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The price of unhealthy food relative to healthy food and its association with diet quality,
diabetes, and insulin resistance in a multi-ethnic population
A Thesis
Submitted to the Faculty
of
Drexel University
by
David Michael Kern, MS
in partial fulfillment of the
Requirements for the degree
of
Doctor of Philosophy
December 2016
ii
© Copyright 2016
David M. Kern. All Rights Reserved.
iii
Dedications
This dissertation is dedicated to my cousin Joe “Ozer” Matteo, whose life reminds me to
take nothing for granted.
Carpe diem.
iv
Acknowledgements
Completing this dissertation and degree wouldn’t have been possible without the
love and support of my wife, Tabby. We accomplished a ton these past few years and I’m
looking forward to going back to spending lazy weekends together. Unfortunately, we
can no longer use my dissertation as an excuse to get out of plans.
I also want to thank my advisor, Amy Auchincloss, for her invaluable support and
mentoring from day one. Amy knew when I needed to be pushed to finish things up, and
when to give me time when thing were hectic at work and in my personal life. I’ve never
met anyone who works as hard as Amy; I don’t know where she finds the time to do
everything she does – teaching, mentoring, research, writing grants, reading this
dissertation, etc. – but it’s inspiring. Her input and revisions of my work helped me turn
this dissertation into something I’m incredibly proud of. The other members of my
dissertation committee also helped shape this research – Lucy Robinson for her statistical
insight, Ana Diez Roux for her unrivaled expertise in social and neighborhood
epidemiology, Genny Pham-Kanter for her blend of public health and economic
knowledge, and Mark Stehr for his economic expertise and for spending a great deal of
time explaining various economic concepts which provided this work with a perspective
that complemented the epidemiologic focus and resulted in a well-rounded study.
Lastly, I’d like to thank my family (there are too many of you to list here, you
know who you are) for believing in me and pushing me to always do my best. Love you
all.
v
Funding and legal disclaimers
Data acquisition and compilation was partially supported by U.S. Department of
Health and Human Services. National Institutes of Health (NIH), P60 MD002249-05
(National Institute of Minority Health and Health Disparities) and R01 HL071759
(National Heart, Lung, and Blood Institute [NHLBI]). Funding for the MESA parent
study came from NIH NHLBI contracts: HHSN268201500003I, N01-HC-95159 through
95169, UL1-TR-000040 and UL1-TR-001079.
Mention of trade names, commercial practices, or organizations does not imply
endorsement by the authors, the institutions where the authors work, nor by the funding
entities. The authors take sole responsibility for all data analyses, interpretation, and
views expressed in this paper. Any errors in the manuscript are the sole responsibility of
the authors, not of Information Resources Inc. (IRI, who supplied the pricing dataset).
vi
TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION .................................................................................................................... 1
1.1 INTRODUCTION ................................................................................................................... 1
1.2 DIABETES ............................................................................................................................. 2
1.3 DIET QUALITY ...................................................................................................................... 5
1.4 ACCESS TO HEALTHY FOODS ............................................................................................... 9
1.5 FOOD PRICES ..................................................................................................................... 10
1.6 PRIOR WORK EXAMINING FOOD PRICES AND HEALTH OUTCOMES .................................. 15
1.7 SPECIFIC AIMS AND INNOVATION ..................................................................................... 18
CHAPTER 2: THE VARIATION OF HEALTHY AND UNHEALTHY FOOD PRICES ACROSS NEIGHBORHOODS AND THEIR ASSOCIATION WITH NEIGHBORHOOD SOCIOECONOMIC STATUS AND PROPORTION BLACK/HISPANIC .................................................................................. 20
2.1 INTRODUCTION ................................................................................................................. 22
2.2 METHODS ......................................................................................................................... 23
2.3 RESULTS ............................................................................................................................ 30
2.4 DISCUSSION ...................................................................................................................... 33
2.5 CONCLUSIONS ................................................................................................................... 37
CHAPTER 3: THE PRICE OF HEALTHY FOOD RELATIVE TO UNHEALTHY FOOD AND ITS ASSOCIATION WITH DIET QUALITY: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS ............................... 45
3.1 INTRODUCTION ................................................................................................................. 47
3.2 METHODS ......................................................................................................................... 48
3.3 RESULTS ............................................................................................................................ 55
3.4 DISCUSSION ...................................................................................................................... 58
3.5 CONCLUSIONS ................................................................................................................... 61
CHAPTER 4: THE PRICE OF HEALTHY FOOD RELATIVE TO UNHEALTHY FOOD AND ITS ASSOCIATION WITH TYPE 2 DIABETES AND INSULIN RESISTANCE: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS ...................................................................................................... 68
4.1 INTRODUCTION ................................................................................................................. 70
vii
4.2 METHODS ......................................................................................................................... 71
4.3 RESULTS ............................................................................................................................ 79
4.4 DISCUSSION ...................................................................................................................... 80
4.5 CONCLUSIONS ................................................................................................................... 84
CHAPTER 5: DISCUSSION ........................................................................................................................ 90
5.1 DISCUSSION ...................................................................................................................... 90
5.2 CONTEXT OF CURRENT RESEARCH WITH PREVIOUS WORK .............................................. 93
5.3 LIMITATIONS ..................................................................................................................... 97
5.4 CONCLUSIONS ................................................................................................................. 100
LIST OF REFERENCES ............................................................................................................................. 103
APPENDIX 1: FOR CHAPTER 2 ............................................................................................................... 123
APPENDIX 1.1 CHOICE OF PRODUCTS IN THE IRI DATASET .................................................................... 123
APPENDIX 1.2 DETAILS ON HEALTHY AND UNHEALTHY PRODUCTS INCLUDED IN THE PRICE CALCULATIONS ..... 126
APPENDIX 1.3 PRINCIPAL COMPONENT ANALYSIS ............................................................................... 133
APPENDIX 1.4 DISTRIBUTION OF THE HEALTHY-TO-UNHEALTHY PRICE RATIO ........................................... 135
APPENDIX 1.5 VALIDATION OF THE IRI PRICE DATASET AGAINST C2ER .................................................. 136
APPENDIX 1.6 ADDITIONAL SUPPLEMENTAL TABLES FOR CHAPTER 2 ...................................................... 138
APPENDIX 2: FOR CHAPTER 3 ............................................................................................................... 146
APPENDIX 2.1 SUPPLEMENTAL TABLES FOR CHAPTER 3 ....................................................................... 146
APPENDIX 3: FOR CHAPTER 4 ............................................................................................................... 150
APPENDIX 3.1 SUPPLEMENTAL TABLES FOR CHAPTER 4 ....................................................................... 150
APPENDIX 3.2 HOMA-IR VALIDITY ................................................................................................ 154
VITA ................................................................................................................................. 155
viii
LIST OF TABLES
TABLE 2-1 NEIGHBORHOOD DEMOGRAPHICS AND FOOD PRICES FOR STUDY SAMPLE ................... 39
TABLE 2-2 PRICE OF FOODS BY DEMOGRAPHIC CHARACTERISTICS .................................................. 41
TABLE 2-3 VARIANCE DECOMPOSITION BY GEOGRAPHIC AREA BEFORE AND AFTER ADJUSTING FOR REGION, URBANICITY, AND TOILET PAPER PRICE ............................................................. 42
TABLE 2-4 HIERARCHICAL MODEL ESTIMATES REGRESSING PRICE OUTCOMES ON NEIGHBORHOOD SOCIOECONOMIC STATUS (SES) AND THE PROPORTION OF INDIVIDUALS WHO ARE HISPANIC OR BLACK, NESTED WITHIN COUNTY AND STATE, N = 1953 ............................ 43
TABLE 3-1 CHARACTERISTICS OF THE INDIVIDUALS INCLUDED IN THE ANALYSIS BY TERTILES OF HEALTHY-TO-UNHEALTHY FOOD PRICE RATIO (N=2,642) ................................................ 62
TABLE 3-2 PROPORTION OF PARTICIPANTS WITH A HIGH QUALITY DIET BY TERTILE OF THE HEALTHY-TO-UNHEALTHY PRICE RATIO AND THE AVERAGE SERVING PRICE OF HEALTHY AND UNHEALTHY FOODS (N=2,765) ................................................................................. 64
TABLE 3-3 ODDS RATIOS OF HAVING A HIGH QUALITY DIET ASSOCIATED WITH THE PRICE RATIO, AND WITH THE PRICES OF HEALTHY FOODS AND UNHEALTHY FOODS AFTER SEQUENTIAL ADJUSTMENT FOR CONFOUNDERS WITHIN THE FULL POPULATION (N=2,765) AND AFTER REMOVING NEW YORK/BRONX (N=2,225) ................................... 65
TABLE 3-4 ODDS RATIOS OF HAVING A HIGH QUALITY DIET ASSOCIATED WITH THE PRICE RATIO, AND WITH THE PRICES OF HEALTHY FOODS AND UNHEALTHY FOODS, STRATIFIED BY INCOME-WEALTH AND BY EDUCATION (N=2,765) ........................................................... 66
TABLE 3-5 INSTRUMENTAL VARIABLE ANALYSIS RESULTS USING TOILET PAPER AS THE INSTRUMENT AND TWO-STAGE RESIDUAL INCLUSION MODELS FOR THE HEALTHY-TO-UNHEALTHY PRICE RATIO, HEALTHY FOOD PRICE, AND UNHEALTHY FOOD PRICE (N=2,765) A ........................................................................................................................ 67
TABLE 4-1 MESA PARTICIPANT CHARACTERISTICS BY HEALTHY-TO-UNHEALTHY PRICE RATIO AT THE TIME OF EXAM 5 (N=3,408) .............................................................................................. 85
TABLE 4-2 PROPORTION OF PARTICIPANTS WITH TYPE 2 DIABETES, INCIDENT TYPE 2 DIABETES, AND INSULIN RESISTANCE LEVELS BY TERTILE OF THE HEALTHY-TO-UNHEALTHY PRICE RATIO AND THE AVERAGE SERVING PRICE OF HEALTHY AND UNHEALTHY FOODS ......... 87
TABLE 4-3 MULTIVARIABLE MODELS FOR EACH OF THE THREE OUTCOMES OF INTEREST: PREVALENCE OF TYPE 2 DIABETES AT EXAM 5, INCIDENCE OF TYPE 2 DIABETES FROM EXAM 4 TO EXAM 5, AND LEVEL OF INSULIN RESISTANCE AT EXAM 5 A .......................... 88
SUPPLEMENTAL TABLE 1 ROTATED COMPONENT MATRIX FROM THE PRINCIPAL COMPONENT ANALYSIS OF HEALTHY FOODS ILLUSTRATING THE COMPONENT LOADING SCORES OF EACH ITEM FOR DAIRY (MILK, YOGURT AND COTTAGE CHEESE) AND FRUITS AND VEGETABLES (ORANGE JUICE AND FROZEN VEGETABLES) ............................................. 134
ix
SUPPLEMENTAL TABLE 2 ROTATED COMPONENT MATRIX FROM THE PRINCIPAL COMPONENT ANALYSIS OF UNHEALTHY FOODS ILLUSTRATING THE COMPONENT LOADING SCORES OF EACH ITEM FOR SODA, SWEETS (CHOCOLATE AND COOKIES) AND SALTY SNACKS ....... 134
SUPPLEMENTAL TABLE 3 SPEARMAN RANK CORRELATIONS FOR PRODUCTS FOUND IN THE IRI DATASET AND C2ER ........................................................................................................................ 136
SUPPLEMENTAL TABLE 4 PRODUCT SELECTION CRITERIA FOR EACH OF THE FOOD CATEGORIES USED IN THIS STUDY ..................................................................................................................... 138
SUPPLEMENTAL TABLE 5 NUTRITION INFORMATION FOR EACH OF THE HEALTHY AND UNHEALTHY FOOD CATEGORIES INCLUDED IN THIS STUDY ................................................................ 139
SUPPLEMENTAL TABLE 6 SERVING SIZES AND WEIGHTS FOR COMPOSITE PRICE CALCULATIONS USING TWO DIFFERENT WEIGHT CALCULATIONS – BASED ON NATIONAL CONSUMPTION AVERAGES AND USING EQUAL WEIGHTS WITHIN EACH FOOD CLASS ........................... 140
SUPPLEMENTAL TABLE 7 FOOD PRICES ACCORDING TO WEIGHTS BASED ON NATIONAL CONSUMPTION AVERAGES AND EQUAL WEIGHTS ................................................................................... 141
SUPPLEMENTAL TABLE 8 PRICE OF EACH PRODUCT CATEGORY BY DEMOGRAPHIC CHARACTERISTICS142
SUPPLEMENTAL TABLE 9 SENSITIVITY ANALYSIS OF THE HIERARCHICAL MODELS EXAMINING PRICE OUTCOMES ON NEIGHBORHOOD SOCIOECONOMIC STATUS (SES) AND THE PROPORTION OF INDIVIDUALS WHO ARE HISPANIC OR BLACK USING A 2-MILE BUFFER 144
SUPPLEMENTAL TABLE 10 CHARACTERISTICS OF INCLUDED AND EXCLUDED INDIVIDUALS IN THE ANALYSIS OF DIET QUALITY ............................................................................................ 146
SUPPLEMENTAL TABLE 11 RESULTS OF SENSITIVITY ANALYSES USING EQUAL WEIGHTS FOR THE PRICE OUTCOMES, USING A 5-MILE RADIUS TO CAPTURE PRICES FOR ALL INDIVIDUALS, AND USING A 1-MILE RADIUS FOR THOSE LIVING IN NEW YORK CITY AND A 3-MILE RADIUS FOR ALL OTHERS ............................................................................................................. 148
SUPPLEMENTAL TABLE 12 PATIENT CHARACTERISTICS OF THOSE WHO WERE INCLUDED IN THE STUDY (N=3,408) AND THOSE WHO WERE EXCLUDED (N=1,308) ............................................. 150
SUPPLEMENTAL TABLE 13 ANALYSIS OF INTERACTIONS BETWEEN SES AND RACE WITH THE PRICE RATIO FOR EACH OF THE THREE OUTCOMES OF INTEREST A ......................................... 152
x
LIST OF FIGURES
FIGURE 1 DISTRIBUTION OF THE HEALTH-TO-UNHEALTHY PRICE RATIO FOR THE 1953 STORES IN THE IRI DATABASE. .......................................................................................................... 135
xi
ABSTRACT
The price of unhealthy food relative to healthy food and its association with diet quality, diabetes, and insulin resistance in a multi-ethnic population
David Michael Kern
OBJECTIVE: This dissertation first evaluates food price variation within and between
neighborhoods in order to improve our understanding of access to healthy foods and
potential economic incentives and barriers to consuming a higher quality diet. The study
then spatially links individuals to their nearby supermarkets to study the association
between food price and dietary quality, insulin resistance (IR), and diabetes.
METHODS: Prices of healthy foods (dairy, fruits, and vegetables) and unhealthy foods
(soda, sweets, and salty snacks) were obtained from 1953 supermarkets across the US
during 2009-2012 from the Information Resources Inc. (IRI) database. In Aim 1, prices
of healthy and unhealthy foods, and the relative price of healthy foods compared with
unhealthy foods (healthy-to-unhealthy price ratio) were linked to census block group
socio-demographics in order to analyze associations between food prices with
neighborhood SES and proportion Black/Hispanic. Linear hierarchical regression models
were used to explore geospatial variation and adjust for confounders. The second and
third aims of this study linked average price of healthy foods, unhealthy foods and their
ratio to participants in The Multi-Ethnic Study of Atherosclerosis (MESA). For Aim 2,
individuals who completed MESA exam 5 (2010-2012) and the food frequency
questionnaire were included (N=2765). A Healthy Eating Index (HEI-2010) was
calculated for each individual according to their FFQ. Logistic regression estimated
xii
adjusted odds ratios of a high quality diet (top quantile of HEI-2010) associated with each
price exposure. Sensitivity analyses used an instrumental variable approach in which the
price of brand name toilet paper served as an instrument for food prices. For Aim 3,
individuals from MESA who completed exam 5 and exam 4 (administered five years
prior, diabetes incidence analysis only) were included. Type 2 diabetes status was
confirmed at each exam and IR was measured according to the homeostasis model
assessment index of IR. Adjusted logistic, modified Poisson and linear regression models
were used to model diabetes prevalence, incidence and IR, respectively.
RESULTS: Overall, the price of healthy foods was nearly twice as high as the price of
unhealthy foods ($0.590 vs. $0.298 per serving; healthy-to-unhealthy price ratio of 1.99).
This trend was consistent across all neighborhood characteristics. After adjusting for
covariates, no association was found between food prices (healthy, unhealthy, or the
healthy-to-unhealthy ratio) and neighborhood SES, while small positive and negative
associations were detected with the proportion Black/Hispanic, respectively.
A larger healthy-to-unhealthy price ratio was associated with lower odds of having a high
quality diet (Odds Ratio [OR]=0.76 per SD increase in the ratio, 95% CI=[0.64 to 0.91]).
Instrumental variable analyses largely confirmed these findings although confidence
intervals were wider and the result was no longer statistically significant (OR=0.82 [0.57
to 1.19]). A higher ratio of healthy-to-unhealthy food had a positive association with IR
(4.8% increase in IR for each standard deviation increase in price ratio (estimate=0.048,
95% CI=[0.00 to 0.10]) after adjusting for confounders. No association with diabetes
xiii
incidence (relative risk=1.11, 95% CI=[0.85 to 1.44]) or prevalence (OR=0.95, 95%
CI=[0.81 to 1.11]) was observed.
CONCLUSIONS: The price of healthy food was twice as expensive as unhealthy food
per serving on average. A higher price of healthy food relative to unhealthy foods appears
to be negatively associated with a high quality diet and with insulin resistance. There did
not appear to be an association with diabetes prevalence or 5-year incidence. This study
provides new insight into the relationship between food prices with diet quality, IR and
diabetes. Policies to address the large price differences between healthy and unhealthy
foods may help improve diet quality and downstream health effects in the U.S.
1
CHAPTER 1: INTRODUCTION
1.1 INTRODUCTION
Affordability may play an important role in determining whether individuals truly
have access to healthy foods. The price of food influences the purchasing decisions of
consumers (Andreyeva et al., 2010), and there is mounting evidence that healthy food is
significantly more expensive than unhealthy alternatives (Rao et al., 2013). This
discrepancy in price may be influencing consumers to purchase a larger quantity of
unhealthy foods than is recommended by dietary guidelines, especially those of lower
socioeconomic status whom are more price sensitive.
Diet plays an important role in one’s health, as individuals with a healthy diet are
less likely to be obese (Guo et al., 2004) or develop diabetes (Chiuve et al., 2012), along
with an array of other chronic conditions. Thus, the availability of healthy foods may be
critical in preventing downstream, debilitating health complications. Currently, 29
million individuals in the United States are living with diabetes – a condition that comes
with a major risk of severe complications and death (Kochanek et al., 2016). Reducing
the incidence of this disease can have enormous effects on our healthcare system,
including a reduction in mortality, morbidity of other chronic conditions, and economic
burden.
Studies have found that county and metropolitan level food prices are associated
with BMI and obesity; however, prior work has not examined neighborhood level prices
and their association with diet quality and diabetes. Because there is large variability in
neighborhood characteristics within a single metropolitan area, it is important to account
for this when examining the association between food price and health. This dissertation
2
will address the gaps of prior work by examining food prices local to each individual in
the study, include a variety of healthy and unhealthy foods, and evaluate the association
of prices with various endpoints along a clinical pathway (diet quality, insulin resistance,
and diabetes).
1.2 DIABETES
1.2.1 Epidemiology and trends of diabetes
The CDC estimates that nearly 2 million Americans, typically middle-age and
older adults, are newly diagnosed with diabetes every year and more than 29 million
individuals in the United States are currently living with diabetes as of 2012, accounting
for 9.3% of the population(Centers for Disease Control and Prevention, 2014). Of those
with diabetes more than a quarter (8.1 million) are undiagnosed, and therefore untreated
by a physician. The prevalence of diabetes is growing at an alarming rate with a 2.6%
increase in the prevalence of diagnosed diabetes since 2000(Centers for Disease Control
and Prevention, 2016).
The prevalence of diagnosed diabetes occurs more often and earlier for African-
Americans (13.2% prevalence, median age of diagnosis 49 years) and Hispanics (12.8%,
49 years), compared with Whites (7.6%, 55 years) (Centers for Disease Control and
Prevention, 2014, Centers for Disease Control and Prevention, 2013); and the incidence
of diabetes is increasing at a faster rate within Black and Hispanic individuals than it is
for Whites(Geiss et al., 2014), leading to even further divergences in the disease burden
between races. There is also a clear association between education level and diabetes
prevalence. Those with less than a high school education have much higher rates of
3
diagnosed diabetes (12.9%) compared to those with a high school diploma (9.5%) and
those with some college or more (6.7%) (Centers for Disease Control and Prevention,
2015), and the rate of the increase in prevalence is higher in those with a high school
degree or less compared with those who have more than a high school education (Geiss et
al., 2014).
1.2.2 Downstream effects of diabetes
Reducing the incidence of diabetes is important because of the complications that
result from the disease. The risk of cardiovascular disease, including stroke, is the most
prominent complication of diabetes and accounts for the largest proportion of morbidity,
mortality, and costs due to diabetes (Go and et al, 2014, American Diabetes Association,
2013), and nearly 65% of people with diabetes in the United States die from
cardiovascular disease (Donahoe et al., 2007). Other serious complications of diabetes
include neuropathy (Boulton et al., 2005), nephropathy (Mogensen et al., 1983), foot
complications especially due to peripheral arterial disease which can result in amputation
(Jude et al., 2001), eye complications (diabetic retinopathy)(Wilkinson et al., 2003) and
dyslipidemia(American Diabetes Association, 2013, Centers for Disease Control and
Prevention, 2014).
Due to such complications, diabetes was the seventh leading cause of death in the
United States in 2010 with a death rate of 20.8 per 100,000 persons (Murphy et al.,
2013). Overall, due to both direct and indirect costs resulting from morbidity and
mortality, diabetes was estimated to result in $245 billion in costs in 2012, and patients
4
with diabetes incurring more than double the average costs of patients without the disease
(Centers for Disease Control and Prevention, 2014).
Reducing the rate of diabetes through prevention of disease would have a
significant impact on morbidity, mortality, and economic healthcare burden in our
country.
1.2.3 Diabetes risk factors
Risk factors of diabetes include race/ethnicity (African American, Latino, Native
American, Asian American, Pacific Islander), familial history of diabetes, hypertension,
prediabetes, low HDL and/or high triglycerides levels, low socioeconomic status and a
history of cardiovascular disease (American Diabetes Association, 2013, Robbins et al.,
2001); however, the largest risk factor of diabetes is being overweight or obese. Large
cohort studies have found that those who are obese are at a much greater risk of
developing diabetes(Abdullah et al., 2010, Hu et al., 2001), and that an individual’s
waist/hip ratio is an important risk factor of diabetes (Vazquez et al., 2007, Janiszewski et
al., 2007). Because the disease is mainly driven by obesity, type 2 diabetes is thought to
be largely preventable through lifestyle modifications, especially diet and exercise
(Diabetes Prevention Program Research Group, 2002, Tuomilehto et al., 2001), and life
style interventions that emphasize the importance of a healthy diet and exercise have
shown to significantly reduce the risk of developing diabetes (Diabetes Prevention
Program Research Group, 2002, Tuomilehto et al., 2001). The importance of a healthy
diet on reducing the risk of diabetes is more than just preventing weight gain, as part of
5
the association between the healthfulness of a diet and diabetes is independent of weight
(de Koning et al., 2011a, Hu et al., 2001).
1.2.4 Insulin resistance
A precursor to diabetes is a condition known as insulin resistance. For an
individual with insulin resistance, muscle, fat, and liver cells fail to respond properly to
the presence of insulin resulting in the inability to properly absorb blood glucose
(Reaven, 1995). To compensate for the inability of the cells to efficiently absorb glucose,
the beta cells in the pancreas produce more insulin; however, over time the body is
unable to produce enough insulin to aid absorption of the glucose resulting in increased
blood sugar levels (Reaven, 1995). The increasing levels of glucose in the blood stream
eventually results in pre-diabetes and diabetes. Preventing insulin resistance, as well as
identifying and treating the condition after it occurs, can help prevent the onset of type 2
diabetes. Many of the same risk factors of diabetes are also risk factors of insulin
resistance, with the major cause thought to be excess intra-abdominal fat due to lipids
accumulating within insulin responsive tissues (Samuel and Shulman, 2012, Kahn and
Flier, 2000). As with diabetes, diet plays an important role in the development of insulin
resistance and so understanding factors associated with poor diet quality is an important
step in preventing the condition.
1.3 DIET QUALITY
1.3.1 Characteristics of the American diet
The quality of the average diet in the US is well below recommended dietary
guidelines due to low fruit and vegetable consumption and high intake of added sugars,
6
saturated fats, and sodium (United States Department of Agriculture and United States
Department of Health and Human Services, 2015). Eating habits in the United States
have changed significantly over the last three decades, with an increased frequency of
snacking on desserts, salty snacks, and sweetened beverages, among other highly
processed foods with low nutrient value (Piernas and Popkin, 2009). Since 2000 in the
US, total fat, especially those from salad and cooking oils, have risen dramatically, and
there has been a significant downward trend in the number of fruits and vegetables
consumed (United States Census Bureau, 2012). Additionally, the consumption of milk, a
healthier alternative to soda and other sweetened beverages, has been on the decline for a
number of years (Stewart et al., 2013, Krebs, 2013).
1.3.2 Measure of overall diet quality
A number of dietary guidelines exist, each with their own definition of what
constitutes a healthy diet. Such examples include the Mediterranean Diet Scale
(Trichopoulou et al., 2003, Trichopoulou et al., 1995), the Healthy Food Index (Osler et
al., 2002), the Dietary Approaches to Stop Hypertension (DASH) (Appel et al., 1997), the
Healthy Eating Index (HEI) (Kennedy et al., 1995, Guenther et al., 2013) and the
alternative HEI (Chiuve et al., 2012), as well as empirically derived dietary measures
(Nettleton et al., 2006, Nettleton et al., 2008b, Fung et al., 2001).
