Food Insecurity and Obesity in Low-Income Women:

The Monthly Cycle of Food Abundance and Food Shortage

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

By

Qian Ye, M.S.

The Ohio State University Nutrition Graduate Program

The Ohio State University

2011

Dissertation Committee:

Dr. Hugo Melgar-Quiñonez, Advisor

Dr. Carla Miller

Dr. Chris Taylor

Dr. Sarah Anderson

Copyright by

Qian Ye

2011

ii

ABSTRACT

Food insecurity has been associated with overweight/obesity in U.S. women.

Several hypotheses have been proposed to address this paradoxical association, but none

has yet been tested. This dissertation is designed to test the ―monthly cycle of food

abundance and food shortage‖ hypothesis, and to examine the effects of food stamp

program (FSP) participation, disordered eating, and dietary intake patterns on the

association. It is hypothesized that food insecure women would experience a monthly

cycle with higher total energy intake (TEI) and household food stores at the beginning of

the month, followed by a more limited TEI and food supply at the end.

The dissertation compared food insecure and overweight/obese (FIS/ovob)

women with three other women groups: food secure and normal weight (FS/norm), food

secure and overweight/obese (FS/ovob), and food insecure and normal weight

(FIS/norm). The monthly variations in TEI and food stores were assessed in a sample of

low-income women in Ohio, by comparing the energy intake from the first ten days with

that of the last ten days of the month during three continuous months. For FIS/ovob

women, significant decreases were found in the total number of food items (Month 1:

87.74 vs. 68.26, Month 2: 83.3 vs. 72.2, Month 3: 88.81 vs. 75.3, p<0.05) and in essential

food groups including grains, vegetables, fruits, meat & beans, and milk; in TEI (2114.19

vs. 1843.06 kcal, p<0.05) and fat intake (804.1 vs. 649.93 kcal, p<0.05) in Month 1.

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Among food insecure women, food stamp recipients showed a higher BMI (38.24 vs.

30.94, p<0.01) and more severe decreases in three-month food items (61.58 vs. 8.22,

p<0.01) than non-recipients. In addition, deeper food insecurity was marginally

correlated with more severe Eating Concern in disordered eating (Pearson’s correlation:

0.23, p=0.09). Using the National Health and Nutrition Examination Survey (NHANES)

1999–2008 data, a higher carbohydrates/energy ratio and a lower protein/energy ratio was

found in FIS/ovob women compared to food secure women; no differences of TEI or

fat/energy ratio were observed. Furthermore, FIS/ovob women showed higher prevalence

of a 4.54 kg (10 lbs) 1-yr weight gain (28.81%) than other women groups. The results

suggest the existence of the monthly cycle of food abundance and food shortage in

FIS/ovob women, which may be caused by the interaction in food insecurity with FSP

participation; carbohydrate intake may increase, and daily energy intake and fat intake

may fluctuate in response to the monthly cycle and result in gradual weight gain over

long periods of time. Policy changes may be necessary; nutrition education integrating

with community-based intervention programs and efforts from private sectors like food

providers are needed for FIS/ovob women to have a more even distribution of available

food sources throughout the month, and a reduction of the potentially episodic overeating

behaviors.

iv

Dedicated to my parents

v

ACKNOWLEDGMENTS

I would like to take this opportunity to thank people who have supported me

during my research process and the PhD period. Thanks to my advisor, Dr. Hugo Melgar-

Quiñonez, for his guidance and encouragement. He is the one who opens the door of

public health nutrition for me, and he is also a mentor and a friend to me. I would also

like to thank my doctoral committee members, Dr. Carla Miller, Dr. Chris Taylor and Dr.

Sarah Anderson, for their patience, expertise and help.

I would like to recognize people who contributed to this research project: Daniel

Remley, Deb Angell, Melinda Fischer, Ana Claudia Zubieta, Cindy Long and Dawn

Winkle. Thanks to their work in The Ohio State University (OSU) Extension to help to

set up the research and to recruit and interview the study subjects. I would also like to

thank these students, Guillermo Bervejillo, Jenny Geoge, Ellen Greathouse, Brittney

Keller, Sheryl Mims, and Paola Seguil, for their help in data processing. The study would

not have been completed without the commitment of all these people.

I am thankful for programs, scholarships and awards supporting me during the

PhD period: the OSUN program (The Ohio State University Interdisciplinary Nutrition

Ph.D. Program), the Graduate Dissertation Fellowship from College of Education and

Human Ecology, the Russell Klein Research Award, the Excellent Abstract Award from

vi

Society for Nutrition Education Annual Conference, the Summer Survey Research

Award from Graduate Interdisciplinary Specialization in Survey Research at OSU, and

the summer internship in the North America Nutrition Group in Mead Johnson Nutrition.

Finally, I would like to thank my family and friends. Thanks to my parents, my

parents-in-law for being so patient and supportive during the process, and special thanks

to my husband, Ke Hu, for his continuous support, understanding and love.

vii

VITA

Oct 9, 1981................................................... Born - Ningbo, Zhejiang, China

1999............................................................ B.S. Biotechnology, Zhejiang

University

2003-2006..................................................... Graduate Research and Teaching

Associate, Molecular Biology,

Zhejiang University

2006............................................................ M.S. Molecular Biology,

Zhejiang University

2006 to 2009................................................. Graduate Research Associate,

Department of Human Nutrition,

The Ohio State University

2009 to June, 2010........................................ Graduate Dissertation Fellowship,

College of Education and Human

Ecology, The Ohio State University

July, 2010 to Present..................................... Graduate Research Associate,

College of Social and Behavioral

Science, The Ohio State University

viii

Publications

- Alvarez-Uribe MC, Melgar-Quiñonez H, Ye Q. 2009. Food insecurity and weight

status in Colombia: inverse association in adults and children. Abstract. International

Congress of Nutrition (2009), Bangkok, Thailand. Ann Nutr Metab. 2009; 55 (suppl

1): 365.

- Chen G, Melgar-Quinonez H, Ye Q. 2009. Differences in the psychometric

characteristics by gender of respondents to a household food security scale in

Yunnan, China. Abstract. International Congress of Nutrition (2009), Bangkok,

Thailand. Ann Nutr Metab. 2009; 55 (suppl 1): 365.

- Ye Q, Zubieta AC, Remley D, Angell D, Mims S, Melgar-Quiñonez H. 2009.

Assessing the Monthly Food Abundance-Shortage Cycle in Food insecure

Overweight/Obese Women in Ohio. Abstract. International Congress of Nutrition

(2009), Bangkok, Thailand. Ann Nutr Metab. 2009; 55 (suppl 1): 602.

- Ye Q, Zubieta AC, Remley D, Angell D, Mims S, Melgar-Quiñonez H. Household

food supply is affected by food abundance-food shortage monthly cycle among food

insecure women in Ohio. Experimental Biology (2009), New Orleans, LA. FASEB J.

2009; 23: Abstract 737.21.

- Ye Q, Zubieta AC, Remley D, Long C, Angell D, Mims S, Melgar-Quiñonez H.

Assessing the monthly food abundance-shortage cycle in food insecure overweight

women. Abstract. Society for Nutrition Education Conference (2009), New Orleans,

LA. J Nutr Educ Behav. 2009; 41(suppl 1): S4.

- Ye Q, Qiu YX, Quo YQ, Chen JX, Yang SZ, Fu CX. Species-specific SCAR markers

for authentication of Sinocalycanthus chinensis. J Zhejiang Univ Sci B. 2006; 7: 868-

872.

- Ye Q, Li ZH, Zheng YP, Qiu YX, Fu CX. Analysis of Machilus thunbergii types using

ISSR-PCR marker assays. J Zhejiang Univ Agric & Life Sci. 2006.

ix

Fields of Study

Major Field: The Ohio State University Nutrition Graduate Program

x

TABLE OF CONTENTS

ABSTRACT ........................................................................................................................ ii

ACKNOWLEDGMENTS .................................................................................................. v

VITA ................................................................................................................................. vii

TABLE OF CONTENTS .................................................................................................... x

LIST OF TABLES ............................................................................................................ xii

LIST OF FIGURES ......................................................................................................... xiii

CHAPTER 1 INTRODUCTION ........................................................................................ 1

1.1 Background ................................................................................................................1

1.2 Study Design, Research Hypotheses and Objectives .................................................5

1.2.1 Study 1 ......................................................................................................... 5

1.2.2 Study 2 ......................................................................................................... 7

1.3 Significance ................................................................................................................8

1.4 Subsequent Chapters ................................................................................................10

CHAPTER 2 REVIEW OF LITERATURE ..................................................................... 11

2.1 Food Insecurity and Obesity ....................................................................................11

2.1.1 Food Insecurity ........................................................................................... 11

2.1.2 Obesity ........................................................................................................ 18

2.1.3 The Paradox in Food Insecurity and Obesity in Women ........................... 21

2.2 Questionnaire-based Measurements and Dietary Intake Assessment ......................33

2.2.1 Household Food Insecurity Measurement .................................................. 33

2.2.2 Self-reported Eating Disorders Examination Questionnaire (EDE-Q) ...... 38

2.2.3 Dietary Assessment Methods ..................................................................... 42

2.3.4 Shelf Food-Inventory.................................................................................. 48

CHAPTER 3 Addressing the Association of Food Insecurity and Overweight/Obesity by

Testing the ―Monthly Cycle of Food Abundance and Food Shortage‖ Hypothesis ......... 51

3.1 Introduction ..............................................................................................................51

3.2 Methods ....................................................................................................................54

3.2.1 Subjects ....................................................................................................... 54

3.2.2 Data Collection ........................................................................................... 58

3.3 Data Analysis ...........................................................................................................62

3.3.1 Data Processing .......................................................................................... 63

3.3.2 Variables of Interest ................................................................................... 65

3.3.3 Statistical Analysis ..................................................................................... 65

3.4 Results ......................................................................................................................67

3.4.1 Subjects ....................................................................................................... 67

3.4.2 Characteristics of Subjects ......................................................................... 68

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3.4.3 Monthly Variations in Household Food Supply ......................................... 69

3.4.4 Monthly Variation of Nutrient Intake ......................................................... 73

3.4.5 Disordered Eating ....................................................................................... 75

3.4.6 Food Stamp Program (FSP) Participation .................................................. 77

3.5 Summary and Results of Hypothesis Testing ..........................................................78

CHAPTER 4 Addressing the Association of Food Insecurity and Overweight/Obesity.. 95

Using data from NHANES 1999-2008 ............................................................................. 95

4.1 Introduction ..............................................................................................................95

4.1.1 Hypothesis Testing ..................................................................................... 96

4.2 Methods ....................................................................................................................96

4.2.1 Sample ........................................................................................................ 96

4.2.2 Data Collection ........................................................................................... 97

4.2.3 Data Analysis............................................................................................ 103

4.3 Results ....................................................................................................................105

4.4 Summary and Results of Hypothesis Testing ........................................................111

CHAPTER 5 DISCUSSION AND CONCLUSIONS .................................................... 121

5.1 Discussion ..............................................................................................................122

5.1.1 Study Populations and TEIs Comparison ................................................. 122

5.1.2 Energy Requirements and Over-buying Behaviors .................................. 123

5.1.3 Food Insecurity and Dietary Intake Patterns (TEI and Macronutrient Intake)

................................................................................................................................. 125

5.1.4 Food Insecurity and Disordered Eating ................................................. 128

5.1.5 The Mediating Effects of Food Stamp Program (FSP) Participation ....... 129

5.1.6 Other Issues .............................................................................................. 132

5.2 Limitations and Future Studies ..............................................................................136

5.2.1 Limitations ................................................................................................ 136

5.2.2 Future studies............................................................................................ 138

5.3 Conclusions ............................................................................................................141

REFERENCE .................................................................................................................. 142

APPENDIX A ................................................................................................................. 163

Family Record Questionnaire ......................................................................................... 163

APPENDIX B ................................................................................................................. 166

Modified Household Food Security Survey Module (HFSSM) ..................................... 166

APPENDIX C ................................................................................................................. 172

Shelf Food-Inventory Survey .......................................................................................... 172

APPENDIX D ................................................................................................................. 181

Eating Disorders Examination Questionnaire (EDE-Q) ................................................. 181

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LIST OF TABLES

Table 2. 1 US-HFSSM questions used to assess household food security in the Current

Population Survey (CPS) Food Security Survey ............................................ 50

Table 3.1 Sample sizes by food security, weight status and interview dates 80

Table 3.2 Characteristics of women completed the six surveys ....................................... 81

Table 3.3 Total shelf food items (SD) by food security and weight status ....................... 83

Table 3.4 Food groups significantly decreased food groups in the three months ............. 84

Table 3.5 Monthly differences (SD) in household food stores by USDA essential food

groups (including holidays).............................................................................. 85

Table 3.6 Monthly differences (SD) in household food stores by USDA essential food

groups (excluding holidays) ............................................................................. 87

Table 3.7a Monthly differences in total energy intake (TEI) ........................................... 89

Table 3.7b Monthly differences in macronutrient intakes ............................................... 90

Table 3.8 Monthly Differences (SD) in micronutrient intakes in Month 1 ...................... 91

Table 3.9a EDE-Q subscale scores (SD) by food security status and weight status ........ 92

Table 3.9b EDE-Q subscale scores (SD) by food security status and by weight status ... 92

Table 3.10 Correlations of EDE-Q subscale scores with BMI and food security status .. 93

Table 3.11 Featured behaviors of eating disorders by food security status ...................... 93

Table 3.12 Monthly differences of household food stores by food security and food

stamps receiving status ..................................................................................... 94

Table 4.1 Characteristics of women by household food security status ......................... 113

Table 4.2 Characteristics of low-income women by household food security status ..... 115

Table 4.3 Comparison of 1-yr weight change and macronutrient intakes by food security

status ............................................................................................................... 117

Table 4.4 Comparison of 1-yr weight change and macronutrient intakes by weight status

and food insecurity ......................................................................................... 118

Table 4.5 Comparison of 1-yr weight change and macronutrient intakes in food insecure

women by food stamp participation ............................................................... 120

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LIST OF FIGURES

Figure 1 The prevalence of food insecurity in the United States from 1995-2009 .......... 13

Figure 2 Conceptualization in food insecurity and its risk factors ................................... 16

Figure 3 The total energy intake (TEI) in food insecure and overweight/obese (FIS/ovob)

women in the Ohio study and in the NHANES study ...................................... 110

1

CHAPTER 1

INTRODUCTION

This chapter briefly reviews the concepts and prevalence of food insecurity and

overweight/obesity and the paradoxical relationship between the two, the design and

objectives of the study, and the significance of the research.

1.1 Background

What is food insecurity? It is defined as ―the limited or uncertain availability of

nutritionally adequate and safe foods‖ [1]. The prevalence of food insecurity is high

globally and has been increasing recently, particularly after the onset of food crisis and

the economic crisis [2]. In 2009, about 1.02 billion people worldwide suffered from food

insecurity (measured by dietary energy supply1) [2]. In U.S., food insecurity is measured

by a questionnaire-based instrument: the U.S. Household Food Security Survey Module

(US-HFSSM); the module assesses household food insecurity experiences and coping

strategies to subsequent events from worrying about running out of food to reduction of

quality and quantity of foods during the previous 12 months [3]. The most recent national

survey data showed that in 2008, the prevalence of food insecurity in U.S. households has

1 The dietary energy supply method is used by the Food and Agriculture Organization (FAO) to

report undernourishment [100].

2

reached the highest percentage since the first national food security survey was conducted

in 1995: 17 million (14.6%) U.S. households experienced low and very low food security

in 2008 [4].

Risk factors in food insecurity include socioeconomic characteristics such as

household income [5], parental education [6], household size [7] [8] and parental age [9].

Low income is considered as one of the strongest predictors in food insecurity In U.S.

and in some developing countries [5]. In U.S., low-income households, single-parent

households, Black and Hispanic households, and households in large cities and rural

areas have considerably higher prevalence of food insecurity, compared to their

corresponding counterparts [4]. Moreover, more than half (55%) of food insecure

households had participated in some types of food assistance programs in 2008 [4].

Household food insecurity leads to food consumption reduction in both quality

and quantity, and therefore reduces the nutrient intake needed for a healthy life. Indeed,

food insecurity as measured by current food security scales is related with poor nutrition,

such as lower diet diversity [10], less consumption of fruits/vegetables [11], [12] and

animal-source products [12], and lower nutrient intakes [11], [12], [13], [14], and poor

health outcomes including chronic illness [15], [16], [17] and psychological problems

[13], [18]. In addition, increased participation in assistance programs is associated with

household food insecurity [19], [11].

Like food insecurity, overweight and obesity, defined as ―abnormal or excessive

fat accumulation that may impair health‖ [20], are serious public health concerns in many

parts of the world [20], [21], [22], [23]. Industrialized countries hold the highest

3

prevalence of obesity. In U.S., for example, the prevalence of obesity has experienced a

dramatic increase in the past twenty years [24]. According to the most recent data, more

than two thirds of U.S. adults (68%) are overweight or obese [25], and almost one in

three U.S. children and adolescents aged 2 to 19 years (31.7%) has a BMI over the 85th

percentile (i.e. childhood overweight or obesity) [26].

Overweight and obesity are risk factors for a number of chronic diseases and

psychological consequences. These diseases include type 2 diabetes, coronary heart

disease, certain cancers, hypertension, and dyslipidemia [27], and mental stress like

weight control and body image [28]. Accordingly, the high prevalence of overweight and

obesity poses a real burden to the society. A fact to prove this burden is that

cardiovascular disease accounts for 40% of deaths in developed countries [29]. As a

consequence, the economic costs for treating obesity or obesity-related diseases are

amplified, and hence the social burden of overweight and obesity is increased [30].

Conventional belief associated obesity with affluence due to the excessive energy

consumption. However, more and more studies have reported a paradoxical association

of obesity and food insecurity [21], [23], [31], [32], [33], [34], [35]. In U.S., a number of

cross sectional studies have shown a significant trend of increased severity in food

insecurity and greater obesity rates, particularly among women [36], [37], [38], [39],

[40], [41]. Townsend et al. [38] analyzed data from the 1994–1996 Continuing Survey of

Food Intakes by Individuals (CSFII) and concluded that food insecurity was much more

highly correlated with overweight compared to food security. This conclusion was further

confirmed by more recent national survey data (National Health and Nutrition

4

Examination Survey, NHANES) in 1999-2000 and 2001-2002 [41] and regional studies

[36], [39], [40]. For example, a study in California women found that food insecurity was

associated with obesity in all ethnic groups, including non-Hispanic whites, Hispanics,

African Americans, and Asians [36]. This inverse association was weaker or even less

consistent in men [35], [42], [43].

