “yes” or “no.” The rater also

records the price per package of hot dogs. If hot dogs are on sale, only the regular price is recorded.

Frozen dinners. The NEMS-S assesses the availability and price of regular and reduced-fat

frozen dinners. Stouffer’s and Lean Cuisine are the preferred reference brands, but other brands

may be assessed if Stouffer’s and Lean Cuisine are unavailable (blank spaces are provided to record

alternative brands). The preferred frozen dinners for assessment are lasagna, roast turkey breast, and

meatloaf, but other frozen dinners may be assessed if the preferred dinners are unavailable. The rater

indicates the availability of each dinner by marking “yes” or “no.” The rater then estimates the

proportion of reduced-fat compared to regular frozen dinners available among the brands assessed.

For each frozen dinner, the rater records the ounces, calories, grams of fat, and price per package. If

any frozen dinners are on sale, only the regular prices are recorded.

P a g e | 10

Table 1. NEMS-S Measure

Item Assessed Factors Assessed Healthier vs. Less-

Healthy Varieties

Compared*

Reference Brand*

Milk Availability, price, &

shelf space

Skim/1% vs. Whole Store brand

Fruits Availability, price, &

quality

N/A N/A

Vegetables Availability, price, &

quality

N/A N/A

Ground Beef Availability & price Standard (80/20%) vs.

Lean (90/10%)

Whichever occupies

the most shelf space

Hot Dogs Availability & price Regular vs. 98% fat-free Oscar Meyer

Frozen Dinners Availability & price Regular vs. reduced-fat Stouffer's/Lean

Cuisine

Baked Goods Availability & price Muffins vs. bagels Whichever occupies

the most shelf space

Beverages Availability & price Regular vs. diet soda;

juice drink vs. 100%

juice

Coke & Diet Coke;

Minute Maid

Bread Availability & price Regular vs. 100% whole

wheat

Nature's Own

Chips Availability & price Regular vs. baked Lay’s

Cereal Availability & price Regular vs. lower-sugar

(< 7g)

Cheerios

* The NEMS-S measure provides alternative procedures if preferred varieties and brands are unavailable.

Baked goods. The NEMS-S assesses the availability and price of regular and healthier

baked goods. Muffins are the preferred regular baked good, but danishes or other baked goods may

be assessed if muffins are unavailable (blank spaces are provided to specify the type of alternative

baked good assessed). Bagels are the preferred healthier baked good, but English or low-fat muffins

may be assessed if bagels are unavailable (blank spaces are provided to specify the type of alternative

healthier baked good assessed). The rater indicates availability by marking “yes” or “no.” The rater

also records grams of fat, calories, and regular price for each baked good assessed.

Beverages. The NEMS-S assesses the availability and price of regular soda, diet soda (0

kcal), 100% fruit juice, and juice drink (some fruit juice with added sugar and water). Coke and Diet

Coke are the preferred reference brands for soda, whereas Minute Maid is the preferred reference

brand for 100% juice/juice drink. If those brands are unavailable, available brands may be assessed

as alternates. The rater indicates the availability of 12-packs of regular and diet soda by marking

11 |P a g e

“yes” or “no.” If 12-packs of the beverages are unavailable, the rater may alternatively assess the

availability of 6-packs. The rater indicates the availability of a half-gallon of 100% juice and a

comparable juice drink by marking “yes” or “no.” The rater also records the regular price of the

assessed quantity of sodas, 100% juice, and juice drink.

Bread. The NEMS-S assesses the availability and price of white and 100% whole wheat

bread. The preferred reference brand for bread is Nature’s Own, but Sara Lee or another brand may

be assessed if the preferred brand is unavailable (blank spaces are provided to specify the brand

assessed). The rater indicates the availability of each type of bread by marking “yes” or “no” and

records the size and price of the loaves. If bread is on sale, only the regular price is recorded. The

rater also counts and records the total number of available varieties of 100% whole wheat bread.

Chips. The NEMS-S assesses the availability and price of regular and baked chips (< or =

3g fat per oz.). Lay’s is the preferred reference brand, but other brands of chips may be assessed if

the preferred brand is unavailable (blank spaces are provided to record the alternate brand assessed).

The rater indicates the availability of each type of chips by marking “yes” or “no” and records the

size (in ounces) and regular price of each available type. The rater also counts and records the total

number of available varieties of low-fat chips.

Cereal. The NEMS-S assesses the availability and price of regular and lower-sugar (< 7g

sugar) cereal. Cheerios is the preferred reference brand, but other brands of cereal may be assessed if

the preferred brand is unavailable (blank spaces are provided to record the alternate brand assessed).

The rater indicates the availability of each type of cereal by marking “yes” or “no” and records the

size (in ounces) and price of each available type. If a cereal is on sale, only the regular price is

recorded. The rater also counts and records the total number of available varieties of healthier cereal.

The NEMS-S instrument has received very high rates of agreement and kappa statistics for

inter-rater (from 92-100% and 0.83 -1.00) and test-retest reliability (from 90.2%-100% and 0.75-

1.00), which lend support for the construct validity of the measure (Glanz et al., 2007). The study

was exempt from Human Subjects review, as the study evaluated supermarket products and utilized

secondary data sources for health outcomes.