The HEI was built to reflect the USDA’s recommendations for a healthy diet and
is in turn used by the USDA to monitor and assess diet quality in the US (Kennedy et al.,
1995), with the most recent version based on the 2010 USDA dietary guidelines (United
States Department of Agriculture and United States Department of Health and Human
7
Services, 2010, Guenther et al., 2013). The 2010 USDA dietary guidelines were designed
with specific regard to the growing obesity epidemic (United States Department of
Agriculture and United States Department of Health and Human Services, 2010), a
condition that puts individuals at high risk of diabetes and cardiovascular disease, among
many other chronic medical conditions (Grundy, 2002, Kopelman, 2000, Nelson et al.,
1988, Sowers, 1998, Sturm, 2002, Wilson et al., 2002). Thus, adherence to a diet that
would have a HEI score is expected to be associated with a decreased risk of diabetes and
the wide array of resulting chronic diseases (Guo et al., 2004).
Because the HEI is focused on the type of diet that is associated with a wide range
of chronic diseases rather than any specific disease area, and because it is endorsed by the
USDA as measuring the prototypical healthy diet, it was used in this research to measure
overall diet quality.
1.3.3 Diet quality and health
The importance of a healthy diet, and what constitutes a healthy diet, has been
studied extensively, and diet quality has been linked to a number of health outcomes. A
healthy diet, as measured by the Healthy Eating Index (HEI), has been found to be
inversely associated with obesity (Guo et al., 2004), waist circumference (Tande et al.,
2010), diabetes (Chiuve et al., 2012), cardiovascular disease (Chiuve et al., 2012,
McCullough et al., 2000), stroke (Chiuve et al., 2012), cancer (Arem et al., 2013, Li et
al., 2014) and mortality (Rathod et al., 2012, Kappeler et al., 2013, Harmon et al., 2015,
Reedy et al., 2014). Unfortunately, the average consumer purchases foods that result in a
diet that falls well short of the recommendations in the USDA dietary guidelines (Volpe
8
and Okrent, 2012, United States Department of Agriculture, 2015). This is due in part to
purchasing too few fruits and vegetables and too many fats, sugars, and sweets. With a
large proportion of the American population failing to achieve a healthy diet, it is
important to understand the causes of the worsening diet quality in this country and
develop interventions to reverse this trend in order to reduce the prevalence of obesity,
diabetes, and all other health complications resulting from poor diets.
1.3.4 Diet quality and SES
Diet quality is associated race and socioeconomic status (Beydoun and Wang,
2008). African Americans – independent of income – have been shown to have an overall
poorer diet compared with all other races, as measured by the Healthy Eating Index, as
are those with a lower family income – independent of race – compared with individuals
with a higher income (Forshee and Storey, 2006). The association between SES and diet
quality has appeared to have gotten stronger over time with a widening gap in diet quality
between those of high and low SES (Wang et al., 2014). In addition to the overall diet,
lower individual and neighborhood SES (Ball et al., 2006, De Irala-Estevez et al., 2000,
Dubowitz et al., 2008) and race (Dubowitz et al., 2008) have been found to be associated
with decreased consumption of fruits and vegetables. Unsurprisingly, these risk factors of
poor diet are consistent with the risks of diabetes; i.e., those that are of lower
socioeconomic status and racial minorities are more likely to have poor diets and
diabetes.
9
1.4 ACCESS TO HEALTHY FOODS
1.4.1 Physical access to healthy foods
Throughout the United States there are individuals who do not have access to
foods that constitute a healthy diet. A large portion of food access research has focused
on food deserts and the lack of physical access to fresh fruits and vegetables and
supermarkets (Cummins and Macintyre, 2002, Walker et al., 2010). The importance of
increasing physical access to healthy foods in food deserts, such as through the
construction of supermarkets, stems on the hypothesis that improving physical access
allows more individuals the ability to purchase nutritious foods which were previously
unavailable. However, the impact of such interventions has been mixed. While early
work showed a correlation between supermarkets and obesity and diet quality (Morland
et al., 2006, Powell et al., 2007b, Rose and Richards, 2004, Black and Macinko, 2008), a
handful of more recent studies, including longitudinal and interventional designs, have
found no or minimal effect of supermarket access on diet quality or risk of obesity
(Boone-Heinonen et al., 2011, Cummins et al., 2014, Elbel et al., 2015, Ford and
Dzewaltowski, 2010). Thus, when thinking about access to healthy food, it is important
to not only consider physical proximity, but also consider other factors such as the
affordability of those foods.
1.4.2 Economic access and affordability of healthy foods
Economic access – or affordability – may play an important role in determining
whether healthy foods are truly accessible to individuals in a neighborhood. If healthy
food is physically available in the neighborhood, but many are unable to afford it, then
10
access is still poor; this situation is referred to as a food mirage (Breyer and Voss-
Andreae, 2013). The inability for individuals to afford sufficient food has resulted in
food insecurity for more than 14% of households in the US, and those most likely to be
food insecure are African Americans and those of low SES (Coleman-Jensen et al.). For
individuals whose main concern regarding access to food is to avoid hunger, and for
whom nutritional concerns are secondary, price is a major consideration. If unhealthy
food is more affordable than healthy food – even if slightly – it would be likely to follow
that food purchases would tend to be more unhealthy and the resulting diet would be
poorer (Bernstein et al., 2010, Lopez et al., 2009, Rehm et al., 2011, Ryden and Hagfors,
2011, Schroder et al., 2006), and the risk for chronic health problems would increase
(Guo et al., 2004, Chiuve et al., 2012, Li et al., 2014).
Differential availability across sociodemographic factors may also contribute to
disparities in intake, especially for individuals of lower SES who are more sensitive to
food prices (Powell et al., 2009b) due to a higher proportion of income spent on food
(Frazao et al., 2007). Black and Hispanic populations often have lower SES, thus price
differentials could be contributing to their increased risk of obesity and diabetes (Ogden
et al., 2014).
1.5 FOOD PRICES
1.5.1 Diet costs
The poor diet quality of many individuals in our country may be due in part to
purchasing too few fresh fruits and vegetables and instead purchasing more affordable
processed fats, sugars, and sweets. Low cost diets are associated with higher calorie
11
intake at the expense of fewer nutrients(Andrieu et al., 2006), while healthier diets tend to
be more expensive (Rehm et al., 2011, Ryden and Hagfors, 2011, Schroder et al., 2006,
Rehm et al., 2015). A meta-analysis found that, on average, the healthiest diets cost $1.48
more per day compared with the least healthy (Rao et al., 2013). Of the 8 measures of
overall dietary quality reported, across 6 different studies(Bernstein et al., 2010, Lopez et
al., 2009, Rehm et al., 2011, Ryden et al., 2008, Ryden and Hagfors, 2011, Schroder et
al., 2006) all but one(Ryden et al., 2008) found that the healthiest diets were significantly
more expensive than the least healthy.
Furthermore, differences in nutrient intake by individual SES can be explained
primarily by the cost of a diet (Monsivais et al., 2012, Konttinen et al., 2013), and a
systematic literature review found that individuals of lower SES tended to select food that
was of lower nutritional value and diets that were of lower-quality due to the lower price
of these foods (Darmon and Drewnowski, 2015). In fact, the cost of a healthy diet is
beyond the allotted food budget for many individuals in poverty whom already devote a
significantly greater proportion of their income to food compared with those of higher
SES (Darmon and Drewnowski, 2015, Drewnowski and Specter, 2004), and has been
estimated that up to 70% of a low-income family’s food budget would need to be devoted
to fruits and vegetables to meet recommended dietary guidelines (Cassady et al., 2007).
The higher price of healthy diets compared with more affordable unhealthy diets
is due to differences in the price of individual healthy and unhealthy foods which
comprise those diets. Food prices have been measured in a variety of ways, including the
price per calorie (Drewnowski, 2009, Andrieu et al., 2006, Drewnowski and Darmon,
2005), the price per unit of edible food weight (e.g., price per gram or ounce) (Todd et
12
al., June 2011, Lipsky, 2009), or the price per serving (Lipsky, 2009, Reed et al., 2004,
Stewart et al., February 2011, Kern et al., 2016). Depending on how price of food is
operationalized, healthier foods have been found to be more expensive than unhealthy
foods (Drewnowski, 2010, Drewnowski and Darmon, 2005, Lipsky, 2009, Andrieu et al.,
2006, Monsivais et al., 2010, Kern et al., 2016). The price of a calorie is substantially
cheaper when obtained from unhealthful, energy-dense foods, such as soda, instead of
from more healthful, less-dense foods. For example, while sugar and oil cost less than $1
per 10 MJ (2,388 kCal), milk costs roughly five times as much, ground beef nearly 10
times as much, and vegetables are $30-$100 for the same amount of energy (Drewnowski
and Darmon, 2005). However, when considering the price per serving, the cost of healthy
and healthy foods are closer, though healthy foods, specifically fruits and vegetables,
remain more expensive then unhealthy foods (Lipsky, 2009, Kern et al., 2016).
1.5.2 Price measurement
This study used the price per serving as the primary unit of analysis rather than
the price per calorie or per unit of weight. Using price per calorie has been criticized
because consumers don’t have information regarding the price per calorie immediately
available to them (Lipsky, 2009). A person would have to calculate the total number of
calories in a package and divide by the product price, and it isn’t likely that this
measurement would be responsible for the majority of purchasing decisions. And while
price per gram or ounce is useful when the foods being compared have similar forms and
serving sizes, the comparison becomes more difficult to interpret when the types of foods
(e.g., soft drinks versus produce) and/or the serving sizes differ substantially (Carlson and
Frazão, May 2012). Thus, we chose the unit of analysis that could be compared across all
13
product types, and which may be most meaningful to consumers: price per serving, which
has been used previously in similar research (Stewart et al., February 2011, Lipsky, 2009,
Reed et al., 2004).
1.5.3 Price elasticity of foods
To measure the effect of pricing differences on buying patterns, the price
elasticity of demand (or simply “price elasticity”) of foods has been studied. Price
elasticity refers to the proportional change in demand (i.e., purchasing) when the price of
a product is changed 1%. Inelasticity is defined as a product having price elasticity <1.0
(i.e., a 1% change in price leads to a <1% change in purchases), while elasticity is when
the price elasticity is >1.0 (i.e., a 1% change in price leads to a >1% change in
purchases).
In a thorough review of the literature, Andreyeva et al summarize the price
elasticity for a variety of food types (Andreyeva et al., 2010). They found the price of soft
drinks was modestly elastic at 0.79, implying that a 1% increase in the price of soda
would result in a 0.79% decrease in purchases. The result was similar for juice (0.76) and
fruit (0.70), while more inelastic for milk (0.59) and vegetables (0.58). This is consistent
with economic and marketing research which has shown that the price of food – in
addition to taste, nutrition, convenience, and other factors – affects the purchasing
choices of individuals who must navigate environments with numerous choices and
inundated with advertising(Connors et al., 2001, Glanz et al., 1998, Andreyeva et al.,
2010, Aggarwal et al., 2016). Another study examined the effect of soda taxes on a state
wide basis and found that when the price of soft drinks increased one percentage point
14
consumption of soda decreases, and that there is an increase in whole milk consumption
(Fletcher et al., 2010b). This effect is indicative of cross product elasticity in which the
price of one product influences the demand of a potential substitute product. Because of
this cross-product elasticity, as the price of unhealthy foods relative to healthy foods
becomes larger we’d expect to see lower consumption of unhealthy foods and an increase
in consumption of healthy foods, leading to better overall diet quality.
And while it appears food costs are not strongly elastic, it has been found that
increased prices would have the largest effect in decreasing consumption in populations
that are most at risk for being overweight and/or obese (Powell and Chaloupka, 2009,
Powell et al., 2009b). Because a greater proportion of income for lower income
individuals is spent on food (Frazao et al., 2007), they are more price elastic and would
be more influenced by price differences between exchangeable products (Powell and
Chaloupka, 2009). Thus, those at highest risk of diabetes due to a poor diet and high
weight appear to be those whose diet would be most influenced by changes in food
prices.
The potential effect of food prices on consumption is important because healthier
food appears to be more expensive than unhealthy food, and the consumption of healthy
food can be influenced by both its own price and the price of unhealthy food. If there are
price differences across neighborhoods, where unhealthier food is cheaper than healthy
food compared with other areas, then the residents of that neighborhood may be put at an
increased risk of obesity, diabetes, cardiovascular disease, and many other chronic
conditions.
15
1.6 PRIOR WORK EXAMINING FOOD PRICES AND HEALTH OUTCOMES
A previous paper examining the variation in healthy and unhealthy food prices
was limited to soda and milk only (Kern et al., 2016). The current study expands upon
previous research of healthy and unhealthy beverages to include proxies for fresh fruits
and vegetables as well as foods high in sugar, fat, sodium and preservatives. Like the
previous study, the current study integrated the prices of various healthy and unhealthy
foods in supermarkets across the US and measured their association with neighborhood
SES and racial makeup. The current study goes beyond the scope of the prior research by
examining the association of food prices with diet quality, diabetes, and insulin
resistance.
Research on the effect of changes in food price on health has been growing in
response to the bevy of proposed taxes on soda and other sugar sweetened beverages.
These studies have largely been ecologic in scope (looking at regional tax levels of
unhealthy foods and the rates of obesity in those same areas) or have used economic
modeling assuming specific elasticity measurements to estimate the effect of price
changes on consumption and resulting BMI. In large part, these studies have argued that
price has a modest effect on health outcomes. This is likely due to small taxes that have
been implemented or proposed (hypothesized that larger tax differences would show a
more pronounced effect (Fletcher et al., 2010a, Brownell et al., 2009)) and that a single
product – even when it is a highly consumed product -- is unlikely to have a very large
effect (Finkelstein et al., 2014). Research has concluded soda taxes may have a very
modest effect in decreasing BMI (Powell et al., 2009a, Smith et al., 2010), but could have
16
a significant impact on reducing diabetes incidence and resulting medical costs in the
country as a whole (Wang et al., 2012).
Observational research examining actual food prices and individual level
outcomes, rather than experimental or simulation studies, have typically used food prices
at the county or metropolitan level. In these studies, the price of fruits and vegetables has
primarily been found to be weakly positively associated with BMI, while other food
prices (e.g., fast food, food at home, food in restaurants) have been found to be weakly
associated with weight outcomes (Beydoun et al., 2008, Chou et al., 2004, Han and
Powell, 2011, Sturm and Datar, 2008, Powell et al., 2007a, Powell and Chaloupka, 2009,
Powell et al., 2013). Evidence of the association between food price and levels of insulin
resistance has been found to be weak (Rummo et al., 2015), may exist only within those
in the middle income range and lower education (Meyer et al., 2014), or exists only for
specific unhealthy foods (Duffey et al., 2010). Studies examining NHANES data provide
evidence that higher prices of healthy and low glycemic foods are associated with higher
blood sugar levels in those with diabetes (Anekwe and Rahkovsky, 2014) and without
(Rashad, 2007). While one prior study examined the hypothetical effect taxes may have
on diabetes prevalence by simulating specific scenarios and assumptions (Wang et al.,
2012), to our knowledge no previous research has linked area level food prices to
diabetes.
Previous work has examined general associations between food prices and health
outcomes, but there is a lack of research examining local healthy and unhealthy food
prices with outcomes, including type 2 diabetes or levels of insulin resistance. For
example, in the study by Duffey et al (Duffey et al., 2010) the prices were metropolitan-
17
level food prices from the Council for Community and Economic Research and were not
broken down by specific neighborhood. Thus, while they found an association between
the regional price of pizza and soda with insulin resistance, it isn’t known what effect
pricing had within individual neighborhoods of a metro region. There may be large
variation in what the prices were for individuals within the same metropolitan area, and
the effect of those prices on insulin resistance (and in turn an increased risk of diabetes)
within a given neighborhood may be much different than what was observed at the
metropolitan area level. Furthermore, there may be underlying differences between
metropolitan areas other than food prices that caused the observed differences in health,
or that may have masked true effects greater than what was seen.
A pair of studies by Drewnowski et al (Drewnowski et al., 2012, Drewnowski et
al., 2014) aimed to better understand the relationship between local supermarket food
prices and their impact on obesity in Seattle and Paris, with the latter expanding the scope
to consider the role of SES in the association (Drewnowski et al., 2014). Individuals
shopping at low price supermarkets had 2.5 times the risk of obesity compared with those
who shopped at high price supermarkets after adjusting for age, gender, SES, and other
covariates. The authors’ conclusion neatly sums up the motivation for the current
research: “low income communities may be vulnerable to obesity not because the nearest
supermarket is several kilometers away but because lower-cost foods tend to be energy
dense but nutrient poor. In other words, access to food needs to be measured also in
economic terms” (Drewnowski et al., 2014). While the Drewnowski studies are an
improvement over many previous studies in terms of spatial resolution of the food
18
environment, it lacks detail about the specific foods individuals were purchasing, and
instead focuses on the price of stores as a whole.
By building off of the work of our predecessors to provide new knowledge
regarding the effect of food prices on diet, insulin resistance and diabetes, this study can
provide key information to policy makers about the impact that the price of healthy and
unhealthy foods has on dietary behavior and downstream health outcomes. Even a
modest reduction of the incidence of diabetes would have a pronounced public health
effect due to the current high rate of new cases in the United States each year.
1.7 SPECIFIC AIMS AND INNOVATION
This study utilizes a large, multi-ethnic cohort of older adults for which details
regarding their diet, insulin resistance level, and diabetes status is available. Using their
geographic home location, the study was able to link individuals to their nearby
supermarkets in order to determine their local food prices. This allowed the investigation
of three specific aims:
Aim 1: Evaluate the variation of healthy and unhealthy food prices across
neighborhoods and their association with neighborhood socioeconomic
status and the proportion Black/Hispanic
Aim 2: Evaluate the association between the price of healthy food relative to
unhealthy food with diet quality, and evaluate whether individual
socioeconomic status moderates this relationship.
Aim 3: Evaluate the association between the price of healthy food relative to
unhealthy food with insulin resistance, diabetes prevalence, and diabetes
19
incidence, and evaluate whether individual socioeconomic status modifies
these relationships.
There remains a gap in the current body of research to synthesize the three
following key pieces: (1) differentiating between healthy and unhealthy foods and
evaluating their price relative to each other; (2) assessing food prices at the neighborhood
level, rather than metropolitan level, to understand the association between food price and
characteristics of the immediate surrounding area; (3) linking local food prices to diet and
health outcomes of individuals, specifically diabetes and insulin resistance. This
dissertation aimed to fill these gaps by incorporating each of these three pieces into a
single study to analyze the association between the price of healthy and unhealthy foods
with diet, insulin resistance, and diabetes in an older population. This work will expand
our knowledge of the effect that food prices have on individuals’ diet and risk of diabetes
and may inform future policies regarding the taxation and subsidization of food costs.
20
CHAPTER 2: THE VARIATION OF HEALTHY AND UNHEALTHY FOOD PRICES ACROSS NEIGHBORHOODS AND THEIR ASSOCIATION WITH
NEIGHBORHOOD SOCIOECONOMIC STATUS AND PROPORTION BLACK/HISPANIC
ABSTRACT
OBJECTIVE: To evaluate food price variation within and between neighborhoods in
order to improve our understanding of access to healthy foods and potential economic
incentives and barriers to consuming a higher quality diet.
METHODS: Prices of healthy foods (dairy, fruits, and vegetables) and unhealthy foods
(soda, sweets, and salty snacks) were obtained from 1953 supermarkets across the US
during 2009-2012, and were linked to census block group socio-demographics. Analyses
evaluated associations between neighborhood SES and proportion Black/Hispanic and
the prices of healthy and unhealthy foods, and the relative price of healthy foods
compared with unhealthy foods (healthy-to-unhealthy price ratio). Linear hierarchical
regression models were used to explore geospatial variation and adjust for confounders.
RESULTS: Overall, the price of healthy foods was nearly twice as high as the price of
unhealthy foods ($0.590 vs. $0.298 per serving; healthy-to-unhealthy price ratio of 1.99).
This trend was consistent across all neighborhood characteristics. After adjusting for
covariates, no association was found between food prices (healthy, unhealthy, or the
healthy-to-unhealthy ratio) and neighborhood SES. Similarly, there was no association
between the proportion Black/Hispanic and healthy food price, a very small positive
association with unhealthy price, and a modest negative association with the healthy-to-
unhealthy ratio.
21
CONCLUSIONS: No major differences were seen in food prices across levels of
neighborhood SES and proportion Black/Hispanic; however, the price of healthy food
was twice as expensive as unhealthy food per serving on average.
22
2.1 INTRODUCTION
The quality of the average diet in the US is well below recommended dietary
guidelines due to low fruit and vegetable consumption and high intake of added sugars,
saturated fats, and sodium (United States Department of Agriculture and United States
Department of Health and Human Services, 2015). Dietary quality is even lower among
African Americans, Hispanics, and those with low socio-economic status (SES)
(Beydoun and Wang, 2008, Forshee and Storey, 2006).
Research has primarily studied physical access to healthier versus unhealthy foods
by examining availability of supermarkets in a neighborhood. Areas without
supermarkets are often classified as food deserts (Walker et al., 2010). However,
economic access – or affordability -- may also play an important role in determining
whether healthy foods are truly available to individuals in a neighborhood. If healthy
food is physically available in the neighborhood, but many are unable to afford it, then
access is still poor; this situation is referred to as a food mirage (Breyer and Voss-
Andreae, 2013). In addition, if unhealthy food is cheaper than healthy food, this may
contribute to disparities in dietary quality since individuals of lower SES are more
sensitive to prices and to food price in particular (Powell et al., 2009b) due to higher
proportion of income spent on food (Frazao et al., 2007). Black and Hispanic populations
often have lower SES, thus price differentials could be contributing to their increased risk
of obesity and diabetes (Ogden et al., 2014).
The difference in price between unhealthy and healthy foods has been studied
previously (Andrieu et al., 2006, Drewnowski, 2010, Drewnowski and Darmon, 2005,
23
Lipsky, 2009, Monsivais et al., 2010), finding that unhealthy foods -- such as snacks
(Lipsky, 2009) or high energy dense macronutrients (sugars, fats and oils) (Drewnowski,
2010) – were much less expensive compared with healthy foods such as fruits and
vegetables. However, very little is known about small area price variations in healthy
and unhealthy foods throughout the U.S. and whether prices vary according to
neighborhood socio-demographics. Prior U.S. work examining the relationship between
food prices and area-level demographics have used within-city food store audits and
report mixed results (Gustafson et al.). Holding constant the type of food store (and
focusing on supermarkets), food in lower vs. higher socio-economic status areas was
commonly found to be cheaper (Andreyeva et al., 2008, Jetter and Cassady, 2006,
Latham and Moffat, 2007).
The current study utilized a novel, large dataset of prices of healthy and unhealthy
foods in supermarkets across the U.S. and assessed variation by neighborhood and price
associations with neighborhood SES and proportion Black or Hispanic. Understanding
how prices vary within and between neighborhoods will improve our understanding of
access to healthy foods and potential economic incentives and barriers to consuming a
higher quality diet.
2.2 METHODS
2.2.1 Price data
Product pricing data was obtained from Information Resources Inc. (IRI), a
market research group focused on consumer packaged goods sold in large chain
supermarkets and superstores across the U.S. (Albertson’s, A&P, Food Emporium,
24
Pathmark, Super Fresh, Kmart, BJ’s, Sam’s Club, Walmart, etc.) (IRI, 2014, IRI, 2015,
Bronnenberg et al., 2008, Symphony IRI, 2015). In total, data used in this study span
January 2009 through December 2012 and came from 1953 stores located in 21 states
(including Washington DC), 193 counties, and 1849 census block groups. Data elements
were product category, item information (Universal Product Code [UPC] and package
size), number of units sold, price of the item, store identifier and store address. IRI’s
pricing database included 299 categories of packaged items – foods like cream/creamers,
mustard/ketchup, spaghetti sauce, and non-foods such as household cleaners, diapers,
tobacco. Fresh fruits and vegetables were not available. For more information regarding
the type of data obtained from IRI see Appendix 1.1.
Data were purchased for 9 available product categories to serve as proxies of
unhealthier and healthier foods. For unhealthier foods, packaged, highly processed, long-
shelf life products were selected: soda, chocolate candy, cookies, and salty snacks. For
healthier foods, refrigerated products were selected in order to roughly approximate costs
of fresh fruit and vegetable spoilage and storage/distribution: refrigerated milk, yogurt,
cottage cheese, frozen vegetables, and fresh orange juice (APPENDIX 1, Appendix 1.1).
For further details on the justification of the products included in the healthy and
unhealthy domains see Appendix 1.2. In order to purchase these data, it was necessary to
request data via UPC code. In total, 171 UPC codes were selected that had the highest
sales across multiple regions and stores. For this reason, all UPCs were top-selling
branded items (n=61 brands, e.g.: Coca Cola, Hershey, Keebler, Lay’s, Tropicana,
Breakstone, Farmland, Dannon, Bird's Eye, etc.). A non-food product category, toilet
25
paper, was also selected and used in statistical models in order to proxy the cost of doing
business in a particular supermarket location (see more information below).
2.2.2 Price variables
Prices reflect the shelf price and included store-level promotions and retailer
coupons, but did not include changes in price applied at the cash register, including taxes
and manufacturer coupons. Volume equivalent prices were calculated as the price of an
item divided by the number of ounces the item contained and multiplied by the typical
serving size of each item (according to the FDA (United States Food and Drug
Administration, 2014)) to create a price per serving size.
Prices per serving were averaged over the 4 year period (2009-2012) for each
product category (e.g., milk). Temporal aggregation was done in order to maximize the
presence of the same UPCs across regions/stores, increase sample size, and stabilize
prices. There was little variability in inflation-adjusted prices between years during this
time period (Kern et al., 2016)). The average price of all items in a product category was
weighted according to volume sold (reported in IRI dataset) to create price per serving for
each product category. Factor analyses used UPC-level price to identify product classes
for healthy and unhealthy domains (See Appendix 1.3 for factor analyses details). This
resulted in two healthy food classes: 1. milk, yogurt, and cottage cheese (AKA “dairy”)
and 2. fresh orange juice and frozen vegetables (AKA “fruits and vegetables”); and three
unhealthy food classes: 1. soda, 2. cookies and chocolate candy (AKA “sweets”), and 3.
chips, onion rings, and pretzels (AKA "salty snacks"). Product prices were then averaged
within the healthy and unhealthy domains and weighted according to national
26
consumption estimates. In general, results were similar when healthy and unhealthy
domains were weighted equally across food classes, APPENDIX 1, Supplemental Table
7.