The paradoxical association of food insecurity with obesity has been studied for

about twenty years. Several possibilities have been proposed to explain this association.

The most popular one is that when nutritionally-balanced diets are less available in food

insecure households [44], [45], high-fat-high-calorie food turns out to be the most

affordable energy source to prevent hunger [46], [47], [48]. An extended scenario of this

hypothesis is that food insecure people may indulge in highly palatable and energy-rich

food and hence an increased risk of obesity [46]. Another hypothesis for the paradoxical

association is that obesity could be a result of a periodic cycle of food availability and

food shortage; such a cycle may result in an increase of the efficiency in fat deposition

when food is available again [46], [47], [49]. The third hypothesis focuses on the

psychosocial stress prevalent among food insecure people, resulting in endocrine

abnormalities and promoting visceral obesity [46], [50].

Although a number of hypotheses were proposed for the paradoxical relationship

in food insecurity and obesity, none of them have yet been tested. In addition, most of the

previous studies were from cross-sectional data, which could not provide enough

information needed to explain the dietary and weight change during the food insecure

5

process. For these reasons, there is a need for a prospective study to test any of these

hypotheses in food insecure people.

1.2 Study Design, Research Hypotheses and Objectives

With the aim of understanding the paradoxical association in food insecurity and

overweight/obesity in U.S., this dissertation divided women into four groups: food

insecure and overweight/obese (FIS/ovob), food secure and normal weight (FS/norm),

food secure and overweight/obese (FS/ovob), and food insecure and normal weight

(FIS/norm). The dissertation includes two studies: a prospective study to test ―monthly

cycle of food abundance and food shortage‖ hypothesis in a group of low-income women

in Ohio, and a cross-sectional study using the survey data from the continuous National

Health and Nutrition Examination Survey (NHANES) 1999–2008.

1.2.1 Study 1 – Addressing the Association in Food Insecurity and Overweight/Obesity

among Women by the “Monthly Cycle of Food Abundance and Food Shortage”

Hypothesis

This study examined a group of low-income women in Ohio, which will be called

the Ohio study from now on.

Most of the studies reporting the paradoxical relationship of food insecurity with

obesity were from cross-sectional data [3], [4]. However, it is difficult to answer the

question about how food insecurity relates to the consumption of nutrients and total

6

energy intake because of the simultaneous nature of cross-sectional study designs:

participants’ usual energy intake cannot be accurately assessed. In contrast, a prospective

design allows exploring the changes of dietary intake to answer the research questions.

Moreover, a prospective study can provide repeated measurements to reduce the intra-

individual random errors of dietary intake measurement (see section 3.2.2.3 for more

details). Therefore, it allows testing the existence of the monthly cycle of food shortage

and food abundance in food insecure families, and can provide useful information to

address ―why‖ food insecurity is related to overweight.

This study is one of the few studies using a prospective study design to address

the relationship in food insecurity and obesity among women [51], [52], [53]. According

to the proposed hypothesis, food insecure households have access to abundant food

sources during the first few weeks of the month, while face problems of food shortage at

the end. Thus, higher energy consumption may occur in food insecure people at the

beginning of the month when food supply is plentiful, and restricted energy intake may

happen at the end of the month. Such a monthly cycle of food abundance and food

shortage may repeat month to month when the household keeps food insecure, and may

lead to overweight/obesity in food insecure women.

In this dissertation, the Ohio study is designed to test the existence of the monthly

cycle of food abundance and food shortage among food insecure women, by measuring

the total energy intake (TEI) and household food stores in a group of low-income women

in Ohio at the beginning and the end of three continuous months.

Specifically, the following hypotheses are proposed to be tested in this study:

7

1. FIS/ovob women will have higher TEI at the beginning of the month compared to

the end of the month in three continuous months.

2. FIS/ovob women will have more household food items at the beginning of the

month compared to the end of the month in three continuous months.

3. The monthly decrease of TEI and household food items will be greater in

FIS/ovob women compared to FS/norm, FS/ovob, and FIS/norm women.

4. FIS/ovob women will have more severe disordered eating behaviors than

FS/norm, FS/ovob, and FIS/norm women.

5. Among food insecure women, food stamp recipients will have higher body mass

index (BMI) and greater monthly decrease in TEI and household food items than

food stamp non-recipients.

1.2.2 Study 2 – Addressing the Association in Food Insecurity and Overweight/Obesity

among Women by Macronutrient Intakes and 1-Year Weight Change - NHANES

1999-2008 Data

This study used the national survey data from the continuous National Health and

Nutrition Examination Survey (NHANES) 1999–2008, and will be called the NHANES

study from now on.

The NHANES study is another way to address the paradoxical association in food

insecurity and obesity. The study was designed to test if this paradoxical relationship

exists in NHANES women sample with similar characteristics (i.e. Non-Hispanic white,

reproductive age, non-pregnant, non-lactating) as Ohio study; if the paradox exists, is it

8

mediated by macronutrient intakes (i.e. energy, fat, protein, and fiber) and Food Stamp

Program (FSP) participation? In addition, the average TEI in U.S. FIS/ovob women

calculated from the NHANES study may allow some introspection into the monthly cycle

tested in Ohio study.

Specifically, the following hypotheses are proposed to be tested in this study:

1. Food insecure women will have higher body mass index (BMI) than food secure

women, and low-income food insecure women have higher BMI than low-income

food secure women.

2. Compared to FS/ovob women, FIS/ovob women will have greater 1-yr body

weight increase, higher total energy intake (TEI), and higher percentage of energy

from fat, lower percentage of energy from protein and lower grams of fiber

intake.

3. Among food insecure women, food stamp recipients will have higher BMI,

greater 1-yr body weight increase, higher TEI, higher percentage of energy from

fat, lower percentage of energy from protein and lower grams of fiber intake than

food stamp non-recipients.

4. FIS/ovob women in the NHANES study will have lower TEI than FIS/ovob

women in the Ohio study at the beginning of the month, but higher at the end of

the month.

1.3 Significance

9

The paradoxical relationship of deepening food insufficiency and increasing

obesity rate has been studied for about twenty years. In spite of the many proposed

possibilities, no theory has yet been tested. The Ohio study contributes key information

on how episodic food intake patterns mediate the association in food insecurity and

obesity, illustrating the need to promote consistent eating patterns in food insecure

people. The use of a prospective design in the Ohio study is necessary to explore the

monthly variation of dietary intake, meanwhile providing repeated measurements to

reduce intra-individual random errors on dietary intake assessment [54]. Additionally, the

results of the Ohio study provide information for policy makers to make changes in

federally funded food assistance programs, such as the Supplemental Nutrition

Assistance Program (SNAP2, formerly Food Stamp Program) which assigns its benefits

on a monthly basis.

The Ohio study was supported through a seed grant by the Ohio Agricultural

Research and Development Center (OARDC). The limited resource of a seed grant

restricts the generalization ability of this study. For example, although multiple 24-hour

dietary recalls were applied in the study, the number of interview days could only provide

the estimation for TEI, but not for macronutrient intakes. However, the larger sample size

and more complicated survey design in NHANES make the NHANES data provide more

2 Effective October 1, 2008, the U.S. Department of Agriculture's Food Stamp Program was

renamed Supplemental Nutrition Assistance Program (SNAP). In addition, this program has many

different state-specific names. To keep consistency, the program is called Food Stamp Program (FSP) in

the dissertation.

10

nationally representative data, including regular dietary intake data. Therefore, the

dissertation includes the NHANES study as well to address the paradoxical association in

food insecurity and obesity in U.S. women. With the NHANES data, the study can

further analyze the macronutrient intakes and macronutrient/energy ratios in the

population of interest, for example, in the food insecure and overweight/obese women,

and the effect of FSP participation in the association in food insecurity and

overweight/obesity. In addition, the NHANES study can help to estimate the association

in food insecurity and overweight/obesity on a national level among sub-groups of

interest. Therefore, the findings of this dissertation provide comprehensive information

from both a prospective study and a cross-sectional study, to address the association of

food insecurity and overweight/obesity in U.S. women.

1.4 Subsequent Chapters

Chapter 1 briefly introduces the hypotheses to be tested in the dissertation and the

significance of the research. A review of literature is in Chapter 2, introducing the

prevalence, risk factors and consequences of food insecurity and obesity, and their

association. Questionnaire-based instruments used in this dissertation and the 24-hour

dietary recalls are also introduced in Chapter 2. The analysis methods and results from

the Ohio study and the NHANES study are presented in Chapter 3 and Chapter 4

respectively. Chapter 5 compares the results from the Ohio study and the NHANES study,

and discussed the limitations and direction of future research based on the study findings

and conclusions.

11

CHAPTER 2

REVIEW OF LITERATURE

This chapter contains two parts reviewing relevant studies for the dissertation.

The first part reviews the definition, prevalence and correlated consequences of

overweight/obesity and food insecurity, the association of food insecurity and obesity,

and possible explanations to this association. The second part of the chapter provides

information about the development and validation of tools used for food insecurity

measurement, disordered eating behaviors, dietary intake and household food stores.

2.1 Food Insecurity and Obesity

2.1.1 Food Insecurity

Food insecurity is defined as ―the limited or uncertain availability of nutritionally

adequate and safe foods or the limited or uncertain ability to acquire acceptable foods in

socially acceptable ways‖, while food security is the ―access by all people at all times to

enough food for an active and healthy life‖ [1].

Food insecurity has long been a concern of world leaders [55]. In 1996, the World

Food Summit in Rome reaffirmed the human right to get access to adequate, safe and

nutritious foods [56]. In the same year the United Nations set the goal of cutting the

number of people suffering from hunger by half to ―no more than 420 million‖ by 2015

12

[57]. Unfortunately, the number of people worldwide affected by food insecurity had

been increasing ―slowly but steadily‖ before the onset of food crisis and the economic

crisis in 2009, and increased ―sharply‖ after these crises [2]. In 2009 Food and

Agriculture Organization (FAO) estimated that 1.02 billion people worldwide were

suffering from undernourishment [2].

In the United States, household food insecurity as a public health concern has

been monitored nationally since 1995 (Figure 1) by United States Department of

Agriculture (USDA) Economic Research Service (ERS) using data from the Current

Population Survey (CPS) Food Security Supplement [4]. The 2-yr cycle of the prevalence

of food insecurity in 1995-2000 suggests the seasonal effects resulted from the food

security survey collection patterns: administered in April in odd-numbered years and

August/September in even-numbered years. To avoid such a seasonal effect, from 2001

food security surveys were conducted in early December every year [4]. From 2001 to

2004, the prevalence food insecurity in U.S. increased from 10.69% to 11.95%, and

declined to 11.0% in 2005, and remained around until 2007 (11.10%). In 2008, this

number increased substantially to 14.6% (i.e. 17 million of U.S. households) and

remained basically unchanged in 2009 (14.7%) [4]. In Ohio, the estimated prevalence of

food insecurity during 2006 – 2008 was 13.3% [4].

13

Figure 1 The prevalence of food insecurity in the United States from 1995-2009 [4]

Food insecurity in the U.S. includes two categories: low food security and very

low food security. The two categories constitute food insecure levels with coping

strategies related to decreasing food quality and variety, and reduced food intake as well

as interrupted eating patterns. Food insecure households ―obtained enough food to avoid

substantially disrupting their eating patterns or reducing food intake by using a variety of

coping strategies‖ are considered with low food security; comparatively, food insecure

households with interrupted eating patterns and reduced food intake due to lack of money

or other resources are with very low food security (formerly known as ―food insecure

with hunger‖ prior to 2006). The prevalence of very low food security in U.S. increased

slowly but steadily from 2001 (3.26%) to 2007 (4.1%); in 2008 and 2009, 5.7% (i.e. 6.7

million) of U.S. households experienced very low food security. These two percentages

were the highest since the first food security survey conducted nationally in 1995[4].

14

Risk Factors in Food Insecurity

A conceptualization in food insecurity and its risk factors was elaborated by

Campbell in Figure 2 [58]. In this conceptual model, three sources of food acquisition

were identified: normal food system like grocery stores and food service operations,

governmental food assistance programs, and alternate food sources like gardening,

hunting and fishing. The first two are considered as conventional food sources.

Therefore, risk factors in food insecurity are anything that limits the household resources

(e.g. money, time, information, health, etc.), or increases the proportion of non-food

expenditures in household resources, such as employment, income, social assistance,

housing, health care, taxes, emergency expenditure and so on [58].

A number of risk factors has been associated with household food insecurity, such

as low income [4], [53], [59], being headed by a single parent [4], [53], [59], minority

race [4], [53], [59], with children and the number of children [4], [53], [59], education

[6], [53], household size [8], [7], [53], and household location [4], [53]. Income level has

proven to be the strongest predictor for food insecurity [4], [5], [60], [6], [53] [59]. Early

CPS food security surveys have proved declined hunger rates along with increased

income levels [5]. In 2009, 43.0% of U.S. households with incomes below the poverty

line were food insecure [4]. Furthermore, compared to the national level, the prevalence

of food insecurity in U.S. is higher in: households with children (21.3%), with children

and headed by a single parent (36.6% for single women and 27.8% for single man), Black

households (24.9%), Hispanic households (26.9%), and households living in the

15

metropolitan areas of principal cities (17.2%) [4]. Similar results were found in

households participated in the Panel Study of Income Dynamics (PSID) study in 1999

and 2001 [53]; fewer years of education and higher rates of unmarried/divorced/separated

were also found in food insecure women than their food secure counterparts. In addition,

food insecure households have a higher participation rate in food assistance programs

than food secure households [4] [53]. For example, more than half (55%) of U.S. food

insecure households had participated in some food assistance programs in 2008; under

the same income range, food stamp receiving households showed prevalence of very low

food security almost twice as high as non-participating households (25.7% vs. 13.4%)

[4].

16

Figure 2 Conceptualization in food insecurity and its risk factors

*W.I.C. - Women, Infants and Children Supplemental Food Program, S.N.A.P. -

Supplemental Nutrition Assistance Program [58]

17

Health Consequences of Food Insecurity

Reduced food acquisition leads to household food supply restriction and related

food anxiety, and reduction of individual food intake in diversity and quantity [58]. One

study in California found that it was hard for Mexican American preschool-children in

food insecure households to meet the Food Guide Pyramid guidelines, particularly in

food groups of vegetables and meat [61]. As a result, the nutrient intake, serum nutrient

concentration, health status including physical health and mental health are affected in

food insecure families. Undernutrition, including protein energy malnutrition (PEM,

including stunting, wasting and underweight) due to lack of protein or energy intake and

micronutrient deficiency, are one of the major health outcomes of food insecurity in the

world. PEM are prevalent mostly in developing countries [62], [63]. In developed

countries like U.S., deepening food insecurity is associated with lower nutrient intakes,

increased weight, higher risks of chronic diseases, and psychological problems. For

example, lower intake of nutrients including protein, vitamin C and B6, folate and

minerals like iron, zinc, and magnesium have been reported in food insecure adults and

children [11], [12], [13], [14]. The PSID study [53] observed higher smoking rate and

poorer self-evaluated health status in food insecure women. Similarly, a study in Canada

[15] showed food insecure women were associated with poor/fair health status, high risk

of chronic diseases, poor chronic disease management, restricted activity, food allergies,

poor maternal health and nutrition, and immunodeficiency virus infection [15]. Another

study in Mississippi adults [64] found that the association of food security with general

18

health (i.e. physical and mental health) were dependent on race: white or black food

secure people were more likely to report good health than black food insecure people,

while white and food insecure people were less likely to report good health than black

food insecure people. Furthermore, the increased risk of overweight/obesity in food

insecure women [52], [36], [37], [46], [38], [39], [40] will be discussed in section 2.1.3.

In addition, hospital data showed patients with high rates of hunger in the emergency

department were more likely to choose to buy food instead of medications [16]; food

insecure children were more likely to be hospitalized since birth [65]. Poor chronic

disease management has also been reported in food insecure women [66], [16], [67].

Except poor physical health, psychological issues like depression and distress due to

preoccupation of access to enough food, disparagement of self-image and unhealthy

eating behaviors have been found in food insecure women. For example, maternal

depression was observed to be associated with poor child health status and household

food insecurity [59], [18], and food insecure women were more likely to report

disordered eating than food secure women [11].

2.1.2 Obesity

In general, overweight and obesity are defined as ―abnormal or excessive fat

accumulation that may impair health‖ [20]. According to the clinical guideline from

National Heart, Lung and Blood Institute (NHLBI) in U.S., adult overweight and obesity

are determined as a Body Mass Index (BMI) of 25 to 29.9 kg/m2 and of 30 kg/m

2 or

higher, respectively [27].

19

The prevalence of obesity has been increasing globally [20], [21], [22], [23],

regions [68], [69], [70], [71], [72]. In 1997, the WHO formally recognized obesity as a

global epidemic in both industrialized and developing countries [20]. Industrialized

countries hold the highest prevalence of obesity. In U.S., the successive nationally

representative surveys make it possible to follow the trends in overweight/obesity. Three

phases have been identified in this trend. In Phase I from the 1960’s to the 1980’s, the

prevalence of obesity increased slightly: 12.8% in the National Health Examination

Survey (NHES I, 1960-1962), and 14.1% and 14.5% in the first and the second National

Health and Nutrition Examination Surveys (NHANES I, 1971-1974; NHANES II, 1976-

1980), respectively [73]. A dramatic increase was detected in Phase II from the 1980’s to

the 1990’s, where obesity prevalence increased to 22.5% in NHANES III (1988-1994)

[73], and continued increasing to 30.5% in NHANES 1999-2000 [74]; the crude

prevalence of overweight/obesity increased from 55.9% in NHANES III to 64.5% in

NHANES 1999-2000 [74]. In Phase III (1999-2008), the prevalence of obesity seemed to

be more stable over the 10-year period compared to the previous phase: no significant

changes were observed in women [25]; a significant linear trend was found in men, but

the prevalence in 2003-2004, 2005-2006, and 2007-2008 was not significantly different

from each other [25]. In 2007-2008, more than one third of U.S. adults were obese

(33.8%), and women had a little higher prevalence than men (35.5% vs. 32.2%). The

prevalence of overweight and obese combined in U.S. adults were 68% [25]. Obesity

prevalence varies by racial/ethnic populations. Data from Behavioral Risk Factor

Surveillance System (BRFSS) surveys in 2006 – 2008 reported higher prevalence of

20

obesity in Non-Hispanic blacks (35.7%) and Hispanics (28.7%), compared to non-

Hispanic whites (23.7%) [75]. In Ohio, the prevalence of overweight and obesity was

36.9% and 29.7% respectively in adults in 2009 [24].