The Oklahoma County Wellness Score

The Oklahoma County Wellness Score was developed in 2010 through a partnership

between the Oklahoma City-County Health Department’s Epidemiology Services Program and

Central Oklahoma Turning Point (Wellness Now, 2010). The Oklahoma County Wellness Score

utilizes a variety of data types to analyze the overall health status of 51 ZIP codes within Oklahoma

County. Each ZIP code is scored according to its economics, housing, education, transportation,

chronic disease, cancer, infectious disease, emergency department utilization, and maternal/child

health outcomes. These scores are then aggregated to form a composite Wellness Score indicating

the overall health status of the ZIP code. The Wellness Score data highlights a wide disparity in

health status between ZIP codes within Oklahoma County (see Figure 1). The five highest scoring

P a g e | 12

ZIP codes in Oklahoma County are characterized by a disproportionately high concentration of

white residents, whereas the five lowest scoring ZIP codes are characterized by a disproportionately

high concentration of racial and ethnic minority residents (Wellness Now, 2010; Wellness Now,

2014). In addition, approximately 20% of households in the five lowest-scoring ZIP codes lack

access to a personal vehicle, compared to fewer than 5% of households in the highest-scoring ZIP

codes (U.S. Census Bureau, 2000; U.S. Census Bureau, 2013).

Figure 1. Oklahoma County Wellness Score by ZIP Code

Notes: A star symbol designates the location of a supermarket.

ZIP codes that do not contain supermarkets are not shaded

ZIP Code Selection

Oklahoma County Wellness Score data from the Oklahoma County Wellness Score 2014:

Community Health Status Assessment (from the Oklahoma City-County Health Department) were

utilized as indicators of overall health and wellness at the ZIP code level (Wellness Now, 2014). 51

Oklahoma County ZIP codes were divided into two groups according to median cut-point

composite Wellness Score. ZIP codes with below-median Wellness Scores were categorized in the

low Wellness Score (less healthy) group, and ZIP codes with above-median Wellness Scores were

categorized in the high Wellness Score (healthier) group. Socioeconomic/demographic indicators

were based on 2010 Census data.

13 |P a g e

Supermarket Selection

Supermarkets were identified, enumerated, and mapped using multiple data sources,

including a supermarket database and GIS map from the City of Oklahoma City Planning

Department, as well as Google Maps. Only food outlets that sell a general line of food including

produce, fresh meat and poultry, dairy, dry and packaged foods, and frozen foods were considered

eligible supermarkets for the purposes of this study. A total of 56 eligible supermarkets were

identified. For the purposes of analysis, the supermarkets were divided into two groups according to

the composite Wellness Scores of the ZIP codes in which they are located. All eligible supermarkets

within the 51 ZIP codes were included for assessment. In sum, 26 supermarkets were located in ZIP

codes with higher composite Wellness Scores, and 30 supermarkets were located in ZIP codes with

lower composite Wellness Scores. Some ZIP codes did not contain any supermarkets.

Rater Training

All supermarket assessments were conducted by two trained raters. Both raters completed

the supermarket portion of an online NEMS training course hosted by the University of

Pennsylvania prior to conducting supermarket assessments. The online course modules took

approximately 6 hours for each rater to complete and included training on identifying and

enumerating food outlets, conducting NEMS-S surveys in supermarkets, and customizing the

NEMS-S measure for specific research needs

Data Analysis Methods

Availability, price, quality, and composite (sum total of availability, price, and quality)

NEMS-S scores were calculated for each supermarket using the Scoring Systems for NEMS Store

Measures (see Figure 2, Appendix 2, & Appendix 3). The potential ranges for each NEMS-S score

are 0 to 27 for availability, 0 to 6 for quality, -8 to 17 for price, and -8 to 50 for composite scores. All

data analysis was completed using IBM SPSS Statistics Version 21. Descriptive statistics and

supermarket ZIP code comparisons were computed via independent sample t-tests. Pearson

correlation coefficients were computed to assess the relationships between NEMS-S scores and the

following ZIP code characteristics: Wellness Score, median household income, percent racial-ethnic

minority composition, and percent of zero-vehicle households.

P a g e | 14

Results

ZIP code level Wellness Scores ranged from 10.74 to 27.62, with a median and mean of

19.31 (SD = 5.06) (See Table 2). ZIP code level composite NEMS-S scores ranged from 15 to 43,

with a median of 35 and a mean of 33.88 (SD = 6.83). Supermarkets in high Wellness Scoring ZIP

codes were found to have significantly higher composite NEMS-S scores than supermarkets in low

Wellness Scoring ZIP codes (36.80 vs. 31.52, p = .002) (see Table 3 and Appendix 3). Supermarkets

in high Wellness Scoring ZIP codes were significantly more likely than those in low Wellness

Scoring ZIP codes to have healthy food available, as indicated by their availability subscores (27.08

vs. 22.65, p = .001). Correlation analyses demonstrated that ZIP code-level median household

income was positively associated with composite NEMS-S scores (r = .396, p = .003), ZIP code-

level percentage of racial-ethnic minority residents was negatively associated with composite NEMS-

S scores (r = -.475, p < .001) (See Appendices 4 & 5). Similarly, the percentage of zero-car

households within a ZIP code was negatively associated with composite NEMS-S scores (r = -.422,

p = .001) (see Appendix 6). No significant differences were found in price or quality between high

and low Wellness Scoring ZIP codes

Table 2. NEMS-S Scores

NEMS-S Score

Minimum Maximum Median Mean Standard Deviation

Availability 9 29 27 24.63 5.22

Price -5 10 3 3.11 3.45

Quality 5 6 6 5.98 0.134

Composite 15 43 35 33.88 6.83

Table 3. Differences in NEMS-S Scores between High and Low Wellness Scoring ZIP Codes

ZIP Code

NE

MS

-S S

co

re T

yp

e High Wellness Score Low Wellness Score Sig.