The average price of brand name toilet paper was calculated for each store in
order to control for baseline costs specific to each store which may influence the price of
food (e.g., rent, distribution, and employee wages) and may not be captured through other
variables. Toilet paper is a good proxy for the basic cost of doing business for the
following reasons: it inhabits significant shelf space, is non-perishable, is unlikely to
suffer from location specific supply shocks, and is unlikely to experience large shifts in
demand (which could also lead price to reflect factors other than store level cost)
(Turcsik, 2014).
2.2.3 Outcome variables
Outcome variables were prices per serving of each of the following product
classes: dairy, fruits and vegetables, soda, sweets, and salty snacks; as well as overall
healthy and unhealthy price and relative price of healthy foods compared with unhealthy
foods (AKA healthy-to-unhealthy price ratio). The relative price was operationalized as
the ratio of the average price per serving of healthy food divided by the average price per
serving of unhealthy food. A ratio >1.0 indicates that the average price of a single serving
of healthy food is more expensive than the price of a serving of unhealthy food (i.e., 1.92
means the price of a serving of healthy food is 92% or 1.92 times more expensive than
the price per serving of unhealthy food on average). A ratio <1.0 indicates that a serving
of unhealthy food is more expensive than healthy food.
27
2.2.4 Census variables
Each store was assigned to the population-weighted centroid of their block group.
Block groups within 1-mile of each store were selected and census data were averaged
for those block groups in order to characterize the census composition around the stores.
All block groups intersecting the 1-mile buffer were included as part of the store’s
neighborhood, and census data were weighted according to the population of each
included block group. The 1-mile buffer was chosen to expand the supermarket’s
neighborhood beyond the block group in which it was located, which may be an
industrial area with a low population and not representative of the surrounding
neighborhood. A 1-mile buffer has been referred to as a relevant consumer market area
for a supermarket (California Center for Public Health Advocacy et al., 2008). A
sensitivity analysis used a 2-mile buffer and results were similar (APPENDIX 1,
Supplemental Table 9).
Census data were obtained from the 2007-2011 American Community Survey
(ACS) 5-year summary file. Neighborhood SES index was created using six variables
from the ACS representing wealth and income, housing value, education, and managerial
or professional occupations, and was operationalized as a single continuous measure as
described by Diez-Roux et al (Diez Roux et al., 2001) (see Table 2-2 footnote).
Neighborhood proportion of Black or non-white Hispanic (AKA “Black/Hispanic”) and
neighborhood SES were converted into percentiles and units represent 20-percentile
increments. To control for potential differences in age distribution and population across
block groups the proportion of individuals aged 20 to 39 years (standardized as a z-
28
score), and the population density (individuals per square kilometer) were included as
covariates in multivariable models.
2.2.5 Geographic variables
Geographic region (Northeast, Midwest, South, and West) and urbanicity of each
store location were included as covariates in the regression analysis. Both were included
to control for differences in infrastructure and other aspects of the built environment
unique to regions or to cities versus rural areas which could affect the shelf price of
foods. Urbanicity was based on county population size, and was operationalized as a
categorical variable with three levels: large metropolitan area of 1+ million residents,
small metropolitan area of less than 1 million residents, and micropolitan (centered on an
urban area with population 10,000 to 49,999) and non-core areas (all other areas smaller
than micropolitan) (United States Department of Agriculture and Economic Research
Service, 2013).
2.2.6 Supermarket density
Supermarket density data were obtained from the National Center for Chronic
Disease Prevention and Health Promotion (NCCDPHP) of the CDC. The NCCDPHP
compiled the number of chain and non-chain supermarkets within each census tract or
within 0.5 miles of the tract boundary (Grimm et al., 2013). Supermarket density was
included as a covariate to control for potential differences in market competition that may
affect product prices (Lamichhane et al., 2013).
29
2.2.7 Statistical analysis
Bivariate associations between socio-demographics (neighborhood SES,
Black/Hispanic, region, and urban class) and food/beverage prices were analyzed using
unadjusted normal linear models. SES and Black/Hispanic were treated as continuous
variables (after verifying approximately linear relationships with price) and region and
urbanicity were treated as categorical variables.
Hierarchical models nesting stores within counties and states were used to
account for the spatial dependence of prices between stores proximal to each other. The
first models aimed to describe the effect of geography on food prices. The model
controlled for geographic region and urbanicity as geographic fixed effects along with
store-level toilet paper price to control for baseline costs. Random intercepts for state and
county allowed mean price to vary across space, and accounted for the correlation
between prices within a given geographic area. The variance of price that was explained
at the state and county level was calculated, with the remaining error considered variation
at the store level.
Lastly, multivariable hierarchical models adjusting for additional census variables
and supermarket density were analyzed. Black/Hispanic and SES effects were first
modeled separately with no covariates. The models then controlled for age, region,
urbanicity, population density, supermarket density, toilet paper price, and
Black/Hispanic (in the SES model) and SES (in the Black/Hispanic model). The effects
of 20 percentile increases in Black/Hispanic and SES were reported along with 95%
confidence intervals and p-values.
30
All statistical analysis was performed in SAS v9.3 (Cary, NC). Spatial methods
linking stores to their neighborhoods were performed using ArcGIS v10.0.
2.3 RESULTS
2.3.1 Descriptive results
Most of the 1,953 stores were located in large metropolitan areas (82.2%, 0) and
in the south (51.7%). The price of healthy foods was nearly twice as high as the price of
unhealthy foods ($0.590 vs. $0.298 per serving [PS]) a trend that was consistent across
all measured neighborhood characteristics and resulted in a healthy-to-unhealthy price
ratio of 1.99 (Table 2-2).
For the healthy food domain, there was a slight decrease in the price of healthy
foods, averaged over product classes, as neighborhood SES decreased ($0.611 vs $0.581
PS in highest to lowest SES quintile). However, within the healthy food domain, the
product category gradient differed: fruits and vegetables decreased in price as
neighborhood SES decreased ($0.526 vs. $0.460 PS) while dairy increased in price
($0.747 vs. $0.788 PS) (APPENDIX 1, Supplemental Table 8). For the unhealthy food
domain, prices were nearly identical between low and high SES quintiles ($0.299 vs.
$0.306 PS) and there were no noteworthy variations within unhealthy food domain
product categories. Thus, prices of healthy food drove variation in the healthy-to-
unhealthy price ratio by neighborhood SES. The ratio was slightly weaker in lower SES
areas: 2.01 vs. 1.95 PS in the highest vs. lowest quintiles, respectively. The association
between neighborhood Black/Hispanic and food prices followed a similar yet inverted
pattern as the SES: areas with higher Black/Hispanic had higher dairy prices, lower fruit
31
and vegetable prices, and little difference in the price of unhealthy foods. Similarly, the
healthy-to-unhealthy price ratio mirrored what was found for SES (2.00 vs 1.97 PS for
neighborhoods with the lowest vs highest proportion Black/Hispanic). Neighborhood
SES and Black/Hispanic were highly correlated with a Spearman rank correlation
coefficient of -0.69 (p<0.001).
2.3.2 Model results
To understand the contribution of area-level characteristics to price differences, a
variance decomposition analysis was performed, first including only the random effects
of state and county (“empty model”), and then and adjusted for fixed effects of region,
urbanicity, and price of toilet paper at the store. In the empty model state-county level
attributes accounted for approximately 70% of unexplained variability in unhealthy prices
and 50% of unexplained variability in healthy prices (Table 2-3). After fixed effect
adjustment for region and urbanicity, unexplained price variation at the state-county level
was slightly lower for unhealthy prices (dropped from 67% to 61%) and a lot lower for
healthy price (dropped from 48% to 26%). The fixed effects primarily accounted for
unexplained variability at the state-level (rather than the county-level). These results
conform to prior work that found fairly low spatial variability in unhealthy prices but
moderate variability in healthy prices (Kern et al., 2016). Variability in the healthy-to-
unhealthy price ratio was similar to the unhealthy food price variability after adjusting for
the fixed effects. Further adjustment for area-level SES and Black/Hispanic had minimal
effect on the decomposition results (data not shown).
32
Table 2-4 shows the estimated associations of neighborhood SES and proportion
Black/Hispanic with each of the price outcomes, including composite healthy and
unhealthy food prices and their ratio. In general, model results were similar to the SES
gradient observed in descriptive results.1 After adjustment for geographic variables,
neighborhood SES was not associated with the composite index for healthy food prices
(due to opposing SES gradients for fruits/vegetables and dairy), the composite index for
unhealthy food price (no association with soda price, a small positive association with the
price of sweets, and a small negative association with salty snacks), or the healthy-to-
unhealthy price ratio.
After adjustment, neighborhood Black/Hispanic was positively associated with
composite index for unhealthy food price (positive associations with price of sweets and
salty snacks, but no association with soda). Black/Hispanic was not associated with
healthy food prices (no associations for fruits/vegetables or dairy). This resulted in a
small negative association with the healthy-to-unhealthy price ratio, indicating that the
price differential was slightly attenuated with increases in Black/Hispanic (for every 20
percentile increase in proportion of Black/Hispanic, the PS price of healthy food relative
to unhealthy food was 1.3% lower [95% CI: -0.7% to -1.9%]). Results from sensitivity
analysis using a 2-mile buffer were consistent and similar in magnitude to primary results
using a 1-mile buffer (APPENDIX 1, Supplemental Table 9).
1 While higher levels of SES were associated with slightly higher prices of fruits and vegetables (approximately a $0.01 increase in price per serving for a 20 percentile increase of SES) higher SES was also associated with lower dairy prices ($0.018 decrease in serving price per 20 percentile SES increase) resulting in an association with a slightly higher composite healthy food price, though this association disappeared after adjusting for covariates.
33
2.4 DISCUSSION
Across all regions and neighborhoods, the average price per serving of healthy
food was nearly twice as high as unhealthy food. There were no strong associations
between neighborhood SES and neighborhood Black/Hispanic and composite indices for
price of healthy foods or unhealthy foods. The only notable associations were within
product categories for the healthy food domain. For example, the price of dairy appeared
to be higher in neighborhoods of lower SES and with higher proportion of
Black/Hispanic, whereas the price of fruits and vegetables was lower in those same
neighborhoods. There was little variation in price of unhealthy food across neighborhood
demographics regardless of the product examined. The healthy-to-unhealthy price ratio
was slightly lower (unhealthy food more expensive than healthy food) as the proportion
of Black/Hispanic increased (even after adjusting for neighborhood SES and other
covariates); there was no evidence of an adjusted association between and the price ratio
with neighborhood SES.
Large absolute differences in price between healthy and unhealthy foods is
consistent with previous work that found prices of soft drinks much lower than fluid milk
(Kern et al., 2016), and sugars, fats, and oils much lower than fruits, vegetables, meat and
poultry in a second study (Drewnowski, 2010), and a third study that reported higher diet
costs were associated with a higher quality diet as measured by the Healthy Eating Index-
2005 (Rehm et al., 2011). These differences are likely due to the increased costs of
refrigeration, farming, and transportation for perishables versus lower such costs for long
shelf-life packaged/processed foods. The combination of lower price and increased
availability of packaged/processed foods may be having profound effects on food
34
purchases and result in less-than-optimal diet quality across a broad spectrum of the
population (Kearney, 2010). The large price divide between healthier and unhealthy
foods is concerning; dietary guidelines emphasize the consumption of fruits, vegetables,
and low-fat dairy while limiting intake of sugar, saturated fats, and sodium (United States
Department of Agriculture and United States Department of Health and Human Services,
2015). If healthy foods cost twice the amount per serving as unhealthy foods, meeting
these dietary guidelines will be difficult for many people, especially those of lower SES.
Additionally, recent work has reported higher prevalence of soda/fast-food advertising in
neighborhoods with higher proportion Black/Hispanic and lower income (Powell et al.,
2014). The confluence of high availability, exposure to advertising, and lower price may
contribute to high consumption of unhealthy foods in these populations (Chandon and
Wansink, 2012, Drewnowski, 2009, Appelhans et al., 2012).
Economists note that the demand for a food product is influenced by its price and
the price of potential substitutes (Andreyeva et al., 2010). For every 10% increase in the
price of fruits and vegetables the quantity demanded of sugars and sweets rises of
upwards to 1%, and the quantity demanded of snacks and fats and oils increases up to
0.6% (Okrent and Alston, August 2012). Because of these cross product elasticities, it is
possible that the unhealthy and healthy food price differentials that we observed may be
responsible for a potential 10% increase in the consumption of unhealthy foods high in
sugar and fat. Given the large discrepancy in price, it would take a radically large, and
unlikely, tax on junk food (or similar intervention) to make the two prices equal.
We used a UPC level dataset and were only able to purchase a small number of
items to represent healthy and unhealthy domains. This is similar to prior research that
35
has heavily relied on index food and beverage products. For example, researchers have
used index items from the Council for Community and Economic Research (C2ER;
formerly the American Chamber of Commerce Researchers Association), a price dataset
for 34 food/beverages sold in U.S. metropolitan areas (pizza, steak, ground beef, bacon,
etc.) (Duffey et al., 2010, Powell and Bao, 2009, The Council for Community and
Economic Research (C2ER), 2015), and average national prices for more than 3,000
foods from the Center for Nutrition Policy and Promotion have been used to study the
relationship between food prices and nutritional value (Drewnowski, 2010, USDA/CNPP,
2008). The primary problem with databases used in prior work is they lack geographic
specificity thus cannot be used to link prices to neighborhood socio-demographics.
Furthermore, our study lacked individual level data on food prices and
sociodemographics, thus the findings of this study are limited to neighborhood level
associations rather than associations between individuals’ race, SES, and food prices at
nearby stores.
Results suggest that healthier and unhealthy food prices vary only slightly by
neighborhood sociodemographics and in the case of neighborhood SES, the direction was
inverse our hypothesis. These findings offer some good news: while healthier foods were
twice the price of unhealthier foods, we found no evidence that within chain supermarket
venues, healthier foods were more expensive and unhealthy foods cheaper in lower SES
and Black/Hispanic areas. However, prior studies have noted that areas with lower SES
and higher proportion minority are more likely to have smaller grocery stores and fewer
large supermarkets and food costs are generally higher in smaller stores (Gustafson et al.,
2012, Kaufman et al., 1997, Chung and Myers, 1999). Thus, price differentials may exist
36
between lower and higher SES communities due to area-level differences in types of food
stores. Nevertheless, it is a strength of this study that we focused on supermarkets as they
are the major venue for all U.S. retail food sales (63% of all retail food sales from 2009 to
2012 (United States Department of Agriculture, 2014a)) and a very rich supermarket
pricing dataset was available. In sum, major advantages of the dataset used in the current
study are: 1. Food/beverage prices were geographically disaggregated to store
locations/neighborhoods; and 2. The dataset offered good generalizability to large chain
supermarkets in urbanized areas across multiple areas of the U.S. for multiple years
(rather than prices for a single city as some others have done (Drewnowski et al., 2014,
Andreyeva et al., 2008)).
This study relied on price per serving as the primary unit of analysis. Other
measures of price that have been used in previous research include the price per calorie
(Drewnowski, 2009, Andrieu et al., 2006) and price of edible food weight (e.g., price per
gram or ounce) (Todd et al., June 2011). Using price per calorie has been criticized
(Burns et al., 2010, Lipsky, 2009) due to a statistical artifact the measurement creates.
And while price per gram or ounce is useful when the foods being compared have
comparable forms and serving sizes, the comparison becomes more difficult to interpret
when the types of foods (e.g., soft drinks versus produce) and/or the serving sizes differ
substantially (Carlson and Frazão, May 2012). Thus, we chose the unit of analysis that
could be compared across all product types, and which may be most meaningful to
consumers: price per serving, which has been used previously in similar research
(Stewart et al., February 2011, Lipsky, 2009, Reed et al., 2004).
37
Ideally we would have included fresh fruits and vegetables but they were
unavailable in the source dataset. It is known that price of orange juice is highly
correlated with the price of fresh oranges (Morris, 2011), and the price of frozen
vegetables is largely similar to those that are fresh, though the level of correlation varies
according to the type of vegetable (Stewart et al., February 2011). Only branded products
were included (see Methods for the rationale). Using brands ensured comparability of
products with and across regions. Brands dominate market share for most of the
unhealthy products (e.g., soda (Beverage World), salty snacks (Grocery Headquarters),
chocolate candy (Grocery Headquarters)) and some (orange juice (Beverage Industry
Magazine))) but not all (dairy (Grocery Headquarters) and frozen vegetables (Store
Brands)) of the healthier products. Additionally, we chose perishable foods to be
representative of healthy foods in order to reflect the price of fresh produce and dairy
whose costs are largely due to transport and perishability. However, not all healthy foods
are necessarily perishable and thus the cost of perishable foods may not be representative
of all healthy foods, although processed fruits and vegetables, such as those that are
canned, are not consistently more or less expensive than fresh produce (Stewart et al.,
February 2011). Ideally we would have also included prices for other non-perishable
healthy foods such as legumes and whole grains, however these data were not available.
Because of this, it is unclear how the results may have differed had the entire universe of
healthy and non-healthy foods been included.
2.5 CONCLUSIONS
This study is one of the first to examine the relationship between healthy and
unhealthy foods and their relationship within and between neighborhoods in regions
38
across the U.S. While no major differences were seen in supermarket food prices across
levels of neighborhood SES and Black/Hispanic, overall the price of healthy food was
twice as expensive as unhealthy food. Such large differences in the affordability of
healthy food compared with unhealthy substitutes may be resulting in less than optimal
diet across all population groups and in particular individuals of lower SES who are more
sensitive to price differences (Powell et al., 2009b). Further research is needed to
determine the extent to which price influences overall diet quality and potential
downstream health effects such as obesity, diabetes, and cardiovascular disease.
39
TABLES AND FIGURES - 2
Table 2-1 Neighborhood demographics and food prices for study sample
Mean/N SD/%
Number of stores 1,953
Neighborhood demographics
Proportion Black/Hispanic (mean, SD) 0.37 0.25
Urban classification
Large metro (pop. ≥1 million) 1,606 82.2
Small metro (pop. <1 million) 296 15.2
Rural (pop <50k) 51 2.6
Region
Northeast 207 10.6
Midwest 173 8.9
South 1,009 51.7
West 564 28.9
Age, proportions (mean, SD)
18 years or younger 0.24 0.05
18-34 years 0.24 0.08
35-64 years 0.40 0.05
65 or older 0.12 0.05
Supermarket density (mean, SD)a 3.34 2.25
Population density (mean, SD) 1,930 3,091
Food prices per serving (mean, SD)
Healthy foods
Frozen vegetables $0.472 $0.073
Orange juice $0.506 $0.037
Fruits and vegetables (OJ and frozen veggies) $0.489 $0.051
Dairy $0.760 $0.100
Healthy food composite $0.590 $0.056
Unhealthy foods
Soda $0.217 $0.029
Chocolate $0.535 $0.050
40
Mean/N SD/%
Cookies $0.214 $0.035
Sweets (Chocolate and cookies) $0.375 $0.037
Salty snacks $0.290 $0.014
Unhealthy food composite $0.298 $0.024
Healthy vs. unhealthy food price
Healthy-to-unhealthy ratio (weight 1) 1.990 0.190 a number of supermarkets within the supermarket census tract or within ½ mile of the tract
41
Table 2-2 Price of foods by demographic characteristics
Number of stores
Healthy food price per serving a
Unhealthy food price per
serving a
Healthy-to- Unhealthy price
per serving a Mean SD Mean SD Mean SD Overall 1953 $0.590 $0.056 $0.298 $0.024 1.990 0.190
Neighborhood SES quintile b
Lowest quintile (least advantaged) 391 $0.581 $0.047 $0.299 $0.019 1.950 0.175
Second quintile 391 $0.581 $0.048 $0.295 $0.020 1.974 0.177
Middle quintile 390 $0.589 $0.053 $0.295 $0.017 2.003 0.196
Fourth quintile 391 $0.590 $0.057 $0.294 $0.016 2.014 0.204
Highest quintile (most advantaged) 390 $0.611 $0.068 $0.306 $0.039 2.010 0.190
Proportion Black/Hispanic quintile
Lowest quint. (0.8% to 14.0% bl/Hisp) 390 $0.592 $0.058 $0.297 $0.033 $2.002 $0.182
Second quint. (14.0% to 24.0%) 392 $0.591 $0.058 $0.298 $0.026 $1.990 $0.187
Middle quint. (24.0% to 37.8%) 390 $0.593 $0.060 $0.297 $0.022 $2.004 $0.203
Fourth quint. (37.9% to 58.4%) 391 $0.587 $0.054 $0.297 $0.019 $1.981 $0.194
Highest quint. (58.5% to 98.8%) 390 $0.588 $0.050 $0.299 $0.018 $1.974 $0.184
Urban classification
Large metro (pop. ≥1 million) 1606 $0.596 $0.058 $0.298 $0.026 $2.007 $0.192
Small metro (pop. <1 million) 296 $0.568 $0.042 $0.298 $0.015 $1.912 $0.163
Rural (pop <50k) 51 $0.553 $0.023 $0.291 $0.015 $1.904 $0.139
Region
Northeast 207 $0.594 $0.074 $0.314 $0.052 $1.912 $0.179
Midwest 173 $0.599 $0.026 $0.279 $0.014 $2.155 $0.106
South 1009 $0.555 $0.024 $0.295 $0.018 $1.888 $0.138
West 564 $0.650 $0.043 $0.302 $0.013 $2.152 $0.143 a Composite healthy and unhealthy food prices were weighted based on national consumption averages of each food category b Neighborhood SES was derived from log of the median household income; log of the median value of housing units; the percentage of households receiving interest, dividend, or net rental income; the percentage of adults 25 years of age or older who had completed high school; the percentage of adults 25 years of age or older who had completed college; and the percentage of employed persons 16 years of age or older in executive, managerial, or professional specialty occupations.
42
Table 2-3 Variance decomposition by geographic area before and after adjusting for region, urbanicity, and toilet paper price
Healthy-to-Unhealthy price ratio
Healthy food price
Unhealthy food price
Variance (SE) Variance (SE) Variance (SE) Empty model
Between states 0.0146 (0.0054) 0.0006 (0.0002) 0.0002 (0.0001) Between counties (in states) 0.0047 (0.0008) 0.0003 (0.0001) 0.0002 (0.0000) Within counties 0.0118 (0.0004) 0.0010 (0.0000) 0.0002 (0.0000) ICC (state level) 47.0% 31.5% 38.1% ICC (county level) 15.1% 16.8% 28.7% Residual 37.9% 51.6% 33.2%
Control for region, urbanicity, and toilet paper price
Between states 0.01 (0.0042) 0.0001 (0.0001) 0.0002 (0.0001) Between counties (in states) 0.0048 (0.0008) 0.0002 (0.0000) 0.0001 (0.0000) Within counties 0.0118 (0.0004) 0.0008 (0.0000) 0.0001 (0.0000) ICC (state level) 37.6% 9.7% 43.8% ICC (county level) 18.1% 15.9% 17.2% Residual 44.3% 74.4% 39.0%
CI, confidence interval; ICC, Intraclass correlation, the proportion of variation in price that is accounted for by differences at the state and county levels; SE, standard error
43
Table 2-4 Hierarchical model estimates regressing price outcomes on neighborhood socioeconomic status (SES) and the proportion of individuals who are Hispanic or black, nested within county and state, N = 1953
Estimate a
95% CI Model Lower Upper P-value SES MODELS: Mean difference in price per 20 percent increase in neighborhood socioeconomic index
Healthy food price Unadjusted b 0.00452 0.0034 0.0056 <0.0001 Adjusted c 0.00090 -0.0007 0.0025 0.2836 Fruits and veggies Unadjusted b 0.01386 0.0126 0.0151 <0.0001 Adjusted c 0.01096 0.0091 0.0128 <0.0001 Dairy Unadjusted b -0.01323 -0.0153 -0.0112 <0.0001 Adjusted c -0.01836 -0.0215 -0.0152 <0.0001 Unhealthy food price Unadjusted b 0.00057 0.0001 0.0011 0.0234 Adjusted c 0.00055 -0.0001 0.0012 0.1110 Soda Unadjusted b 0.00051 -0.0001 0.0011 0.0937 Adjusted c 0.00065 -0.0003 0.0016 0.1682 Sweets Unadjusted b 0.00267 0.0018 0.0035 <0.0001 Adjusted c 0.00153 0.0005 0.0026 0.0054 Salty snacks Unadjusted b -0.00216 -0.0025 -0.0018 <0.0001 Adjusted c -0.00125 -0.0018 -0.0007 <0.0001 Healthy-to-Unhealthy price ratio
Unadjusted b 0.01146 0.0076 0.0153 <0.0001 Adjusted c 0.00037 -0.0059 0.0066 0.9088
BLACK/HISPANIC MODELS: Mean difference in price per 20 percent increase in neighborhood proportion of Black/Hispanic
Healthy price Unadjusted b -0.00319 -0.0043 -0.0020 <0.0001 Adjusted c 0.00007 -0.0016 0.0017 0.9377 Fruits and veggies Unadjusted b -0.01069 -0.0120 -0.0094 <0.0001 Adjusted c -0.00089 -0.0028 0.0010 0.3484 Dairy Unadjusted b 0.01105 0.0089 0.0132 <0.0001 Adjusted c 0.00223 -0.0009 0.0054 0.1687 Unhealthy price Unadjusted b 0.00058 0.0001 0.0011 0.0224 Adjusted c 0.00212 0.0014 0.0028 <0.0001 Soda Unadjusted b 0.00022 -0.0004 0.0008 0.4764 Adjusted c 0.00077 -0.0002 0.0017 0.1063 Sweets Unadjusted b -0.00053 -0.0014 0.0003 0.2244 Adjusted c 0.00360 0.0025 0.0047 <0.0001 Salty snacks Unadjusted b 0.00220 0.0018 0.0026 <0.0001 Adjusted c 0.00122 0.0007 0.0018 <0.0001 Healthy-to-Unhealthy price ratio
Unadjusted b -0.01486 -0.0187 -0.0110 <0.0001 Adjusted c -0.01274 -0.0190 -0.0065 <0.0001
a per 20 percentile change in neighborhood SES or Black/Hispanic b Models did not include covariates but were adjusted for county and state via model nesting
44
c Includes covariates: Age, region, urbanicity, population density, supermarket density, toilet paper price, and either race (in the SES models) or neighborhood SES (in the race models).