Obesity and overweight are risk factors of the following diseases: type 2 diabetes,

coronary heart disease, certain cancers (endometrial, breast, and colon), hypertension,

dyslipidemia, stroke, liver and gallbladder disease, sleep apnea and respiratory problems,

osteoarthritis, and gynecological problems [27]. For example, the Nurses’ Health Study

reported that compared to women with lower BMI (< 21), the relative risks (RR) for

Coronary Heart Disease were more than twice as high for women with BMI 25 - 28.9,

and greater than three and half times as high for women with BMI > 29 [76]. In addition,

psychological consequences like disparagement of body image and negative emotional

reactions to dieting represent another part of health concerns associated with obesity [28].

Unhealthy weight control behaviors were reported in women from both high and low

socioeconomic status (SES) [77]. In addition to these health and social consequence,

expenditure directly and indirectly owing to overweight and obesity and their associated

health problems is a major expense for the society. For example, medical cost in U.S.

attributed to overweight and obesity in 1998 was estimated to be $78.5 billion dollars

($92.6 billion in 2002 dollars) [30].

The epidemic of overweight/obesity and related health consequences are major

public health concerns in U.S... Possible risk factors for obesity include generic factors,

birth weight, social factors, and behavioral factors like physical activity, dietary intake

patterns, and psychological factors [78]. For example, lower socioeconomic status in

21

early childhood is associated with increased fatness in later life time [78]. And

consumption of food with high energy density but low in micronutrients is considered to

be a reason in the obesity epidemic [79]. The following section will give a literature

review about the association of food insecurity and obesity and its possible explanations.

2.1.3 The Paradox in Food Insecurity and Obesity in Women

It is known that food insecurity has a positive association with socioeconomic

characteristics such as household income [5], [60], parental education [6] and household

size [8], [7]. Conventional wisdom considers food insecurity as correlated with

undernourishment due to insufficient food supply, while overnutrition (overweight and

obesity) is associated with higher food security [46]. This rationale still holds in some

populations in developing or non-western countries where overnutrition is reported to be

associated with higher socioeconomic status (SES) [21], [22].

The inverse association of food insecurity and related social factors with weight

status has been observed in the developed societies [21], [23], [31], [42], [43]. In specific,

this association differs by gender: women of lower SES have a higher prevalence of

obesity, regardless of race or ethnicity [34], [35] while men shows weaker or even less

consistent association in food insecurity and overnutrition [35], [42], [43]. In U.S.,

―disproportionately higher risk‖ of overweight/obesity has been found in food insecure

women [46]. Early in 1965, a study for a sample of residents in Midtown Manhattan,

New York City reported an obesity rate among women of lower SES (30%) six times as

high as that in women of upper SES (5%) [35]. In 1995, Dietz reported a case study of a

22

7-year-old African American girl, discussing the coexistence of hunger and obesity

within one person [47]. Later on, Townsend et al. [38] used national survey data from

1994–1996 Continuing Survey of Food Intakes by Individuals (CSFII) and found that

food insecurity was highly correlated with overweight than food security; food insecurity

was a significant predictor of overweight even when 15 potential confounders, including

factors of demographics (i.e. age and ethnicity), SES (i.e. income, education, and

occupation), assistance programs, environment (e.g. household size, urbanization), and

lifestyle (i.e. physical activity and food choices) were adjusted [38]. Another cross-

sectional study using national data from the 1999-2000 and 2001-2002 found that women

living in households of marginal food security and food insecurity without hunger had a

higher risk of obesity compared to their fully food secure counterparts, after adjusting for

race/ethnicity, household income, education, and current health status; moreover,

marginally food secure women were more likely to gain weight [41]. Significant trends

between worsening food insecurity and increased obesity rates have also been reported

from state-level survey data [36], [40] and community studies in U.S. women [32], [39],

[41]. For example, in the data from the 1998 and 1999 California Women's Health

Survey, the obesity rate in food insecure women were about twice as high as that in food

secure women (31% vs. 16%) [36]. Another study investigated BMI and its association

with SES characteristics in 13,167 participants (45 - 64 yr) in the Atherosclerosis Risk in

Communities Study and reported a negative association in women but not in men [32]. In

one study from a group of 2,580 adults in an Appalachian Ohio county (rural population),

23

it was found that women’s BMI and obesity rate in women were higher in food insecure

households compared to those in food secure households [80].

The possible causes of the paradoxical correlation of household food insecurity

and high rate of obesity have been studied [52], [46], [38], [47], [41], [51]. In the case

study reported by Dietz, the mother of a 7-year old African American girl said they could

hardly afford high-quality food for weight reduction after they paid the rent fee with the

welfare check; from then till they received their second check, they had to supply their

daughter with high-calorie food like pasta and hot dogs [47]. Based on this report two

explanations are proposed for the paradox: 1) the low-quality but high-fat and high-

calorie food that food insecure people eat to prevent hunger, and 2) the periodic food

availability and shortage frequently experienced by these people [47]. Besides, the

association in food insecurity and obesity is hypothesized also be mediated by dietary

behaviors, like disordered eating patterns [11].

Based on these explanations, factors involved from food insecurity to

overweight/obesity in women include dietary intake, periodic food availability and food

shortage, and abnormal dietary behaviors. Based on these, the dietary intake patterns,

food stamp program participation, and binge eating behavior and their potential

mediating effects are reviewed in the following sections.

2.1.3.1 Dietary Intake Patterns

Researchers believe that food insecure people may have access to ―sufficient or

even excessive energy‖ but limited foods of high nutritional quality and diversity, such as

24

fruits and vegetables [48], [52]. Furthermore, food insecure people may indulge in these

highly palatable and energy-rich foods [46]. Lower diet diversity [10], less consumption

of fruits/vegetables [60], [11], [12] and animal-source products [60], [12], and lower

nutrient intakes [11], [12], [13], [14] have been reported in food insecure people

compared to food secure people. In the data from the Third National Health and Nutrition

Examination Survey (NHANES III), adults (20–59 yr) in food insufficient households

had lower calcium intakes, lower frequency of milk/fruits/vegetable consumption, and

lower serum nutrients like total cholesterol, vitamin A and specific carotenoids than those

in food sufficient households [12]. Using the same survey data, Bhattacharya et al. [10]

found household food insecurity would reduce the Healthy Eating Index (HEI, an index

for diet diversity) in whites and increase the risk of low serum nutrients in whites and

blacks for adults 18-64 yr. Significant decrease in the frequency of fruits and vegetable

consumption in food insecure women was also reported in a sample of women (15-40 yr)

in a rural New York State county [11]; lower intakes of potassium and fiber were found

to be associated with more severe food insecurity. Another study in Toronto, Canada

analyzed the nutritional adequacy in a group of women (19-48yr) receiving emergency

food assistance by measuring dietary intake data and household food security status over

the past 30 days; low levels of intakes in micronutrients like Vitamin A, folate, iron, and

magnesium were observed to be associated with severe household food insecurity [13].

Since weight increase is associated with energy intake, it is more helpful to know

the differences in total energy intake (TEI) as well as macronutrient intake patterns (i.e.

protein, carbohydrates, and fat) in food secure women compared with in food insecure

25

women. The results of macronutrient intake patterns are varied by studies. The study in

Toronto for women receiving emergency food assistance [13] found women in

households without hunger consumed more energy and protein than those in households

with hunger, while controlling for other socioeconomic and behavioral confounders;

however no significant difference was found in fat intake or carbohydrate intake. Another

study in Canada [14] used data from the 2004 Canadian Community Health Survey found

similar association of energy intake (marginal difference, p<0.1) and protein intake with

food insecurity among women (19-50 yr), while lower fat intake in food insecure women

who were 31-50 yr. In terms of carbohydrates intake, this study reported marginally

higher intake in food insecure women 31-50 yr after controlling for income adequacy,

respondent education, immigrant status, current daily smoking status, and household size,

but no significant difference in women 19-30 yr [14]. In U.S., using data from the

NHANES III [12], Dixon et al. found no significant difference in TEI or in fat intake

between food insufficient adults and food sufficient adults (20-59 yr), while Zizza et al.

[81] reported no significant difference of TEI or carbohydrates intake in women (18-60

yr) in NHANES 1999-2002 but higher fat intake in women in food insecurity with hunger

compared to fully food secure women (18-60 yr).

2.1.3.2 Food Stamp Participation

Food Stamp Program (FSP) is a food and nutrition assistance program provides

food stamps/benefits to low- income population who live in U.S... The program is

sponsored by the federal government and administered by the U.S. Department of

26

Agriculture; benefits are distributed by the individual states in US. To be eligible for FSP,

households should meet certain criterion like the income tests to receive food stamps.

Since low income is a strong predictor for food insecurity, food insecure people have a

higher possibility to receive food stamps. However, due to the definition of food

insecurity focusing on food availability rather than income level, food stamp recipients

are not meant to be food insecure. National data in 2008 [4] reported that 56% of food

stamp receiving households were food insecure. Under the same income range, food

stamp receiving households showed prevalence of very low food security almost twice as

high as non-participating households (25.7% vs. 13.4%) [4]. Due to the high correlation

between FSP participation and food insecurity, the periodic distribution cycle of FSP

benefits is hypothesized to lead to a periodic cycle of food availability and shortage, and

a higher risk of weight gain in food insecure recipients.

Associations of FSP participation and weight increase have been studied by cross-

sectional studies [38] [82] and longitudinal studies [83] [84] [53] [85]. Townsend et al.

[38] examined 9451 women from the 1994–1996 Continuing Study of Food Intake of

Individuals (CSFII). After controlling for potential confounders (including food

insecurity), they reported women FSP recipients had a 38% increased odds of being

overweight (classified as BMI> 27.3 kg/m2) compared to non-recipients. However,

inconsistent results have been reported in other cross-sectional studies. For example,

using NHANES 1976–1980, 1988–1994, and 1999–2002, positive association of FSP and

obesity in women was observed in early waves but not in most recent waves [82]:

compare to early waves, the most recent data showed similar BMI in food stamp

27

participants and in income-eligible non-participants. In addition, the study also found this

association is varied by age and ethnic groups. Therefore, cross-sectional data may have

its limitation to investigate the effects of FSP participation on weight status. Gibson [83]

analyzed data from the National Longitudinal Survey of Youth (NLSY), a nationally

representative survey interviewed people born between 1957 and 1964; it was estimated

that current FSP participation is associated with an increase of 9% in women’s obesity

risk and a five-year participation in FSP is associated with a 20.5% increase in the obesity

risk, holding all available confounders. Using the same NLSY survey with a sample of

20,922 women, Zagorsky & Smith [85] reported a 1.15 unit rise in BMI with FSP

participation in the overall sample; specifically, BMI increased the most when women

were in FSP (0.40 unit per year), compared to lower increasing rates when women left

FSP (0.20 unit per year) or before women’s food stamp receipt (0.07 unit per year).

Cross-sectional analysis in this study also found a higher BMI in FSP participants than

non-participants (28.2 vs. 26.6) [85]. Another longitudinal study by Meyerhoefer and

Pylypchuk used the Medical Expenditure Panel Survey (MEPS) and estimated a 6 – 7%

increase in women’s risk of obesity when participating FSP [84].

Although many studies show supportive evidence for the long-term association of

FSP and weight increase, very few studies have done to investigate the FSP participation

and its effect on weight status in food insecure women. One longitudinal study [53]

investigated the effects of FSP participation on weight change in a sample of 5303 food

insecure women from 1999 to 2001[53]. Their study found that a full participation in FSP

would result in a weight increase only among ―persistently food insecure‖ women, while

28

in women who were ―became food secure or food insecure‖, or ―persistently food

secure‖; food insecurity was associated with a weight decrease [53].

2.1.3.3 Disordered Eating

Disordered eating is not the same as eating disorders. Eating disorders are very

complex illnesses marked by extreme food intake or food intake reduction, and related

extreme concern about body weight/shape [86]. There are two main types of eating

disorders: anorexia nervosa and bulimia nervosa, and a third type "eating disorders not

otherwise specified (EDNOS)". In contrast, disordered eating is irregular eating

behaviors such as binge eating with loss of control, or extreme weight-control behaviors

like self-induced vomiting, or the use of diet pills, laxatives, and diuretics. People who

are affected by disordered eating may be diagnosed with an EDNOS. For example, binge

eating disorder is one type of EDNOS [86]. Binge eating disorder refers to behaviors of

episodically consuming large quantities of food in a short time period; unlike bulimia

nervosa, binge eating disorder is not accompanied by compensatory behaviors like

excessive exercise, fasting, inducing vomiting or using laxative [87]. People with binge

eating disorder have both the syndromes of episodically large-amount ingestion and the

guilt sense of losing control over eating [88]. Binge eating disorder can lead to serious

chronic diseases like severe obesity, diabetes, hypertension and cardiovascular diseases

[86] [88].

It has been proposed that the association of food insecurity and

overweight/obesity is mediated by disordered eating, particularly binge eating behaviors

29

[11] [38] [52]. Although little is known about how binge eating behaviors start,

psychosocial factors such as low self-esteem and anxiety have been hypothesized to be

important [52]. Since food insecure women have similar psychological characteristics

due to the uncertainty in life, food insecure women may under high psychosocial stress

which then leads to binge eating and subsequent weight gain. To the best of our

knowledge, only one study - the study in a sample of 193 women in a rural county in

New York State [11] [52] - has investigated the association of food insecurity and

disordered eating patterns. More severe disordered eating patterns was observed along

with worsening food insecurity; using multivariate regression models, the study

concluded that severe food insecurity itself was associated with lower BMI and less

likelihood of obesity, but severe food insecurity was also associated with higher risk of

disordered eating and hence the risk of obesity was increased; because the former one had

a larger effect, women of severe food insecurity were less likely to become obese

compared to food insecure women of less severe levels [52]. In this study the disordered

eating behaviors were evaluated using a 4-item scale: the Stanford Eating Disorders

Questionnaire [89]. The four questions in the scales were relevant to binge eating

behaviors: ―(1) has abnormal or unusual eating patterns compared to others in terms of

how much she eats or how fast she eats; (2) ever eats large amounts of food very quickly

in a short period of time; (3) ever has episodes of overeating that she would refer to as

binges, and (4) eats large quantities of food deliberately out of the sight of other people‖

[52].

30

In addition, Townsend et al. [38] reported the association of food insecurity and

overweight in women from the 1994–1996 Continuing Study of Food Intake of

Individuals (CSFII), and proposed that overweight may be related to ―involuntary,

temporary food restriction‖. Furthermore, researchers believe that a periodic pattern of

food deprivation/food restriction tends to promote binge eating behaviors and excessive

weight gain when a plentiful food supply is available. For example, experimental studies

observed significantly stronger requests and attempts in children to restricted palatable

snack foods compared to similar non-restricted foods, or to the same foods during non-

restriction session [90]; such restriction may increase their subsequent selection and

intake of these foods [90]. Furthermore, longitudinal studies exploring the eating in the

absence of hunger (EAH) in girls (5-9y) have found that food restriction level at younger

age was a strong predictor for higher EAH at older age [91].

2.1.4 The “Monthly Cycle of Food Abundance and Food Shortage” Hypothesis

Based on the possible explanations discussed above, the ―monthly cycle of food

abundance and food shortage‖ hypothesis was proposed. In this hypothesis, obesity could

be a result of the periodic food availability and shortage existing among food insecure

people, which may cause the body to change permanently and increase fat deposition

when food is available [46], [47], [49]. In particular, abundant food sources may be

available in food insecure households during the first week(s) of the month, but restricted

at the end; such an abundance-shortage cycle may continue during following months.

31

Additionally, due to the first explanation, it is possible that most of the available foods

for food insecure households during the first week(s) are high-fat and high-calories.

There are two important assumptions in this hypothesis. First, the episodic

abundance and shortage of food intake can lead to obesity. This assumption was first

supported by the ―thrifty genotype‖ hypothesis proposed by Neel early in 1962,

suggesting that a cycle with ―periods of gorging alternated with periods of greatly

reduced food intake‖ would have helped to develop an adaptive method called ―thrifty‖

metabolism [49]. This hypothesis states that individuals experiencing such a fluctuating

cycle would have high efficiency in fat deposition when calories are available, and the

accumulated fat would be used during the next food shortage period [49]. The ―thrifty

genotype‖ hypothesis is supported by some animal studies, showing that multiple cycles

of the starvation-refeeding strategy in rats were followed by the up-regulation of white

adipose tissue (WAT) lipogenesis [92]. In addition, researchers believe that a periodic

pattern of food deprivation/food restriction tends to promote binge eating behaviors and

excessive weight gain when a plentiful food supply is available [93], [94], [92], [90],

[95]. Experimental studies among children suggested causality between child weight gain

and food restriction: children had significantly stronger requests and attempts to restricted

palatable snack foods compared to similar non-restricted foods, or to the same foods

during non-restriction session. Therefore, such restriction may increase their subsequent

selection and intake of these foods [90]. Similar results were reported by other

observational studies, showing the strong association of childhood overweight and

restriction-feeding strategy in children [95]. Furthermore, longitudinal studies exploring

32

the eating in the absence of hunger (EAH) in girls (5-9y) have found that food restriction

level at younger age was a strong predictor for higher EAH at older age [91].

Interestingly, many of the studies on food restriction and weight gain are found in girls

rather than boys [95], [96]. For example, a study showed that maternal restriction of

snack food was a stronger predictor of levels of snack food intake only in girls [96].

The second assumption is that the episodic pattern of food supply and food intake

does exist in food insecure households. Very few studies have been conducted to testify

this assumption. However, several studies have shown an episodic lack of food followed

by increased food servings during times of relative abundance in low-income families or

in food insecure households. For example, one study using the 1989–1991 Continuing

Survey of Food Intakes by Individuals (CSFII) data reported that food expenditure in

food stamps receiving households peaked dramatically in a couple few days right after

the benefits were received but flattened out throughout the rest of the month; in addition,

energy intake significantly dropped from the first week to the last week in households

doing grocery shopping once per month or less [51]. Another study showed low-income

families had decreased number of food servings in the last week of the month [97].

Moreover, a study reported that in food insecure households, meat and energy intake

dropped significantly in children as payday came close [98]. A similar study in Canada

analyzed the dietary intake using 24-hour dietary recalls in low-income women by their

food security status; after the receipt of income, within-month declines of TEI was found

in food sincere women [99].However, all these studies lacked the data of women’s

weight status to be associated with the monthly declines.

33

2.2 Questionnaire-based Measurements and Dietary Intake Assessment

This section introduces tools used in the study for measuring household food

security, disordered eating behaviors, and dietary intake data.