Availability 27.08 22.65 p = .001

Price 3.80 2.55 NS

Quality 6.00 5.97 NS

Composite 36.80 31.52 p = .002

Note: For all NEMS-S scores/subscores, higher scores are preferable (e.g. indicate better availability,

more affordability, and higher quality)

15 |P a g e

Figure 2. Mean NEMS-S Score by ZIP Code



P a g e | 16

Discussion

In addition to the challenges presented by the community nutrition environment in

Oklahoma County and the ZIP code-level health disparities already identified by Wellness Score

data, the results of this study reveal disparities in the consumer nutrition environment and

availability of healthy foods in supermarkets between ZIP codes with low Wellness Scores and high

Wellness Scoring ZIP codes in Oklahoma County. Moreover, the findings of this research suggest a

positive relationship between median household income and the consumer nutrition environment of

supermarkets. The research also reveals a negative relationship between the percentage of ZIP code

population that is of a racial-ethnic minority and composite and availability scores, which suggests

that as the racial-ethnic minority percentage of the population increases, the consumer nutrition

environment (in particular the availability of healthy food) in supermarkets decreases. Similarly, as

the percentage of ZIP code population that lacks access to a personal vehicle increases, the

composite NEMS-S scores and availability of healthy food within stores decreases. This finding

suggests a relationship between the economic resources and racial-ethnic composition of ZIP codes

and the consumer nutrition environment in local supermarkets.

Figure 3. Racial/Ethnic Minority Density by ZIP Code



17 |P a g e

As previously mentioned, the five healthiest ZIP codes in Oklahoma County are

characterized by a disproportionately high concentration of white residents, whereas the five

unhealthiest ZIP codes are characterized by a disproportionately high concentration of racial and

ethnic minority residents (see Figure 3). The findings of this study indicate that the disparity in the

consumer nutrition environment in supermarkets disproportionately impacts people of color and the

economically disadvantaged. Additionally, the relationship between zero-vehicle households and the

availability of healthy food suggests that healthy food is least available in areas with high proportions

of households that face the largest obstacles to traveling outside of their ZIP codes for groceries (see

Figure 4).

Figure 4. Density of Households with Zero Personal Vehicles by ZIP Code



Limitations

As is the case with most consumer nutrition environment research, the cross-sectional,

ecological-level design of this study precludes assumptions of causality. It is therefore unclear to

what extent the products stocked by supermarkets may reflect or influence consumer demand.

Moreover, as previously mentioned with regard to other consumer nutrition environment research,

the potential impact of self-selection bias, confounding, and the ecological fallacy are also of

concern.

P a g e | 18

In addition, it is possible that the scoring of the NEMS-S measure may not accurately reflect

the actual consumer nutrition environment in supermarkets. The authors share specific concerns

about the validity of the quality subscore, particularly because only the perceived appearance

(quality) of produce is included in the measure, which may not be an accurate indicator of the overall

quality of available foods within a supermarket. Moreover, the procedures involved in assessing

quality are subjective, based on appearance, and arguably insensitive to consumer impressions. For

example, a rater must deem over half of the bananas available in a supermarket to be “unacceptable”

in order for bananas to receive an “unacceptable” rating on the NEMS-S measure. In addition, in

order for a supermarket to lose “quality” points in the NEMS-S scoring system, 3 out of the 10

types of fruits or vegetables assessed would have to receive ratings of “unacceptable.” The authors

hypothesize that stricter, more comprehensive measures of food quality would reveal differences

between supermarkets that the current NEMS-S measure does not capture.

Finally, by ignoring the seasonality of fruits and vegetables and where the produce is grown,

the NEMS-S measure may inadvertently penalize smaller grocers that purchase produce locally. The

fact that a particular supermarket does not stock watermelons and tomatoes in December may not

be reflective of a lack of healthy food availability so much as an intentional choice to only stock

fruits and vegetables that are in season. As it currently stands, a large national chain that ships

artificially-ripened, nutritionally inferior tomatoes during the winter might receive higher NEMS-S

produce scores than a local grocer who stocks naturally-ripened, nutritionally superior tomatoes, but

only stocks them when they are in season.

Policy Implications

Despite the limitations of the study, the disparities revealed highlight the need for

interventions and policies that ensure equitable access to healthy foods across all sectors and

demographic groups within Oklahoma County. Interestingly, a 2005 feasibility study commissioned

by the Greater Oklahoma City Chamber of Commerce and the City of Oklahoma City determined

that “a food store of approximately 35,000 square feet is clearly needed in Northeast Oklahoma

City” based on the fact that the $7.8 million worth of demand for food and drink retail is currently

not being satisfied by the existing $1.8 million worth of supply (The Kilduff Company, 2005, p. 40).

Northeast Oklahoma City contains two of the five unhealthiest ZIP codes in Oklahoma County,

both of which also received low consumer nutrition environment scores using the NEMS-S measure

(Wellness Now, 2010). Northeast Oklahoma City also contains several census tracts that the United

States Department of Agriculture Economic Research Service has determined to be food deserts

(Economic Research Service, 2012).