45
CHAPTER 3: THE PRICE OF HEALTHY FOOD RELATIVE TO UNHEALTHY FOOD AND ITS ASSOCIATION WITH DIET QUALITY: THE MULTI-
ETHNIC STUDY OF ATHEROSCLEROSIS
ABSTRACT
BACKGROUND: The price of food influences the purchasing decisions of individuals.
This study examined if the price of healthy food relative to unhealthy food is associated
with dietary quality.
METHODS: Cross-sectional data came from The Multi-Ethnic Study of Atherosclerosis
(exam 5, 2010-2012 N=2765) and supermarket food/beverage prices (Information
Resources Inc. n=794 supermarkets). For each individual, average price of healthy foods,
unhealthy foods and their ratio was computed for supermarkets within 3-miles. Logistic
regression estimated odds ratios of a high quality diet (top quantile of Healthy Eating
Index 2010) associated with healthy-to-unhealthy price ratio, adjusted for demographics,
smoking, geographic region, neighborhood socioeconomic index, supermarket and
population density, and cost of living. Sensitivity analyses used an instrumental variable
approach.
RESULTS: Healthy foods cost nearly twice as much as unhealthy foods on average
(mean±SD healthy-to-unhealthy ratio=1.97±0.14). A larger healthy-to-unhealthy price
ratio was associated with lower odds of a high quality diet (OR=0.76 per SD increase in
the ratio, 95% CI=[0.64 to 0.91]). Instrumental variable analyses largely confirmed these
findings although confidence intervals were wider and the result was no longer
statistically significant (OR=0.82 [0.57 to 1.19]).
46
CONCLUSIONS: A higher price of healthy food relative to unhealthy foods appears to
be negatively associated with a high quality diet. Policies to address the large price
differences between healthy and unhealthy foods may help improve diet quality in the
U.S.
47
3.1 INTRODUCTION
An individual’s healthy food environment encompasses more than just physical
access to fresh fruits, vegetables and other nutritious foods. Economic access and the
affordability of healthy foods in the neighborhood is also a key component of one’s food
environment. The price of food – in addition to taste, nutrition, convenience, and other
factors – affects the purchasing choices of individuals who must navigate environments
with numerous choices and saturated by advertising(Connors et al., 2001, Andreyeva et
al., 2010, Aggarwal et al., 2016). Healthy foods have been found to be more expensive
than unhealthy foods, whether measured per calorie (Drewnowski, 2010, Andrieu et al.,
2006) or per serving (Lipsky, 2009, Reed et al., 2004, Stewart et al., February 2011, Kern
et al., 2016). For this reason, low cost diets are associated with higher calorie intake at the
expense of fewer nutrients (Andrieu et al., 2006), while healthier diets tend to be more
expensive (Rehm et al., 2011, Ryden and Hagfors, 2011, Schroder et al., 2006).
A healthy diet, as measured by the Healthy Eating Index (HEI), is inversely
associated with obesity (Guo et al., 2004), waist circumference (Tande et al., 2010),
diabetes (Chiuve et al., 2012), cardiovascular disease (Chiuve et al., 2012, McCullough et
al., 2000), stroke (Chiuve et al., 2012), cancer (Arem et al., 2013, Li et al., 2014) and
mortality (Rathod et al., 2012, Kappeler et al., 2013, Harmon et al., 2015, Reedy et al.,
2014). The average consumer purchases foods that result in a diet that falls well short of
the recommendations in the USDA dietary guidelines (Volpe and Okrent, 2012). This
may be due in part to purchasing too few fresh fruits and vegetables and instead
purchasing more affordable processed fats, sugars, and sweets.
48
Lower SES (Ball et al., 2006, De Irala-Estevez et al., 2000, Dubowitz et al., 2008)
has been associated with decreased consumption of fruits and vegetables, and prior work
has found that these SES-differences can be explained primarily by the cost of a diet
(Monsivais et al., 2012, Konttinen et al., 2013). It is reasonable to expect that large
differences in price between healthy and unhealthy foods would lead to differences in
purchasing patterns and resulting diets, and that those differences would be more
prominent for individuals of lower SES (Powell and Chaloupka, 2009, Powell et al.,
2009b). An experimental study found that food taxation and subsidy policies have a
differential effect by income level (Darmon et al., 2016), while observational research
found that the effect of metropolitan area food prices on diet quality was not modified by
income level (Beydoun et al., 2008). However, there is a lack of research examining how
local neighborhood food prices may affect diet quality differently across levels of SES.
This cross-sectional study spatially linked 2,765 participants from the Multi-
Ethnic Study of Atherosclerosis (MESA) to nearby supermarkets to examine the
association between neighborhood food price and diet quality.
3.2 METHODS
3.2.1 MESA data
MESA is a population-based longitudinal cohort study of ethnically diverse adults
aged 45-84 years (Bild et al., 2002). Individuals were recruited from six sites across the
United States: Bronx/Upper Manhattan, NY; Baltimore City and Baltimore County,
Maryland; Forsyth County, North Carolina; Chicago, Illinois; St. Paul, Minnesota; and
49
Los Angeles County, California. MESA included a baseline examination (2000-2002)
and four follow-up exams; 4716 respondents participated in exam 5 (2010-2012).2
Food pricing was only available concurrent to exam 5, thus this study only
included participants who completed exam 5, administered during April 2010 through
January 2012. MESA person-level data included in this study were: diet (see details
below), age (continuous), sex, race/ethnicity (Caucasian, Chinese-American, African-
American, Hispanic), smoking status (never, former, current), marital status, body mass
index (BMI), physical activity, education level (high school diploma, GED, or less; some
college, technical or associat0es degree; bachelor’s degree or higher), and income/wealth
index (an ordinal measure ranging from 0 to 8 based on the combination of income level
and ownership of four assets: car, home, land, and investments).
3.2.2 Food Frequency Questionnaire (FFQ)
The FFQ used in the MESA was a modified-Block-style, 128-item questionnaire
adapted from the questionnaire designed for the Insulin Resistance and Atherosclerosis
Study (IRAS) (Mayer-Davis et al., 1999) and has been described elsewhere (Nettleton et
al., 2006).3 Responses to the FFQ were used to calculate an HEI score for each
individual.
2 MESA sampling frame of the cohort differed by site, but all had a strategy to recruit individuals to meet a priori defined distributions across strata defined by gender, ethnicity, and age group rather than to represent the demographic distribution of the source communities. Some field centers used stratified random sampling according to age and gender, others used recruitment based on geographic boundaries rather than demographics, while others used random digit dialing across the entire sampling frame. Sampling frames of individuals and households were obtained through a variety of sources across sites, including a Department of Motor Vehicles, employment records, commercial mailing services, and a county assessor’s office. 3 Modifications to the FFQ included additional items to reflect the racial composition of the MESA cohort. For each of the food items on the FFQ, respondents chose their consumption frequency (rare or never, 1 per month, 2-3 per month, 1 per week, 2 per week, 3-4 per week, 5-6 per week, 1 per day, and 2+ per day); their frequency of consumption was then weighted by a multiplier according to their reported typical serving size (× 0.5, × 1.0, and × 1.5 for small, medium, and large, respectively). IRAS was validated against 24-hour dietary recalls (Mayer-Davis et al., 1999) and the MESA
50
3.2.3 Outcome: Healthy Eating Index-2010 (HEI)
HEI-2010 was used to assess dietary quality. The HEI reflects 2010 federal
dietary guidelines, has been used to monitor and assess diet quality in the U.S.(Kennedy
et al., 1995, United States Department of Agriculture and United States Department of
Health and Human Services, 2010, Guenther et al., 2013), and has (1) adequate content
validity (Guenther et al., 2013) (2) sufficient construct validity, and (3) acceptable
reliability (Guenther et al., 2014). The HEI-2010 includes twelve components (whole
fruit, total vegetables, sodium, etc.), each of which contribute a minimum of 0 to a
maximum of 5, 10, or 20 points, resulting in a range of 0 to 100 for the total score and
higher scores indicate a healthier diet(Guenther et al., 2013).
Preliminary analyses revealed a roughly normal distribution of HEI scores. We
ranked the index into quintiles and operationalized “high quality diet” as the top quintile
(>80th percentile) of all scores in the sample. The top quintile has been used to define a
healthy diet in prior work (Arem et al., 2013, Harmon et al., 2015, Reedy et al., 2014)
and ranking the dietary values acknowledges the low precision inherent in dietary self-
reports.
3.2.4 Price data
Data on food prices were obtained from Information Resources Inc. (IRI), a
market research group that monitors prices of 299 consumer packaged goods sold in large
chain supermarkets and superstores across the US (IRI, 2014, IRI, 2015, Bronnenberg et
diet data correlated as expected with HDL-cholesterol and TAG concentrations (Nettleton et al., 2009b) and cardiometabolic conditions (Nettleton et al., 2008a, Gao et al., 2008, Abiemo et al., 2013, Nettleton et al., 2008b, Levitan et al., 2016).
51
al., 2008, Symphony IRI, 2015). From the 109 supermarket chains in our study area, 8
parent companies (representing 27 supermarket chains) did not agree to release their
stores’ data. Ultimately data were included from 794 stores (82 chains) located in 11
states (including Washington DC), 72 counties, and 757 census block groups. Data years
were 2009-12.
Nine food/beverage product categories were selected to serve as proxies for either
healthier or unhealthier foods. Because data for fresh fruits and vegetables were not
available, refrigerated products were selected as a proxy for fresh produce under the
assumption that they had similar spoilage and storage/distribution costs as fresh produce.
Healthier food was represented by dairy (refrigerated milk, yogurt, cottage cheese) and
fruits and vegetables (frozen vegetables, and fresh orange juice). Unhealthier food was
represented by packaged, highly processed, long-shelf life products: soda, sweets
(chocolate candy, cookies) and salty snacks. Detailed methods are reported elsewhere
(Kern et al.).
Prices in the database did not include taxes and manufacturer coupons, but instead
reflected the shelf price and included store-level promotions. The primary exposure of
interest was the price of healthy foods relative to unhealthy foods, which was
operationalized as the ratio of the average price per serving of healthy food divided by
the average price per serving of unhealthy food and referred to as the healthy-to-
unhealthy price ratio. A ratio >1.0 indicates serving of healthy food was more expensive
than a serving of unhealthy food. The price of healthy foods and unhealthy foods were
also modeled separately as secondary exposures of interest. Each price exposure was
converted to a z-score: representing an increase of 14% in the relative price of healthy
52
food compared with unhealthy food for the healthy-to-unhealthy ratio, and an increase of
$0.04 and $0.03 in the average price per serving of healthy and unhealthy food prices,
respectively.
The average price of brand name toilet paper in stores within 3 miles of each
MESA participant was also calculated and used as an instrument for unhealthy food price
and the price ratio in the sensitivity analyses described in the ‘Statistical Methods’
section.
The number of IRI supermarkets within 3-miles of each MESA participant’s
address at exam 5 was created and referred to as the supermarket density.
3.2.5 Census data
US Census data came from the American Community Survey 2007-2011.
Geographic regions (Northeast, Midwest, South, West) and population density 4 were
assigned to each participant. A neighborhood block group SES index was created using
six variables representing wealth and income, such as household income, housing value,
investment income, education level, and managerial occupations, and was operationalized
as a single continuous measure, as described elsewhere (Diez Roux et al., 2001).
4 Population density per square mile was obtained using the 2010 census. Total population within a 1 mile buffer was calculated based on block-level census population and each block was weighted by the percent of the block area that fell within the participant buffer. The total population within that block was then multiplied by this weight and the weighted populations were summed together for the total population within the buffer. The total population was divided by total buffer area in square miles.
53
3.2.6 Cost of living data and supermarket density
The cost of living index 2010 was obtained from the Council for Community and
Economic Research for each metropolitan area (Council for Community and Economic
Research, 2016).
3.2.7 Data linking
Addresses of MESA participants and supermarket addresses from the pricing
dataset were used to link individuals to the average food/beverage prices at supermarkets
within a 3-mile buffer (radius) of each MESA participant residence using ArcGIS 10.0.
Median number of supermarkets per MESA participant in the analytic sample was 5
(25th-75th percentile 3-6). A 3-mile radius was used for consistency with other research
examining neighborhood food environments, and is in line with prior research estimating
the average distance individuals travel to their primary supermarket (The Capitol Forum,
2014, Drewnowski et al., 2012, Fuller et al., 2013, Michimi and Wimberly, 2010).
Sensitivity analyses using equal weights rather than those based on national consumption
averages, using supermarkets within 5-miles instead of 3-miles, and using a 1-mile buffer
for those living in New York City produced similar results which can be found in
APPENDIX 2 Supplemental Table 11.
3.2.8 Statistical methods
Multivariable logistic regression was used to model the relative odds of having a
high quality diet for every standard deviation increase in the healthy-to-unhealthy price
ratio. Model 1 adjusted for geographic region, age (continuous), and gender. Model 2
added income/wealth index, education, race/ethnicity, and smoking status, and model 3
54
added neighborhood level SES and supermarket density. Other variables (marital status
and physical activity) were considered as covariates in the model, but were not included
due to a lack of association with the outcome and not having any impact on the effect
estimate of price in the multivariable models.
We tested the interaction of price with education level and income/wealth tertile
by including appropriate interaction terms (separate models examining education and
income/wealth) and conducting stratified analyses by tertile of income/wealth (low,
medium, and high) and education level (high school degree or less, some college, or
bachelor’s degree or more). Our hypothesis was that the effect of price on diet would be
stronger for lower levels of SES.
In addition to the above multivariable models, sensitivity analyses used an
instrumental variable approach in order to remove potential unmeasured confounders that
would affect food prices and diet such as the local food culture and the types of foods
typically available in the neighborhood. The use of instrumental variables (IV) has been
increasing in epidemiologic research as a means to estimate causal effects (Greenland,
2000, Hernan and Robins, 2006, Martens et al., 2006). Toilet paper price was chosen as
the instrument for the price of unhealthy foods, and in turn the healthy-to-unhealthy price
ratio, because it (1) has a strong association with food prices in the same stores
(particularly unhealthy long-shelf life foods), (2) has no anticipated causal association
with participants’ diet quality other than through its correlation with food price, and (3) is
in principle not associated with unmeasured confounders mentioned above. These
characteristics satisfied the three major assumptions of an instrument: it is (1) strongly
associated with the exposure; (2) any effect it has on the outcome is fully mediated by the
55
exposure; and (3) it shares no unmeasured common causes with the outcome (Rassen et
al., 2009).
A two-stage residual inclusion (2SRI) analysis was used to perform the
instrumental variable analysis. A 2SRI model was used because they are statistically
consistent in non-linear models, such as the logit model used in this study, while the two-
stage predictor substitution model commonly used in linear models is not (Terza et al.,
2008). In the first stage, linear regression was used to regress the price ratio on toilet
paper price and covariates (age, gender, region, income/wealth index, education, race,
smoking status, neighborhood SES, supermarket density, population density, and cost of
living). The residuals from the stage 1 regression were then included as a covariate in the
second stage. The second stage model used a logit model to regress the indicator for a
high quality diet on the price ratio (the main exposure of interest), the residuals obtained
from stage 1, and all covariates used in stage 1. Bootstrapping of 10,000 samples was
used to calculate 95% credible intervals for the stage 2 estimates.
3.3 RESULTS
Among 4716 MESA participants who contributed data to exam 5, 1047 were
excluded due to not having dietary data. Further exclusions were: 835 due to not being
within 3 miles of a supermarket in our dataset (of which 674 were from the St. Paul
MESA site where supermarket data were unavailable) and 68 who did not have full
covariate data. The final analytic sample was 2765. Sample characteristics for included
vs. excluded are shown in APPENDIX 2 Supplemental Table 10. On average, the
excluded sample was similar to those who were included on most characteristics. There
56
were differences in regional and racial characteristics due to the exclusion of those living
in the St. Paul area, and small differences in education levels as a smaller proportion of
college graduates were in the excluded sample.
Demographics and other characteristics of the MESA participants are reported in
Table 3-1. Overall the serving price of healthy food was nearly twice as expensive as
unhealthy food (mean±SD of healthy-to-unhealthy price ratio = 1.97±0.14, and on an
absolute scale: $0.60±$0.04 vs. $0.31±$0.03 per serving for healthy and unhealthy food,
respectively, Table 3-2). While the ratio was analyzed as a continuous z-score in all
analytic models, the tertiles of healthy-to-unhealthy price ratio are used in the table for
descriptive purposes. Large difference across the tertiles was only apparent for region
(highest ratio in west and lowest in south) and for race/ethnicity (Chinese resided in
highest ratio areas and Black in lowest ratio areas, Table 3-1). Prior to adjusting for
covariates, the proportion of individuals with a high quality diet was 24% for those with
the highest healthy-to-unhealthy ratio compared with 18% of other individuals (Table 3-
2).
3.3.1 Primary analysis
Table 3-3 displays sequential adjustment for covariates in each of the three
models. Results of the final model indicate a modest inverse association between the
healthy-to-unhealthy ratio and high quality diet. That is, for every one standard deviation
increase (14% higher) in the price of healthy food to unhealthy food, the odds of having a
high quality diet decreased by 24% (OR=0.76, 95% CI=[0. 64 to 0.91]). No association
was found between healthy food price alone with diet (OR=1.04), but a higher price (per
57
standard deviation increase, approximately $0.03) of unhealthy food was associated with
increased odds of having a high quality diet (OR=1.16, 95% CI=[1.02 to 1.33]).
There was a statistically significant interaction between individual income/wealth
and the price ratio on diet while adjusting for all covariates included in the final model (p
for interaction 0.001, Table 3-4). All stratified point estimates were in the expected
direction but the gradient was somewhat unexpected: the strongest and only statistically
significant point estimate was in the middle income/wealth tertile (OR=0.61). The next
largest effect was found within the lowest income/wealth tertile (OR=0.79), while those
in the highest tertile had the weakest effect (OR=0.87), as expected. The interaction was
not statistically significant between education and the price ratio (p for interaction 0.095),
nevertheless, stratified estimates showed the weakest association in least educated and the
strongest in most educated (OR 0.85 for high school or less and OR 0.77 for Bachelor’s
or more), counter to expectation.
3.3.2 Instrumental Variable Analyses
Results of the 2SRI IV analysis are shown in Table 3-5. In stage 1, the instrument,
toilet paper price, was positively associated with unhealthy food price (spearman
correlation coefficient, r=0.56) and negatively associated with the healthy-to-unhealthy
price ratio (r= 0.41). As expected the correlation was higher with unhealthy food prices
(r=0.56) than with healthy food prices (r=0.29). The F-statistic was 1113 and guidelines
specify the minimum value should be 10 (Stock et al., 2002). In stage 2, the association
between healthy-to-unhealthy price ratio and high quality diet was similar in magnitude
to the results obtained in the primary analysis; however, the results was not statistically
58
significant due to wide confidence intervals (OR=0.82, 95% bootstrapped CI=[0.57 to
1.19]).
3.4 DISCUSSION
Higher prices of healthy foods relative to unhealthy foods were found to be
associated with lower odds of having a high quality diet: per 14% higher ratio of healthy
food to unhealthy food, there was 24% lower odds of having a high quality diet. Despite
healthy foods costing more than unhealthy foods, there was no association between diet
and prices of healthy foods alone, but there was a positive association with unhealthy
food price alone (per $0.03 higher unhealthy food price per serving there was a 16%
higher odds of having a healthy diet). To our knowledge, no prior study examining
healthy and unhealthy food prices has linked neighborhood food prices (rather than
aggregate level prices) to a cohort of individuals throughout the U.S. to examine the
association with diet.
Consumers report that price is a main driver in food purchase decision making
(Connors et al., 2001, Glanz et al., 1998, International Food Information Council
Foundation, 2015) and our study illustrates how this may impact diet. While much work
has been done to improve the quality of diets by understanding and improving the lack of
physical access to supermarkets in food deserts (Walker et al., 2010), additional paths
should be considered. Improving the affordability of healthy foods relative to their
unhealthy substitutes may be an effective option. Examples of options proposed for
improving the relative affordability of healthy foods include taxation of unhealthy foods,
subsidizing healthy food, and combinations of these practices. One study suggests taxing
59
unhealthy foods has the benefit of raising revenue and could be an effective strategy to
influence dietary changes (Thow et al., 2014). Another study suggests that subsidizing
healthy food has the benefit of directly making healthy food more affordable and appears
to increase the consumption of those foods, though with a smaller effect as the unhealthy
food taxes (Powell et al., 2013) and may require government funding. It has been
suggested that combining strategies to influence consumption patterns would be much
more effective than any single method alone (Epstein et al., 2012, Thow et al., 2014) and
may satisfy consumers generally opposed to ‘sin’ taxes by providing them simultaneous
savings on their grocery bill due to cheaper healthy foods (O’Hara and Musicu, 2015).
We expected differences in the price exposure to have more of an effect within
those of lower SES; however, our results suggested it was instead those in the middle
range of the income/wealth index, and those with the highest level of education for which
there was a stronger effect. This result may be explained by the following: healthy food
may still be too expensive for the lowest SES group even at its most affordable, as the
lowest observed healthy-to-unhealthy price ratio for any individual was 1.55. Given that
low-income families need to devote approximately half of their food budget to meet the
dietary guidelines for fruit and vegetable intake (Cassady et al., 2007), the relative price
of healthy food may have to be even lower than what was observed in this study before a
strong effect is seen within the lowest SES individuals. These results are consistent with
findings from a recent study which found food price policies had much larger effects in
middle-income individuals compared with those of low-income (Darmon et al., 2016).
Because of this, large subsidies for healthy foods paired with high taxes of unhealthy
foods may be required that move the relative price of healthy food lower than the levels
60
measured in this study to have an effect within low SES populations (McGill et al.,
2015).
Instrumental variable analyses confirmed odd ratios found in the main analyses
although confidence intervals were wider. The IV analysis adds some face validity to the
standard regression results; however, as with all models, IV methods have assumptions
(Rassen et al., 2009), some of which are not verifiable – notably the third assumption
prohibiting any association between the IV and unmeasured cofounders (Martens et al.,
2006). Confidence intervals from the IV analysis were wider than those obtained from the
primary analysis due to the two stage structure of the models and because the prediction
of the price ratio by the IV was imperfect (Rassen et al., 2009).
3.4.1 Limitations
While the validity of the FFQ used in this study has been documented (Nettleton
et al., 2009b, Mayer-Davis et al., 1999) the limitations to FFQ are well-known (Willett
and Hu, 2006). The FFQ contained an a priori food list that may miss foods consumed by
a diverse population; however, a strength of the MESA FFQ was that it included many
foods that reflect the diversity of the multi-ethnic population. By defining a high quality
diet relative to the population (top quintile) we attempted to address some of its
limitations regarding underreporting. Selection bias may have been introduced by
limiting analyses to individuals living within 3 miles of a supermarket. However, an
examination of the characteristics of those included and excluded found few differences
with the exception of region and race/ethnicity, which was due to the excluded being
largely concentrated in a single recruitment site where supermarket data were
61
unavailable. Other measurements of food price may have been considered for this
analysis, including the price per unit of weight, or price per calorie (Andrieu et al., 2006,
Drewnowski, 2009); however, we chose the price per serving as it is a unit of analysis
that can be compared across all product types, may be most meaningful to consumers,
and which has been used previously in similar research (Lipsky, 2009, Reed et al., 2004,
Stewart et al., February 2011). Lastly, there are limitations to the products included in the
price measures. Data on fresh fruits and vegetables were not available and instead a
proxy using frozen vegetables and refrigerated orange juice was used which is highly
correlated with the price of fresh ranges (Morris, 2011) and data on other healthy foods
such as whole grains and legumes was not available in this analysis. Additionally, data
were limited to branded products in order to compare the same products across the
country.
3.5 CONCLUSIONS
With a large proportion of the U.S. population failing to achieve a healthy diet it
is important to understand the root causes. This study suggests that the large difference in
price between healthy and unhealthy food may play a role. If this is true, interventions
may be considered to improve the affordability of healthy foods relative to unhealthy
alternatives.