2.2.1 Household Food Insecurity Measurement

In U.S., the development of reliable measures for hunger was promoted by a

Presidential Task Force Report in U.S. in 1984, which reported hunger in U.S. but could

not quantity the extent [1]. This report brought up the uncertainty of the exact number of

people affected by food insecurity and to what extent they were affected, which makes it

necessary to have a food insecurity measurement tool. Therefore, in 1990 the National

Nutrition Monitoring and Related Research Act was enacted to emphasize the importance

of developing a standard and consistent measurement for food insecurity in U.S. and

defining food insecurity [1]. A tool that provides a valid and comparable measurement in

food insecurity can help researchers to estimate the prevalence of food insecurity,

correctly identify the causes and consequences, target high-risk population groups, and

establish, monitor, and evaluate intervention programs.

Several methods have been used to assess household food insecurity [100], such

as national economic indicators like food production and dietary energy supply, and

within-country level methods like household income and expenditure, individual dietary

intake, nutritional status and anthropometric measurement. These indicators are

intensively dependent on time, skills and resources; more importantly, none of them can

34

help to understand the conceptualization in food insecurity [100], [101]. Therefore, a

qualitative food security scale (questionnaire) is necessary to account for the experience

of food insecurity in the household. In addition, a qualitative food security scale costs

less, and can be applied easily in different populations and in multiple levels (e.g.

individual, household, and community level).

In order to quantify, monitor and evaluate food insecurity with an easy-to-use and

valid tool, researchers from various U.S. federal agencies, academics, and the private-

sector have been involved in the development of questionnaire-based measures to

quantify the extent in food insecurity over the last twenty years [102], using approaches

like in-depth interview, focus group, and validation studies to develop tools and measure

the validity of developed tools in different population groups.

One of the first studies in the food security scale development was part of the

Community Childhood Hunger Identification Project (CCHIP), which was based on the

1983 Massachusetts Nutrition Survey. This study developed an 8-item tool specifically

for low-income households with children, asking about coping methods and sequential

events to deal with food insecurity process from mild food insecurity to severe hunger in

the previous 12 months [103]. The study defined hunger as ―food insufficiency due to

lack of resources‖ [104]. At approximately the same time as the CCHIP tool was

developed, Radimer and colleagues at Cornell University constructed the

Radimer/Cornell scale by conducting open-ended personal interviews with low-income

women to assess their perceptions in food insecurity and hunger for themselves as well as

their children. The conceptual definition of hunger and the 12-item tool were developed

35

based on these women’s statements and further factor and psychometric analysis. Half of

the items were about the perceptions in food insecurity and half were about food

insecurity related behaviors [105].

The CCHIP and Radimer/Cornell scale provide basic foundation for the

development of the U.S. Core Food Security Module (CFSM) later on in 1990 [106].

This module uses 18 items as a whole to assess the underlying level of severity of

household food insecurity and hunger, assessing food insecurity experiences and coping

strategies to subsequent events during the previous 12 months.

The CFSM was further modified to generate the U.S. Household Food Security

Survey Module (US-HFSSM) by the U.S. Department of Agriculture (USDA) in the mid-

1990s [3]. This module is composed of 18 questions based on the CFSM, 10 of which are

for adult food insecurity experience, and 8 are for children [3]. It asks about experience

and coping approaches to food insecurity from worrying about running out of food to

reduction of quality and quantity of foods during the previous 12 months. Using this tool,

a food security score can be generated for each household to categorize the households

into four food security status: food secure, food insecure without hunger, food insecure

with moderate hunger and food insecurity with severe hunger. In 2005, the category food

insecure without hunger was changed to low food security, and food insecurity with

hunger was changed to very low food security. The new four categories since 2005 are:

high food security, marginal food security, low food security, and very low food security

[107]. The US-HFSSM (Table 2.1) is the module currently applied in NHANES and has

been widely adapted and applied to different populations (see 2.3.2).

36

In addition, there is a short form of the US-HFSSM developed for application in

screening, monitoring, and evaluating intervention programs when resources are limited.

It contains 6 items from the 10-item adult component in; no child component was

included. The short form was comparable to US-HFSSM when approximating the three

major categories of food secure, food insecure without hunger, and food insecure with

hunger in US-HFSSM, but performed a little better in households without children than

households with children [108], [109].

Validation of Questionnaire-based Household Food Insecurity Measurement

After developing a questionnaire-based tool, it is essential to verify the validity of

this measurement: to ensure the very tool analytically captures the exact underlying

construct of what to be measured. To be a valid tool in a specific population, a household

food insecurity scale should measure the exact experience and coping approaches of

household food security during a period of time in the very population. Two major types

of validity measures have been commonly used in validating household food security

scales: internal validity and criterion-related validity.

Internal validity refers to the capability of the tool to capture each single

underlying construct in food insecurity precisely, meanwhile maintaining consistency

with empirical data and patterns of confirmative responses. One common method to

evaluate the internal validity in food insecurity tools is the Rasch Model. This model

assesses the psychometric characteristics of items in measurement tools by detecting

37

whether there is any problem within the items, about the order of the items within the

questionnaire, and about the score interpretation of the tool [3].

The internal validity of qualitative food security scales have been confirmed

[110], [111], [112], [113], [7], [60]. For example, tools translated from CCHIP and US-

HFSSM have been locally adapted and tested separately by Rasch Model in Antioquia,

Colombia [7] and Campinas, Brazil [60]. Both tools showed reasonable responses

compared to expected values of each item, and consistent patterns of affirmative

responses. These encouraging results made the two countries incorporate these two tools

into their national nutrition surveys. Another study in Trinidad, an English-speaking

Caribbean country showed that the six-item short form of HFSSM is suitable for food

insecurity classification in adults and adolescents in the country [112], [113]. In U.S., the

CFSM has been confirmed to fit well in the Hawaii population as well as with national

data [110]. Similar as the results reported by studies using Rasch Models, another

validation study applied generalized linear model to evaluate the 1995, 1997 and 1999

Food Security Module used by USDA, and confirmed the validation and robustness of

the questionnaire-based tool in measuring food insecurity [111].

Criterion-related validity is defined as the comparability of the results of the

tested tool with those of one or more criterion-related methods which are theorized to be

related to the measured concept [114]. Since there is no gold-standard for measuring food

insecurity, researchers have assessed the criterion-related validity of food security scales

by evaluating their correlation with socioeconomic and demographic indicators like

income, education, dietary intake, nutrition status, and health outcomes [104][115]. These

38

indicators are hypothesized to be related to and vary by household food insecurity status.

As discussed in section 2.1.1, a number of social factors like low income, single parent,

being Black or Hispanic, large household size, and lower education [4], [53], [59], [6],

[53], and health outcomes including lower nutrient intake [11], [12], [13], [14] and poor

health status [15], [64] , [65], [66], [16], [67], [59], [18] have been associated with

household food insecurity, measured by adapted questionnaire-based tools.

In spite of internal and criterion-related validity, external validity is of greater

interest in some situations. External validity is also called ―generalizability‖, indicating

that the tool can be universally used in different population settings. In U.S., HFSSM

shows its suitability in capturing the experience in food insecurity and hunger in different

populations including minority groups like Asian and Pacific Islanders and Latinos [116],

[110], [19], [61]. Adapted HFSSM also demonstrate a similar suitability in countries

inside and outside the America, including Canada, Latin America and Caribbean

countries, UK, Russia, Java, Thailand, Bangladesh, Philippines, Republic of Korea,

Southwest Pacific Islands countries, and countries in Africa and West Africa [104]. This

generalizability may be due to the similarity of experience to food insecurity from

worrying to coping approaches.

2.2.2 Self-reported Eating Disorders Examination Questionnaire (EDE-Q)

EDE-Q is the self-reported version of the Eating Disorder Examination (EDE)

[117], an investigator-based instrument. EDE is considered as the ―gold standard‖ for

assessing the complex psychopathological features in eating disorders [118], [119]; as a

39

self-reported tool, EDE-Q is more welcome because of its privacy, efficiency, and

reduced time and money expenditure. EDE-Q contains 28 items to comprehensively

assess the aspects of ―specific psychopathology‖ in eating disorders in the past 28 days

[117]. There are two types of data collected by EDE-Q: the frequency data on key

behaviors of eating disorders (6 items - times of overeating episodes, days of overeating

episodes, extreme dietary restraint, use of self-induced vomiting, use of laxative, and

excessive exercise) and the subscale scores on the severity of four psychopathological

aspects (i.e. Restraint, Eating Concern, Shape Concern and Weight Concern) [120]. Here

are the items included in each subscale: restraint (restraint over eating, avoidance of

eating, food avoidance, dietary rules, and empty stomach), eating concern (preoccupation

with food/eating/calories, fear of losing control over eating, eating in secret, social eating,

and guilt about eating), shape concern (flat stomach, preoccupation with shape or weight,

importance of shape, fear of weight gain, dissatisfaction with shape, discomfort seeing

body, avoidance of exposure, and feelings of fatness), and weight concern (importance of

weight, reaction to prescribed weighting, preoccupation with shape or weight,

dissatisfaction with weight, and desire to lose weight). The subscale data are obtained

from 22 items using a 7-point rating scheme [117]: a particular subscale score is

calculated by dividing the sum of the ratings of items belonging to this subscale by the

total item numbers. Thus a higher score represents greater severity of the certain

behavior. Finally, a global score is generated by dividing the sum of subscale scores by

the total number of subscales [120]. Besides, EDE-Q also asks about self-reported weight

40

and height, missed menstrual periods in the past three to four months, and use of any diet

pills [117].

There are several self-report instruments developed for binge eating assessment,

including general measures for eating disorders: the Eating Attitudes Test [121] and the

Eating Disorder Inventory [122], and specific measures for binge eating behaviors: Binge

Eating Scale [123], the Bulimia Test [124], and the Questionnaire on Eating and Weight

Patterns [125]. However, these measures have been criticized [118] by the lack of

definition of ―binge eating‖, providing only a complicated index rather than clear

measures of specific binge eating behaviors, and without a specific time frame.

Comparatively, the EDE-Q focuses on the overeating episodes and accompanied feelings

of loss of control on the past 28 days. Specifically, the EDE-Q assesses binge eating

behaviors by yielding both a comprehensive index (the Eating Concern subscale score) as

well as the frequency of number of days/times on which binge eating occurred by the

following two items: (1) ―Over the past 28 days, how many times have you eaten what

other people would regard as an unusually large amount of food?‖, and (2) ―On how

many of these times did you have a sense of having lost control over your eating (at the

time that you were eating)?‖. Here the EDE-Q defines binge eating as ―eating what others

would regard as an unusually large amount of food for the circumstances, accompanied

by a sense of having lost control over eating‖ [120]. Furthermore, EDE-Q is a

comprehensive tool for measuring eating disorders. The use of EDE-Q can help to assess

binge eating behaviors and potentially associated eating disorder psychopathology in

food insecure and overweight/obese women in the dissertation.

41

EDE-Q has been used to identify binge eating in previous studies in women [126].

One study classified binge eaters for reporting at least four times of binge eating in the

past 28 days, and binged in the previous three months [126]. Another study measured

binge eating by assessing how many days was the number of days on which the subjects

―eating an unusually large amount of food for the circumstances, accompanied by a sense

of loss of control‖ [127].

The validity of EDE-Q have been studied in the general community population

[120], [128], college students [129], female substance abusers [130], and people with

binge eating disorder and uncontrolled overeating [127], [131]. The external validity was

tested by comparing EDE-Q and interviewer-based EDE: high correlations are confirmed

between the two on subscales and on key behavioral features [120], [130], [127].

However, some studies have reported that it is more accurate for EDE-Q to identify

unambiguous behaviors rather than more complex features such as binge eating and

shape concerns [120], [130], and vomiting episodes and objective binge eating [131].

Another study reported that EDE-Q detected higher scores in complex behaviors than

EDE [127]. The internal validity of EDE-Q is tested as well: great internal consistency

and test-retest reliability for all four subscales were reported in a sample of 139 female

undergraduate students who were tested and re-tested after two weeks [129]. But this

study also showed that items to measure key behavioral features were a little less stable

than those of subscales [129]. In addition, community norms for EDE-Q have been

developed for young adult women [132] and adolescent girls [133]. The norms for young

adults women were developed from a large sample (n = 5,255) of women in Australia

42

[132]. The study assessed features of EDE-Q for each of five-yr age range in women

aged 18 – 42 yr: 18–22, 23–27, 28–32, 33–37, and 38–42 yr [132]. [132]The Mean scores

(SDs) for the Restraint, Eating Concern, Weight Concern and Shape Concern subscales

were 1.30 (1.40), 0.76 (1.06), 1.79 (1.51) and 2.23 (1.65), respectively, and the mean

global score was 1.52 (1.25) for the total sample; and the frequency for any objective and

subjective overeating episodes was 17.3% and 22.1%, respectively.

2.2.3 Dietary Assessment Methods

There are three dietary assessment methods commonly used in nutrient

epidemiology: food records, food frequency questionnaires (FFQ), and 24-hour dietary

recalls. Each method has its advantages and limitations. In this section a brief review of

the three methods is provided, and he reasons for choosing 24-hour dietary recalls rather

than other dietary assessment methods in this study are presented as well.

In the method of food records, participants are asked to estimate (or weigh) and

record the amounts of all the foods and beverages they consumed from all meals and

snacks for a period of time, sometimes including food description, preparation methods,

brand names, and other details relating to food consumption [134]. Food records are

usually used to obtain exact individual dietary intake of energy or specific macro-/micro-

nutrients, or to give reference values for another dietary assessment method in validity

studies. The limitations of this method include: 1) selection bias – this method requires

the tested population with a relatively high literacy level as well as a high degree of self-

motivation; 2) the estimated food record method tends to put research burden on

43

participants. This may affect study results and yield underestimation, such as incomplete

records and reduced dietary intake, especially when the number of recording days

increases [134]. Consequently, the method itself can change food consumption behaviors;

3) this method could bias the dietary intake estimation by changing individuals’ food

consumption patterns: participants may begin to pay attention to their dietary intake

during food recording process, and try to change their diet purposely.

Food frequency questionnaires (FFQs) are methods used to assess the usual

dietary intake by asking the frequency of consumptions of certain groups of food during a

period of time [134]. Typical FFQs emphasize the frequency rather than exact portion

sizes, but some FFQs also gather information on rough estimation of portion sizes. The

most important advantage of FFQs is that it is a more reliable way to measure

individuals’ usual (long-term) dietary intake compared to food records or 24-hour dietary

recalls. In addition, the questionnaire form of FFQs makes it easy to administer, and

hence less burdensome for participants compared to food records. However, FFQs do not

measure many details of dietary intake, and hence cannot quantify dietary intakes as

accurately as 24-hour recalls or food records, particularly in a short time period. FFQs are

usually used to rank individuals by their dietary intake rather than estimating the exact

dietary intake values during specific time periods. Furthermore, FFQs can lead to report

bias and recall bias since it is usually self-reported and asks for dietary consumption in

previous months or even years.

In 24-hour dietary recalls, interviewers ask individuals to report their food

consumption during the preceding 24 hours or on the previous day [134]. 24-hour recalls

44

are usually used to obtain group dietary intake or individual dietary intake of energy or

specific macro-/micro-nutrients [134]. This requires detailed information about dietary

intake. To obtain such information, interviewers usually use probing questions and

certain procedures during the process: more of the procedures are discussed in the

following ―Multiple-Pass Approach‖ section. 24-hour recall data can be gathered via

either in-person or phone interviews. There have been concerns on the accuracy of 24-

hour recall results gathered by phone interviews [135]. However, it is shown that as long

as interviewees and interviewers have a face-to-face interview at the beginning, the

following intake estimation from in-person interviews and telephone recalls are

―interchangeable‖, since interviewees have experience on portion size estimation and

interviewers have some information on basic food consumption habits of interviewees

[136], [135].

There are several reasons to choose 24-hour dietary recalls in assessing the dietary

intake in this study.1) Compared to other dietary assessment methods like FFQs, 24-hour

dietary recalls can assess the exact varieties and provide quantitative estimation of dietary

intake in a short time period [134], [137]. The quantification of dietary intake is critical in

this study to find the variations of TEI among low-income women. 2) Most subjects in

the study would be of low literacy level and socioeconomic status (SES). Therefore, a 24-

hour dietary recall administered by trained interviewers puts low burden to participants,

reduces selection bias (a type of bias which is commonly reported in self-conducting

methods like food records) [134], [137], and obtain more information on dietary intake

by using probing questions. In addition, this method will not change participants’ dietary

45

behaviors. 3) The short recall period in this method, around 24 hours, can help

respondents to recall most of their preceding food consumption, which in turn reduces the

recall bias [134].

Based on the reasons listed above, 24-hour dietary recalls are an appropriate

method for dietary assessment in this study. However, there are limitations for this

method. One of the limitations is that the accuracy of 24-hour recalls is highly depending

on interviewing skills. And since this is still a self-report method, reporting bias (e.g. TEI

underreport in obese participants) still exists in 24-hour recalls [134]. Such limitations

can be improved by standardized data collection protocols and comprehensive quality

assurance system (see 3.2.2). Another shortcoming of 24-hour dietary recalls is its

uncertainty to measure individuals’ usual dietary intake, since the single-day food

consumption measured by this method may vary day by day. More details and the

solution to this limitation are provided in the following section.

The Variation in Dietary Intake Collected by 24-hour Dietary Recalls

It has been widely concerned by researchers that 24-hour dietary recalls cannot

reflect the usual dietary intake of individuals [134], [137]. That is because the usual

dietary intake of a free-living person is featured as an underlying consistent eating pattern

along with day-to-day variation [137], while a single 24-hour dietary recall can only

catch food consumption on a single day. To fully understand this contradiction, the

sources of variation in 24-hour dietary recalls and their influences to the estimation of

different nutrient intake is explained here.

46

There are two major components in the variation: the inter-individual variance (or

between-person variation) and the intra-individual variance (or within-person variation)

[138]. The inter-individual variance is the difference of interest: the true difference of

dietary intake among different individuals. On the contrast, the intra-individual variance

should be as low as possible in order to catch the usual dietary intake pattern of

individuals. The day-to-day variation within individuals is the most significant

contributor to the intra-person variance when estimating different nutrient intake: TEI,

macronutrients (i.e. carbohydrates, fat, and protein), and micronutrients (i.e. vitamins and

minerals) [139]. Among all the nutrients, the TEI is quite well regulated by physiologic

mechanisms and has the lowest day-to-day variation [54], [137]. It was reported that the

intra-individual variance was equal to the inter-individual variance in TEI [138].

Comparatively, specific nutrients have much higher ratios of inter-individual variance

(i.e. greater than 1), which means these nutrients have higher intra-individual variance.