However, in spite of the feasibility study’s recommendation that city leaders actively recruit a

supermarket to locate in the area—a project that the study’s authors indicated could take three or

more years to complete—no new supermarkets have moved into the area since the completion of

the feasibility study (The Kilduff Company, 2005). The situation in Northeast Oklahoma City is

consistent with the observation that “despite ample evidence that low-income neighborhoods are a

profitable and untapped market, research provides a strong correlation between poverty, race, and a

lack of grocery stores” (Conroy & McDavis-Conway, 2006, p. 10).

19 |P a g e

To address disparities in food access across Pennsylvania, The Food Trust collaborated with

public and private partners to create the Pennsylvania Fresh Food Financing Initiative, which

“provides financing for supermarket operators that plan to operate in underserved communities

where infrastructure costs and credit needs cannot be filled solely by conventional financial

institutions” (Giang et al., 2008). The Fresh Food Financing Initiative began with a $30 million

allocation from the State of Pennsylvania that was leveraged 3:1 through private sources and New

Market Tax Credits to create a $120 million financing pool. The Fresh Food Financing Initiative has

since committed to fund 32 food stores that will serve an estimated 320,000 residents and create or

retain approximately 2,500 jobs.

In order to recruit supermarkets to underserved areas in Oklahoma County, such as

Northeast Oklahoma City, community leaders and stakeholders should consider implementing an

initiative modeled after Pennsylvania’s successful example, which has been selected by the Centers

for Disease Control and Prevention’s Pioneering Innovation Award. The potential benefits of

supporting the development of supermarkets extend beyond improving the community and

consumer nutrition environments and increasing the availability of healthy food. According to The

Reinvestment Fund (2006), the opening of a new supermarket increases economic activity in the

neighborhood and region, creating jobs and immediately boosting property values. Based on the

success of the Pennsylvania Fresh Food Financing Initiative, President Obama allocated over $400

million to establish a national Healthy Food Financing Initiative that provides funding for similar

projects across the country (Shier et al., 2012).

Additional community-based research needs to be conducted to investigate individual

behaviors, perceptions, and demands as they relate to the community nutrition environment in

Oklahoma County. This information could be used by decision makers to encourage and inform

positive changes to the consumer nutrition environment in local supermarkets, which may in turn

result in improvements to health outcomes over time.

P a g e | 20

Appendix 1: NEMS-S Survey

Nutrition Environment Measures Survey (NEMS)

Food Outlet Cover Page

Rater ID:

Store ID: -

Date://Month Day Year

Grocery Store

Restaurant ID: -

Date: //Month Day Year

:

:

::

Start Time:

Start Time:

End Time:

End Time:

SD


Cover Page

Comments:

AMPM

AM

PM

AM PMAM PM

Number of cash registers:

Menu/Internet Review Date: //Month Day Year

::

Start Time:End Time:

AM PMAM PM

Site Visit

Date: //Month Day Year

::

Start Time:

End Time:AM PM

AM PM

Other Visit/Interview

FF Specialty OtherConvenience Store

Other

- -

- -

FC

© 2006 Rollins School of Public Health, Emory Universi ty All rights reserved

Not for reproduction or redistribution without permission8250013302

21 |P a g e

P a g e | 22

1. Bananas

Availability and Price

2. Apples

3. Oranges

4. Grapes

5. Cantaloupe

6. Peaches

7. Strawberries

8. Honeydew Melon

9. Watermelon

10. Pears

Produce ItemAvailable Price Unit

pc lb

Quality

A UA

.

.

.

.

.

.

.

.

.

.

11. Total Types:

$

$

$

$

$

$

$

$

$

$

Comments


Measure #2: FRUIT

Rater ID: Store ID:

Date:Month Day Year

#

Red delicious

Navel

Red seedless

Seedless

Anjou

Yes No

- - -

Grocery Store Convenience Store Other

Measure Complete

/ /

(Count # of yes responses)

0450176946

23 |P a g e


Measure #3: VEGETABLES

1. Carrots


2. Tomatoes

3. Sweet Peppers

4. Broccoli

5. Lettuce

6. Corn

7. Celery

8. Cucumbers

9. Cabbage

10. Cauliflower

Produce ItemAvailable Price Unit

pc lb

Quality

A UA

.

.

.

.

.

.

.

.

.

.

11. Total Types:

$

$

$

$

$

$

$

$

$

$

Comments

Rater ID: Store ID:

Date:Month Day Year

#

1 lb bag

Loose

Green bell peppers

Bunch

Green leaf

Regular

Head

Yes No

- - -


Measure Complete

/ /

(Count # of yes responses)

6577396766

P a g e | 24


MEASURE #4: GROUND BEEF


Item

5. Standard ground beef, 80% lean,

20% fat

Alternate Item:

6. Standard alternate ground beef, if above is not available

1. Lean ground beef, 90% lean,

10% fat (Ground Sirloin)

4. # of varieties of lean ground beef ( <10% fat)

2. Lean ground beef, (<10% fat)

3. Ground Turkey, (<10% fat)

AvailablePrice/lb. Comments

.$

.$

0 1 2 3 4 5 6+

% fat

% fat

.$

.$

% fat

.$

Comments

Alternate Items:

Rater ID: Store ID:

Date:Month Day Year

Yes No N/A

- - -


Healthier option:

Regular option:

Measure Complete

/ /

4349643520

25 |P a g e


MEASURE #5: HOT DOG

Availability and PriceItem

7. Oscar Mayer Wieners(turkey/pork/chicken)-regular 12g fat

8. Beef Franks (regular)

1. Oscar Mayer 98% Fat Free Wieners(turkey/beef) 0.5g fat

2. Fat-free other brand 0g fat

3. Light Wieners (turkey/pork)

Available Price/pkg.Comments

.$

.$

Kcal/svg

.$

.$

.$

4. Light beef Franks (usually 1/3 less calories, 50% less fat)

.$

5. Turkey Wieners (1/3 less fat)

.$

6. Other .$

Brand name

Alternate Items: (< 9g fat)