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TABLES AND FIGURES - 3
Table 3-1 Characteristics of the individuals included in the analysis by tertiles of healthy-to-unhealthy food price ratio (n=2,642)
All participants
Lowest ratio, smallest
differential (1.55 - 1.88)
Middle ratio (1.88 - 2.01)
Highest ratio, largest
differential (2.01 - 2.39)
N /
Mean Col % /
SD n /
Mean Row %
/ SD n /
Mean Row %
/ SD n /
Mean Row %
/ SD Number of participants (N) 2765 938 903 924 MESA recruitment site (n, %) a
Forsyth County, NC 539 19.5% 239 44.3% 299 55.5% 1 0.2% New York, NY 538 19.5% 262 48.7% 226 42.0% 50 9.3% Baltimore, MD 464 16.8% 339 73.1% 122 26.3% 3 0.6% St. Paul, MN 8 0.3% 6 75.0% 0 0.0% 2 25.0% Chicago, IL 651 23.5% 89 13.7% 221 33.9% 341 52.4% Los Angeles, CA 565 20.4% 3 0.5% 35 6.2% 527 93.3%
Region of residence (n, %)
Northeast 534 19.3% 259 48.5% 226 42.3% 49 9.2% Midwest 642 23.2% 85 13.2% 220 34.3% 337 52.5% South 1021 36.9% 593 58.1% 421 41.2% 7 0.7% West 568 20.5% 1 0.2% 36 6.3% 531 93.5%
Supermarket density (3 mile) (mean, SD) 1.19 1.42 1.51 1.88 1.22 1.30 0.84 0.74 Female (n, %) 1466 53.0% 483 32.9% 502 34.2% 481 32.8% Age (mean, SD) 70.3 9.5 70.6 9.1 70.3 9.3 69.9 9.9 Race/ethnicity (n, %)
White 1101 39.8% 422 38.3% 485 44.1% 194 17.6% Chinese American 359 13.0% 10 2.8% 56 15.6% 293 81.6% Black 834 30.2% 413 49.5% 231 27.7% 190 22.8% Hispanic 471 17.0% 93 19.7% 131 27.8% 247 52.4%
Education (n, %) High school diploma or less 777 28.1% 231 29.7% 251 32.3% 295 38.0% Some college 761 27.5% 259 34.0% 233 30.6% 269 35.3% Bachelor’s degree or more 1227 44.4% 448 36.5% 419 34.1% 360 29.3%
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All participants
Lowest ratio, smallest
differential (1.55 - 1.88)
Middle ratio (1.88 - 2.01)
Highest ratio, largest
differential (2.01 - 2.39)
N /
Mean Col % /
SD n /
Mean Row %
/ SD n /
Mean Row %
/ SD n /
Mean Row %
/ SD Per capita household income (in $10k) 2.6 1.9 2.8 1.8 2.8 1.9 2.3 1.8 Wealth index 2.6 1.2 2.6 1.2 2.6 1.2 2.5 1.2 Income/wealth index 5.1 2.2 5.3 2.1 5.2 2.2 4.9 2.3 Marital status (n, %)
Not married or living with partner 1107 40.0% 419 37.9% 364 32.9% 324 29.3% Married/Living with partner 1658 60.0% 519 31.3% 539 32.5% 600 36.2%
BMI (mean, SD) 28.2 5.6 28.9 5.5 28.4 5.9 27.2 5.2 <25 (n, %) 855 30.9% 236 27.6% 268 31.3% 351 41.1% 25-29.9 (n, %) 1043 37.7% 347 33.3% 341 32.7% 355 34.0% ≥30 (n, %) 867 31.4% 355 40.9% 294 33.9% 218 25.1%
Smoking status (n, %) Never smoked 1281 46.3% 366 28.6% 412 32.2% 503 39.3% Former smoker 1283 46.4% 487 38.0% 433 33.7% 363 28.3% Current smoker 201 7.3% 85 42.3% 58 28.9% 58 28.9%
Physical activity, MET min per week (mean, SD) 2774 3552 3239 4237 2765 3441 2310 2749 a This is the MESA location of the participant, not necessarily their area of residence
64
Table 3-2 Proportion of participants with a high quality diet by tertile of the healthy-to-unhealthy price ratio and the average serving price of healthy and unhealthy foods (n=2,765)
All participants Lowest ratio (1.55 - 1.88)
Middle ratio (1.88 - 2.01)
Highest ratio (2.01 - 2.39)
Number of participants (N) 2765 938 903 924
Number with high quality diet (n, %) 545 19.7% 165 17.6% 161 17.8% 219 23.7%
Average food prices per serving
Healthy food price per serving a
(mean, SD, median) $0.60 $0.04 $0.60 $0.61 $0.06 $0.57 $0.59 $0.03 $0.58 $0.62 $0.03 $0.62
Unhealthy food price per serving b
(mean, SD, median) $0.31 $0.03 $0.30 $0.33 $0.04 $0.31 $0.30 $0.01 $0.30 $0.29 $0.01 $0.29
Ratio of Healthy-to-Unhealthy
(mean, SD, median) 1.97 0.14 1.93 1.83 0.05 1.85 1.95 0.05 1.93 2.14 0.09 2.13 a Healthy foods were represented by frozen vegetables, orange juice, and dairy (milk, yogurt, and cottage cheese) b Unhealthy foods were represented by soda, salty snacks (chips, pretzels, onion rings), and sweets (chocolate candy and cookies)
65
Table 3-3 Odds ratios of having a high quality diet associated with the price ratio, and with the prices of healthy foods and unhealthy foods after sequential adjustment for confounders within the full population (n=2,765) and after removing New York/Bronx (n=2,225)
Exposure of interest
Healthy-to-unhealthy ratio Healthy food price Unhealthy food price 95% CI 95% CI 95% CI
Model covariates Odds ratio Lower Upper
p-value
Odds ratio Lower Upper
p-value
Odds ratio Lower Upper
p-value
Full sample (n=2,765)
Model 1: region, age, gender 0.97 0.83 1.13 0.6978 0.98 0.88 1.10 0.7655 1.00 0.91 1.10 0.9639
Model 2: Model 1 plus income/wealth,
education level, smoking status, and race 0.86 0.73 1.01 0.0571 0.97 0.86 1.09 0.5709 1.03 0.93 1.13 0.6045
Final Model: Model 2 plus neighborhood
SESa and neighborhood supermarket density 0.76 0.64 0.91 0.0027 1.04 0.88 1.22 0.6371 1.16 1.02 1.33 0.0267 a Neighborhood SES was derived from log of the median household income; log of the median value of housing units; the percentage of households receiving interest, dividend, or net rental income; the percentage of adults 25 years of age or older who had completed high school; the percentage of adults 25 years of age or older who had completed college; and the percentage of employed persons 16 years of age or older in executive, managerial, or professional specialty occupations.
66
Table 3-4 Odds ratios of having a high quality diet associated with the price ratio, and with the prices of healthy foods and unhealthy foods, stratified by income-wealth and by education (n=2,765)
Exposure of interest Healthy-to-unhealthy ratio Healthy food price Unhealthy food price 95% CI 95% CI 95% CI OR Lower Upper p-value OR Lower Upper p-value OR Lower Upper p-value Wealth/income tertile a
Lowest (1-4), n=956 0.79 0.58 1.06 0.1149 0.99 0.76 1.28 0.9200 1.16 0.90 1.49 0.2654
Middle (5-6), n=793 0.61 0.43 0.87 0.0067 1.13 0.81 1.57 0.4746 1.32 1.02 1.70 0.0365
Highest (7-8), n=893 0.87 0.64 1.18 0.3621 1.11 0.83 1.49 0.4846 1.10 0.89 1.36 0.3825
Education level b
HS degree or less, n=777 0.85 0.58 1.24 0.3897 0.79 0.58 1.09 0.1513 0.90 0.65 1.25 0.5354
Some college, n=761 0.81 0.57 1.16 0.2552 1.11 0.78 1.59 0.5586 1.20 0.89 1.62 0.2339
Bachelor’s degree or more, n=1227 0.77 0.59 0.99 0.0412 1.15 0.91 1.47 0.2512 1.18 0.99 1.41 0.0621
a p-value for interaction between the income-wealth and the price ratio p = 0.0012; healthy price p=0.4047 ; unhealthy price p=0.0053 b p-value for interaction between the education level and the price ratio p = 0.0949; healthy price p=0.4415; unhealthy price p=0.2111
67
Table 3-5 Instrumental variable analysis results using toilet paper as the instrument and two-stage residual inclusion models for the healthy-to-unhealthy price ratio, healthy food price, and unhealthy food price (n=2,765) a
Stage 1 Stage 2 Price outcome
N N
events t-value F-
statistic
Odds Ratio
Lower CLb
Upper CLb
Healthy-to-unhealthy price ratio
2765 543 -33.36 1113 0.82 0.57 1.19
Healthy food price 2765 543 59.86 3583 1.15 0.91 1.45
Unhealthy food price 2765 543 98.59 9720 1.10 0.93 1.30
a Covariates in each stage included age, gender, geographic region, wealth/income index, race, smoking status, neighborhood SES, supermarket density, population density, and cost of living index b Confidence limits were obtained from a bootstrapped analysis using 10,000 replications
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CHAPTER 4: THE PRICE OF HEALTHY FOOD RELATIVE TO UNHEALTHY FOOD AND ITS ASSOCIATION WITH TYPE 2 DIABETES AND INSULIN RESISTANCE: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS
ABSTRACT
OBJECTIVE: To examine the association between the price of healthy food relative to
unhealthy food and type 2 diabetes prevalence, incidence and insulin resistance (IR).
METHODS: Data came from the Multi-Ethnic Study of Atherosclerosis exam 5
administered 2010-2012 (exam 4, five years prior, was used only for diabetes incidence)
and supermarket food/beverage prices derived from Information Resources Inc. For each
individual, average price of healthy foods, unhealthy foods and their ratio was computed
for supermarkets within 3-miles. Type 2 diabetes status was confirmed at each exam and
IR was assessed via the homeostasis model assessment index. Logistic, modified Poisson
and linear regression models were used to model diabetes prevalence, incidence and IR,
respectively as a function of price and covariates; 2,353 to 3,408 participants were
included in analyses (depending on the outcome).
RESULTS: A higher ratio of healthy-to-unhealthy food was associated with greater IR
(4.8% increase in IR for each standard deviation higher price ratio [0.048, 95% CI 0.00 to
0.10] after adjusting for region, age, gender, race/ethnicity, family history of diabetes,
income/wealth index, education, smoking status, physical activity, and neighborhood
socioeconomic status). No association with diabetes incidence (relative risk=1.11, 95%
CI 0.85 to 1.44) or prevalence (odds ratio=0.95, 95% CI 0.81 to 1.11) was observed.
69
CONCLUSIONS: Higher prices of healthy food relative to unhealthy food were
positively associated with IR, but not with either of the diabetes outcomes. This study
provides new insight into the relationship between food prices with IR and diabetes.
70
4.1 INTRODUCTION
An estimated 29 million individuals are living with diabetes, accounting for more
than 9% of the population (Centers for Disease Control and Prevention, 2014), and the
prevalence of diagnosed diabetes has grown sharply since 2000 (Centers for Disease
Control and Prevention, 2016). It is vital that we identify potential areas of intervention to
reverse this trend.
Type 2 diabetes is mainly driven by obesity, and diet quality plays an important
role in its development. Diets high in unhealthy, sugary, energy-dense and nutrient-poor
foods are associated with an increased risk of obesity and diabetes (Malik et al., 2010b,
Brunner et al., 2008), while consumption of healthier foods – such as fresh fruits and
vegetables and dairy – has shown protective effects (Mursu et al., 2014, Margolis et al.,
2011, Choi et al., 2005).
Unfortunately, foods that are energy dense and generally considered unhealthy
tend to be the most affordable (Drewnowski, 2010, Drewnowski and Darmon, 2005,
Lipsky, 2009, Andrieu et al., 2006, Monsivais et al., 2010, Kern et al., 2016). The price
of food is associated with food purchasing decisions and consumption (Connors et al.,
2001, Glanz et al., 1998, Andreyeva et al., 2010, Aggarwal et al., 2016), and thus price
differences between healthy and unhealthy foods are expected to be associated with
downstream effects of diet, including diabetes and insulin resistance.
Prior work that has examined prices of healthy and unhealthy foods and health
outcomes have focused on the relationship between food prices and body weight. Most
71
(Powell et al., 2007a, Powell and Bao, 2009, Chaloupka and Powell, 2009, Chou et al.,
2004, Finkelstein et al., 2014) but not all (Beydoun et al., 2008, Han and Powell, 2011) of
these studies have found higher healthier food prices associated with higher BMI/obesity
and/or higher unhealthier prices associated with lower BMI/obesity.
No studies have examined food price association with type 2 diabetes and only a
few studies have linked food prices directly to insulin resistance. Those studies focused
primarily on metropolitan-area fast-food prices (hamburger, pizza, fried chicken) (Duffey
et al., 2010, Rummo et al., 2015, Meyer et al., 2014). Duffey et al (2010) found
metropolitan area prices inversely associated with insulin resistance (Duffey et al., 2010),
while Rummo et al (2015) found no association (Rummo et al., 2015) and Myer et al
(2014) found associations only among less advantaged residents(Meyer et al., 2014). No
work to date has examined food prices at stores nearby residents and examined
associations with type 2 diabetes and insulin resistance.
This study spatially linked participants from a large multi-ethnic sample to nearby
supermarkets to examine associations between neighborhood food price and type 2
diabetes prevalence, type 2 diabetes incidence and insulin resistance.
4.2 METHODS
4.2.1 MESA data
This study utilized data gathered by the Multi-Ethnic Study of Atherosclerosis
(MESA). The MESA is a population-based longitudinal cohort study of ethnically diverse
adults aged 45-84 years (Bild et al., 2002). Individuals were recruited from six sites
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across the United States: Bronx/Upper Manhattan, NY; Baltimore City and Baltimore
County, Maryland; Forsyth County, North Carolina; Chicago, Illinois; St. Paul,
Minnesota; and Los Angeles County, California. The first examination was conducted in
2000-2002 and four examinations occurred during 10 years of follow-up. Food prices
were only available concurrent to exam 5, thus this study only included participants who
completed exam 5 (April 2010-January 2012). Participant characteristics for the incident
diabetes analysis were gathered from exam 4 (which occurred five years earlier: 2005-07,
considered the “baseline” in this analysis) while exam 5 was used for all other analyses.
4.2.2 Outcomes - Diabetes and insulin resistance
Type 2 diabetes status was based on the 2003 American Diabetes Association
criteria, defined as fasting glucose ≥126 mg/dl and/or use of antidiabetic
medications(2003). Prevalent type 2 diabetes at exam 5 was defined as meeting the
diabetes criteria at any of the five examinations. Incident type 2 diabetes at exam 5 was
defined as meeting the diabetes criteria at the time of exam 5 with no evidence of
diabetes at any prior exam.
Fasting glucose was measured by rate reflectance spectrophotometry using thin-
film adaptation of the glucose oxidase method on the Vitros analyzer (Johnson &
Johnson Clinical Diagnostics, Rochester, NY). Insulin was measured in serum or EDTA,
heparin or citrate plasma on a Roche Elecsys 2010 Analyzer (Roche Diagnostics
Corporation) using a sandwich immunoassay method. Insulin resistance at exam 5 was
measured according to the homeostasis model assessment index of insulin resistance
73
(HOMA-IR). This index is well correlated with measures from the gold-standard
hyperinsulinemic clamp and is calculated as (Matthews et al., 1985):
𝐻𝐻𝐻𝐻 𝐼𝐼 =𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝑔𝑔𝑔𝑔𝐹𝑔 �𝑚𝑚𝑔𝑔𝐿 � × 𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝑔𝑔𝐹𝐹 (𝐹𝐹 𝑚𝐹𝑔𝑚𝑔𝑔𝐹𝐹𝐹𝐹 𝑝𝑔𝑚 𝑔𝐹𝐹𝑔𝑚)
22.5
A log transformation was used to reduce skewness of the distribution and created
a normal distribution of values for use in multivariable analyses. See Appendix 3.2 for
details regarding the validity of the HOMA-IR measurement used in this study.
4.2.3 Covariates
Other MESA person-level data included in this study were: diet (see details
below), age (continuous), sex, race/ethnicity (Caucasian, Chinese-American, African-
American, Hispanic), family history of diabetes, smoking status (never, former, current),
education level (high school diploma, GED, or less; some college, technical or associates
degree; bachelor’s degree or higher), income/wealth index (an ordinal measure ranging
from 0 to 8 based on the combination of income level and ownership of four assets: car,
home, land, and investments), and physical activity (measured as total hours of physical
activity per day; operationalized into tertiles for modeling).
4.2.4 Price data
Data on food prices were obtained from Information Resources Inc. (IRI) (IRI,
2014, IRI, 2015, Bronnenberg et al., 2008, Symphony IRI, 2015), a market research
group that monitors prices of 299 consumer packaged goods sold in large chain
supermarkets and superstores across the U.S.(IRI, 2014, IRI, 2015, Bronnenberg et al.,
74
2008, Symphony IRI, 2015). Twenty-nine parent companies owned 100 supermarket
companies in our study area. Among those, 8 parent companies (that owned 28
companies) did not agree to release their stores’ data and no further information was
available for those companies. Data used in this study were from 794 stores located in 11
states (including Washington DC), 72 counties, and 757 census block groups. Data years
were 2009-2012.
Nine food/beverage product categories were selected to serve as proxies for either
healthier or unhealthier foods. Because data for fresh fruits and vegetables were not
available, refrigerated products were selected in order to roughly approximate costs of
fresh fruit and vegetable spoilage and storage/distribution, and proxy fresh produce.
Healthier food was represented by dairy (refrigerated milk, yogurt, cottage cheese), fruits
and vegetables (frozen vegetables, and fresh orange juice). While the healthfulness of
orange juice is questionable, the product was chosen to represent healthy foods due to its
high correlation with the price of fresh oranges (Morris, 2011) not for its own nutritional
value. Unhealthier food was represented by packaged, highly processed, long-shelf life
products: soda, sweets (chocolate candy, cookies), and salty snacks.
The primary exposure of interest was the price of healthy foods relative to
unhealthy foods, which was operationalized as the ratio of the average price per serving
of healthy food divided by the average price per serving of unhealthy food and referred to
as the healthy-to-unhealthy price ratio. The price of healthy foods and unhealthy foods
were also modeled separately as secondary exposures of interest. Average prices of
healthy and unhealthy foods were calculated using weights for each product class based
75
on national consumption estimates (i.e., food types – e.g., fruits and vegetables – that are
consumed more received larger weights) and converted to z-scores. A one unit change in
the z-scores for the price ratio were equivalent to a 14% change in the price of healthy
food relative to unhealthy food while a one unit change in the z-scores for healthy and
unhealthy foods represented differences of $0.04 and $0.03 per serving, respectively. A
sensitivity analysis which calculated overall healthy and unhealthy food prices using
equal weights for each product class (e.g., dairy) was also performed and model results
were similar to the results using the original weighting methodology.
4.2.5 Other data sources
US Census data came from the American Community Survey (ACS) 2007-2011.
Geographic regions (Northeast, Midwest, South, West) and population density 5 were
assigned to each participant. A neighborhood block group SES index was created using
six variables representing wealth and income, housing value, education, and managerial
or professional occupations, and was operationalized as a single continuous measure as
described by Diez-Roux et al (Diez Roux et al., 2001).
Addresses of MESA participants and supermarket addresses from the pricing
dataset were used to link individuals to the average food/beverage prices at supermarkets
within a 3-mile buffer (radius) of each MESA participant residence using ArcGIS 10.0.
Median number of supermarkets per MESA participant in the analytic sample was 5
5 Population density per square mile was obtained using the 2010 census. Total population within a 1 mile buffer was calculated based on block-level census population and each block was weighted by the percent of the block area that fell within the participant buffer. The total population within that block was then multiplied by this weight and the weighted populations were summed together for the total population within the buffer. The total population was divided by total buffer area in square miles.
76
(25th-75th percentile 3-6). A 3-mile radius was used for consistency with other research
examining neighborhood food environments, and is in line with prior research estimating
the average distance individuals travel to their primary supermarket (2014, Drewnowski
et al., 2012, Fuller et al., 2013, Michimi and Wimberly, 2010).
4.2.6 Statistical models
The following sequence of models was examined for each outcome: unadjusted
for covariates (model 0), and then adjusted for potential confounders: model 1,
geographic region, age, gender, and family history of diabetes; model 2, plus
income/wealth index, education, race, smoking status and physical activity; and model 3,
plus neighborhood level SES.
The analysis of diabetes prevalence included all individuals completing exam 5
and who lived within 3 miles of a supermarket from our dataset. Individuals with type 1
diabetes, those missing data for covariates, and those missing type 2 diabetes status at
exam 5 were excluded (n=258). For diabetes prevalence, logistic regression was used to
compute odds ratios (ORs) and 95% confidence intervals (CIs).
The analysis of diabetes incidence included all individuals free of type 2 diabetes
at exam 4 and who lived within 3 miles of a supermarket at any time between exam 4 and
exam 5. Of those included for analysis, 97% lived within 3 miles of a supermarket for the
entire duration between the two exams, and just 1.3% lived at an address near a
supermarket for less than half the time. A modified Poisson regression model with robust
error variance(Zou, 2004) was used to measure the relative risk of developing diabetes
77
between exam 4 and 5 for each unit increase in the z-score of the price exposure.
Covariates were measured during exam 4 and included the same variables described
above. Risk ratios (RRs) and 95% CIs are reported.
The analysis of insulin resistance levels included all individuals free of type 2
diabetes at exam 5 and with complete data for fasting insulin and glucose levels. HOMA-
IR levels were assessed using a normal linear model. Because HOMA-IR was heavily
skewed a log transformation was used and the resulting distribution was normal, and thus
the log-transformed HOMA-IR values were modeled. Regression coefficients of the
exposure illustrate the mean change in the log of HOMA-IR (reported as the percent
change of HOMA-IR for more meaningful interpretation) for every one unit change in
the z-score of the price exposure (healthy price, unhealthy price, or the ratio). Log-linear
effect estimates and 95% CIs are reported.
There was no evidence of non-linearity in the association between prices and the
outcome variables, thus prices were analyzed as continuous exposures, operationalized as
z-scores, in all analytic models. Tertiles of healthy-to-unhealthy price ratio are used in
tables 1 and 2 for descriptive purposes.
The multiplicative interaction 6 between individual SES – measured separately by
the income/wealth index and education level – with the price ratio was examined,
adjusting for all covariates from model 3, for each outcome of interest. Our hypothesis
was that the effect of price on the outcomes would be stronger within lower levels of
6 Departure from multiplicative assumption was tested within the regression models. Departure from additivity was also tested (results not shown). Additive results were similar for the prevalence models but additivity in incidence models was not fully assessed due to model convergence problems
78
SES. To illustrate how the effect differed by level of SES, stratified analyses were
performed within each tertile of income/wealth (low, medium, and high) and education
(high school degree or less, some college, or bachelor’s degree or more). Similarly, the
analysis tested for an interaction between race with the price ratio and a stratified analysis
was conducted within each race category (White, Chinese, Black, and Hispanic).
All analyses were performed using SAS software version 9.3 (SAS Institute,
Cary, NC).
4.2.7 Sample
Of the 4,716 individuals completing exam 5 there were 3,666 living within 3
miles of a supermarket in our dataset. Participants retained for analyses of each outcome
were: 3,408 for diabetes prevalence (excluded type 1 diabetes n=7, missing data for
covariates n=181, and unknown diabetes status at exam 5 n=70); 2,829 for diabetes
incidence (excluded prevalent or unknown diabetes status at exam 4); 2,353 for HOMA-
IR (excluded diabetes n=680 and those missing glucose or insulin data n=336). More
than half of the 1,308 excluded from prevalence analyses were from the St. Paul site (a
site with many Hispanics), as supermarket data was not available for this area. This led to
a higher proportion of excluded participants being Hispanic (see APPENDIX 3
Supplemental Table 12 for comparison of excluded and included). In addition, those
excluded had slightly higher BMI and education; but otherwise excluded and included
were generally similar.
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4.3 RESULTS
Demographics and other characteristics of the MESA participants are reported in
Table 4-1. Overall the serving price of healthy food was nearly twice as expensive as
unhealthy food (mean±SD of healthy-to-unhealthy price ratio = 1.97±0.14; $0.61±$0.04
vs. $0.31±$0.03 average price per serving for healthy and unhealthy food, respectively).
Large difference across the healthy-to-unhealthy price ratio tertiles was only apparent for
region (highest ratio in west which was primarily the Los Angeles area and lowest in
south which was primarily North Carolina and Baltimore) and for race/ethnicity (Chinese
resided in highest ratio areas and Black in lowest ratio areas) (Table 4-1). Participants
with the lowest income (primarily Hispanic and Chinese) lived in areas with the highest
ratio. There were no apparent differences in the proportion of individuals with prevalent
type 2 diabetes or levels of insulin resistance across tertiles of the price ratio (Table 4-2).
There appeared to be a slight increase in the rate of incident diabetes as the price ratio
increased (4.8%, 5.2%, and 6.4% from lowest to highest).
4.3.1 Multivariable models
Results from the analytic models adjusting for covariates are shown in Table 4-3.
Our hypotheses were that the ratio of healthy-to-unhealthy price would be positively
associated with insulin resistance and diabetes. The healthy-to-unhealthy price ratio was
modestly associated with the level of insulin resistance: a one unit increase in the price
ratio z-score was associated with a 5.2% higher HOMA-IR score (Estimate=0.051, 95%
CI=[0.00 to 0.10], adjusted for age, gender, race/ethnicity, family history of diabetes,
80
income/wealth index, education level, smoking status, and physical activity). After
additional adjustment for neighborhood SES, the estimate remained largely the same
though confidence intervals included the null (0.047, 95% CI=[-0.00 to 0.10]). For
diabetes prevalence and incidence, there was no evidence of an association with healthy-
to-unhealthy ratio (OR model 3 =0.95, 95% CI=[0.81 to 1.11] and adjusted RR=1.11,
95% CI=[0.85 to 1.44], respectively).
Separate models for healthy and unhealthy food prices yielded similar results to
those reported above: significant negative associations with insulin resistance were
detected, while no significant associations were detected with either diabetes outcome.
As expected, unhealthier food price was negatively associated with HOMA-IR; but
contrary to expectation, healthier food price was also negatively associated with HOMA-
IR.
There was no evidence of effect modification -- by income/wealth, education or
race -- of the healthy-to-unhealthy price ratio association with any of the three outcome
measures (p for interactions ranged 0.07 to 0.82). See APPENDIX 3 Supplemental Table
13 for results.
4.4 DISCUSSION
This study of healthy-to-unhealthy food price ratios at local supermarkets found
positive associations with insulin resistance, indicating that residents living in areas with
larger price differentials between healthier and unhealthy food have greater insulin
81
resistance. No association was found between healthy-to-unhealthy food prices and
prevalence or incidence of type 2 diabetes.
No previous studies have examined the price of unhealthy foods at nearby grocery
stores and insulin resistance, but the association detected between local food prices and
insulin resistance in this study is consistent with prior research linking insulin resistance
to other neighborhood factors including the availability of healthy food and physical
activity resources (Auchincloss et al., 2008), neighborhood deprivation (Andersen et al.,
2008), and neighborhood SES (Diez Roux et al., 2002). Prior work has examined fast
food price and insulin resistance in a cohort of young adults (CARDIA) and found mixed
results: one study found expected results with soda and pizza prices and insulin resistance
(Duffey et al., 2010) but results were null in another study (Rummo et al., 2015) or
significant only within relatively disadvantaged groups (middle income range, lower
education, Black (Meyer et al., 2014)).
In our study, no significant association between price and diabetes was observed.
This may not be surprising given the chronic nature of diabetes. The lack of association
with prevalence is especially unsurprising given that diabetes may have been diagnosed
in the far past and an individual’s most recent price exposure may not be reflective of the
exposures faced many years prior. However, an association with diabetes incidence was
more likely to be observed given the concurrent timing of diagnosis and the price
exposure. In fact, the effect trended in the expected direction, but was not significant due
to limited power and wide confidence intervals. And, while the price exposure was
measured synchronously with the diagnosis of diabetes, risk factors of diabetes, such as
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diet and weight, tend to be chronic exposures, and not limited to the time period during
which the disease was diagnosed. Thus, while we measured the price of food around the
time of first diagnosis of diabetes in the incidence analysis, it would have been beneficial
to know what the long-term exposures to food prices were, but these data were not
available. There is little prior literature with which to compare to our results. Studies
examining NHANES data suggested that higher prices of healthy and low glycemic foods
were associated with higher blood sugar levels in those with diabetes (Anekwe and
Rahkovsky, 2014) and without (Rashad, 2007). Compared to our dataset, the NHANES
population was younger, less educated and had a higher proportion of White individuals.