For this reason, it is necessary to have more days in data collection to obtain more

accurate estimation. Among these specific nutrients, most of the macronutrients, due to

their high contribution to the TEI, have a somewhat lower day-to-day variation compared

to micronutrients [137]. Other nutrients, like polyunsaturated fat, cholesterol, and most

micronutrients (e.g. Vitamin A) have much higher degrees of day-to-day variation [137],

[138]. This high variation is because these nutrients are highly concentrated in certain

foods; the consumption of these foods is depending on the individual’s food choice on the

particular day [137]. Besides the day-to-day variation, days of the week effect has been

found to be another component of the intra-individual variance only in females [138].

47

Data showed that working women consumed more food on Sunday than on weekdays

[138]. Seasonal variation is another reported component of intra-individual variance,

which may not be a significant contributor in the United States [137]. It is shown that the

correlations between 1-week diet records did not vary substantially within three, six, nine

or twelve months [140].

Because of the large intra-individual variation, a single 24-hour dietary recall is a

poor estimate for the usual dietary intake of individuals; multiple-day recalls are needed.

But how many days are necessary for estimation? The optimal number of recalls can vary

depending on the nutrients or food of interest [54]: the larger day-to-day variation of the

nutrient of interest, the more recalls are needed to get an accurate estimation. For certain

nutrients, it is even beyond practical possibilities to get a highly accurate estimate by

repeated recalls [137]. For example, it is estimated that 424 repeated days are needed to

allow for 95% of the observed values being within 10% of the true mean of an

individual’s Vitamin A intake. Comparatively, only 7 days are needed for the true mean

of TEI to achieve the same accuracy [137]. In addition, to reduce the days of week effect,

multiple-day dietary recalls should include both weekdays and weekends proportionally

[134], [138].

Multiple-Pass Approach

As mentioned before, the interview process is critical to obtain accurate data in

24-hour food intake recalls. The multiple-pass approach introduced here has been widely

used in 24-hour dietary recalls in national surveys [141], [142], [143]. The multiple-pass

48

method has been proved as a validated way to estimate group energy intake: no

significant difference between reported energy intake and total energy expenditure

measured by doubly labeled water method (DLW) [144], [145], [53]. In the multiple-pass

approach, 3 to 5 standard steps are applied to assure highly-qualified results. The passes

in this study include ―a quick list, detailed description, and review‖. In the first step,

subjects are asked to list all the foods and beverages consumed on the previous day in any

order they liked. The next step is a detailed description of all the listed food. Interviewers

use probing questions to gather detailed information on the ingredients, portion sizes,

brand names, and preparation methods. Food models can be used to help subjects for

portion size estimation. In the final review step, interviewers go through all listed

information with the subjects, making sure the accuracy and any forgotten foods or

beverages. Finally, interviewers should check all the information again after each step.

2.3.4 Shelf Food-Inventory

Shelf food-inventory is a questionnaire with an extensive list of food items

commonly found in U.S. households. There are in total 19 food groups consisting of 222

items, with an open-ended question at the end inquiring about any other items in the

household.

Household inventory questionnaires have been validated to assess food selection

behaviors. The self-reported shelf inventory was first described by Crockett et al. [146] as

part of a nutrition education project in 1992: the self-report shelf inventory was compared

with a food frequency questionnaire for its overall accuracy, and an interviewer-based

49

shelf inventory administered on the same day for the specificity and sensitivity. Results

confirmed the validity for the tool to measure the presence/absence of food items in

households [146]. As such, it may be a useful tool for measuring the household food

stores in this study. Another validation study in Chicago found the self-report inventory

was effective in assessing food purchase behaviors in low-income black and Hispanic

households, using a food frequency questionnaire and the 24-hour food diary as reference

methods [147]. The shelf-inventory instrument has been successfully used in many

studies [19], [61], [39]. For example, a study measuring food insecurity and household

food supply in Latino households found that the most commonly reported food supply by

Californian Latino families was very similar to that reported in the Chicago validation

study [19].

50

1. ―We worried whether our food would run out before we got money to buy

more.‖ Was that often, sometimes, or never true for you in the last 12 months?

2. ―The food that we bought just didn’t last and we didn’t have money to get more.‖

Was that often, sometimes, or never true for you in the last 12 months?

3. ―We couldn’t afford to eat balanced meals.‖ Was that often, sometimes, or never

true for you in the last 12 months?

4. In the last 12 months, did you or other adults in the household ever cut the size of

your meals or skip meals because there wasn’t enough money for food? (Yes/No)

5. (If yes to question 4) How often did this happen—almost every month, some

months but not every month, or in only 1 or 2 months?

6. In the last 12 months, did you ever eat less than you felt you should because

there wasn’t enough money for food? (Yes/No)

7. In the last 12 months, were you ever hungry, but didn’t eat, because there wasn’t

enough money for food? (Yes/No)

8. In the last 12 months, did you lose weight because there wasn’t enough money

for food? (Yes/No)

9. In the last 12 months did you or other adults in your household ever not eat for a

whole day because there wasn’t enough money for food? (Yes/No)

10. (If yes to question 9) How often did this happen—almost every month, some

months but not every month, or in only 1 or 2 months?

Questions 11-18 were asked only if the household included children age 0-18:

11. ―We relied on only a few kinds of low-cost food to feed our children because

we were running out of money to buy food.‖ Was that often, sometimes, or never

true for you in the last 12 months?

12. ―We couldn’t feed our children a balanced meal, because we couldn’t afford

that.‖ Was that often, sometimes, or never true for you in the last 12 months?

13. ―The children were not eating enough because we just couldn’t afford enough

food.‖ Was that often, sometimes, or never true for you in the last 12 months?

14. In the last 12 months, did you ever cut the size of any of the children’s meals

because there wasn’t enough money for food? (Yes/No)

15. In the last 12 months, were the children ever hungry but you just couldn’t afford

more food? (Yes/No)

16. In the last 12 months, did any of the children ever skip a meal because there

wasn’t enough money for food? (Yes/No)

17. (If yes to question 16) How often did this happen—almost every month, some

months but not every month, or in only 1 or 2 months?

18 In the last 12 months did any of the children ever not eat for a whole day

because there wasn’t enough money for food? (Yes/No)

Table 2. 1 US-HFSSM questions used to assess household food security in the Current

Population Survey (CPS) Food Security Survey [4]

51

CHAPTER 3

Addressing the Association of Food Insecurity and Overweight/Obesity by Testing

the “Monthly Cycle of Food Abundance and Food Shortage” Hypothesis

This chapter presents the background, study design, data collection and analysis

methods, and results and conclusions in the Ohio study. The hypothesis tested in the

study is ―the monthly cycle of food abundance and food shortage‖ hypothesis, which

suggests that women in food insecure households experience a monthly cycling pattern of

food intake featured by periods of food deprivation or limited food selection, followed by

periods of binge eating characterized by increased total energy intake (TEI).

3.1 Introduction

The paradoxical association in food insecurity and obesity has been studied for

about twenty years. Several possibilities have been proposed to explain this association.

The most popular one is that when nutritionally-balanced diets are less available in food

insecure households [44], [45], high-fat-high-calorie food is be the most affordable

energy source to prevent hunger [46], [47], [48]. An extended scenario of this hypothesis

is that food insecure people may indulge in highly palatable and energy-rich food and

hence have an increased risk of obesity [46]. Another hypothesis for the paradoxical

association is that obesity could be a result of a periodic cycle of food availability and

52

food shortage; such a cycle may result in body change and increase the efficiency of fat

deposition when food is available again [46], [47], [49]. The third hypothesis focuses on

the psychosocial stress prevalent among food insecure people, resulting in endocrine

abnormalities and promoting visceral obesity [46], [50]. Although a number of

hypotheses were proposed for the paradoxical relationship in food insecurity and obesity,

none of them have yet been tested. In addition, most of the previous studies were from

cross-sectional data, which could only provide correlated information of dietary intake,

food insecurity and weight status at a defined time. For these reasons, there is a need for a

prospective study to test these hypotheses in food insecure people.

To address this paradoxical association, the ―monthly cycle of food abundance

and food shortage‖ hypothesis is proposed in the study. According to this hypothesis,

food insecure households have access to abundant food sources during the first few

weeks of the month, while face problems of food shortage at the end. Thus, higher energy

consumption may occur in food insecure people at the beginning of the month when food

supply is plentiful, and restricted energy intake may happen at the end of the month. Such

a monthly cycle of food abundance and food shortage may repeat month to month when

the household keeps food insecure. This study is one of the few studies using a

prospective study design to address the relationship in food insecurity and obesity among

women [51], [52], [53]; a prospective design allows exploring the changes of dietary

intake and the monthly varying patterns as well.

The ―monthly cycle of food abundance and food shortage‖ hypothesis has not yet

been tested. Nevertheless, several studies have shown support for this hypothesis. One

53

study found that food expenditure in food stamps receiving households peaked

dramatically in the first three days right after the benefits were received but flattened out

throughout the rest of the month; in addition, energy intake significantly dropped from

the first week to the last week in households doing grocery shopping once per month or

less [51]. Another study in public health showed low-income families demonstrated

decreased number of food servings in the last week of the month [97]. Furthermore,

researchers believe that a periodic pattern of food deprivation/food restriction tends to

promote binge eating behaviors and excessive weight gain when a plentiful food supply

is available [93], [94], [92], [90], [95]; there was evidence supporting a causal

relationship between child-feeding restricting strategies and childhood overweight [95].

The purpose of the study is to testify a monthly cycle of food abundance and food

shortage among a group of low-income women in Ohio, and its association with food

security and weight status. Specifically, the following hypotheses are proposed to be

tested in this study:

1. FIS/ovob women will have higher TEI at the beginning of the month compared to

the end of the month in three continuous month.

2. FIS/ovob women will have more household food items at the beginning of the

month compared to the end of the month in three continuous month.

3. The monthly decrease of TEI and household food items will be greater in

FIS/ovob women compared to FS/norm, FS/ovob, and FIS/norm women.

4. FIS/ovob women will have more severe disordered eating behaviors than

FS/norm, FS/ovob, and FIS/norm women.

54

5. Among food insecure women, food stamp recipients will have higher body mass

index (BMI) and greater monthly decrease in TEI and household food items than

food stamp non-recipients.

The study can contribute key information on how episodic food intake patterns

mediate the association in food insecurity and obesity, illustrating the need to promote

consistent eating patterns in food insecure people. Moreover, the study provides

information for policy makers to make changes in federally funded food assistance

programs which assign benefits on a monthly basis: a more even distribution of such

benefits throughout the month, combined with intervention programs emphasizing better

food shopping strategies and more even intra-household food distribution might result in

a reduction of episodic overeating behavior patterns in food insecure households.

3.2 Methods

The study was designed to compare the total energy intake (TEI) and household

food stores at the beginning of the months with those at the end of the months in three

continuous months, among women groups of different food security status and weight

status. Featured characteristics of disordered eating were also compared among women

groups.

3.2.1 Subjects

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The target population in the study is food insecure and overweight/obese women

in Ohio. The major source of sample recruitment was participants of Ohio Family

Nutrition Program (FNP), a free nutrition education program for low-income adults in

Ohio. The reason to use FNP participants as the major source for subject recruitment was

because these adults are FSP eligible individuals. In a previous study, food stamp

recipients were observed experiencing a monthly pattern of food expenditure [51]. In

Ohio FNP is a program supporting families with nutrition education in over sixty five

counties. Its presence throughout Ohio allows us to assure the local support needed for

this study. Also it helps to count on nutritionists and interviewer staff with rich

experience working in low-income communities, and/or leading the implementation of

FNP in Ohio. In addition, in 2007 77% of FNP participants in Ohio were women

(personal communication with FNP staff).

Subjects were recruited by The Ohio State University (OSU) Extension educators

in three Ohio counties: Butler County (South), Richland County and Huron County

(North). Counties were selected based on the availability of human resources in local

OSU Extension offices, the participation of food assistance program staff, and geographic

locations. Richland County and Huron County are located in the northern area of Ohio.

As of the census of 2000 [148], about 50,000 households were in Richland County and

22,000 households were in Huron County; approximately 30% of the households in these

two counties had children under 18. The percentages of families below the poverty line

were about 8% and 6% in Richland county and Huron county, and the per capita income

was $18,582 for Richland County and $18,133 for Huron County, respectively. Butler

56

County has about 123,000 households and more than 30% of which had children under

18. The per capita income for Butler County was $22,076; nearly 5% of the families in

Butler County were below the poverty line. Since the average per capita income was

$21,003 in Ohio and $21,587 in U.S.in 2000 [148], the income in Richland County and

Huron County were below the average level [148]. In addition, most of the residents in

these three counties were white people [148].

To recruit low-income women, flyers and cards with brief study introduction and

staff contact information were posted and distributed in places where Family Nutrition

Program (FNP) participants are gathered. Registered potential subjects were contacted

and screened for age, race, and pregnancy/lactation status in the local OSU Extension

offices using a short screening form. Before entering the study, participants were read a

script introducing the study and their rights and responsibilities during the research period

after acceptance. A consent form was signed by them after they read it. Each participant

received $15 gift card for each interview. Incentives were given to interviewees after

each interview.

The study was designed to recruit 80 women evenly distributed throughout the

year and followed each of them for three months. For example, group 1=January-March,

group 2=April-June, group 3=July-September, group 4=October-December. This is

because food assistance program participants remain in the program for a short period of

time, and the risk factors of food insecurity such as household income and household

member employment are affected by seasonal factors. Women should be Caucasian,

reproductive age (22-49 years of age), non-pregnant and non-lactating. The sample size

57

of eighty women was determined based on the following assumptions: 1) significant

differences in mean energy intake (kcal and % of RDA) of 10% or greater between food

insecure and food secure women; and 2) probability of 80% to detect differences at a

significance level of 0.05. These calculations are based on data from the CSFII [38]. In

addition, a sample size of 80 allows for a 25% attrition rate. After recruitment,

participants were then classified into four categories by their Body Mass Index

(BMI>=25: overweight/obese; BMI<25: normal weight) and food security status (food

secure and food insecure): food secure and normal weight (FS/norm), food secure and

overweight/obese (FS/ovob), food insecure and normal weight (FIS/norm), and food

insecure and overweight/obese (FIS/ovob). However, it was difficult to recruit enough

sample size of women in each group throughout the year. And some women subjects

dropped in the middle of the study due to the hard economic status at the study time. As a

result of such a difficult situation, the study then expanded its recruitment range to all

low-income women (not only FNP participants). Finally, sixty-six women were present at

the first interview, while only forty women completed all six interviews. The attrition rate

was 40%.

Each woman was interviewed twice every month, during the first and last few

days of the month and six times in total. The first few days are ―beginning of the month‖

which represent the time when food stamps or any other financial resources are available

to the household. This study design allowed us to address the research question and to

identify differences due to seasonal changes. Interviewers who were responsible for

gathering data from the three counties were educators from the OSU Extension.

58

Interviews were face-to-face, conducted in the local OSU Extension offices, and lasted

between 1 and 1.5 hours.

3.2.2 Data Collection

Data collection methods included interviewer-administrated and self-reported

survey questionnaires, anthropometric measurements, and data entry. In this study,

interviewers were experienced OSU Extension educators working with low-income

population for a long time. To maintain high quality of data, interviewers were trained

with standardized data collection guides before subject recruitment and interviews:

details including consent form and script reading, which tool(s) should be administered in

a certain interview, how to ask questions to women subjects during the interview

(particularly questions about dietary intake ), how to measure body weight and height,

and how to review the surveys for completeness. To assure data quality, each county was

assigned a supervisor (OSU Extension officers) to re-review each survey for

completeness and accuracy before sending back to the central office for data entry.

Communications between interviewers and study researchers were kept during the data

collection process. Unexpected situation were discussed and retraining of interviewers

was conducted when data collection methods were modified. Completed surveys were

sent back to the central office (the office of the principle investigator) and were locked

for reasons of confidentiality. The study protocol was approved by the Institutional

Review Board (IRB) at The Ohio State University.

59

Received surveys were first reviewed for obvious errors such as lack of some

surveys, no signature from the supervisor, wrong age, blank survey questions, and so on.

After screening, data entry were conducted in the central office. Data entry personnel

were trained to use standardized coding system and provided sample templates. All

survey data, except for 24-hour recalls, were entered into spreadsheets; data of 24-hour

dietary recalls were recorded in a word document. Entered data were reviewed by another

data entry person, and then rechecked by the project coordinator (myself) for erroneous

items, unreasonable results, or extreme observations before data analysis.

In the following sections, survey tools and data collection methods including

family record questionnaire, household food security survey module (HFSSM), 24-hour

dietary recall, shelf food-inventory questionnaire, eating disorders examination

questionnaire (EDE-Q), and weight and height measurements are introduced.

3.2.2.1 Family Record Questionnaire

Family record questionnaire gathers information on demographics and socio-

economic characteristics about the participant and her household members. Items

assessed included age, household size, family composition, birth and living place,

pregnant or lactating status, income, education, use/access to food assistance, the latest

date for receiving food stamps, housing expenditure, cooking skills and the frequency of

cooking. This tool was administered only during the first interview.

60

3.2.2.2 Household Food Security Survey Module (HFSSM)

The development and validity testing of household food security status

measurement has been addressed in detail in previous sections (section 2.3).The survey

module used in this study was a 16-item questionnaire addressing the experiences on

multiple levels in food insecurity in the household due to financial restraints in the month

previous to the interview. This module was modified from the U.S. Household Food

Security Supplemental Module (US-HFSSM): three items asking about the frequency of

some food insecurity experience were excluded from the US-HFSSM and one item about

whether the household has children under 18 or not was added to the 16-item HFSSM

used in the study. The last seven questions in the module is regarding to children’s food

insecurity status. In addition, an affirmative response to each item led to a frequency of

occurrence question in the modified version, asking about how often the situation

happens: rarely, sometimes, or always. This tool was administered at the beginning of

Month 1 (i.e. survey 1).

3.2.2.3 24-hour Dietary Recalls

The 24- hour dietary recall method was applied to assess the total energy intake

(TEI) of participating individuals at the beginning and the end of the months. In total six

dietary recalls were administered in three months, with each month had one recall at the

beginning and one at the end. The 24-hour dietary recall questionnaire was asked by

trained interviewers in all six interviews (i.e. survey 1 to survey 6). Although multiple

recalls are needed for valid estimation on individual dietary intake, it has been confirmed

61

that a single-day 24-hour recall is a valid way to estimate the unbiased mean of dietary

intake in a group with a greatly-overestimated standard deviation [54], [137]. In this

study, the research purpose is to measure TEI in particular women groups. In addition,

the intra-individual variation of the variable of most interest - TEI is fairly low.