Alternate Items: (>10g fat)

Rater ID: Store ID:Date:

Month Day Year

$ .9. Other

oz pkg Hot dogs/pkg

g fat kcal/svg

oz pkg Hot dogs/pkg

g fat kcal/svg

Yes No N/A

- - -


Healthier option:

Regular option:

Measure Complete

/ /

5986187936

P a g e | 26


MEASURE #6: FROZEN DINNERS

A. Reference Brand

1. Stouffer's brand (preferred) Yes No2. Alternate brand (with reduced-fat dinnersavailable) Brand Name:

Comments:

B. Availability1. Are reduced-fat frozen dinners available? ( <9g fat/8-11 oz.)

Yes No

Rater ID: Store ID:

Date:Month Day Year

Shelf space:(measure only if reduced-fat frozen dinners are available)2. Reduced-fat dinners/regular dinners: Proportion < = 10% 11-33% 34-50% 51%+

C. Pricing (All items must be same brand)Regular Dinner Price/ PkgReduced-Fat Dinner Price/ Pkg Comments

.$1. Lean Cuisine Lasagna .$

.$2. Lean Cuisine Roasted Turkey

Breast.$

.$3. Lean Cuisine Meatloaf

.$

Regular Alternate (> 10g fat)Reduced-Fat Alternate (< 9g fat)

Stouffer's Lasagna

Stouffer's Roasted Turkey

Breast

Stouffer's Meatloaf

- - -


oz. Kcal. g fat

oz. Kcal. g fat

oz. Kcal. g fat

oz. Kcal. g fat

oz. Kcal. g fat

oz. Kcal. g fat

$ . $ .

$ . $ .

$ . $ .

4. Other

5. Other Other

6. Other Other

Other

oz. Kcal. g fat oz. Kcal. g fat



Measure Complete

/ /

Price/ Pkg Price/ Pkg Comments

7422087785

27 |P a g e


MEASURE #7: BAKED GOODS

Rater ID: Store ID:

Date:Month Day Year

Availability & PriceLow-fat baked goods <3g fat/serving

Item Available Amt. per

package

g fat/

per item

kcal/

per item

Price Comments

1. Bagel

$ .

Alternate Items:

2. English muffin

3 a. Low-fat muffin

0 1 2 3+

$ .

$ .

Regular option (> 4g fat/serving or 400 Kcal/serving):

4. Regular muffin $ .

Alternate Items:

5. Regular Danish $ .

6. Other $ .

b. # varieties of low fat muffins

$ .

Package

Single

Yes No

- - -


Yes No N/A

Yes No N/A

Yes No N/A

Healthier option:

Measure Complete

/ /

2816345830

P a g e | 28


MEASURE #8-GS: BEVERAGE

Rater ID: Store ID:

Date:Month Day Year

Availability & Price

Healthier option: Available size Price Comments

1. Diet Coke 12 pack 12 oz.

6 pack 12 oz.

$ .

$ .

2. Alternate brand of diet soda12 pack 12 oz.

6 pack 12 oz

$ .$ .

Healthier option:

5. Minute Maid 100% juice, (64 oz., half gallon) .$

Alternate Items:

6. Tropicana 100% juice, (64 oz., half gallon) .$

.$

Regular option:

8. Minute Maid juice drink, (64 oz., half gallon)

Alternate Items:

$ .

$ .

7. Other:

9. Tropicana juice drink, (64 oz., half gallon)

6 pack 12 oz. $ .

3. Coke 12 pack 12 oz. $ .

4. Alternate brand of sugared soda12 pack 12 oz.

6 pack 12 oz

$ .$ .

Available

$ .10. Other:

Yes No

Yes No

- - -


Yes No N/A

Yes No N/A

Yes No

Yes No N/A

Regular option:

Yes No N/A

Yes No

Yes No N/A

Yes No N/A

Measure Complete

/ /

2399270080

29 |P a g e


MEASURE #9: BREAD

Rater ID: Store ID:

Date:Month Day Year


Item

1. Nature's Own 100% Whole Wheat Bread

4. # of varieties of 100% whole wheat bread and whole grain (all brands)

2. Sara Lee Classic 100% Whole Wheat Bread

3. Other:

Available Price/loaf Comments

.$

0 1 2 3 4 5 6+

.$

.$

Alternate Items:

Healthier Option: Whole grain bread (100% whole wheat bread and whole grain bread)

Regular Option: White bread (Bread made with refined flour)5. Nature's Own Butter Bread .$

6. Sara Lee Classic White Bread

7. Other:

.$

.$

Alternate Items:

Loaf size

(ounces)Yes No N/A

- - -


Measure Complete

/ /

8320121759

P a g e | 30


MEASURE #10: BAKED CHIPS


Month Day Year

Availability & PriceLow-fat chips <3g fat/1 oz. serving

Item

1. Baked Lays Potato Chips

Price Comments

.$

Healthier Option :

.$

Alternate Item:

3. # of varieties of low-fat chips (any brand) 0 1 2 3 4 5 6+

Regular Option (select most comparable size to healthier option available):

4. Lays Potato Chips Classic .$

.$

Alternate Item:

2.