Market region food prices from 10 distinct food groups obtained from Nielsen Homescan
consumer surveys were used in one study (Anekwe and Rahkovsky, 2014) while the
other used aggregate national food prices for four high glycemic index foods from the
Bureau of Labor and Statistics (Rashad, 2007). Thus, our study differentiates itself from
prior work by using prices found at stores within the immediate neighborhood of
individuals.
Food price is just one piece of a complex food environment, and the lack of
association or weak association between food/beverage prices ratio and health may be
due to the influence of other factors such as the neighborhood built environment
(Christine et al., 2015, Auchincloss et al., 2008, Diez Roux and Mair, 2010), food
marketing(Zimmerman, 2011) and nutrition information in retail environments (Campos
et al., 2011), among others, which were not captured in this study.
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Intervention studies suggest that price interventions modify purchases of the
targeted foods (Waterlander et al., 2012, Harnack et al., 2016), yet nutritional quality
does not necessarily improve due to substitution with other untaxed less healthy items
(Epstein et al., 2012, Mursu et al., 2014, Ni Mhurchu et al., 2010). Combining subsidies
for healthy foods with restrictions or increased prices of unhealthy foods can partially
address negative substitutions (Harnack et al., 2016, Thow et al., 2014, Epstein et al.,
2012, Duhaney et al., 2015, Niebylski et al., 2015). For this reason, our study focused
mostly on the price ratio of healthy-to-unhealthier foods/beverages as the main exposure.
Separately modeled estimates of the adjusted association between unhealthier and
healthier prices on HOMA-IR indicated lower levels of insulin resistance for both (when
prices of unhealthier soda, sweets and salty snacks were more expensive and, counter-
intuitively, when price of healthier food was more expensive). Prior work suggests that
incentivizing purchase and use of healthier foods may have weaker impacts relative to
disincentivizing the purchase of unhealthy foods (Epstein et al., 2010, Mayne et al.,
2015). Associations between availability and price of healthier foods/beverages and
health are complex and potentially mediated or moderated by available time, education,
and skills in food preparation (Duhaney et al., 2015, Niebylski et al., 2015).
4.4.1 Limitations
Requiring individuals to live within 3 miles of a supermarket in our dataset may
have introduced selection bias. An examination of the characteristics of those included
and excluded found differences with region and race/ethnicity, which was due to those
excluded being largely concentrated in a single recruitment site where supermarket data
84
were unavailable, as well as differences in education and income levels. Data on fresh
fruits and vegetables were not available and instead a proxy was refrigerated products
(frozen vegetables and refrigerated orange juice and dairy). Lastly, data were limited to
branded products in order to include products that are available in many regions of the
country.
4.5 CONCLUSIONS
As the prevalence of type 2 diabetes increases we must understand what factors
are contributing to the development of this disease and what steps need to be taken to
reverse the trend. While a large amount has been written regarding the hypothetical effect
of subsidizing and taxing foods to influence consumption and the resulting health effects,
very little work has examined real world data to understand how changes in price effect
rates of diabetes and levels of insulin resistance. This study was the first of its kind to
examine the association between neighborhood food prices with insulin resistance and
diabetes for individuals in multiple areas across the U.S. While prior work has examined
food prices at the metropolitan level, current literature regarding the direct association
between local neighborhood food prices and diabetes is lacking, and our study provides
the only currently published data examining this relationship.
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TABLES AND FIGURES - 4
Table 4-1 MESA participant characteristics by healthy-to-unhealthy price ratio at the time of exam 5 (N=3,408)
All
participants
Lowest healthy-to-unhealthy
ratio (1.55 - 1.88)
Middle healthy-to-unhealthy
ratio (1.88 - 2.01)
Highest healthy-to-unhealthy
ratio (2.01 - 2.39)
N /
Mean Col %
/ SD N /
Mean Row
% / SD N /
Mean Row
% / SD N /
Mean Row
% / SD Number of participants (N) 3408 1148 1141 1119 MESA location (n, %)
Forsyth County, NC 645 18.9% 295 45.7% 349 54.1% 1 0.2% New York, NY 665 19.5% 322 48.4% 273 41.1% 70 10.5% Baltimore, MD 570 16.7% 420 73.7% 147 25.8% 3 0.5% St. Paul, MN 7 0.2% 6 85.7% 0 0.0% 1 14.3% Chicago, IL 814 23.9% 102 12.5% 333 40.9% 379 46.6% Los Angeles, CA 707 20.7% 3 0.4% 39 5.5% 665 94.1%
Region of residence (n, %) Northeast 660 19.4% 318 48.2% 273 41.4% 69 10.5% Midwest 797 23.4% 95 11.9% 331 41.5% 371 46.5% South 1236 36.3% 732 59.2% 496 40.1% 8 0.6% West 715 21.0% 3 0.4% 41 5.7% 671 93.8%
Age (mean, SD) 70.2 9.4 70.4 9.2 70.4 9.4 69.8 9.8 Female (n, %) 1809 53.1% 603 33.3% 636 35.2% 570 31.5% Race/ethnicity (n, %)
White 1274 37.4% 475 37.3% 563 44.2% 236 18.5% Chinese American 481 14.1% 10 2.1% 89 18.5% 382 79.4% Black 1072 31.5% 549 51.2% 321 29.9% 202 18.8% Hispanic 581 17.0% 114 19.6% 168 28.9% 299 51.5%
Family history of diabetes (n, %) 1529 44.9% 546 35.7% 490 32.0% 493 32.2% BMI (mean, SD) 28.2 5.6 29.1 5.6 28.3 5.8 27.1 5.2
<25 (n, %) 1040 30.5% 277 26.6% 337 32.4% 426 41.0% 25-29.9 (n, %) 1283 37.6% 422 32.9% 446 34.8% 415 32.3% ≥30 (n, %) 1085 31.8% 449 41.4% 358 33.0% 278 25.6%
Education level (n, %) HS/GED or less 1022 30.0% 310 30.3% 331 32.4% 381 37.3% Some college, Technical or Associate degree 945 27.7% 328 34.7% 306 32.4% 311 32.9%
86
All
participants
Lowest healthy-to-unhealthy
ratio (1.55 - 1.88)
Middle healthy-to-unhealthy
ratio (1.88 - 2.01)
Highest healthy-to-unhealthy
ratio (2.01 - 2.39)
N /
Mean Col %
/ SD N /
Mean Row
% / SD N /
Mean Row
% / SD N /
Mean Row
% / SD Bachelor’s degree or higher 1441 42.3% 510 35.4% 504 35.0% 427 29.6%
Per capita income (in $10k) 2.6 1.9 2.7 1.8 2.8 2.0 2.2 1.8 Wealth index 2.6 1.2 2.6 1.2 2.6 1.2 2.5 1.2 Income/wealth index 5.0 2.2 5.2 2.1 5.2 2.2 4.7 2.4 Marital status (n, %)
Not married or living with partner 1395 40.9% 533 38.2% 474 34.0% 388 27.8% Married/Living with partner 2013 59.1% 615 30.6% 667 33.1% 731 36.3%
Smoking status (n, %) Never smoked 1575 46.2% 444 28.2% 507 32.2% 624 39.6% Former smoker 1585 46.5% 598 37.7% 556 35.1% 431 27.2% Current smoker 248 7.3% 106 42.7% 78 31.5% 64 25.8%
Physical activity, hours per day (mean, SD) 8.8 5.4 9.5 5.8 9.1 5.3 7.7 4.8 Daily calorie intake (mean, SD) 1643 782 1668 793 1641 765 1619 789
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Table 4-2 Proportion of participants with type 2 diabetes, incident type 2 diabetes, and insulin resistance levels by tertile of the healthy-to-unhealthy price ratio and the average serving price of healthy and unhealthy foods
All participants Lowest ratio (1.55 - 1.88)
Middle ratio (1.88 - 2.01)
Highest ratio (2.01 - 2.39)
Number of participants (N) 3,408 1,148 1,141 1,119 Number with diabetes at exam 5 (n, %) 790 23.2% 275 24.0% 247 21.6% 268 23.9% Number with incident diabetes at exam 5 (n, %) a 154 5.4% 45 4.8% 49 5.2% 60 6.4% Insulin resistance level at exam 5 (mean, SD, median) b 2.32 0.67 2.33 2.31 0.66 2.32 2.32 0.68 2.33 2.31 0.69 2.34 Average food prices per serving
Healthy food price per serving (mean, SD, median) $0.61 $0.04 $0.60 $0.60 $0.06 $0.57 $0.63 $0.05 $0.61 $0.59 $0.03 $0.58 Unhealthy food price per serving (mean, SD, median) $0.31 $0.03 $0.30 $0.33 $0.04 $0.31 $0.32 $0.03 $0.31 $0.30 $0.01 $0.30 Ratio of Healthy-to-Unhealthy (mean, SD, median) 1.97 0.14 1.94 1.83 0.05 1.85 1.96 0.07 1.98 1.95 0.05 1.94
a percentages are within the analytic population for the incidence analysis, n=2,829 b values are from within the analytic population for the insulin resistance analysis, n=2,353; insulin resistance is measured as the log of HOMA-IR
88
Table 4-3 Multivariable models for each of the three outcomes of interest: prevalence of type 2 diabetes at exam 5, incidence of type 2 diabetes from exam 4 to exam 5, and level of insulin resistance at exam 5 a
Exposure of interest Healthy-to-unhealthy ratio Healthy food price Unhealthy food price
95% CI 95% CI 95% CI
Type 2 diabetes prevalence (n=3,408) OR Lower Upper p-value OR Lower Upper p-value OR Lower Upper p-value
Model 0: No covariates 1.003 0.927 1.085 0.9443 0.943 0.869 1.022 0.1529 0.949 0.874 1.031 0.2180 Model 1: Region, age, gender, race, family history of diabetes 0.959 0.824 1.116 0.5862 0.888 0.784 1.005 0.0589 0.948 0.855 1.051 0.3128 Model 2: SES, smoking, PA 0.968 0.831 1.129 0.6796 0.896 0.791 1.016 0.0862 0.950 0.856 1.055 0.3375 Model 3: Neighborhood SES 0.948 0.812 1.106 0.4984 0.931 0.816 1.061 0.2839 0.981 0.879 1.093 0.7233
Type 2 diabetes incidence (n=2,829) RR Lower Upper p-value RR Lower Upper p-value RR Lower Upper p-value
Model 0: No covariates 1.179 1.025 1.355 0.0208 0.985 0.855 1.134 0.8285 0.828 0.684 1.002 0.0530 Model 1: Region, age, gender, race, family history of diabetes 1.146 0.882 1.490 0.3066 0.864 0.698 1.071 0.1821 0.857 0.695 1.057 0.1485 Model 2: SES, smoking, PA 1.138 0.874 1.481 0.3367 0.868 0.700 1.077 0.1981 0.862 0.698 1.064 0.1659 Model 3: Neighborhood SES 1.107 0.850 1.441 0.4503 0.941 0.754 1.174 0.5885 0.912 0.734 1.134 0.4075
Insulin resistance (n=2,353) Estimate Lower Upper p-value Estimate Lower Upper p-value Estimate Lower Upper p-value Model 0: No covariates -0.007 -0.034 0.020 0.6128 -0.025 -0.052 0.003 0.0768 -0.014 -0.041 0.013 0.3023 Model 1: Region, age, gender, race, family history of diabetes 0.051 0.002 0.100 0.0419 -0.052 -0.091 -0.012 0.0101 -0.046 -0.079 -0.014 0.0050 Model 2: SES, smoking, PA 0.051 0.002 0.100 0.0410 -0.051 -0.090 -0.011 0.0115 -0.046 -0.078 -0.014 0.0055
89
Exposure of interest Healthy-to-unhealthy ratio Healthy food price Unhealthy food price
95% CI 95% CI 95% CI
Model 3: Neighborhood SES 0.047 -0.002 0.097 0.0623 -0.046 -0.087 -0.004 0.0307 -0.042 -0.076 -0.008 0.0143 CI, confidence interval; OR, odds ratio; RR, risk ratio; SES, socioeconomic status; PA, physical activity. a Model 0 includes only the price exposure; Model 1 covariates include region, age, gender, race/ethnicity, and family history of diabetes; Model 2 covariates include those in Model 1 plus income/wealth index, education level, smoking status, and physical activity; Model 3 covariates include those in Model 2 plus neighborhood SES
90
CHAPTER 5: DISCUSSION
5.1 DISCUSSION
The affordability of healthy food relative to unhealthy food may be a key
consideration for individuals when deciding what foods to purchase at their local
supermarket. These purchasing decisions may directly affect their diet quality and may
lead to considerable downstream health outcomes, including obesity, insulin resistance
and diabetes. This study linked individuals from the Multi-Ethnic Study of
Atherosclerosis (MESA) – a large, multi-site, longitudinal cohort study – to supermarkets
close to their home. We examined the relationship between the price of foods at these
supermarkets with outcomes collected from MESA, including the quality of diet, level of
insulin resistance, and presence of diabetes. We also examined the potential effect
modification of socioeconomic status on this relationship.
In the first aim of this study we found that across 1,953 supermarkets the average
price per serving of healthy food was nearly twice as high as unhealthy food ($0.590 for
healthy food vs. $0.298 for unhealthy food per serving). In fact, the average per serving
price of healthy food was more expensive than unhealthy food in every one of the
supermarkets examined (range of the healthy-to-unhealthy ratio: 1.48 – 2.45). The
evaluation of neighborhood SES and neighborhood Black/Hispanic showed no strong
association with food prices, including the healthy-to-unhealthy price ratio, and the
individual composite prices of healthy and unhealthy foods. Notable associations were
found when examining individual product categories within the healthy food domain.
The price of dairy, for example, was higher in neighborhoods of lower SES and
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neighborhoods with a higher proportion of Black/Hispanic individuals, while the price of
fruits and vegetables was lower in those same neighborhoods. A similar examination of
products within the unhealthy food domain yielded little; there was little variation in
price of any unhealthy food products. The relative price of healthy food versus unhealthy
food was slightly more affordable as the proportion of Black/Hispanic increased after
adjusting for neighborhood SES and other covariates, while there was no evident
association between neighborhood SES and the price ratio in covariates adjusted models.
In the second aim of the study we found that higher relative prices of healthy
foods compared to unhealthy foods were found to be associated with decreased odds of
having a high quality diet. Although healthy foods were more expensive than unhealthy
foods, consistent with findings in the first aim of this research, there was no association
between diet and the absolute (i.e., non-relative) price of healthy food, while there was a
significant association with the price of unhealthy foods alone. It appears that the relative
price of healthy food compared with unhealthy alternatives is a more meaningful metric
of affordability rather than the absolute price of healthy food without consideration of
potential unhealthy substitutes. Thus, the examination of any food or food class should
consider the price of substitutes.
This study found an interaction between SES and the healthy-to-unhealthy price
ratio on the odds of having a high quality diet; however, the direction of the interaction
was not in line with our a priori hypothesis. Instead of the price ratio having more of an
effect within those of lower SES, the largest effects were observed in those with the
highest level of education and those in the middle range of the income/wealth index. One
explanation for this may be that because healthy food was twice as expensive as
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unhealthy food on average and 55% more expensive at its most affordable, healthy food
may have been too expensive for those with the lowest income, regardless of what the
price ratio was. Those with the highest level of income and wealthy may have the
necessary means to purchase healthy food regardless of how high or low its price was
relative to unhealthy substitutes, and so fluctuations in price had little effect on their
purchasing habits. Thus, it could be those who fall in the middle of the SES spectrum that
are most sensitive to the relatively small variations in the price ratio observed in this
study. To have an impact on lower SES individuals it may require strong subsidies of
healthy foods and high taxes of unhealthy foods to make a significant difference in the
diet of the most vulnerable populations. In fact, subsidies may be the preferred
intervention as they would raise the real income of lower income individuals and make
them more responsive to price differences (McGill et al., 2015).
In the third aim of this study we detected a positive association between the
healthy-to-unhealthy price ratio and level of insulin resistance as measured by HOMA-
IR, indicating that individuals living in areas with larger price differentials between
healthy and unhealthy foods had a higher prevalence of insulin resistance. However, no
significant association was detected between the price ratio with either the prevalence or
incidence of diabetes. There was also no interaction detected between the price ratio with
any of the three outcomes. While slightly higher effects were found for those in the
middle income/wealth tertile, the confidence intervals were wide and showed no clear
separation from other levels of SES.
The null association with diabetes may not be surprising given the chronic nature
of the disease. Food prices for this study were for the years 2009-2012, yet in the
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prevalence analysis diabetes may have been first diagnosed years or decades prior and
thus recent food prices may not have been relevant if they weren’t a reflection of the
prices faced prior to developing diabetes. Furthermore, for the analysis of incident
diabetes, the price of food was captured near the time of first diagnosis of diabetes;
however, data regarding the long-term food prices were not available, and thus we don’t
know what the exposure was for the majority of the time leading up to the first diagnosis
of diabetes. The causes of insulin resistance precede diabetes, and HOMA-IR levels – a
combination of current insulin and glucose levels – are more likely than diabetes to be a
direct result of recent exposures such as recent food prices and the subsequent diet.
Therefore, our ability to detect an association was greatest for insulin resistance, and it
may require a much greater observation period for food prices to detect an association
with the development of type 2 diabetes.
5.2 CONTEXT OF CURRENT RESEARCH WITH PREVIOUS WORK
The large absolute differences in price between healthy and unhealthy foods
observed in this study is consistent with previous work that found prices of soft drinks
much lower than fluid milk (Kern et al., 2016), and the price of sugars, fats, and oils
much lower than fruits, vegetables, meat and poultry (Drewnowski, 2010), that higher
diet costs were associated with a higher quality diet (Rehm et al., 2015), and overall,
healthy food tends to cost more than unhealthy food (Darmon and Drewnowski, 2015).
These differences are likely due to the increased costs of refrigeration, farming, and
transportation for perishables versus lower such costs for long shelf-life
packaged/processed foods. The combination of lower price and increased availability of
packaged/processed foods may be having profound effects on food purchases and result
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in less-than-optimal diet quality across a broad spectrum of the population (Kearney,
2010). The large price divide between healthier and unhealthy foods is concerning;
dietary guidelines emphasize the consumption of fruits, vegetables, and low-fat dairy
while limiting intake of sugar, saturated fats, and sodium (United States Department of
Agriculture and United States Department of Health and Human Services, 2015), yet the
differential price of these foods may be a deterrent to achieving a healthy diet (Darmon
and Drewnowski, 2015).
While it is well established that fresh food is generally more expensive than
unhealthy food, our study is unique in that it linked individuals to their nearby
supermarkets to examine the association between food price with their diet quality. To
our knowledge, this is the first study to do so. Recent work has found that healthier diets
are more expensive than unhealthy diets (Rehm et al., 2015, Rao et al., 2013, Monsivais
et al., 2012), but these studies did not link local food prices to individual diet quality
across a geographically diverse cohort in the US. Instead, previous studies have
calculated diet costs according to national food price estimates (Rehm et al., 2015, Morris
et al., 2014) rather than food prices local to an individual’s neighborhood or have been
limited to a single city (Monsivais et al., 2012).
If healthy foods are twice as expensive as unhealthy foods, as found to be in this
study, meeting established dietary guidelines will be difficult for many people, especially
those of lower SES. A pair of recent studies utilizing data from a single city, the Seattle
Obesity Study (SOS), examined the role of individual SES and types of supermarkets
where individuals shopped and found that low SES consumers shopped at lower price
supermarkets and had a higher risk of obesity compared to those of higher SES, and that
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SES was positively associated with diet quality as measured by the HEI (Drewnowski et
al., 2016, Drewnowski et al., 2014). A third study from the SOS determined that SES
was positively associated with food prices and that food price mediated the relationship
between SES and diet quality (Aggarwal et al., 2011). A systematic review of the
literature found that these findings are consistent across the majority of research, and that
discrepancies in the price between healthy and unhealthy food are likely a key component
of socioeconomic disparities in diet quality (Darmon and Drewnowski, 2015). Further
worsening the food environment for many lower SES and non-White individuals is a
higher prevalence of soda and fast-food advertising in neighborhoods with higher
proportion of lower income and Black/Hispanic persons (Powell et al., 2014). The union
of high availability, exposure to advertising, and lower price may contribute to high
consumption of unhealthy foods in these populations leading to poorer diets (Chandon
and Wansink, 2012, Drewnowski, 2009, Appelhans et al., 2012).
There is ample evidence that individuals of lower SES tend to have poorer, less
expensive diets compared with individuals of higher SES (Darmon and Drewnowski,
2015); however, it is not clear how neighborhood food prices may differentially effect the
diet of different groups of individuals according to their SES. A greater proportion of
income for lower SES individuals is spent on food (Frazao et al., 2007), and lower SES
individuals tend to be more price elastic consumers (Powell and Chaloupka, 2009), and
so it was hypothesized that the largest effects of price on diet would be observed for
individuals with the lowest levels of SES. However, that was not the case, and instead we
found largest effects within the middle income category consistently across our
outcomes, and this study provides insight to a previous gap in the literature regarding the
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role SES may play in the causal pathway between food price and diet quality. This
finding is consistent with a recently published experimental economics study which
simulated food subsidies and taxes on diets across levels of SES, and found that the effect
would be greatest for those in the medium-income group rather than those with the lowest
incomes (Darmon et al., 2016).
No previous studies have examined the price of unhealthy foods at nearby grocery
stores and insulin resistance, but prior work has examined metropolitan area fast food
prices and insulin resistance in a cohort of young adults (CARDIA) and found mixed
results. One study found a negative association between soda and pizza prices and insulin
resistance (Duffey et al., 2010), while results were null in another study (Rummo et al.,
2015), and significant only within relatively disadvantaged groups (middle income range,
lower education, Black) in a third study (Meyer et al., 2014). As an investigation of
neighborhood level exposures, our results are consistent with prior research linking
insulin resistance to neighborhood availability of healthy food and physical activity
resources (Auchincloss et al., 2008), neighborhood deprivation (Andersen et al., 2008),
and neighborhood SES (Diez Roux et al., 2002).
There is little prior literature with which to compare to our results for the diabetes
outcomes, as to our knowledge no study has examined the direct association between
food prices and diabetes. One study examining NHANES data paired with food price data
from Nielsen Homescan consumer surveys found that higher prices of healthy foods were
associated with higher blood sugar levels in those with diabetes (Anekwe and
Rahkovsky, 2014). A second study utilizing NHANES data examined aggregate regional
food prices from the Bureau of Labor Statistics and found a non-statistically significant
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inverse association between the price of high glycemic index foods and HbA1c level, and
a positive association of similar magnitude between low glycemic index food prices and
HbA1c (Rashad, 2007).
Instead of focusing on a specific condition such as diabetes or insulin resistance,
the majority of work examining the relationship between food prices and health outcomes
has focused on weight gain and BMI. These studies have largely concluded that price
changes would have weak, but non-negligible effect on weight outcomes (Powell and
Chaloupka, 2009, Finkelstein et al., 2014), and so it isn’t surprising that no strong
association was detected between food price and diabetes in our study, while modest
associations were detected with insulin resistance.
5.3 LIMITATIONS
There are some limitations of this study that are worth noting. The IRI data set did
not have prices for fresh fruits and vegetables and thus we were limited to using proxies
of fresh fruits and vegetables, specifically orange juice and frozen vegetables. The price
of orange juice is highly correlated with the price of fresh oranges (Morris, 2011) and the
prices of frozen vegetables are similar to those that are fresh, with some variation in the
amount of correlation across vegetable types (Stewart et al., February 2011), thus these
items appear to be acceptable proxies for their fresh counterparts.
Only branded products were included in the price calculations. This was done in
order to maximize product penetration across all stores included in the analysis to ensure
comparability of products across stores and avoid biasing the results due to the inclusion
of a large number of store- or region-specific products. Brands dominate market share for
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most of the unhealthy products, including soda (Beverage World), salty snacks (Grocery
Headquarters), and chocolate candy (Grocery Headquarters). Branded products are
somewhat dominant for orange juice (Beverage Industry Magazine), but much less for dairy
(Grocery Headquarters) and frozen vegetables (Store Brands), and our price estimates for
healthy products may be less representative of the overall healthy food landscape than
our price estimates for unhealthy foods.
The products chosen to represent healthy foods in this study were those that were
to proxy the price of fresh, perishable foods; i.e., fruits, vegetables, and dairy. In doing so
we selected only a portion of all foods that can generally be considered healthy. Canned
fruits (packaged in water, not syrup) and vegetables may also be considered healthy but
were not represented in our data; however, processed fruits and vegetables, including
frozen and canned varieties are not consistently more or less expensive than fresh options
(Stewart et al., February 2011), and so our proxies are likely sufficient for these items.
Beyond fruits, vegetables and dairy, there are other healthy foods such as legumes and
whole grains that were not included in this analysis because they were not available in the
price dataset. It is not clear how our price estimates, and ultimately the results of our
study, would have differed had we had access to all possible healthy foods.
It is possible that limiting our analysis to individuals from MESA who lived
within 3 miles of an IRI supermarket may have introduced selection bias to our study.
However, there was little difference in the characteristics of those who were and were not
included with the exception of race/ethnicity and region due to the exclusion of one of the
MESA sites for which supermarket data was unavailable.
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There are limitations to self-report dietary data obtained via a food frequency
questionnaire (FFQ), as was used in this study. One of the major limitations of an FFQ is
the underreporting of calories; however, the validity of the FFQ used in this study is well
documented and FFQs may be a better measure of long-term diet – rather than recent or
current diet – than other self-reported diet measures taken at a single point in time
(Willett and Hu, 2006). In an attempt to address underreporting across all participants we
defined a high quality diet relative to the population rather than according to an absolute
scale. If underreporting did exist in this study, we do not believe there is a theoretical
reason for the potential measurement error of the FFQ to differ by the price exposure, and
thus any measurement error is likely to be non-differential and would bias results towards
the null hypothesis rather than away from it. The FFQ used in this study was based on an
a priori list of foods that may not have captured every possible food item consumed by
every individual in the study; however, the FFQ used in the MESA study was modified
from a previous FFQ in order to reflect the diversity of the population by including many
foods not typically used that are specific to the cultures of the individuals included in the
cohort study.