24-hour recall data were collected with the assistance of the multiple-pass

approach [141] [142]. The passes in this study included ―a quick list, detailed description,

and review‖. In the first step, subjects were asked to list all the foods and beverages

consumed on the previous day in any order they liked. The next step was a detailed

description of all the listed food. Interviewers used probing questions to gather detailed

information on the ingredients, portion sizes, brand names, and preparation methods.

Food models were also used to help subjects for portion size estimation. In the final

review step, interviewers went through all listed information with the subjects, making

sure the accuracy and any forgotten foods or beverages. In addition, interviewers were

required to check all the information again after each step.

Collected 24-hour dietary intake data were entered by trained data entry personnel

and were processed by ―The Food Processor‖ Program [149]. To obtain high quality

data, interviewers in the interviews and coders in the data processing period received

training and re-training. Other quality control methods including data review and data

recoding by another coder were applied.

3.2.2.4 Shelf Food-Inventory

62

Shelf food-inventory was used to collect information about household food supply

at the time of the interview. There are in total 19 food groups consisting of 222 items,

with an open-ended question at the end inquiring about any other items in the household.

A self-report version of this instrument was administered at each interview (i.e. survey 1

to survey 6) to assess the monthly variation pattern of household food supply.

3.2.2.5 Eating Disorders Examination Questionnaire (EDE-Q)

To further understand the experience of disordered, particularly binge eating

among food insecure and overweight/obese women, a self-reported Eating Disorders

Examination Questionnaire (EDE-Q) was administrated in the study at the fourth

interview (i.e. survey 4), when interviewers have established a closer relationship with

participants.

3.2.2.6 Anthropometrics

Participants’ height (centimeters) and weight (kilograms) were measured at the

first interview (i.e. survey 1). Trained interviewers were required to measure the weight

and height at least twice at each interview. A third measurement would be taken when

there was a difference of 0.3 kg or more in weight or 0.5 cm or more in height . Body

mass indexes (BMI) were then calculated by dividing weight by height squared (kg/m2)

[150].

3.3 Data Analysis

63

3.3.1 Data Processing

Data collected by certain tools were processed and analyzed based on the research

purposes. The 16-item HFSSM was coded as follows: negative responses to items were

coded as ―0‖, while affirmative responses were coded as ―1‖. A food security score was

assigned to each household as the sum of all affirmative points. Interviewed women were

subsequently categorized food security – including high food security (score 0) and

marginal food security (score 1-2), or food insecurity – including low food security (score

3-7 in households with children; 3-5 in households without children) and very low food

security (score 8 and above in households with children; 6 and above in households

without children) [107].

Data of 24-hour dietary recalls were analyzed by ―The Food Processor‖ program

[149] to calculate daily total energy intake (TEI) and other nutrient intake. Each subject

has six recalls which can yield three dietary intake data at the beginning of the months

and three at the end of the months. Average daily dietary intakes for the beginning and

the end of the month were then calculated by dividing the sum of intake values by three

days.

Food items in the Shelf Food-Inventory were categorized into six essential food

groups (i.e. grains, vegetables, fruits, milk, meat & beans, and oils) according to

Mypyramid Food Guidance System [151]. Each food group was analyzed by the number

of food items: the sum of all confirmed food item types during the interview. Only food

groups/individual food items present at both the beginning and the end of the month were

compared. To assess the monthly variation, number of food items were compared

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between the beginning and the end of the three months. In addition, particular food items

accounting for the major variations in food groups were identified in the target

population: food insecure and obese women.

EDE data were analyzed using descriptive statistics and their associations with

food security and weight status. Two types of data were generated from EDE-Q: scores

of four subscales3 (i.e. Restraint, Eating Concern, Weight Concern and Shape Concern)

as well as a global score based on the subscales, and the frequency of key behaviors of

eating disorders. The subscale and global scores of EDE-Q were reported as the mean

scores and standard deviations of each subscale by individuals’ food security and weight

status, while each of the featured behaviors were analyzed by computing the proportion

of women reporting the specific behavior (either occasionally or frequently) in different

food security and weight status groups. The reported occurrence of key eating disorder

features (i.e. times of overeating episodes, days of overeating episodes, extreme dietary

restraint, use of self-induced vomiting, use of laxative, and excessive exercise) were

calculated for comparison of women from the food insecure and obese group with those

from other groups.

Anthropometric data (weight and height) were used to calculate BMI (kg/m2),

measured weight (kilograms) divided by measured height squared (meter square). BMI

was used to categorize subjects into two weight status groups: normal weight (BMI <

18.5-24.9 kg/m2), overweight/obesity (BMI >= 25 kg/m

2) [27]. Due to the small sample

3 It is required that more than half of the items need to be rated to calculate subscale scores [120].

65

size of women who were not overweight in this study, all women with BMI less than 25

were considered as normal weight.

3.3.2 Variables of Interest

This section provides a brief introduction for the variables of interest in the study:

1) Total energy intake (TEI), collected by 24-hour dietary recalls at the beginning

and the end of three continuous months.

2) Number of food item types, including total food items, food items in groups (18

food groups in the Food Shelf Inventory Questionnaire, and 6 essential food

groups recommended by USDA), and individual food items (i.e. food items

decreasing in two or three months). Data on Food Shelf Inventory were collected

at the beginning and the end of three continuous months.

3) The Eating Concern subscale score and the frequency of number of days/times on

which binge eating occurred.

4) Household food security status: food secure, and food insecure.

5) Weight status: normal weight, and overweight/obesity.

6) Demographic and household controls: women’s age, education (school in years),

household size, household income, housing expenditure last month, number of

receiving food assistance programs, and receiving food stamps or not.

3.3.3 Statistical Analysis

66

Women in the study were categorized into four groups based on their BMI and

food security status: food secure and normal weight (FS/norm), food secure and

overweight/obese (FS/ovob), food insecure and normal weight (FIS/norm), and food

insecure and overweight/obese (FIS/ovob). Demographic and household controls were

analyzed by descriptive statistics.

Paired t-tests were applied to examine the differences of household food supply

(total food items and food items in particular food groups) and the dietary intake (TEI,

macro/micro nutrients) between the beginning and the end of the months within each

women group.

Oneway analysis of variance (oneway-ANOVA) was used to test for differences

in EDE-Q subscale scores across the four women groups, while Chi-Square tests were for

differences in the occurrence frequency of featured eating disorder behaviors. Since the

sample sizes of four women groups in the study were different, the assumption of equal

variances (homogeneity) in oneway-ANOVA might be violated. To avoid a too liberal or

too conservative estimation, a simulated p-value was also calculated for oneway ANOVA

for more robust results4.

Two multivariate logistic regression models were generated to assess the risk

factors of showing a decreased TEI of 200 kcal or more in each month. Independent

variables were two dummy variables (i.e. food insecure or not, overweight/obese or not)

4 Assuming the means across groups are equal, STATA can perform ANOVA simulation many

times with given sample sizes and standard deviations, and calculate simulated p-values with confidence

intervals (CIs) given the very data pattern.

67

in the first model, and one dummy variable (i.e. women in the FIS/ovob group or not) in

the second model. Control variables included age, education (school years), household

income category, household size, housing expenditure last month, number of receiving

food assistance programs, and interviewed during holiday seasons or not.

All the statistical analyses were performed using the software package STATA

[152]. Statistically significant differences were determined at p < 0.05, and differences at

p < 0.1 was determined as marginally different.

3.4 Results

3.4.1 Subjects

Sample Sizes and Women Groups

The study was originally designed to recruit a sample size of 80 women, allowing

for a 25% attrition rate. The first interview (survey 1 in Month 1) recruited 66 eligible

women. However, women started to drop the study because of different reasons, like

unaffordable gas fee or not enough incentives. And some of the participants were not

available during the first or the last week of the months for dietary intake and household

food store measurements. To keep more participants in the study, the interview days were

extended to the first and last ten days of the months. Table 3.1 shows the sample sizes of

interviewed women in each women group (i.e. FS/norm, FS/ovob, FIS/norm, and

FIS/ovob) during the three months: 59 women in Month 1, 48 in Month 2, and 44 in

Month 3. In total 40 women had completed the six surveys.

68

Holiday Interviews

Most of the interviews were conducted during non-holiday seasons. But some of

the participants were interviewed during holiday seasons (i.e. November or December).

Due to the irregular food consumption in holiday times, participants’ dietary intake may

be affected by the interview time. A list of the numbers of participants interviewed during

holiday seasons were listed in Table 3.1. It was found that Month 2 had the highest

percentage of holiday season interviews (November 22.92%, December 27.08%), and

Month 3 had more participants interviewed in December (27.27%) and less in November

(6.82%) compared to Month 1 (November 23.73%, December 10.17%).

3.4.2 Characteristics of Subjects

In total forty women had completed six surveys. The demographics and featured

characteristics of these women were compared among the four groups of women in Table

3.2. On average households in this study had 4 household members. Most of the women

were in low-income households: 82.50% of the households had a monthly income less

than $2000 and 62.50% of the households were under $1750 per month; only one

household made more than $2,500 per month (data not shown). The median of household

monthly income was between $1251-1500. According to the 2009 Poverty Guideline by

the Department of Health and Human Services (HHS) which is $22,050 per year (i.e.

$1837.5 per month) for a 4-member household [153], the majority of respondents should

be women living in low-income households.

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No significant differences were found in women’s age, household size, household

income, and school years. Seventy percent of the women were from food insecure

households: fifty percent were low food secure and twenty percent were very low food

secure. The number of received assistance programs was higher in food insecure women

(p<0.05) than food secure women.

The study also tested the cooking skills among women (Table 3.2). Three-fourths

of the women evaluated their cooking skills as ―adequate‖. FIS/norm women tended to

have a lower percentage (50%) of self-rating cooking skills as ―adequate‖. The frequency

of preparing a green salad in last three months was lower in food insecure women than in

food secure women (p < 0.05), particularly in FIS/ovob women. Almost two-thirds of

FIS/ovob women (62.5%) prepared a green salad once a month or never in the last three

months. No difference was found among women groups in the frequency of preparing

beef/chicken/fish/vegetables or an entire dinner in the last three months: the majority of

the women reported ―once a week or daily‖.

3.4.3 Monthly Variations in Household Food Supply

3.4.3.1 Total Food Items

The total shelf food items were composed of all the food items listed in the Shelf

Inventory Questionnaire and other items reported by participants. These items were

counted to obtain the number of total food items in the household during the interview

time (Table 3.3). For FIS/ovob women, the total food items significantly decreased from

the beginning to the end of the months in three continuous months. In addition, FS/ovob

70

women had the highest total food items at both the beginning and the end of the three

month among four women groups, and significant drop of total food items were observed

in Month 1 and Month 3 in FIS/ovob women. Less consistent and less significant

decreasing patterns were observed in women of normal weight (Table 3.3).

3.4.3.2 Food Groups

As mentioned before, there are in total 19 food groups in the Shelf Inventory

Questionnaire. After combining food group ―sauce‖ with food group ―soups‖, food items

in the questionnaire were categorized into eighteen groups: 1) milk, dairy, ice cream and

yogurt, 2) cheese, 3) cereals, 4) bread and cakes, 5) tortillas, pasta and rice, 6) cookies,

crackers and chips, 7) vegetables (fresh, frozen or canned), 8) legumes, 9) fruits (fresh,

frozen or canned), 10) beverages, 11) meat (beef and pork), ham and sausages (fresh or

frozen), 12) poultry (fresh or frozen), 13) sea food (fresh, frozen or canned), 14) oils and

other fats, 15) mayonnaise, sauce and salad dressing, broth and soups, 16) condiments,

17) miscellaneous food items, 18) baby food.

Items by food groups were analyzed to see whether a monthly decrease exists or

not.5 Table 3.4 lists food groups that significantly decreased in the three months by

household food security and weight status. It was observed that FIS/ovob women had a

much less stable food shelf inventory in three continuous months compared to other three

groups of women: almost all food groups dropped in Month 1 (except for baby food), five

5 Three significant increases were observed: oils in FS/ovob women in Month 1, cereals in

FS/norm women in Month 1, and poultry in FS/ovob women in Month 2.

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and eight food groups dropped in Month 2 and Month 3, respectively. Specifically, food

groups like vegetables, fruits, and meat dropped considerably (vegetables: 11.23 vs. 8.72,

fruits: 5.26 vs. 3.46, meat: 5.69 vs. 3.64, p<0.05) in Month 1; in Month 2, the storage of

legumes, fruits, meat, condiments and other miscellaneous food were significantly higher

at the beginning than the end of the month; in Month 3, eight food groups (i.e. cereals,

bread, pasta, meat, poultry, sauce and soups, condiments, and miscellaneous)

significantly decreased. Comparatively, no significant decrease in fruits was found in the

other three women groups. Meat and legumes decreased happened in FS/ovob women in

Month 1 and Month 3, respectively, and vegetables decrease were found in FS/norm

women in Month 2. Grains (cereals, pasta, and bread) were the only essential food group

(please see the following paragraph for more details) that decreased in two months in

other three groups of women. In addition, normal weight women tended to have a more

stable household food stores than overweight/obese women, but the sample sizes of

FS/norm and FIS/norm women were too small to draw conclusions in these two groups.

Essential Food Groups

To further analyze the effects of the monthly decrease of household food stores,

food groups and food items in the Shelf Inventory Questionnaire were then re-categorized

into six essential food groups: grains, vegetables, fruits, oils, meat & beans, and milk

according to USDA recommendation (http://www.mypyramid.gov/pyramid/index.html).

Among FIS/ovob women, grains decreased significantly in two months, and fruits and

meat & beans decreased in all three months. Comparatively, other groups of women

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showed much less decreasing food groups (Table 3.5). In Month 1, compared to other

women groups, the largest decrease in groups of meat & beans, and vegetables were

observed in FIS/ovob women. Additionally, FIS/norm women had the lowest numbers at

the beginning and the end of Month 1 in most essential food groups, and FS/ovob women

showed the highest numbers of food items at the beginning and the end, particularly

groups of grains, meat & beans, and milk.

In order to remove the influence of holiday seasons, women who were not

interviewed in November or December were analyzed separately. Table 3.6 shows

monthly differences of essential food groups among women who were not interviewed in

November or December. In FIS/ovob women, five essential food groups (grains,

vegetables, fruits, meat & beans, and milk) significantly dropped from the beginning to

the end in the three continuous months.

3.4.3.3 Individual Food Items in FIS/ovob Women

Since FIS/ovob women showed obvious monthly decreasing patterns on

household food stores, it was interesting to know what individual food items were

decreasing during the interview time in this group of women. It was found that 71 food

items were significantly higher at the beginning than the end of Month 1 in FIS/ovob

women, while in FS/ovob women it was only 12 food items higher in the first ten days of

the month. In Month 2 and Month 3, FIS/ovob women showed 33 food items

significantly higher at the beginning of the months.

73

Here is a list of specific food items which were found as decreasing for two or

three months among FIS/ovob women: regular cottage cheese, regular cream cheese,

white bread, muffins, flour tortillas, fruit snacks, mixed vegetables, celery, asparagus,

beans, apples, any kind of berries, regular soda pop, 100% pure fruit juices, beef or pork

frankfurters, bacon, salami/pepperoni/sausages/liver pate (three months), chicken breast,

breaded chicken, shortening, tartar, chicken or beef broth, mustard, vinegar (three

months), condiment powder, powdered mashed potatoes, frozen entrees, and candy.

Therefore, it was found that items from almost every food group (out of the eighteen

groups) showed significantly decrease in certain months, except items from the group of

baby food.

3.4.4 Monthly Variation of Nutrient Intake

3.4.4.1 TEI and Macronutrients

The monthly decrease of TEI (Table 3.7a) and macronutrient intakes were also

compared among the four women groups (Table 3.7b). Among FIS/ovob women, a

significant decrease of energy intake (271.13 kcal, 12.82%) was observed in Month 1;

about 57% of this decrease was from fat (154.17 kcal, 19.15%). The protein and sugar

intake did not show significant decrease in Month 1, but the mean decreasing amount of

protein and sugar were highest in FIS/ovob women than other women groups. In Month 2

and Month 3, FIS/ovob women showed very similar energy intake and macronutrient

intakes at the begging and the end of the months: no significant differences were

observed. For other groups of women, FS/ovob women showed similar mean caloric

74

intake in the first and last ten days of the three months (monthly differences: -80 kcal in

Month 1, 93 kcal in Month 2, -126 kcal in Month 3). The two women groups of normal

weight showed very unstable increase or decrease variation patterns of macronutrient

intakes in the three months.

3.4.4.2 Other Nutrients

The differences of other nutrients (including vitamins and minerals) were also

compared in FIS/ovob women in the three months. It was found that cholesterol, vitamin

B2, vitamin B12, biotin, vitamin D, iodine, molybdenum, and choline (mg) significantly

decreased from the beginning to the end of Month 1, vitamin K, omega-3, and omega-6

significantly decreased in Month 2, and no nutrient decreased in Month 3. Almost every

nutrient decreased in Month 1 was highly lower than the reference values for an adult

with a caloric intake of 2,000 calories (Table 3.8). No continuous decreasing pattern of

these nutrients was observed. This can be explained by the high day-to-day variation of

micronutrient intakes among free-living persons, and the high percentages of holiday-

season interview in Month 2 and 3.

3.4.4.3 Logistic regression analysis

Two multivariate logistic regression models were generated to assess the risk

factors of showing a decreased TEI of 200 kcal or more in every month. The value 200

kcal was chosen is because the median of the TEI decrease in the overall women sample

was around 200 kcal (213.745 kcal) in Month 1. Independent variables of interest were

75

two dummy variables (i.e. food insecure or not, overweight/obese or not) in the first

model, and one dummy variable (i.e. women in the FIS/ovob group or not) in the second

model. Control variables included age, education (school years), household income,

household size, housing expenditure last month, and number of received food assistance

programs.

In Month 1, 59 women were analyzed using logistic regression models. It was

found that being overweight/obese (odds ratio (OR) = 3.58), being food insecure (OR =

2.10), or within the FIS/ovob group (OR = 2.56) had high odds ratios of showing a

decreased TEI of 200 kcal or more, but the p-values were not significant. In addition,

household size in the control variables showed a significant effect on a decreased TEI in

the two models: OR = 2.20 in the first model, OR = 2.25 in the second model, p<0.05,

which means an increased household size indicates a higher risk of showing a monthly

caloric drop 200 kcalor more. In Month 2, the number of food assistance programs

showed protective effects on TEI decrease in the two models (OR=0.61, p<0.05). No

significant results were found in Month 3.