5.

Available

Yes No

- - -


Yes No N/A

Yes No N/A

Yes No

Measure Complete

/ /

Size (oz.)

oz.

oz.

oz.

oz.

9805006716

31 |P a g e


MEASURE #11: CEREAL


Month Day Year

- - -



Item

1. Cheerios (Plain)

2. Other

Available Price Comments

.$

.$

Size

(ounces)Yes No N/A

Healthier cereals < 7 g sugar per serving

3. # of varieties of healthier cereals 0 1 2 3+

Alternate Item:

Measure Complete

/ /

Healthier Option :

Regular Option ( >7g of sugar per serving):

4. Cheerios (Flavored)

5. Other

.$

.$

Alternate Item:

Yes No N/A

Yes No N/A

9848496682

P a g e | 32

Appendix 2: Scoring System for NEMS Store Measures

11/24/08 Page 1

Scoring Systems for NEMS Store Measures Item Availability Price Quality*

1. Milk YES low-fat/skim = 2 points (pts) Proportion (lowest-fat to whole) ≥ 50% = 1 point (pt)

Lower for lowest-fat = 2 pts Same for both = 1 pt Higher for low-fat = -1 pt

- inapplicable –

2. Fruit 0 varieties = 0 pts < 5 varieties = 1 pt 5-9 varieties = 2 pts 10 varieties = 3 pts

[no points; for comparison with convenience stores]

25-49% acceptable = 1 pt 50-74% acceptable = 2 pts 75%+ acceptable = 3 pts

3. Vegetables 0 varieties = 0 pts < 5 varieties = 1 pt 5-9 varieties = 2 pts 10 varieties = 3 pts

[no points; for comparison with convenience stores]

25-49% acceptable = 1 pt 50-74% acceptable = 2 pts 75%+ acceptable = 3 pts

4. Ground Beef YES lean meat = 2 pts 2-3 varieties < 10% fat = 1 pt > 3 varieties < 10% fat = 2 pts

Lower for lean meat = 2 pts Higher for lean meat = -1 pt

- inapplicable -

5. Hot dogs YES fat-free available = 2 pts Light, but not fat-free = 1 pt

Lower for fat-free or light = 2 pts Higher for fat-free or light = -1 pt

- inapplicable -

6. Frozen dinners YES all 3 reduced-fat types = 3 pts YES 1 or 2 reduced-fat types = 2 pts

Lower for reduced-fat (based on majority of frozen dinners) = 2 pts Higher for reduced-fat = -1 pt

- inapplicable -

7. Baked goods YES low-fat items = 2 pts Lower for low-fat (per piece) = 2 pts Higher for low-fat (per piece) = -1 pt

- inapplicable -

8. Beverages YES diet soda = 1 pt YES 100% juice = 1 pt

Lower for diet soda = 2 pts Higher for 100% juice = -1 pt

- inapplicable -

9. Bread YES whole grain bread = 2 pts >2 varieties whole wheat bread = 1 pt

Lower for whole wheat = 2 pts Higher for whole wheat = -1 pt

- inapplicable -

10. Baked chips YES baked chips = 2 pts > 2 varieties baked chips = 1 pt

Lower for baked chips = 2 pts Higher for baked chips = -1 pt

- inapplicable -

11. Cereal YES healthier cereal = 2 pts

Lower for healthier cereal (per box) = 2 pts Higher for healthier cereal (per box) =-1 pt

- inapplicable -

* For scoring quality, it is based on the % of acceptable ratings on the total amount of varieties available. For example, if there were 6 varieties of

fruit available with 4 items having acceptable ratings, then you would score it with 2 points, as it falls within the 50-75% range.

TOTAL POSSIBLE SCORE: 0 to 30 points (availability) -9 points to 18 points (price) 0 to 6 points (quality) Total Summary Score: Up to 54 points possible (availability + price + quality)

Appendix 3: NEMS-S Scores

Store by ZIP Code Location

Total Points Composite

Total Points Availability

Total Points Price

Total Points Quality

Total Points Possible

54 30 17 6

73003 36 27 3 6

73003 39 25 8 6

73003 40 29 5 6

73008 43 28 9 6

73008 43 27 10 6

73013 35 28 1 6

73013 38 29 3 6

73013 41 28 7 6

73020 38 24 8 6

73034 35 29 0 6

73034 38 29 3 6

73034 40 28 6 6

73045 32 24 2 6

73106 39 27 6 6

73107 15 12 -3 6

73107 16 9 1 6

73107 21 13 2 6

73107 34 24 4 6

73109 25 17 2 6

73109 31 21 4 6

73109 34 28 0 6

73109 39 27 6 6

73110 31 27 -2 6

73111 31 12 3 5

73112 35 24 5 6

73112 40 27 7 6

73115 37 27 4 6

73115 40 27 7 6

73116 38 28 6 6

73117 17 16 -5 6

73118 31 27 -2 6

73118 33 27 0 6

73119 21 15 0 6

73119 31 25 0 6

73119 32 28 -2 6

73119 37 25 6 6

73120 33 27 0 6

73120 35 26 3 6

P a g e | 34

73120 37 26 5 6

73122 39 27 6 6

73127 15 9 0 6

73127 32 26 0 6

73127 33 27 0 6

73129 33 25 2 6

73129 36 21 9 6

73130 28 25 -3 6

73130 42 27 9 6

73132 37 27 4 6

73132 38 28 4 6

73134 33 27 0 6

73139 36 27 3 6

73141 36 27 3 6

73142 41 28 7 6

73149 36 27 3 6

73162 35 26 3 6

73162 36 28 2 6

35 | P a g e

Appendix 4: Correlation Between Composite NEMS-S Score &

Median Household Income

(r = .396, p = .003)