Lastly, the analyses of diabetes prevalence and incidence in this study may have
been limited due to the development of the disease occurring over a long period of time
in response to chronic risk factors, while our data captured an exposure period of just a
few years. Ideally, we would have had data on neighborhood supermarket food prices of
each individual for the majority of their adult (and even childhood) life, but instead our
data were limited to a four year period from 2009 to 2012. If this small period of time
was reflective of one’s price environment for the majority of their life than the exposure
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is much more relevant to the outcome of developing diabetes, as opposed to instances in
which the recent place of residence and affordability of foods is vastly different than what
has been faced for many years prior.
5.4 CONCLUSIONS
This dissertation provides meaningful insight to the relationship between the price
of healthy and unhealthy foods with diet quality, diabetes, and insulin resistance within
adults across the U.S. Research on the access to healthy food has gained popularity in
recent years, and was even a cause championed by the first lady Michele Obama as part
of her “Let’s Move!” campaign to fight childhood obesity and the government’s Healthy
Food Financing Initiative (Administration for Children and Families, 2016). There are
several key takeaways resulting from this work, and a few areas that would benefit from
more in depth research in the future.
While there was a large emphasis on the results examining the association
between the health-to-unhealthy price ratio with diet and health outcomes, it is important
to not lose sight of the fact that on average a serving of healthy food costs approximately
twice as much as a serving of unhealthy food. Though this finding isn’t novel on its own,
it adds to a growing collection of literature that has consistently told a similar story over
the last two decades. This large price differential between healthy and unhealthy food
creates a significant barrier to accessible food, and provides the foundation on which the
rest of the research contained in this dissertation stands. This finding reinforces the notion
that access to healthy food is more than just physical access, but also includes economic
access.
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Second, this study found that the price of food, particularly the price of healthy
food relative to unhealthy food, as well as the price of unhealthy food itself, may have an
impact on diet quality. Diet quality plays an essential role in the development and
prevention of health complications, including obesity, diabetes, cardiovascular disease,
and many others, and the implications of the findings in this study could be immense.
Interventions that affect the price of healthy and/or unhealthy food, such as taxation and
subsidization, could result in reduced morbidity in the U.S. population, and in turn the
economic implications that would follow. In fact this study found a positive association
with insulin resistance, a disease largely mediated by one’s diet, though no significant
association was found with diabetes. Data for this study were limited to a few recent
years, but in order to more likely find an association with more chronic disease outcomes,
such as diabetes, a more historical measure of food prices may be necessary.
Furthermore, as this study was limited to a handful of types of healthy and unhealthy
foods, it is important that future research expand these categories, if the data allows, in
order to improve the generalizability and validity of these findings.
Lastly, this study hypothesized that associations between food price and each of
the outcomes of interest would differ according to an individual’s socioeconomic status.
We hypothesized that those of low SES would be the most affected by price differentials,
as they have the fewer means and are more price sensitive than those with higher levels
of income and education. However, it was instead individuals in the middle of the
income/wealth distribution whom were affected the most by price differences for every
outcome examined – diet quality, diabetes prevalence, diabetes incidence, and insulin
resistance. This is an important finding when considering interventions to make healthy
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and unhealthy food more or less affordable, respectively. While the intention would be to
help those most in need, it is possible such interventions would not sufficiently help the
most vulnerable populations and instead could increase inequalities in nutrition between
socioeconomic groups (Darmon et al., 2016). Prior to implementing any policies aimed at
increasing the affordability of healthy foods, a thorough amount of research and
community outreach should be implemented to ensure that those who need help the most
would receive it. Fiscal interventions to influence the price of healthy and unhealthy
foods may be effective in improving diets and health; however, it is unlikely to be a
panacea for the obesity crisis. Instead, a multipath approach should be used to improve
access to a healthy diet – including educational programs to improve nutritional
knowledge, nutritional labeling on food packaging and menus, regulations on marketing
and advertising of junk food, in addition to improving physical and economic access.
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APPENDIX 1 : FOR CHAPTER 2
APPENDIX 1.1 Choice of products in the IRI dataset
The following criteria were used to select items (UPCs) within products from the IRI
supermarket price dataset:
1. Selected based on high volume sales or unit sales
Within category, the item had to be in the top Nth percentile (percentile varied
according to the product) of volume sales sold in 2010 (sales equalized to package units)
or top Nth percentile of package units sold in 2010. Item rankings varied by year and so
2010 was used as the index year because our health data come primarily from that year.
Ideally we wanted to use the top 20th percentile for all product categories but had to
select a lower percentile because either (a) a product category had less brand-name
dominance and/or the brands frequently altered their packaging and so UPCs were not
consistent over many years. High volume of unit sales was defined as the top 20th
percentile for carbonated beverages, chocolate candy, and cookies. High volume of unit
sales was defined as the top 40th percentile for salty snacks, frozen vegetables, and
cottage cheese. Milk was not subset according to high volume or unit sales.
2. Selected based on high market saturation
Within categories, for at least 2 out of 4 years, the item had to be sold at a
relatively high percent of IRI supermarkets. This was determined by examining IRI's
analysis of the "all commodity volume" or ACV. We iteratively determined the ACV
threshold so as to obtain a sufficient number of items per category-brand. The minimum
threshold for >2-year ACV was: chocolate candy ≥ 80% of all IRI stores, carbonated
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beverages ≥ 60%, cookies ≥ 50%, salty snacks ≥ 50%, orange juice ≥ 50%, yogurt ≥
50%, frozen vegetables ≥ 20%, cottage cheese ≥ 20%, and milk ≥ 10%.
3. Diversity of brands
We selected brand-name products because we needed products that had high
potential for being sold in stores across the U.S. Per category, we selected a mean of 5.5
brands (range 3-11) and 15 UPCs (range 5-25). The categories with only 3 brands were
cottage cheese and frozen vegetables, and the lowest number of UPCs was for cottage
cheese (n=9). After brand selection, the researchers confirmed that selected brands were
noted in industry literature as being the top brands.
4. Multiple items in a particular package size
After fulfilling criteria 1-3 above, items were further sorted by product category,
brand name, package size, 2010 volume sales (equivalence), and minimum ACV between
2009-2012 (descending order so highest minimum value). In order to be able to pool
price information we attempted to select at least 2 items with the same package size and
items that were preferably different brands.
Note to criteria #4: Our analyses used a handful of index food product categories
and small number of index UPCs to compare food prices between regions and between
product categories. Because price per ounce declines as package size increases, ideally
we would select items that are similar across product categories according to "intended
servings per package" and "package size". However, this restriction was not feasible
given criteria 1-3 above. Thus, we selected a diversity of package sizes and standardized
to servings per package and package size before averaging price within a product
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category and before comparing relative prices between regions and between product
categories. Mean servings per package was 2-5 for chocolate candy/cottage
cheese/frozen vegetables and 7-8 for carbonated beverages and salty snacks.
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APPENDIX 1.2 Details on healthy and unhealthy products included in the price calculations
Details of products included in each category
Healthy food prices are represented by a combination of the following products:
milk, frozen vegetables, yogurt, and cottage cheese. Brands that had significant regional
and national penetration were selected in order to maintain product uniformity across all
metro areas being examined. Milk includes 15 products (All ½ gallon products. One 1
gallon product was initially included but was not available in 66% of stores, and because
one gallon products had significantly different prices per serving compared with half
gallon products, it was removed). Data include regular and organic milk, and reduced fat
(1% and 2%) and skim milk. Frozen vegetables include 23 products with package sizes
range from 7 to 24 ounces. Cottage cheese includes 9 products, all of which were either
16 or 24 ounces and included 1%, 2%, or 4% milkfat. Yogurt includes 17 products with
package sizes ranging from 6 to 48 ounces and includes low-fat and non-fat products.
Orange juice includes 12 not from concentrate 100% orange juice products with package
sizes ranging from 13.5 to 128 fluid ounces.
Unhealthy foods are represented by the following products: chocolate candy,
cookies, salty snacks, and soda. Chocolate candy includes single and multiple serving
packages of candy bars/pieces ranging from 1.4 ounces (e.g., Hershey’s milk chocolate
bar with almonds) to 12.6 ounces (a bag of M&Ms). Products include packaging of
regular size and “fun” size pieces across 22 different products. Cookies include 13
products in package sizes ranging from 7.5 to 31.8 ounces. These include Oreo, Chips
Ahoy, Nilla Wafers, among other popular products. Salty snacks include 23 products,
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including pretzels, potato chips, tortilla chips and other similar products. Product sizes
range from 6.4 ounces (Pringles) to 18 ounces (Tostitos). Soda includes 25 products
(excluding diet or reduced calorie items), including single 20 ounce and 2-liter bottles, as
well as 12-packs of 12 ounce cans.
Rationale for the selection of unhealthy foods
Representatives for the unhealthy food category were chosen for their long shelf
life and high use of preservatives. Additionally, each of these foods has poor nutritional
quality; they are high in added sugar, saturated fat, and/or sodium, with very little protein
or vitamins. Increased consumption of these foods has been associated with obesity,
diabetes, and other diseases, detailed below.
Soda: Soda may be the ultimate unhealthy food, as it supplies no nutritional value
yet is calorically dense, while ironically providing low satiety (Malik et al., 2006). With
all of its 140 calories per 12 ounce can coming from refined sugars, most often high
fructose corn syrup, and containing no vitamins or minerals, soda has been referred to as
liquid candy (Jacobson, 2005). The USDA recommends no more than 48 grams of solid
fats and added sugars in a 2,000 calorie diet (United States Department of Agriculture
and United States Department of Health and Human Services, 2010), yet a single can of
soda includes 33 grams of added sugar alone United States Department of Agriculture
(2014c). Despite the lack of nutritional value, soda consumption is higher in the United
States than anywhere else in the world, with approximately one-half of the US population
consuming a sugar drink on any given day (Ogden et al., 2011). Because of its sweet
taste, poor nutritional value, low price, and high rates of consumption, soda consumption
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has been linked to a number of diseases, including obesity, cardiovascular disease, and
diabetes (Bray and Popkin, 2014, Dhingra et al., 2007, Hu, 2013, Hu and Malik, 2010,
Malik and Hu, 2012, Malik et al., 2010b, Schulze et al., 2004, de Koning et al., 2011b,
Malik et al., 2010a, Berkey et al., 2004, Ludwig et al., 2001). Thus soda is well suited to
be considered a representative of unhealthy food.
Salty snacks, cookies, and chocolate candy: The remaining unhealthy food
representatives (chocolate candy, salty snacks, and cookies) were chosen because they
are commonly considered “junk foods” due to their low nutritional quality (Brunello et
al., 2014, Gittelsohn et al., 1998, Magee, 2014, Smith et al., 2014, Zahedi et al., 2014).
Specifically because these foods are high in sodium, added sugar and/or saturated fat
(Brunello et al., 2014). The health effects associated with consumption of such junk food
range from an increased risk of diabetes (Gittelsohn et al., 1998) to poorer mental health
(Zahedi et al., 2014). For an example of the poor nutritional content, consider a regular
1.55 oz Hershey’s milk chocolate bar. A single bar contains 210 calories, 8 grams of
saturated fat (13g of total fat, 20% of the RDA), and 24 grams of sugar (Hershey's, 2014).
Additionally, a 30 gram serving of Chips Ahoy! Original Chocolate Chip cookies,
approximately 3 cookies, contains 144 calories, 7.3 grams of fat, and 10 grams of sugar,
and under 1 gram of protein (Nabisco, 2014). And for salty snacks, a single serving of
approximately 12 Nacho Cheese Doritos chips (30 grams) contains 150 calories, 8.6
grams of fat, and 225 mg of sodium (Frito Lay, 2014). All three of these foods, which are
typical of their overall product category, are calorically dense and high in added sugar,
saturated fat, and/or sodium, in addition to being high in preservatives resulting in a long
shelf life, and hence considered unhealthy in our study.
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Rationale for the selection of healthy foods
Foods selected to represent the healthy food category are those that can be
considered a proxy for all fresh foods that have a limited shelf life, such as fresh fruits
and vegetables, and include vegetables (frozen), fruit juice (as a proxy for fruits), and
dairy products. Additionally, many of the foods being considered healthy have been
associated with positive health outcomes, including lower rates of diabetes, obesity, and
heart disease, among others.
Frozen vegetables: Although this study did not have access to the price of fresh
fruits and vegetables we were able to capture the price of frozen vegetables sold in these
supermarkets. The price of frozen vegetables is very similar to that of their fresh
counterparts, with some frozen vegetables slightly more expensive than fresh, while
others are somewhat cheaper (Stewart et al., February 2011). Although the shelf life of
frozen vegetables is longer than their fresh counterparts, they have similar health benefits
(Rickman et al., 2007, The British Frozen Food Federation et al., 2009, Favell, 1998).
Frozen vegetables have high nutritional content; for example, one serving (85
grams)(United States Food and Drug Administration, 2014) of mixed vegetables from
Birds Eye, which includes frozen carrots, corn, peas and green beans, contains 2.4 grams
of fiber, 2.3 grams of protein, 120% of Vitamin A recommended daily allowance (RDA),
and just 67 calories (Birds Eye, 2016). Furthermore, consumption of vegetables (alone or
studied in combination with fruit consumption) has been linked to health benefits,
including lower rates of diabetes in women (Bazzano et al., 2008) and in men (Mursu et
al., 2014), as well as reduced rates of cardiovascular disease (Hung et al., 2004) and
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coronary heart disease (He et al., 2007). Because of these reasons, frozen vegetables
serve as a proxy for healthy food in general.
Orange juice: Orange juice is included as a health food representative mainly to
act as a proxy for the price of fresh fruit. The fruit juice included in this study is 100%
orange juice and thus the price of oranges is directly reflected in the price of the juice.
The price of an edible cup of an orange is exactly the same whether it is from a fresh
navel orange or from ready to drink orange juice ($0.34 per edible cup) (Stewart et al.,
February 2011). And over time the price of orange juice tracks closely with the price of
raw oranges: from 2001 through the fall of 2011 the correlation between the price of
oranges and retail 100% orange juice was 74% (Morris, 2011). While the health benefits
of fresh fruit, including oranges, is well established, the health effects of drinking orange
juice are mixed. Some studies have found a modest increase in risk of diabetes with
greater consumption of fruit juice (Bazzano et al., 2008, Muraki et al., 2013), while
others have found benefits of increased vitamin C and antioxidant biomarker
concentrations (Sanchez-Moreno et al., 2003) and increased HDL levels (Kurowska et
al., 2000). Thus, orange juice is a proxy for healthy foods because of its pricing
similarities to the fresh, healthy, fruit it is created from, rather than its own health
benefits.
Dairy products (milk, yogurt, and cottage cheese): Dairy products were chosen
because of their short shelf life, similar to that of fresh fruits and vegetables, and because
of their high nutritional value and health benefits. Milk and other dairy contain numerous
vitamins and minerals, including vitamin D and calcium, among many others (Runge et
al., 2011). While milk may contain a significant number of calories – a 12 ounce glass of
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1% low-fat milk will contain 154 calories – 32% comes from protein, 21% from fat, and
the remaining amount from the natural sugar lactose (United States Department of
Agriculture, 2014c). This is a stark contrast to the 100% of calories in soda derived from
added sugars. The consumption of skim milk been shown to satiate the appetite and lead
to fewer consumed calories compared with fruit drinks in the morning (Dove et al.,
2009), and thus a healthy option at breakfast. Vitamin D and dairy calcium are associated
with diet-induced weight loss (Shahar et al., 2010) and reduced odds of insulin resistance
syndrome in overweight persons (Pereira et al., 2002), all of which are applicable to the
outcomes being studied in this research.
Yogurt is similar to fluid milk in that it has a limited shelf life and is rich in
nutrients, with the additional benefit of probiotics. There have been numerous studies of
the potential health benefit of yogurt consumption, particularly within older adults (El-
Abbadi et al., 2014), i.e., the population of interest in this dissertation. Yogurt
consumption has been associated with improved bone mineral density (Sahni et al.,
2013), a lower risk of cardiovascular disease (Sonestedt et al., 2011), and a lower risk of
type 2 diabetes (Margolis et al., 2011) within older populations. Cottage cheese is the
third dairy product to be included, which has many of the same benefits of yogurt and
fluid milk. Although cottage cheese consumption in the United States is fairly small when
compared to other dairy products (Davis et al., 2010), it has significant nutritional value.
A single serving of 1% milk-fat cottage cheese contains 14 grams of protein, but just 1
gram of fat and 79 calories total, in addition to being a significant source of calcium,
phosphorus, riboflavin, and vitamin B-12 (United States Department of Agriculture,
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2014b). Cottage cheese consumption is also more prevalent in older adults (Davis et al.,
2010), making it applicable to the population in our study.
Overall, milk products, including cottage cheese and yogurt, are associated with
decreased prevalence of insulin resistance and type-2 diabetes (Tremblay and Gilbert,
2009, Fumeron et al., 2011), metabolic syndrome (Fumeron et al., 2011, Pfeuffer and
Schrezenmeir, 2007, Samara et al., 2013) and obesity.(Major et al., 2008) Because of
these reasons, and their relatively short shelf life, all three of these dairy products are
strong candidates for representatives of the healthy food category.
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APPENDIX 1.3 Principal component analysis
A two-stage principal component analysis (PCA) was conducted to identify
products that statistically grouped together in order to reduce the dimensionality of the
data and the number of outcomes analyzed. We identified product-item prices that were
highly correlated with each other and distinct from other product groups. All retained
items had component loading scores ≥0.60. This analysis was done in two steps.
The first PCA was conducted by analyzing all individual items (e.g., 20 ounce
Coca-Cola Classic) within each product (e.g., soda). Consistent across all products, two
factors were identified within each product according to the volume or weight of the
items; one factor included all lower volume/weight items (e.g., 2-liter and 20-oz
individual bottles of soda) while the other included all higher volume/weight items (e.g.,
cases of twelve 12-oz cans and 6-packs of 20-oz sodas). The only exception to this was
cottage cheese for which all items (16-oz and 24-oz) grouped into a single factor. Two
price variables were then created for each product by grouping items with similar
volume/weight according to the results of the PCA (with the exception of one for cottage
cheese).
The second PCA was then conducted using the newly created product variables.
The PCA found that healthy food products grouped into two categories: dairy (made up
of yogurt, cottage cheese, and milk) and fruits and vegetables (comprised of orange juice
and frozen vegetables). For unhealthy foods, cookies and chocolate candy grouped
together, while soda and salty snacks remained independent of other products, resulting
in three unhealthy product classes. The different sizes within each product identified in
the first PCA always grouped together in the second step, thus all items from the same
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product category were kept together. The five product categories resulting from the
second PCA were used for the remainder of all analyses.
Supplemental Table 1 Rotated component matrix from the principal component analysis of healthy foods illustrating the component loading scores of each item for dairy (milk, yogurt and cottage cheese) and fruits and vegetables (orange juice and frozen vegetables)
Component 1 2
Milk (half-gallon) .764 .295 Orange juice (≤59 ounces) .138 .830 Orange juice (≥89 ounces) .450 .727 Yogurt (≤24 ounces) .740 .244 Yogurt (≥32 ounces) .892 .133 Cottage cheese .833 .288 Frozen vegetables (≤10 ounces) .615 .632 Frozen vegetables (≥12 ounces) .204 .826
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 3 iterations.
Supplemental Table 2 Rotated component matrix from the principal component analysis of unhealthy foods illustrating the component loading scores of each item for soda, sweets (chocolate and cookies) and salty snacks
Component
1 2 3 Soda (multi pack) .149 .914 .189 Soda (single bottle) .364 .837 -.011 Chocolate candy (≤5 ounces) .848 .118 .069 Chocolate candy (≥9 ounces) .775 .173 .173 Cookies (≤12 ounces) .795 .515 -.005 Cookies (≥15 ounces) .719 .360 -.183 Salty snacks (≤11 ounces) .489 .039 .713 Salty snacks (≥13 ounces) -.162 .102 .879
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 5 iterations.
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APPENDIX 1.4 Distribution of the healthy-to-unhealthy price ratio
The healthy-to-unhealthy price ratio followed a roughly normal distribution as
illustrated in the histogram below (Figure 1). The normal distribution of the data allowed
for meaningful analysis when considering the impact of a one standard deviation change
in the measure on the outcomes examined in Chapters 3 and 4.
Figure 1 Distribution of the health-to-unhealthy price ratio for the 1953 stores in the IRI database. The ratio had a mean of 1.97 and standard deviation of 0.19.
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APPENDIX 1.5 Validation of the IRI price dataset against C2ER
As a measure of external validity of the price data we looked at correlations with
the Cost of Living Index (COLI) from the Council for Community and Economic
Research (C2ER). Store prices from the IRI data were rolled-up to the level of core-based
statistical area (CBSA), also referred to as a metropolitan area, to match the spatial
resolution of the COLI data. A subset of products from IRI were then chosen to most
closely match those included in the COLI calculations; this included ½ gallon milk (in
IRI this was 1%, 2%, and fat free, while in COLI it is whole milk), frozen corn (12 to 16
oz. from IRI vs. 16 oz. from COLI), orange juice (48 to 89 oz. in IRI vs. 64 oz. in COLI;
included only Tropicana and Florida’s natural for both data sources), soda (2-liter of
Coca-Cola from both sources), and potato chips (8.5 and 9.5 oz. from IRI vs. 12 oz. plain
chips from COLI). Spearman rank correlations were performed and found moderate
correlations for orange juice (r=0.305, p<0.001), potato chips (r=0.425, p<0.001), and
soda (r=0.540, p<0.001), low correlation for frozen corn (r=0.149, p<0.001), and no
correlation for milk (r=0.013, p=0.592). The lack of correlation for milk may have been
due to only branded and organic low and non-fat products included in the IRI data vs. any
type of whole milk in COLI.
Supplemental Table 3 Spearman rank correlations for products found in the IRI dataset and C2ER
Spearman rank
correlation p-
value Orange juice a 0.305 <0.001 Frozen corn b 0.149 <0.001 Milk c 0.013 0.592 Soda d 0.540 <0.001
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Spearman rank
correlation p-
value Potato chips e 0.425 <0.001
a Orange juice includes 48 o 89 ounce Tropicana or Florida's Natural from IRI and 64 ounce packages of the same brands from C2ER b Frozen corn includes 12 to 16 ounces packages from IRI and 16 ounce packages from C2ER c Milk includes branded half gallon milk (1%, 2%, fat free) from IRI and any half gallon whole milk from C2ER d Soda includes 2-liter bottles of Coca-Cola from IRI and C2ER e Potato chips includes 8.5 and 9ounce Baked Lays, Baked Ruffles, Kettle Cooked Lays from IRI and 12 ounce plain chips from C2ER
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APPENDIX 1.6 Additional supplemental tables for Chapter 2
Supplemental Table 4 Product selection criteria for each of the food categories used in this study
Category Number brands
Number UPCs Example brands Exclusions
Carbonated Beverages
9 25 7-UP, A&W, Canada Dry, Coca Cola, etc.
Chocolate Candy 11 22 Hershey, Kit Kat, M&M, Milky Way, Nestle, etc.
Cookies 4 13 Keebler, Little Debbie, Nabisco, Pepperidge Farm
Cottage cheese 3 9 Breakstone, Daisy, General Mills
Specialty products, such as lactose free cottage cheese, were not included.
Orange juice 3 12 Florida's Natural, Simply, Tropicana
Added sugar, not 100% orange juice
Milk 5 14 Farmland, Hood, Horizon, Organic Valley, Stonyfield
Full fat; specialty milks: soy, almond
Frozen vegetables 3 23 Bird's Eye, Green Giant, Pictsweet
Seasoned, glazed, creamed
Salty snacks 10 23 Lays, Funyuns, Pringles, Snyders, Tostitos, etc.
Yogurt 5 17 Dannon, Fage, Stonyfield, Yoplait
Products marketed towards children (e.g., Trix brand yogurt) or those that are highly sweetened (e.g., crunch and whips).