3.4.5 Disordered Eating

3.4.5.1 Subscales

Subscale scores as well as the global score in EDE-Q across the four women

groups are present in Table 3.9a. No significant differences were found in subscales

Eating Concern and Restraint. Subscales Weight Concern and Shape Concern and the

global score were significantly different among the four groups of women. To further

76

understand 1) the association of disordered eating behaviors and food insecurity and 2)

the association of disordered eating and weight status, subscale scores were compared

between food secure women and food insecure women, and between non-

overweight/obese women and overweight/obese women, respectively (Table 3.9b). The

former comparison showed no significant difference between food secure women and

food insecure women, while the latter comparison found that overweight/obese women

had much higher scores in Weight Concern (2.74 vs. 1.03, p<0.0001), Shape Concern

(3.34 vs. 1.39, p<0.0001), and the global score (2.13 vs. 0.99, p=0.0004) as well.

The correlations of subscale scores with BMI and with food security scores were

then analyzed (Table 3.10). Out of the four subscales, it is shown that three subscales:

Restraint (correlation: 0.27, p=0.05), Weight Concern (correlation: 0.31, p=0.02), Shape

Concern (correlation: 0.35, p=0.01), and the global score (correlation: 0.31, p=0.02) have

significant and positive correlations with BMI. Comparatively, Eating Concern was

marginally correlated with food security scores (correlation: 0.23, p=0.09).

Since Eating Concern was the subscale of interest for investigating binge eating

behaviors in food insecure subjects, items within Eating Concern were then analyzed

separately and compared by women’s food security status (Table 3.11). The following

items are included in Eating Concern: preoccupation with food/eating/calories, fear of

losing control over eating, eating in secret, social eating (i.e. concern of eating with the

presence other people), and guilt about eating [120]. It was shown that food insecure

women had a significantly higher score in the item ―preoccupation with

77

food/eating/calories‖ (1.24 vs. 0.21, p=0.007), and a marginally higher score in ―fear of

losing control over eating, and guilt about eating‖ (1.27 vs. 0.57, p=0.1).

3.4.5.2 Featured Disordered Eating Behaviors

The frequency of occurrence of featured disordered eating behaviors (i.e. the

proportion of individuals reporting the very behavior) across women groups was also

analyzed. Among the 36 FIS/ovob women who completed EDE-Q, 13 women (36.11%)

reported overeating episodes in the past 28 days. In addition, more than 10% of FIS/ovob

women reported extreme dietary restraint (data not shown). No significant differences

were found among the four groups of women.

3.4.6 Food Stamp Program (FSP) Participation

Table 3.2 has showed that sixty percent of the participants in the study were

receiving food stamps during the interview time. In general, food stamp recipients had a

higher BMI than non-recipients (36.81 vs. 31.16, p=0.01); particularly in food insecure

women (n=50), those who were receiving food stamps had a mean BMI much higher than

those not receiving food stamps (38.24 vs. 30.94, p<0.01). Comparatively, no significant

difference of BMI was found between food secure recipients and non-recipients

(recipients: 30.38; non-recipients: 31.54, n=16).

The monthly variations of household food stores as well as TEI were also

compared between food stamp recipients and non-recipients (Table 3.12). No significant

78

differences were observed in food secure women. Comparatively, in food insecure

women the monthly decrease of household food items was more severe in food stamp

recipients than in non-recipients in Month 1 (22.33 vs. 2.67, p=0.01) and Month 3 (21.71

vs. 0.9, p=0.01). No significant difference was observed in the TEI variation.

3.5 Summary and Results of Hypothesis Testing

The study tested the existence of ―monthly cycle of food abundance and food

shortage‖ among a group of food insecure and overweight/obese women in Ohio. Most of

the women in the study had very high BMI (mean BMI=32.90). The majority participants

(70%) were food insecure women, who had a significantly higher mean BMI than food

secure women (36.19 vs. 30.96, p<0.05). Sixty percent of the participants were receiving

food stamps during the interview time.

Five hypotheses were proposed in this study. Hypotheses 1 and 2 were about the

monthly decrease of TEI and household food stores in FIS/ovob women. Results in the

study confirmed Hypothesis 2 by showing consistent decreasing patterns in total

household food items and in essential food groups (i.e. grains, vegetables, fruits, meat &

beans, and milk) in FIS/ovob women in three continuous months. However, Hypothesis 1

is only true in Month 1, since the monthly decrease of TEI was found in Month 1, but no

Month 2 or in Month 3. Hypothesis 3 was proposed to compare the monthly variation

patterns in the four women groups. This one is also accepted since the results showed that

compared to FIS/ovob women, other women groups had less consistent decreasing

patterns of TEI or food stores. The disordered eating data found that worsening food

79

insecurity was marginally correlated with more severe Eating Concern (correlation: 0.23,

p=0.09). However, no significant difference of Eating Concern subscale score or the

frequency of overeating episodes were found among the four women groups, as such,

Hypothesis 4 is not accepted based on the data in this study. Hypothesis 5 is about the

association of FSP participation with food insecurity and obesity. Higher BMI was found

in food-insecure food stamp recipients compared to food-insecure non-recipients (38.24

vs. 30.94, p<0.01), and the sum of the monthly decreased items in the total three months

were significantly greater in recipients than in non-recipients (61.58 vs. 8.22, p<0.01).

however, TEI variation was not different in these two groups of women. Therefore,

Hypotheses 5 is partly accepted.

80

Month 1 Month 2 Month 3

Group total Nov1 Dec

1 total Nov

1 Dec

1 total Nov

1 Dec

1

FS/norm 4 0 0 3 0 0 3 0 0

FS/ovob 10 2 (20%) 0 11 4

(36.36%)

2

(18.18%) 10 0 4 (40%)

FIS/norm 6 0 1

(16.67%) 4 1 (25%) 0 4 0 1 (25%)

FIS/ovob 39 12

(30.77%)

5

(12.82%) 30 6 (20%)

11

(36.67%) 27

3

(11.11%)

7

(25.93%)

Total 59 14

(23.73%)

6

(10.17%) 48

11

(22.92%)

13

(27.08%) 44

3

(6.82%)

12

(27.27%)

Table 3.1 Sample sizes by food security, weight status and interview dates 1- Nov- participants interviewed in November; Dec- participants interviewed in

December

81

Characteristics Total FS/norm FS/ovob FIS/norm FIS/ovob

n 40 3 9 4 24

Demo-

graphics

Age (years), mean (SD) 34.95

(7.55)

34

(10.15)

35.44

(6.17)

34.25

(5.25)

35

(8.4)

School (years), mean (SD) 13.04

(1.8)

14

(2)

13.89

(2.26)

11.75

(1.71)

12.81

(1.5)

Housing spent last month

($), mean (SD)

550.88

(312.76)

415.33

(367.84)

539.44

(430.81)

613.25

(136.08)

561.71

(289.63)

Household size, mean (SD) 4.03

(1.29)

3.33

(0.58)

4.11

(0.93)

4.25

(1.26)

4.04

(1.49)

Household income

0-$1000/month, % 27.50 33.33 22.22 0.00 33.33

$1001-2000/month, % 55.00 66.67 33.33 100.00 54.17

more than $2000/month, % 17.50 0.00 44.44 0.00 12.50

Food

security

Food security score, mean

(SD)

4.38

(3.42)

0.33

(0.58)

0.67

(0.87)

7.75

(3.86)

5.71

(2.49)

Food security status

High food security, % 17.50 66.67 55.56 0.00 0.00

Marginal food security, % 12.50 33.33 44.44 0.00 0.00

Low food security, % 50.00 0.00 0.00 50.00 75.00

Very low food security, % 20.00 0.00 0.00 50.00 25.00

# of food assistance

programs, mean (SD)

3.88

(2.31)

2.00

(2.00)

2.44

(1.51)

5.25

(2.5)

4.42

(2.28)

Receiving food stamps, % 60.00 33.33 44.44 100.00 62.50

Continued

Table 3.2 Characteristics of women completed the six surveys

FS- food secure, FIS- food insecure, ovob- overweight/obese (BMI >= 25), norm- normal

weight (BMI < 25)

82

Table 3.2 continued

Characteristics Total FS/norm FS/ovob FIS/norm FIS/ovob

Sample size 40 3 9 4 24

Weight

status

BMI (kg/m2), mean (SD)

32.91

(9.15)

22.11

(2.85)

33.52

(7.45)

19.67

(1.04)

36.22

(8.03)

Weight status

normal weight, % 17.50 100.00 0.00 100.00 0.00

overweight, % 20.00 0.00 33.33 0.00 20.83

obesity, % 62.50 0.00 66.67 0.00 79.17

Cooking

Self-rated cooking skills

Inadequate, % 25.00 0.00 22.22 50.00 25.00

Adequate, % 75.00 100 77.78 50.00 75.00

Preparing a green salad last

3 months, freq.

Once a month or never, % 47.50 33.33 11.11 50.00 62.50

Once a week or daily, % 52.50 66.67 88.89 50.00 37.50

Preparing beef, chicken,

fish, vegetables last 3

months, freq.

Once a month or never, % 30.00 0.00 0.00 0.00 8.33

Once a week or daily, % 70.00 100.00 100.00 100.00 91.67

Preparing an entire dinner

(>= 2 people) last 3 months,

freq.

Once a month or never, % 5.00 0.00 0.00 0.00 8.33

Once a week or daily, % 95.00 100.00 100.00 100.00 91.67

83

n Mean (SD) p$

Month 1 Beginning End Difference

FS/norm 4 83.00 (14.51) 72.50 (11.70) 10.50 (15.09) 0.13

FS/ovob 10 105.30 (23.78) 95.90 (24.58) 9.40 (7.46)* 0.002

FIS/norm 6 75.83 (32.82) 74.33 (25.85) 1.50 (13.46) 0.4

FIS/ovob 39 87.74 (22.17) 68.26 (24.08) 19.49 (24.68)* <0.0001

Total 59 89.19 (24.10) 73.85 (25.37) 15.34 (21.77)* <0.0001

Month 2 Beginning End Difference

FS/norm 3 91.67 (7.51) 65.67 (9.07) 26.00 (16.52) 0.056

FS/ovob 11 98.64 (23.59) 97.09 (24.68) 1.55 (10.30) 0.31

FIS/norm 4 84.50 (18.70) 72.50 (8.50) 12.00 (21.89) 0.18

FIS/ovob 30 83.30 (25.84) 72.20 (19.55) 11.10 (29.08)* 0.02

Total 48 87.46 (24.43) 77.54 (22.18) 9.92 (24.92)* 0.004

Month 3 Beginning End Difference

FS/norm 3 83.00 (13.00) 80.00 (5.29) 3.00 (9.64) 0.3

FS/ovob 10 103.60 (25.29) 88.30 (22.00) 15.30 (13.87)* 0.003

FIS/norm 4 86.75 (11.00) 61.75 (16.7) 25.00 (17.42)* 0.03

FIS/ovob 27 88.81 (24.89) 75.30 (20.78) 13.52 (26.58)* 0.007

Total 44 91.59 (23.89) 77.34 (20.90) 14.25 (22.65)* 0.0001

Table 3.3 Total shelf food items (SD) by food security and weight status $- p-values of one-tailed t-test for testing food items higher at the beginning than at the

end; *- significantly higher at the beginning of the month compared to the end

84

Month 1 (n=59) Month 2 (n=48) Month 3 (n=44)

n # (Food groups) n # (Food groups) n # (Food groups)

FS/norm 4 1 (cereals) 3

3 (pasta,

cookies,

vegetables)

3 0

FS/ovob 10

6 (cereals, pasta,

cookies,

beverages, meat,

oils, condiments)

11 1 (poultry) 10

7 (cereals, bread,

pasta, legumes,

seafood,

condiments,

miscellaneous)

FIS/norm 6 2 (cereals, bread) 4 2 (beverages,

miscellaneous) 4 2 (bread, pasta)

FIS/ovob 39

17 (all food

groups except

baby food)

30

5 (legumes,

fruits, meat,

condiments,

miscellaneous)

27

8 (cereals, bread,

pasta, meat,

poultry, sauce and

soups, condiments,

miscellaneous)

Table 3.4 Food groups significantly decreased food groups in the three months

FS- food secure, FIS- food insecure, ovob- overweight/obese (BMI >= 25), norm- normal

weight (BMI < 25)

85

Food

groups

Beginn

ing End p1

Beginni

ng End p1

Beginni

ng End p1

FS/no

rm

Month 1 (n=4) Month 2 (n=3) Month 3 (n=3)

Grains$ 12.75

(1.71)

11.25

(3.1) 0.09 14 (1)

11.67

(0.58)

0.0

1*

13.67

(1.53)

12.33

(1.53)

0.03

*

Vegs$ 12.75

(4.5)

10.75

(2.63) 0.08

13.33

(4.16) 7 (3.61)

0.0

2*

10.33

(5.77)

11

(2.65) 0.62

Fruits$ 6.25

(1.89)

5.5

(1.91) 0.36

9.67

(1.53)

5.33

(2.08)

0.0

8 6 (2.65)

6.33

(1.53) 0.63

Oils 3.75

(1.5) 3 (0) 0.2

3.67

(1.15)

3.67

(1.15) 0.5

3.67

(1.15)

3.67

(0.58) 0.5

Meat &

beans$

11.75

(2.63)

10

(1.63) 0.18

10.33

(0.58)

9.33

(4.16)

0.3

7

10.33

(3.06)

10.67

(2.89) 0.55

Milk$ 6.5

(1.29) 7 (1.83) 0.67

7.33

(1.53)

6.67

(1.53)

0.3

3

6.67

(1.15)

6.67

(0.58) 0.5

FS/ov

ob

Month 1 (n=10) Month 2 (n=11) Month 3 (n=10)

Grains$ 15.7

(3.5) 14.3 (4)

0.02

*

14.27

(4.45)

13.09

(4.09)

0.1

3

14.8

(4.66)

12.1

(4.89)

0.00

3*

Vegs$ 12.6

(2.41)

12

(4.11) 0.2

12.91

(3.05)

11.73

(3.07)

0.1

3

13.9

(3.35)

12

(3.37)

0.04

*

Fruits$ 6.4

(2.95)

5.2

(2.35) 0.07

6.36

(2.34)

5.91

(2.63)

0.2

3

5.7

(2.5) 5 (2.16) 0.14

Oils 3.8

(1.23)

4.4

(1.43) 0.97

3.91

(1.14) 4 (1.18)

0.6

4

3.7

(1.34)

3.8

(1.32) 0.58

Meat &

beans$

14.3

(3.62)

12.2

(4.92)

0.00

2*

13.64

(4.46)

14.55

(4.84)

0.9

2

13.7

(4.47)

12.4

(3.98) 0.08

Milk$ 9

(4.47)

9.3

(4.35) 0.61

9.18

(3.95) 9 (2.97)

0.4

2

9.4

(3.53)

8.3

(3.59) 0.08

Continued

Table 3.5 Monthly differences (SD) in household food stores by USDA essential food

groups (including holidays)

FS- food secure, FIS- food insecure, ovob- overweight/obese (BMI >= 25), norm- normal

weight (BMI < 25). 1 – p-values from one-tailed t-tests. *- significantly higher at the

beginning compared to the end. $ - Grains: cereals, bread and cakes, tortillas, pasta, rice;

Vegs: vegetables and legumes; Fruits: fruits and pure fruit juice; Meat & beans: red meat,

poultry, sea food, egg, legumes, nuts, and peanut butter; Milk: milk, dairy, ice cream,

yogurt, and cheese.

86

Table 3.5 continued

Food

groups

Begin

ning End p1

Beginni

ng End p1

Beginni

ng End p1

FIS/n

orm

Month 1 (n=6) Month 2 (n=4) Month 3 (n=4)

Grains$ 12

(5.87)

13

(4.05) 0.78

14.25

(4.03)

14.25

(3.5) 0.5

16.5

(1.73)

10.5

(2.89)

0.03

*

Vegs$ 10.67

(5.57)

9.33

(5.99) 0.23

10.25

(2.63)

8.25

(2.22) 0.2

11.75

(1.26)

7.25

(5.19) 0.06

Fruits$ 4.83

(2.56)

5.5

(1.97) 0.8

6.5

(3.51)

4.25

(0.96) 0.14 7 (1.41)

3.5

(1.73)

0.01

*

Oils 3.33

(1.97)

2.83

(2.04) 0.1

3.75

(0.48) 3 (0) 0.11

2.75

(1.71) 3.5 (1) 0.89

Meat &

beans$

11

(6.2) 10 (5.1) 0.34

11.75

(5.12)

9.75

(3.77)

0.04

6*

11.75

(3.4)

8.75

(4.03) 0.15

Milk$ 5.67

(3.72)

6.17

(3.31) 0.73

7.75

(2.75)

6.75

(2.22) 0.29

7.5

(3.42) 5 (0) 0.12

FIS/o

vob

Month 1 (n=39) Month 2 (n=30) Month 3 (n=27)

Grains$ 14.33

(4.59)

11.08

(4.19)

0.000

1*

13.3

(4.76)

12.07

(4.49) 0.1

14.78

(5.5)

12.15

(4.21)

0.00

3*

Vegs$ 13.05

(4.08)

10.08

(4.2)

0.000

2*

11.6

(4.04)

10.57

(3.87) 0.15

12.19

(4.52)

11.48

(4.6) 0.26

Fruits$ 6.08

(3.13)

4.1

(2.67)

0.000

1*

5.63

(3.15)

4.3

(2.29)

0.01

*

5.63

(2.53)

4.7

(2.63)

0.04

7*

Oils 3.46

(1.02)

3.05

(1.34)

0.007

*

3.4

(1.28) 3 (1.02) 0.06

3.33

(1.04)

3.26

(1.48) 0.39

Meat &

beans$

14.21

(5.24)

9.87

(4.74)

<0.00

01*

12.9

(4.84)

10.2

(4.15)

0.01

*

13.41

(4.9)

11.19

(4.76)

0.01

*

Milk$ 6.1

(2.83)

4.41

(2.48)

0.000

1*

6.13

(3.01)

5.33

(2.63) 0.12

6.96

(3.47)

6.11

(2.78) 0.12

87

Food

groups

Beginni

ng End p1

Beginn

ing End p1

Beginni

ng End p1

FS/no

rm

Month 1 (n=4) Month 2 (n=3) Month 3 (n=3)

Grains$ 12.75

(1.71)

11.25

(3.1)

0.0

9 14 (1)

11.67

(0.58)

0.01

*

13.67

(1.53)

12.33

(1.53)

0.03

*

Vegs$ 12.75

(4.5)

10.75

(2.63)

0.0

8

13.33

(4.16)

7

(3.61)

0.02

*

10.33

(5.77)

11

(2.65) 0.62

fruits$ 6.25

(1.89)

5.5

(1.91)

0.3

6

9.67

(1.53)

5.33

(2.08) 0.08 6 (2.65)

6.33

(1.53) 0.63

oils 3.75

(1.5) 3 (0) 0.2

3.67

(1.15)

3.67

(1.15) 0.5

3.67

(1.15)

3.67

(0.58) 0.5

meat and

beans$

11.75

(2.63)

10

(1.63)

0.1

8

10.33

(0.58)

9.33

(4.16) 0.37

10.33

(3.06)

10.67

(2.89) 0.55

milk$ 6.5

(1.29)

7

(1.83)

0.6

7

7.33

(1.53)

6.67

(1.53) 0.33

6.67

(1.15)

6.67

(0.58) 0.5

FS/ov

ob

Month 1 (n=8) Month 2 (n=5) Month 3 (n=6)

Grains$ 15.5

(3.85)

14.75

(4.33)

0.0

7

13.2

(3.9)

11.2

(4.21)

0.02

*

14.67

(3.72)

11

(3.35)

0.00

6*

Vegs$ 12.375

(2.56)

11.75

(4.33)

0.2

4

11.8

(1.64)

9.4

(1.52)

0.00

2*

13.83

(2.4)

11

(3.74)

0.01

*

fruits$ 6.13

(3.18)

5.13

(2.53)

0.1

6

6.6

(2.61)

5.2

(1.79) 0.07 6 (2.61)

5.33

(2.5) 0.16

oils 3.5 (1.2) 3.88

(0.99) 0.9

3.4

(1.14)

3.6

(0.89) 0.81

3.83

(1.17)

3.33

(1.21) 0.15

meat and

beans$

14.25

(3.88)

12.25

(5.34)

0.0

1*

11.6

(3.05)

11

(2.92) 0.25

13.17

(2.32)

10.67

(2.94)

0.00

3*

milk$ 8 (3.93) 9

(3.42)

0.8

2

7.6

(2.88)

7

(1.41) 0.35

9.5

(3.56)

7.67

(4.18)

0.04

*

Continued

Table 3.6 Monthly differences (SD) in household food stores by USDA essential food

groups (excluding holidays)

FS- food secure, FIS- food insecure, ovob- overweight/obese (BMI >= 25), norm- normal

weight (BMI < 25). 1 – p-values from one-tailed t-tests. *- significantly higher at the

beginning compared to the end. $ - Grains: cereals, bread and cakes, tortillas, pasta, rice;

Vegs: vegetables and legumes; Fruits: fruits and pure fruit juice; Meat & beans: red meat,

poultry, sea food, egg, legumes, nuts, and peanut butter; Milk: milk, dairy, ice cream,

yogurt, and cheese.