Relationship is significant at the 1% level (p value < 0.01)

P a g e | 36

Appendix 5: Correlation Between Consumer Nutrition Environment Scores &

Percent Minority in ZIP Code

(r = -.475, p < .001)


37 | P a g e

Appendix 6: Correlation Between Consumer Nutrition Environment Scores &

Percent Zero Vehicle in ZIP Code

(r = -.422, p = .001)


P a g e | 38

References

Ahern, M., Brown, C., and Dukas, S. (2011). A national study of the association between

food environments and county-level health outcomes. The Journal of Rural Health, 27,

367-379.

Andreyeva, T., Blumenthal, D.M., Schwartz, M.B., Long, M.W., and Brownell, K.D. (2008).

Availability and prices of foods across stores and neighborhoods: The case of New

Haven, Connecticut. Health Affairs, 27, 1381-1388.

Bader, M.D.M., Purciel, M., Yousefzadeh, P., Neckerman, K.M. (2010). Disparities in

neighborhood food environments: Implications of measurement strategies. Economic

Geography, 86(4):409-430.

Baker, E.A., Schootman, M., Barnidge, E., and Kelly, C. (2006). The role of race and poverty

in access to foods that enable individuals to adhere to dietary guidelines. Preventing

Chronic Disease, 3, A76.

Boone-Heinonen, J., Gordon-Larsen, P., Kiefe, C.I., Shikany, J.M., Lewis, C.E., and Popkin,

B.M. (2011). Longitudinal associations with diet in young and middle-aged adults:

The CARDIA study. Archives of Internal Medicine, 171(13):1162-1170.

Cheadle, A., Psaty, B.M., Curry, S., Wagner, E., Diehr, P., Koepsell, T., and Kristal, Al.

(1991). Community-level comparisons between the grocery store environment and

individual dietary practices. Preventive Medicine, 20, 250-261.

Coleman-Jensen, A., Nord, M., Andrews, M., and Carlson, S. (2012, September). Household

food security in the United States in 2011 (ERR-141), U.S Department of Agriculture,

Economic Research Service.

Drewnowski, A. and Specter, S.E. (2004). Poverty and obesity: The role of energy density

and energy costs. American Journal of Clinical Nutrition, 79, 6-16.

Economic Research Service (ERS), U.S. Department of Agriculture (USDA). Food Access

Research Atlas, http://www.ers.usda.gov/data-products/food-access-research-

atlas.aspx.

Farley, T.A., Rice, J., Bodor, N., Cohen, D., Bluthenthal, R.N., and Rose, D. (2009).

Measuring the food environment: Shelf space of fruits, vegetables, and snack foods

in stores. Journal of Urban Health, 86(5): 672-682.

39 | P a g e

Ferdinand, A.O., Sen, B., Rahurkar, S., Engler, S. and Menachemi, N. (2012). The

relationship between built environments and physical activity: A systematic review.

American Journal of Public Health, 102(10): e7-e13.

Franco, M., Diez-Roux, A.V., Nettleton, J.A., Lazo, M., Brancati, F., Caballero, B. et al.

(2009). Availability of healthy foods and dietary patterns: The multi-ethnic study of

arthrosclerosis. The American Journal of Clinical Nutrition, 89. 897-904.

Franco, M., Diez-Roux, A.V., Glass, T.A., Caballero, B., and Brancati, F.L. (2008).

Neighborhood characteristics and availability of healthy foods in Baltimore. American

Journal of Preventive Medicine, 35, 561-567.

Giang, T., Karpyn, A., Laurison, H.B., Hillier, A., and Perry, D. (2008). Closing the grocery

gap in underserved communities: The creation of the Pennsylvania Fresh Food

Financing Initiative. Journal of Public Health Management Practice, 14(3): 272-279.

Glanz, K., Sallis, J.F., Saelens, B.E., and Frank, L.D. (2007). Nutrition environment measures

survey in stores (NEMS-S): Development and evaluation, 32(4): American Journal of

Preventive Medicine, 282-289.

Glanz, K., Clawson, M., Young, M., Carvalho M. (2008a). NEMS store measures rationale.

In Nutrition Environment Measures Training Manual. Retrieved from NEMS Training

Materials website: http://www.med.upenn.edu/nems/materials.shtml

Glanz, K., Clawson, M., Young, M., Carvalho M. (2008b). NEMS store measures survey

instructions. In Nutrition Environment Measures Training Manual. Retrieved from NEMS

Training Materials website: http://www.med.upenn.edu/nems/materials.shtml

Gloria, C.T. and Steinhardt, M.A. (2010). Texas nutrition environment assessment of retail

food stores (TxNEA-S): Development and evaluation. Public Health Nutrition, 13(11):

1764-1772.

Greenland, S. (2001). Ecologic versus individual-level sources of bias in ecologic estimates of

contextual health effects. International Journal of Epidemiology, 30, 1343-1350.

Gustafson, A., Hankins, S., and Jilcott, S. (2012). Measures of the consumer food store

environment: A systematic review of the evidence 2000-2011. Journal of Community

Health, 37, 897-911.