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Supplemental Table 5 Nutrition information for each of the healthy and unhealthy food categories included in this study
FDA serving size1
Key nutrition information2
Healthy Foods
Frozen vegetables 88 g 60 calories, 2 g protein, 2 g fiber Low-fat milk 8 fl oz 102 calories, 8 g protein, 13 g natural sugar, 2 g
saturated fat Orange juice 8 fl oz 110 calories, 2 g protein, 23 g sugar Yogurt 225 g 132 calories, 11 g protein, 16 g sugar, 2 g
saturated fat Cottage cheese 110 g 79 calories, 14 g protein, 1 g saturated fat
Unhealthy foods
Soda 8 fl oz 93 calories, 26 g added sugar, 0 g protein Cookies 30 g 144 calories, 7 g fat, 10 g sugar, 1 g protein Salty snacks 30 g 150 calories, 9 g fat, 225 mg sodium, 2 g
protein Chocolate candy 40 g 190 calories, 7 g saturated fat
1: Source: (United States Food and Drug Administration, 2014) 2: Nutrition information for each product is represented by one item within that group: frozen vegetables (Birds Eye mixed vegetables(Birds Eye, 2016)), low-fat milk (1% milk-fat (United States Department of Agriculture, 2014c)), orange juice (Simply Orange pulp free (Simply Orange Juice Company, 2015)), yogurt (Dannon All Natural Plain Lowfat (Dannon, 2015)), cottage cheese (1% milk-fat (United States Department of Agriculture, 2014b)), soda (Coca-Cola Classic (United States Department of Agriculture, 2014c)), cookies (Chips Ahoy! Original (Nabisco, 2014)), salty snacks (Doritos Nacho Cheese (Frito Lay, 2014)), and chocolate candy (Hershey’s milk chocolate bar (Hershey's, 2014)) Abbreviations: g = grams, fl oz = fluid ounces, mg = milligrams
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Supplemental Table 6 Serving sizes and weights for composite price calculations using two different weight calculations – based on national consumption averages and using equal weights within each food class
Serving size
Servings per day Weight a Weight b
Healthy foods Fruits and vegetables c (rep. by OJ and frozen veg)
1 cup 2.63 0.617 0.5
Dairy d 8 fl oz 1.63 0.383 0.5 Unhealthy foods
Soda e 8 fl oz 0.73 0.450 0.33 Sweets f,g 30g/40g 0.62 0.383 0.33 Salty snacks h 30 g 0.27 0.167 0.33
a Weight based on national consumption averages (servings per day column) b Equal weight given to all products in their respective class c NHANES 2011-2012 survey found adults 20 years and older consumed 1.64 cups of vegetables per day and 0.99 cups of fruit per day(United States Department of Agriculture and Agricultural Research Service, 2014) d NHANES 2011-2012 survey found adults 20 years and older consumed 1.63 cups of dairy per day. (United States Department of Agriculture and Agricultural Research Service, 2014) e Adults ≥60 years old consume 68 calories from SSBs on a daily basis.(Kit et al., 2013) A 12 ounce can of Coca-Cola contains 140 calories,(Coca-Cola, 2014) thus 68 calories equates to 5.8 ounces, or 0.73 daily servings f According to a Canadian agriculture report, 2010 cookie sales was 1 million tons in the United States,(Agriculture and Agri-food Canada, 2012) which equates to 0.27 servings per day. g According to the latest report from the International Cocoa Organization, in 2008 Americans consumed 5.09 kilograms of chocolate on average, roughly 11.2 pounds, which equates to 0.49 ounces per day, or 0.35 servings.(International Cocoa Organization) h A 2004 report from the USDA found that 95.5% of households consumed any chips (potato, corn, etc.) and of those that did eat chips they consumed 7.0 pounds per capita.(Kuchler et al., 2004) After accounting for those that did not buy any chips this becomes 6.7 pounds per capita annually, 0.29 ounces per day, or 0.27 servings. Abbreviations: g = grams, fl oz = fluid ounces
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Supplemental Table 7 Food prices according to weights based on national consumption averages and equal weights
All healthy food
(weight 1)a All healthy food
(weight 2)b Mean SD Mean SD Aggregate healthy food price $0.590 $0.056 $0.619 $0.065 Aggregate unhealthy food price $0.298 $0.024 $0.294 $0.021 Healthy-to-unhealthy price ratio 1.99 0.19 2.11 0.21
a Weight 1: Aggregate healthy food prices and unhealthy food prices use weights according to national consumption estimates of vegetables, fruits, and dairy for healthy foods, and sugar sweetened beverages, chocolate, cookies, and chips (potato, corn, etc.) for unhealthy food. b Weight 2: Aggregate healthy food prices, unhealthy food prices use equal weights within each category (i.e., fruits & vegetables and dairy each received a weight of 1/2 for the healthy food price; and soda, sweets, and salty snacks each received a weight of 1/3 for the unhealthy price
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Supplemental Table 8 Price of each product category by demographic characteristics
Healthy foods Unhealthy foods Ratio
# of
stores
Fruits + vegetables Dairy
All healthy food a Soda Sweets Salty Snacks
All unhealthy food a
Healthy-to- Unhealthy a
Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
Overall 1953 $0.489 $0.051 $0.760 $0.100 $0.590 $0.056 $0.217 $0.029 $0.375 $0.037 $0.290 $0.014 $0.298 $0.024 $1.990 $0.190
Neighborhood SES quantile b Lowest quintile (least advantaged)
391 $0.460 $0.042 $0.788 $0.089 $0.581 $0.047 $0.219 $0.024 $0.370 $0.032 $0.297 $0.017 $0.299 $0.019 1.950 0.175
Second quintile 391 $0.476 $0.040 $0.759 $0.092 $0.581 $0.048 $0.215 $0.025 $0.370 $0.034 $0.292 $0.013 $0.295 $0.020 1.974 0.177
Middle quintile 390 $0.487 $0.039 $0.760 $0.102 $0.589 $0.053 $0.213 $0.024 $0.373 $0.028 $0.288 $0.011 $0.295 $0.017 2.003 0.196
Fourth quintile 391 $0.496 $0.040 $0.747 $0.106 $0.590 $0.057 $0.213 $0.025 $0.373 $0.026 $0.285 $0.011 $0.294 $0.016 2.014 0.204 Highest quintile (most advantaged)
390 $0.526 $0.065 $0.747 $0.103 $0.611 $0.068 $0.226 $0.043 $0.389 $0.055 $0.287 $0.013 $0.306 $0.039 2.010 0.190
Proportion Black/Hispanic quintile Lowest quintile (0.8% to 14.0% Bl/Hisp) 390 $0.501 $0.060 $0.741 $0.086 $0.592 $0.058 $0.216 $0.039 $0.378 $0.047 $0.286 $0.013 $0.297 $0.033 $2.002 $0.182 Second quintile (14.0% to 24.0%) 392 $0.499 $0.051 $0.743 $0.096 $0.591 $0.058 $0.219 $0.031 $0.375 $0.037 $0.288 $0.012 $0.298 $0.026 $1.990 $0.187 Middle quintile (24.0% to 37.8%) 390 $0.494 $0.045 $0.760 $0.112 $0.593 $0.060 $0.216 $0.026 $0.374 $0.034 $0.289 $0.012 $0.297 $0.022 $2.004 $0.203 Fourth quintile (37.9% to 58.4%) 391 $0.486 $0.046 $0.759 $0.102 $0.587 $0.054 $0.217 $0.024 $0.373 $0.032 $0.292 $0.013 $0.297 $0.019 $1.981 $0.194 Highest quintile (58.5% to 98.8%) 390 $0.465 $0.046 $0.798 $0.090 $0.588 $0.050 $0.217 $0.023 $0.374 $0.033 $0.294 $0.017 $0.299 $0.018 $1.974 $0.184
Urban classification
Large metro (pop. ≥1 million) 1606 $0.492 $0.055 $0.769 $0.102 $0.596 $0.058 $0.217 $0.031 $0.377 $0.039 $0.288 $0.013 $0.298 $0.026 $2.007 $0.192
Small metro (pop. <1 million) 296 $0.476 $0.028 $0.725 $0.083 $0.568 $0.042 $0.221 $0.018 $0.368 $0.027 $0.296 $0.013 $0.298 $0.015 $1.912 $0.163
Rural (pop <50k) 51 $0.467 $0.019 $0.701 $0.055 $0.553 $0.023 $0.217 $0.018 $0.354 $0.026 $0.301 $0.014 $0.291 $0.015 $1.904 $0.139
Region
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Healthy foods Unhealthy foods Ratio
# of
stores
Fruits + vegetables Dairy
All healthy food a Soda Sweets Salty Snacks
All unhealthy food a
Healthy-to- Unhealthy a
Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
Northeast 207 $0.509 $0.087 $0.730 $0.095 $0.594 $0.074 $0.237 $0.056 $0.396 $0.069 $0.288 $0.016 $0.314 $0.052 $1.912 $0.179
Midwest 173 $0.489 $0.032 $0.782 $0.038 $0.599 $0.026 $0.172 $0.020 $0.383 $0.034 $0.277 $0.011 $0.279 $0.014 $2.155 $0.106
South 1009 $0.468 $0.024 $0.703 $0.052 $0.555 $0.024 $0.217 $0.019 $0.365 $0.030 $0.296 $0.013 $0.295 $0.018 $1.888 $0.138
West 564 $0.519 $0.057 $0.867 $0.090 $0.650 $0.043 $0.225 $0.015 $0.382 $0.026 $0.283 $0.007 $0.302 $0.013 $2.152 $0.143 a Composite healthy and unhealthy food prices were weighted based on national consumption averages of each food category b Neighborhood SES was derived from log of the median household income; log of the median value of housing units; the percentage of households receiving interest, dividend, or net rental income; the percentage of adults 25 years of age or older who had completed high school; the percentage of adults 25 years of age or older who had completed college; and the percentage of employed persons 16 years of age or older in executive, managerial, or professional specialty occupations.
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Supplemental Table 9 Sensitivity analysis of the hierarchical models examining price outcomes on neighborhood socioeconomic status (SES) and the proportion of individuals who are Hispanic or black using a 2-mile buffer
Estimate a
95% CI Model Lower Upper P-value SES MODELS: Mean difference in price per 20 percent change in neighborhood socioeconomic index
Healthy food price Unadjusted b 0.00434 0.00320 0.00548 <0.0001
Adjusted c 0.00073 -0.00093 0.00240 0.3881
Fruits and veggies Unadjusted b 0.01342 0.01215 0.01469 <0.0001
Adjusted c 0.01080 0.00890 0.01271 <0.0001
Dairy Unadjusted b -0.01304 -0.01514 -0.01093 <0.0001
Adjusted c -0.01869 -0.02192 -0.01546 <0.0001
Unhealthy food price Unadjusted b 0.00035 -0.00015 0.00085 0.1741
Adjusted c 0.00020 -0.00049 0.00089 0.5649
Soda Unadjusted b 0.00038 -0.00023 0.00098 0.2209
Adjusted c 0.00038 -0.00057 0.00132 0.4357
Sweets Unadjusted b 0.00231 0.00145 0.00317 <0.0001
Adjusted c 0.00107 -0.00002 0.00215 0.0547
Salty snacks Unadjusted b -0.00226 -0.00262 -0.00191 <0.0001
Adjusted c -0.00136 -0.00192 -0.00079 <0.0001
Healthy-to-Unhealthy price ratio Unadjusted b 0.01218 0.00826 0.01610 <0.0001
Adjusted c 0.00188 -0.00447 0.00824 0.5615
BLACK/HISPANIC MODELS: Mean difference in price per 20 percent change in neighborhood proportion of Black/Hispanic
Healthy price Unadjusted b -0.00295 -0.00412 -0.00178 <0.0001
Adjusted c 0.00021 -0.00148 0.00190 0.8071
Fruits and veggies Unadjusted b -0.01005 -0.01141 -0.00869 <0.0001
Adjusted c -0.00013 -0.00205 0.00180 0.8973
Dairy Unadjusted b 0.01067 0.00850 0.01284 <0.0001
Adjusted c 0.00115 -0.00214 0.00444 0.4925
Unhealthy price Unadjusted b 0.00079 0.00027 0.00130 0.0027
Adjusted c 0.00199 0.00128 0.00269 <0.0001
Soda Unadjusted b 0.00035 -0.00027 0.00096 0.2676
Adjusted c 0.00068 -0.00028 0.00165 0.1669
Sweets Unadjusted b -0.00019 -0.00108 0.00069 0.6671
Adjusted c 0.00344 0.00234 0.00455 <0.0001
Salty snacks Unadjusted b 0.00231 0.00194 0.00267 <0.0001
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Estimate a
95% CI Model Lower Upper P-value
Adjusted c 0.00121 0.00064 0.00179 <0.0001
Healthy-to-Unhealthy price ratio Unadjusted b -0.01546 -0.01944 -0.01148 <0.0001
Adjusted c -0.01149 -0.01798 -0.00500 0.0005 a per 20 percentile change in neighborhood SES or Black/Hispanic b Models did not include covariates but were adjusted for county and state via model nesting c Includes covariates: Age, region, urbanicity, population density, supermarket density, toilet paper price, and either race (in the SES models) or neighborhood SES (in the race models).
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APPENDIX 2 : FOR CHAPTER 3
APPENDIX 2.1 Supplemental tables for Chapter 3
Supplemental Table 10 Characteristics of included and excluded individuals in the analysis of diet quality
Included
participants Excluded
individuals
N /
Mean Col %
/ SD n /
Mean % / SD Number of participants (N) 2765 1951 MESA recruitment site (n, %) a
Forsyth County, NC 539 19.5% 274 14.0% New York, NY 538 19.5% 272 13.9% Baltimore, MD 464 16.8% 194 9.9% St. Paul, MN 8 0.3% 763 39.1% Chicago, IL 651 23.5% 225 11.5% Los Angeles, CA 565 20.4% 223 11.4%
Region of residence (n, %) Northeast 534 19.3% 241 13.0% Midwest 642 23.2% 937 50.5% South 1021 36.9% 452 24.3% West 568 20.5% 227 12.2%
Total supermarket density (3 mile) (mean, SD) 1.19 1.42 0.79 1.19 Female (n, %) 1466 53.0% 1048 53.7% Age (mean, SD) 70.3 9.5 69.4 9.5 Race/ethnicity (n, %)
White 1101 39.8% 824 42.2% Chinese American 359 13.0% 182 9.3% Black 834 30.2% 417 21.4% Hispanic 471 17.0% 528 27.1%
Education (n, %) High school diploma or less 777 28.1% 718 36.8% Some college 761 27.5% 610 31.3% Bachelor’s degree or more 1227 44.4% 615 31.5%
Per capita household income (in $10k) 2.6 1.9 2.3 1.7 Wealth index 2.6 1.2 2.5 1.2 Income/wealth index 5.1 2.2 4.8 2.2 Marital status (n, %)
147
Not married or living with partner 1107 40.0% 825 42.3% Married/Living with partner 1658 60.0% 1126 57.7%
BMI (mean, SD) 28.2 5.6 28.9 5.7 <25 (n, %) 855 30.9% 512 26.2% 25-29.9 (n, %) 1043 37.7% 710 36.4% ≥30 (n, %) 867 31.4% 729 37.4%
Smoking status (n, %) Never smoked 1281 46.3% 841 43.1% Former smoker 1283 46.4% 938 48.1% Current smoker 201 7.3% 172 8.8%
Physical activity, MET min per week (mean, SD) 2773.7 3552.0 2548.0 3366.9 a This is the MESA location of the participant, not necessarily their area of residence
148
Supplemental Table 11 Results of sensitivity analyses using equal weights for the price outcomes, using a 5-mile radius to capture prices for all individuals, and using a 1-mile radius for those living in New York City and a 3-mile radius for all others
Exposure of interest Healthy-to-unhealthy ratio Healthy food price Unhealthy food price 95% CI 95% CI 95% CI
Odds ratio Lower Upper p-
value Odds ratio Lower Upper p-
value Odds ratio Lower Upper p-
value Using equal weights for food prices a
Model 1: region, age, gender 0.92 0.76 1.11 0.3905 0.96 0.86 1.09 0.552 1.00 0.92 1.10 0.9625 Model 2: Model 1 plus income/wealth, education level, smoking status, and race
0.84 0.69 1.03 0.0869 0.97 0.86 1.10 0.6684 1.02 0.93 1.12 0.6297
Final Model: Model 2 plus neighborhood SES and neighborhood supermarket density
0.72 0.57 0.91 0.0051 1.05 0.90 1.23 0.5163 1.16 1.01 1.32 0.0314
Using a 5-mile radius for food prices b Model 1: region, age, gender 1.01 0.84 1.21 0.9487 0.97 0.85 1.10 0.6063 0.98 0.88 1.09 0.6974 Model 2: Model 1 plus income/wealth, education level, smoking status, and race
0.83 0.68 1.01 0.0562 0.95 0.83 1.07 0.3860 1.01 0.91 1.13 0.8417
Final Model: Model 2 plus neighborhood SES and neighborhood supermarket density
0.78 0.63 0.96 0.0216 0.93 0.79 1.10 0.4071 1.05 0.91 1.21 0.5231
Using a 1-mile radius for those in NYC c Model 1: region, age, gender 0.98 0.85 1.14 0.8098 1.01 0.88 1.16 0.8980 1.03 0.91 1.15 0.6798 Model 2: Model 1 plus income/wealth, education level, smoking status, and race
0.89 0.76 1.04 0.1447 0.98 0.85 1.13 0.7611 1.06 0.93 1.19 0.3815
Final Model: Model 2 plus neighborhood SES and neighborhood supermarket
0.86 0.73 1.01 0.0608 0.97 0.83 1.13 0.6957 1.07 0.94 1.22 0.2792
149
Exposure of interest Healthy-to-unhealthy ratio Healthy food price Unhealthy food price 95% CI 95% CI 95% CI
Odds ratio Lower Upper p-
value Odds ratio Lower Upper p-
value Odds ratio Lower Upper p-
value density
a Each of the two healthy food products (fruits & vegetables, dairy) received a weight of 0.5, while each of the three unhealthy products (soda, chocolate candy & sweets, salty snacks) received a weight of 0.33. The radius for capturing prices remained at 3-miles for this sensitivity analysis. b All IRI supermarkets within 5-miles of a participant’s place of residence at Exam 5 were included c A 1-mile radius was used for individuals living in New York City, while a 3-mile radius was kept for all others
150
APPENDIX 3 : FOR CHAPTER 4
APPENDIX 3.1 Supplemental tables for Chapter 4
Supplemental Table 12 Patient characteristics of those who were included in the study (n=3,408) and those who were excluded (n=1,308)
Included participants Excluded
individuals
N /
Mean Col % /
SD N /
Mean Col % /
SD Number of participants (N) 3408 1308 MESA location (n, %) a
Forsyth County, NC 645 18.9% 168 12.8% New York, NY 665 19.5% 145 11.1% Baltimore, MD 570 16.7% 88 6.7% St. Paul, MN 7 0.2% 764 58.4% Chicago, IL 814 23.9% 62 4.7% Los Angeles, CA 707 20.7% 81 6.2%
Region of residence (n, %) Northeast 660 19.4% 115 8.8% Midwest 797 23.4% 782 59.8% South 1236 36.3% 237 18.1% West 715 21.0% 80 6.1%
Age (mean, SD) 70.2 9.4 69.1 9.5 Female (n, %) 1809 53.1% 705 53.9% Race/ethnicity (n, %)
White 1274 37.4% 651 49.8% Chinese American 481 14.1% 60 4.6% Black 1072 31.5% 179 13.7% Hispanic 581 17.0% 418 32.0%
Family history of diabetes (n, %) 1529 44.9% 540 41.3% BMI (mean, SD) 28.2 5.6 29.2 5.8
<25 (n, %) 1040 30.5% 327 25.0% 25-29.9 (n, %) 1283 37.6% 470 35.9% ≥30 (n, %) 1085 31.8% 511 39.1%
Education level (n, %) HS/GED or less 1022 30.0% 473 36.2% Some college, Technical or Associate degree 945 27.7% 426 32.6% Bachelor’s degree or higher 1441 42.3% 401 30.7%
151
Included participants Excluded
individuals
N /
Mean Col % /
SD N /
Mean Col % /
SD Per capita income (in $10k) 2.6 1.9 2.2 1.6 Wealth index 2.6 1.2 2.6 1.2 Income/wealth index 5.0 2.2 4.9 2.1 Marital status (n, %)
Not married or living with partner 1395 40.9% 537 41.1% Married/Living with partner 2013 59.1% 771 58.9%
Smoking status (n, %) Never smoked 1575 46.2% 547 41.8% Former smoker 1585 46.5% 636 48.6% Current smoker 248 7.3% 125 9.6%
Physical activity, hours per day (mean, SD) 8.8 5.4 9.3 5.8 Daily calorie intake (mean, SD) 1643 782 1728 811 a This is the MESA location of the participant, not necessarily their area of residence
152
Supplemental Table 13 Analysis of interactions between SES and race with the price ratio for each of the three outcomes of interest a
Type 2 diabetes prevalence (n=3,408) OR Lower Upper p-
value p for
interaction Income/wealth
Lowest (1-4), n=1,264 0.759 0.612 0.942 0.0125 0.0647
Middle (5-6), n=1,011 1.227 0.895 1.683 0.2036
Highest (7-8), n=1,133 1.116 0.793 1.570 0.5292
Education level
HS or less, n=1,022 0.826 0.641 1.065 0.1409 0.8139
Some college, n=945 0.986 0.728 1.335 0.9255
BS or more, n=1,441 1.015 0.774 1.331 0.9142
Race
White, n=1,274 0.984 0.669 1.448 0.9348 0.7132
Chinese American, n=481 0.707 0.509 0.981 0.0381
Black, n=1,072 1.082 0.789 1.485 0.6248
Hispanic, n=581 0.847 0.633 1.134 0.2637
Type 2 diabetes incidence (n=2,829) RR Lower Upper p-
value p for
interaction Income/wealth
Lowest (1-4), n=977 1.097 0.722 1.665 0.6652 0.6736
Middle (5-6), n=861 1.180 0.712 1.958 0.5209
Highest (7-8), n=991 0.913 0.596 1.398 0.6755
Education level
HS or less, n=772 1.477 0.840 2.599 0.1758 0.5299
Some college, n=773 0.938 0.602 1.461 0.7757
BS or more, n=1284 1.120 0.727 1.726 0.6068
Race
White, n=1162 1.088 0.579 2.043 0.7940 0.6883
Chinese American, n=402 1.029 0.615 1.724 0.9126
Black, n=815 0.976 0.549 1.733 0.9329
Hispanic, n=450 1.401 0.806 2.437 0.2322
Insulin resistance (n=1,892) Estim
ate Lower Upper p-
value p for
interaction Income/wealth
Lowest (1-4), n=839 0.016 -0.064 0.097 0.6869 0.2544
Middle (5-6), n=686 0.071 -0.027 0.169 0.1552
Highest (7-8), n=828 0.064 -0.023 0.152 0.1483
Education level
153
HS or less, n=671 -0.068 -0.163 0.028 0.1635 0.1812
Some college, n=633 0.083 -0.012 0.178 0.0862
BS or more, n=1049 0.062 -0.014 0.138 0.1104
Race
White, n=931 0.036 -0.056 0.128 0.4454 0.6414
Chinese American, n=355 0.084 -0.032 0.201 0.1546
Black, n=679 0.006 -0.111 0.123 0.9216
Hispanic, n=388 0.012 -0.088 0.113 0.8108
OR, odds ratio; RR, risk ratio; HS, high school; BS, bachelor's degree. a Covariates include all covariates described for model 3 in the primary analysis (geographic region, age, gender, race/ethnicity, family history of diabetes, income/wealth index, education level, smoking status, physical activity, and neighborhood socioeconomic status).
154
APPENDIX 3.2 HOMA-IR validity
HOMA-IR was shown to be a valid measure of insulin resistance according to its
significant association with the insulin resistance index assessed by euglycemic-
hyperinsulinemic clamp (clamp IR) (Emoto et al., 1999). It was found that the log
transformed HOMA-IR measure had a stronger association than the non-transformed
value. Validation and reproducibility of the HOMA-IR measure were measured (again
using clamp IR as the gold-standard) in another study by Sarafidis et al (2007) (Sarafidis
et al., 2007). Correlation analysis demonstrated that HOMA-IR was strongly and
inversely correlated with the M-value (clamp IR index) (r =- 0.572, p<0.001), the
standardized M/I value (r=-0.768, p<0.0001) and the metabolic clearance rate (another
clamp-derived index) (r=-0.576, p<0.0001). The coefficient of variance (CV) for HOMA-
IR between visit 1 and visit 2 was 23.5% and the correlation between the two periods was
r=0.367 (p<0.05).
The thin-film adaptation of the glucose oxidase method on the Vitros analyzer
(Johnson & Johnson Clinical Diagnostics, Inc.) was used to measure fasting serum
glucose at each examination using. From each of the four examinations, 200 samples
were reanalyzed over a short period of time to recalibrate the original observations and
ensure consistency of the fasting glucose assay over the examinations. The RIA method
was used to determine fasting serum insulin levels were determined using the Linco
Human Insulin-Specific RIA Kit (Linco Research, Inc.). All assays were conducted at the
Collaborative Studies Clinical Laboratory at Fairview University Medical Center
(Abiemo et al., 2013, Nettleton et al., 2009a, Nettleton et al., 2008b).
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VITA EDUCATION Doctor of Philosophy in Epidemiology, December 2016 Dornsife School of Public Health, Drexel University, Philadelphia, PA Master of Science in Biostatistics, May 2009 Mailman School of Public Health, Columbia University, New York, NY Bachelor of Science in Mathematics, June 2007 Drexel University, Philadelphia, PA RESEARCH EXPERIENCE Director of Life Sciences Research, HealthCore, Inc. 2016-Present Researcher; Sr. Researcher; Manager; Associate Director; HealthCore, Inc. 2010-2016 Associate Faulty of Biostatistics, Albert Einstein College of Medicine 2009-2010 Data analyst, Research Foundation for Mental Hygiene, 2008-2009 TEACHING EXPERIENCE Drexel University Dornsife School of Public Health Teaching Assistant, 2014-2015: PBHL 630: Intermediate Epidemiology; PBHL 621: Intermediate Biostatistics II Honors and awards: Teaching assistant excellence award nomination SELECT PEER-REVIEWED PUBLICATIONS Kern DM, Auchincloss AH, Robinson LF, Ballester LL. Neighborhood variation in the price of soda relative to milk and its association with neighborhood socioeconomic status and race. Public Health Nutr. Jun 2016; 30:1-11 Wu B, Bell K, Stanford A, Kern DM, Tunceli O, Vupputuri S, Kalsekar I, Willey V. Understanding CKD among patients with T2DM: prevalence, temporal trends, and treatment patterns – NHANES 2007-2012. BMJ Open Diab Res Care. Apr 2016; 11(4):e000154 Kern DM, Mellstrom C, Hunt PR, Tunceli O, Wu B, Westergaard M, Hammar N. Long-term cardiovascular risk and costs for myocardial infarction survivors in a US commercially insured population. Curr Med Res Opin. Apr 2016; 32(4):703-11. Kern DM, Davis J, Williams SA, Tunceli O, Wu B, Graham E, Strange C, Trudo F. Validation of an administrative claims-based diagnostic code for pneumonia in a commercially insured COPD population. Int J Chron Obstruct Pulmon Dis. Jul 2015; 10(1):1417-1425. Kern DM, Davis J, Williams SA, Tunceli O, Wu B, Hollis S, Strange C, Trudo F. Comparative Effectiveness of Budesonide/Formoterol Combination and Fluticasone/Salmeterol Combination among Chronic Obstructive Pulmonary Disease Patients New to Controller Treatment: A US Administrative Claims Database Study. Repir Res. Apr 2015; 16(1):52 Kern DM, Zhou S, Chavoshi S, Tunceli O, Sostek M, Singer J, LoCasale R. Treatment patterns, healthcare resource utilization and costs among chronic opioid users for non-cancer pain using a US administrative claims database. Am J Manag Care. Mar 2015;21(3):e222-e234 Kern DM, DeVore S, Kim J, Tunceli O, Wu B, Hirshberg B. Real world mortality, cardiovascular outcomes, and healthcare costs in type 2 diabetic patients at high risk for cardiovascular disease. Am J Accountable Care. Mar 2015; 3.15:55-61. Kern DM, Balu S, Tunceli O, Anzalone D. Statin Treatment Patterns and Clinical Profile of Patients with Risk Factors for Coronary Heart Disease (CHD) Defined by National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III. Curr Med Res Opin. Dec 2014; 30(12):2443-2551