Horowitz, C.R., Colson, K., Hebert, P.L. and Lancaster, K. (2004). Barriers to buying healthy

foods for people with diabetes: Evidence of environmental disparities. American

Journal of Public Health, 94(9): 1549-1554.

P a g e | 40

Hosler, A.S., Rajulu, D.T., Fredrick, B.L., and Ronsani, A.E. (2008). Assessing retail fruit and

vegetable availability in urban and rural underserved communities. Preventing Chronic

Disease: Public Health Research, Practice, and Policy, 5(4): 1-9.

Inagami, S., Cohen, D., Finch, B.K., and Asch, S.M. (2006). You are where you shop:

Grocery store locations, weight, and neighborhoods. American Journal of Preventive

Medicine, 31(1): 10-17.

Jetter, K.M., and Cassady, D.L. (2006). The availability and cost of healthier food

alternatives. American Journal of Preventive Medicine, 30(1): 38-44.

Jilcott, S.B., Keyserling, T., Crawford, T., McGuirt, J.T., and Ammerman, A.S. (2011).

Examining associations among obesity and per capita farmers’ markets, grocery

stores/supermarkets, and supercenters in US counties. Journal of the American Dietetic

Association, 111(4): 567-572.

Kelly, B., Flood, V.M., Yeatman, H. (2011). Measuring local food environments: An

overview of available methods and measures. Health & Place, 17, 1284-1293.

The Kilduff Company. (2005). Northeast Oklahoma City grocery store location analysis.

Retrieved from City of Oklahoma City website:

http://www.okc.gov/planning/supermarket/supermarket_study.pdf

Larson, N., & Story, M. (2009). A review of environmental influences on food choices.

Annals of Behavioral Medicine, 38(Suppl 1):S56-S73.

Larson, N.I., Story, M.T., Nelson, M.C. (2009). Neighborhood environments: Disparities in

access to healthy foods in the U.S. American Journal of Preventive Medicine, 36(1):74-

81.e10

Lee, R.E., Heinrich, K.M., Medina, A.V., Regan, G.R., Resse-Smith, J.Y., Jokura, Y., et al.

(2010). A picture of the healthful food environment in two diverse urban cities.

Environmental Health Insights, 4, 49-60.

Leone, A.F., Rigby, S., Betterley, C., Park, S., Kurtz, H., Johnson, M.A., et al. (2011). Store

type and demographic influence on the availability and price of healthful foods, Leon

County, Florida, 2008. Preventing Chronic Disease, 8, A140.

Lytle, L.A. (2009). Measuring the food environment state of the science. American Journal of

Preventive Medicine, 36(4 Suppl):S134-S144.

Morland, K. and Evenson, K.R. (2009). Obesity prevalence and the local food environment.

Health & Place, 15:491-495.

41 | P a g e

Morland, K., Wing, S., and Roux, A.D. (2002). The contextual effect of the local food

environment on residents’ diets: the atherosclerosis risk in communities study.

American Journal of Public Health, 92(11): 1761-1767.

Morland, K., Wing, S., and Roux, A.D. (2006). Supermarkets, other food stores, and obesity:

the atherosclerosis risk in communities study. American Journal of Preventive Medicine,

30, 333-339.

Myers, S. (2010). Potential food deserts in Oklahoma County: A preliminary study [PowerPoint

slides]. Oklahoma City: Oklahoma City-County Health Department.

Oklahoma State Department of Health (2011). 2011 state of the state report.

The Reinvestment Fund. (2006). The reinvestment brief. Philadelphia, PA: The Reinvestment

Fund. Retrieved from The Reinvestment Fund website:

http://www.trfund.com/resource/downloads/policypubs/supermarkets.pdf

Shier, V., An, R., and Sturm, R. (2012). Is there a robust relationship between environment

and childhood obesity in the USA? Public Health, 126, 723-730.

Tomer, A. (2011). Transit access and zero-vehicle households. Brookings, 1-13.

Truehaft, S. and Karpyn, A. (2010). The grocery gap: Who has access to healthy food and why it

matters. Oakland, CA and Philadelphia, PA: PolicyLink and The Food Trust.

Retrieved from PolicyLink website:

http://www.policylink.org/atf/cf/%7B97C6D565-BB43-406D-A6D5-

ECA3BBF35AF0%7D/FINALGroceryGap.pdf

U.S. Census Bureau (2000). Census 2000, 2000 SF3 Sample Data, Table QT-H11;

generated by Kristin Culver; using American FactFinder;

http://factfinder2.census.gov; (26 February 2015).

U.S. Census Bureau (2013). American Community Survey, 2009 – 2013 American

Community Survey 5- Year Estimates, Table B08201; generated by Kristin

Culver; using American FactFinder; http://factfinder2.census.gov; (26 February

2015).

Wellness Now. (2010). Oklahoma County wellness at a glance 2010. Oklahoma City: Oklahoma

City-County Health Department.

Wellness Now. (2014). Oklahoma County Wellness Score 2014: Community Health Status

Assessment. Oklahoma City: Oklahoma City-County Health Department.

P a g e | 42

Zenk, S.N., Schulz, A.J., Israel, B.A., James, S.A., Bao, S., and Wilson, M.L. (2006). Fruit and

vegetable access differs by community racial composition and socioeconomic

position in Detroit, Michigan. Ethnicity and Disease, 16, 275-280.

Zhang, X., Cook, P.A., Jarman, I., and Lisboa, P. (2011). Area effects on health inequalities:

The impact of neighbouring deprivation on mortality. Health & Place, 17, 1266-1273.

43 | P a g e

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