88

Table 3.6 continued

Food

groups

Beginni

ng End p1

Beginn

ing End p1

Beginni

ng End p1

FIS/n

orm

Month 1 (n=5) Month 2 (n=3) Month 3 (n=3)

Grains$ 10.6

(5.32)

12.6

(4.39)

0.9

6

15

(4.58)

13.67

(4.04) 0.4

16.33

(2.08)

11.67

(2.08) 0.09

Vegs$ 10.4

(6.19)

10.6

(5.73)

0.5

9

11

(2.65)

7.33

(1.53) 0.09

11.67

(1.53)

5.67

(5.03) 0.05

fruits$ 4.4

(2.61)

5.6

(2.19)

0.9

5

5.67

(3.79) 4 (1) 0.27

7.33

(1.53)

4.33

(0.58)

0.04

8*

oils 3.2

(2.17)

2.6

(2.19) 0.1 4 (1) 3 (0) 0.11

3.33

(1.53)

3.67

(1.15) 0.79

meat and

beans$

10.2

(6.57)

10.6

(5.46)

0.5

7

13.67

(4.16)

11

(3.46)

0.03

*

13.33

(1.53)

8.67

(4.93) 0.09

milk$ 4.8

(3.42)

5.6

(3.36) 0.8

8.33

(3.06)

6.33

(2.52) 0.18 9 (2) 5 (0)

0.04

*

FIS/o

vob

Month 1 (n=22) Month 2 (n=13) Month 3 (n=17)

Grains$ 14.32

(4.52)

12.5

(4.13)

0.0

6*

14.08

(4.72)

11.46

(5.17)

0.02

*

15.65

(5.7)

12.06

(4.22)

0.00

2*

Vegs$ 13.55

(3.62)

11.09

(4.28)

0.0

07*

12.92

(3.57)

10.31

(3.35)

0.02

*

13.59

(5.04)

10.82

(4.35)

0.00

8*

fruits$ 6.5

(2.74)

5

(2.78)

0.0

08*

6.38

(3.71)

4

(2.42)

0.00

6*

6.24

(2.68)

4.53

(2.27)

0.01

*

oils 3.68

(1.13)

3.5

(1.37) 0.2

3.38

(1.26)

3.08

(0.95) 0.13

3.71

(0.92)

3.35

(1.41) 0.17

meat and

beans$

14.27

(5.35)

11.59

(5.14)

0.0

3*

13.38

(5.62)

9.38

(3.04)

0.02

*

13.82

(4.9)

10.47

(3.41)

0.00

4*

milk$ 6.09

(2.64)

5.14

(2.59)

0.0

2*

6.54

(3.28)

4.54

(1.9)

0.02

*

7.47

(3.28)

5.94

(1.92)

0.01

*

89

Table 3.7a Monthly differences in total energy intake (TEI) $Difference = Value in the beginning of the month - values at the end of the month. FS- food secure, FIS- food insecure, ovob-

overweight/obese (BMI >= 25), norm- normal weight (BMI < 25); 1 – p-values from one-tailed t-tests, testing whether the intake

at the beginning of the month was higher than at the end; *- significant monthly decrease

Beginning End Difference$

(kcal) p

1

mean SD SE mean SD SE

Month

1

FS/norm (n=4) 1650.66 389.98 194.99 2018.86 742.88 371.44 -368.2 0.92

FS/ovob (n=10) 2051.18 646.13 204.32 2131.19 773.81 244.7 -80.01 0.6

FIS/norm (n=6) 2086.51 561.8 229.35 2330.34 741.75 302.82 -243.83 0.77

FIS/ovob (n=39) 2114.19 937.26 150.08 1843.06 872.23 139.67 271.13* 0.045

Total (n=59) 2069.27 830.01 108.06 1953.37 834.24 108.61 115.9 0.17

Month

2

FS/norm (n=3) 2214.49 253.57 146.4 2233.44 402.78 232.54 -18.94 0.53

FS/ovob (n=11) 1993.29 616.8 185.97 1900.16 561.09 169.18 93.13 0.36

FIS/norm (n=4) 2013.27 408.03 204.01 2328.37 985.45 492.73 -315.11 0.79

FIS/ovob (n=30) 2097.35 802.36 146.49 2099.06 879.06 160.49 -1.71 0.51

Total (n=48) 2073.82 703.55 101.55 2080.99 791.98 114.31 -7.17 0.53

Month

3

FS/norm (n=3) 1885.53 689.1 397.85 1995.52 627.03 362.02 -109.99 0.82

FS/ovob (n=10) 2276.35 478.02 151.16 2401.93 529.62 167.48 -125.58 0.76

FIS/norm (n=4) 2745.51 1032.91 516.46 2235.07 314.53 157.26 510.44 0.15

FIS/ovob (n=27) 2108.26 1088.73 209.53 2112.96 854.1 164.07 -4.69 0.51

Total (n=44) 2189.21 949.82 143.19 2181.73 736.14 110.98 7.48 0.47

89

90

Table 3.8b Monthly differences in macronutrient intakes

Monthly difference = Value in the beginning of the month - values at the end of the

month. FS- food secure, FIS- food insecure, ovob- overweight/obese (BMI >= 25), norm-

normal weight (BMI < 25). 1 – p-values from one-tailed t-tests, testing whether the intake

at the beginning of the month was higher than at the end; *- significant monthly decrease

Fat

(kcal) p

1

Protein

(g) p

1

Carbohydrates

(g) p

1

Month

1

FS/norm (n=4) -47.3 0.81 -22.29 0.87 -60.69 0.91

FS/ovob (n=10) 70.18 0.3 0.34 0.49 -34.76 0.76

FIS/norm (n=6) -34.6 0.67 -29.25 0.93 -32.2 0.7

FIS/ovob (n=39) 154.17* 0.04 9.12 0.15 20.6 0.11

Total (n=59) 107.08* 0.04 1.6 0.4 0.34 0.49

Month

2

FS/norm (n=3) -46.8 0.56 -13.98 0.82 18.39 0.22

FS/ovob (n=11) -54.5 0.65 7.19 0.24 29.19 0.16

FIS/norm (n=4) -73.6 0.63 -23.17 0.76 -46.09 0.81

FIS/ovob (n=30) 2.92 0.48 -1.25 0.59 -7.59 0.64

Total (n=48) -19.7 0.64 -1.94 0.66 -0.75 0.52

Month

3

FS/norm (n=3) -128 0.66 -12.12 0.99 21.14 0.42

FS/ovob (n=10) -74.2 0.26 6.78 0.28 -60.81 0.91

FIS/norm (n=4) 211.4 0.08 18.73 0.25 52.43 0.26

FIS/ovob (n=27) 52.73 0.27 13.13 0.11 -32.55 0.93

Total (n=44) 59.75 0.17 10.47 0.08 -27.59 0.93

91

Table 3.9 Monthly Differences (SD) in micronutrient intakes in Month 1 1 – p-values from one-tailed t-tests, testing whether the intake at the beginning was higher

than at the end. $- Daily Reference Intakes, representing either Recommended Dietary

Allowances (RDAs) or Adequate Intakes (AIs) [154]

Nutrients Beginning End p1 DRI$

Cholesterol (mg) 257.80 (220.39) 185.45 (137.20) 0.03 as low as

possible

Iodine (mcg) 97.86 (62.27) 51.50 (41.76) 0.0003 150

Molybdenum (mcg) 22.51 (27.17) 6.92 (5.30) 0.01 45

Choline (mg) 125.55 (123.95) 86.32 (70.46) 0.04 425

Riboflavin (mg) 1.72 (1.47) 1.18 (0.79) 0.01 1.1

Vitamin B12 (mcg) 4.15 (4.01) 2.95 (2.59) 0.03 2.4

Biotin (mcg) 14.10 (13.30) 8.62 (10.09) 0.03 30

Vitamin D (IU) 136.29 (129.27) 82.52 (85.36) 0.01 600

92

Subscale measure FS/norm FS/ovob FIS/norm FIS/ovob p$

Restraint 1.87 (1.80) 1.51 (1.40) 0.40 (0.47) 1.29 (1.25) 0.35

Eating concern 0.26 (0.31) 0.87 (0.68) 0.76 (0.91) 1.14 (1.47) 0.24

Weight concern 0.93 (0.83) 2.78 (1.46) 1.08 (0.77) 2.73 (1.88) 0.01

Shape concern 1.29 (1.12) 3.32 (1.37) 1.45 (0.79) 3.35 (1.88) 0.003

Global score 1.09 (0.96) 2.12 (1.06) 0.92 (0.64) 2.13 (1.44) 0.04

Table 3.10a EDE-Q subscale scores (SD) by food security status and weight status

FS- food secure, FIS- food insecure, ovob- overweight/obese (BMI >= 25), norm- normal

weight (BMI < 25). $- p-values of F-test, generated from ANOVA simulation in STATA

Subscale

measure

FS FIS p

1$

norm ovob p

2$

(n = 14) (n = 41) (n = 8) (n = 47)

Restraint 1.59

(1.42)

1.18

(1.21) 0.34

0.95

(1.28)

1.34

(1.27) 0.42

Eating

concern

0.74

(0.66)

1.10

(1.41) 0.21

0.58

(0.75)

1.08

(1.33) 0.13

Weight

concern

2.39

(1.54)

2.53

(1.85) 0.78

1.03

(0.74)

2.74

(1.77) <0.0001

Shape concern 2.88

(1.54)

3.12

(1.88) 0.63

1.39

(0.85)

3.34

(1.76) <0.0001

Global score 1.90

(1.10)

1.98

(1.42) 0.83

0.99

(0.71)

2.13

(1.35) 0.0004

Table 3.11b EDE-Q subscale scores (SD) by food security status and by weight status

FS- food secure, FIS- food insecure, ovob- overweight/obese (BMI >= 25), norm- normal

weight (BMI < 25). $- p-values of F-test, generated from ANOVA simulation in STATA;

1- comparisons between food secure women and food insecure women;

2- comparisons

between overweight (BMI >= 25) women and normal weight women (BMI < 25)

93

EDE-Q Subscales

BMI Food security score

Correlation

coefficients p

$

Correlation

coefficients p

$

Restraint 0.27 0.05 -0.20 0.15

Eating concern 0.11 0.41 0.23 0.09

Weight concern 0.31 0.02 0.04 0.76

Shape concern 0.35 0.01 0.15 0.28

Global score 0.31 0.02 0.07 0.60

Table 3.12 Correlations of EDE-Q subscale scores with BMI and food security status $- significance levels of the pair-wise correlation coefficients

Behavior Measure FS FIS

p$

(n = 14) (n = 41)

Preoccupation with food, eating or

calories 0.21 (0.43) 1.24 (2.11) 0.007

Fear of losing control over eating 0.57 (0.94) 1.27 (2.06) 0.1

Eating in secret 0.21 (0.43) 0.12 (0.40) 0.48

Social eating 1.14 (1.61) 1 (1.88) 0.78

Guilt about eating 1.57 (1.22) 1.85 (2.12) 0.55

Table 3.13 Featured behaviors of eating disorders by food security status

FS- food secure women; FIS- food insecure women; $- p-values of F-test, generated from

ANOVA simulation in STATA

94

Food Stamps n Beginning End

Differ-

ences p

$

FIS

Month 1

Non-recipients 12 83.25

(17.67)

80.58

(25.78)

2.67

(20.39) 0.01

Recipients 33 87.21

(25.75)

64.88

(22.42)

22.33

(23.55)

Month 2

Non-recipients 10 86.1

(26.67)

82.9

(21.17)

3.2

(25.67) 0.26

Recipients 24 82.38

(24.6)

67.83

(15.69)

14.54

(28.84)

Month 3

Non-recipients 10 81.3

(25.24)

80.4

(18.63)

0.9

(16.54) 0.01

Recipients 21 92

(22.33)

70.29

(21.08)

21.71

(26.78)

FS

Month 1

Non-recipients 8 87.25

(11.74)

79.63

(14.56)

7.63

(8.48) 0.38

Recipients 6 114.5

(26.94)

102

(29.03)

12.5

(10.97)

Month 2

Non-recipients 7 92.29

(14.91)

88.29

(15.8) 4 (15.53)

0.53

Recipients 7 102

(26.23)

92.43

(34.21)

9.57

(15.60)

Month 3

Non-recipients 7 93

(10.83)

83.57

(9.52)

9.43

(14.08) 0.41

Recipients 6 105.67

(34.2)

89.67

(27.95)

16

(13.70)

Table 3.14 Monthly differences of household food stores by food security and food

stamps receiving status

FS- food secure women, FIS- food insecure women; $- p-values of F-test, generated from

ANOVA simulation in STATA, comparing monthly differences between food stamp

recipients and non-recipients

95

CHAPTER 4

Addressing the Association of Food Insecurity and Overweight/Obesity

Using data from NHANES 1999-2008

4.1 Introduction

Based on what had been found in the Ohio study (Chapter 3), the monthly cycle

of food abundance and food shortage, and the participation of Food Stamp Program

(FSP) may contribute to the paradoxical relationship in food insecurity and

overweight/obesity. However, the Ohio study was limited by a small sample size,

especially for women of normal weight status or income level. In order to generate more

nationally representative results, and to address this paradoxical relationship in a larger

sample, the study in this chapter analyzed U.S. women with similar characteristics (22-49

years old, non-Hispanic white, non-pregnant, non-lactating) as those in the Ohio study

using data from the continuous National Health and Nutrition Examination Survey

(NHANES) 1999–2008. The study aimed to test whether dietary intake (total energy

intake – TEI, and macronutrient intakes) and food stamp participation have an impact on

the paradoxical in food insecurity and overweight/obesity. In addition, the study

calculated the daily total energy intake (TEI) in FIS/ovob women in U.S. on average to

provide a reference value for the TEI in FIS/ovob women in the Ohio study.

96

4.1.1 Hypothesis Testing

The following hypotheses were proposed to be tested in this study:

1. Food insecure women have higher body mass index (BMI) than food secure

women, and low-income food insecure women have higher BMI than low-income

food secure women.

2. Compared to FS/ovob women, FIS/ovob women have greater 1-yr body weight

increase, higher total energy intake (TEI), and higher percentage of energy from

fat, lower percentage of energy from protein and lower grams of fiber intake.

3. Among food insecure women, food stamp recipients have higher BM, greater 1-yr

body weight increase, higher TEI, higher percentage of energy from fat, lower

percentage of energy from protein and lower grams of fiber intake than food

stamp non-recipients.

4. FIS/ovob women in the NHANES study have lower TEI than FIS/ovob women in

the Ohio study at the beginning of the month, but higher at the end of the month.

4.2 Methods

4.2.1 Sample

To ensure enough sample size, the data from the NHANES 1999-2008 were

analyzed in this study. In total there were 26,263 women in the NHANES 1999-2008,

among which 10,194 were non-Hispanic White. Women whose age were not within 22-

49 years old (n=7257), pregnant (n=721) or breastfeeding (n=72) were excluded from the

study. In addition, women with zero energy intake (n=1) or with energy intake 4 standard

97

deviations above the mean (n=3) were excluded to eliminate outliers. The final sample

size is 2140 in the NHANES study.

4.2.2 Data Collection

The National Health and Nutrition Examination Surveys (NHANES) are

continuous national nutrition and health monitoring surveys collecting data from non-

institutionalized U.S. citizens. NHANES covered a stratified, probabilistic, multistage,

cluster sampling of households representing the fifty U.S. states as well as Washington

DC. The data oversampled hard-to-reach populations like elders, minorities, low-income

and adolescents. The NHANES survey includes health interviews at respondents’ homes,

and examinations in specially-designed and equipped Mobile Examination Center (MEC),

which can travel to survey locations throughout the country [155].

Public-available NHANES 1999-2008 were downloaded from the Centers for

Disease Control and Prevention (CDC) website. Female interviewees who were at

reproductive age (22-49 years old) and with similar characteristics (non-Hispanic white,

non-pregnant, non-lactating) as those in the Ohio study were chosen to obtain the average

dietary intake in this population.

Demographic data

Demographic variables included in this research were gender, age, race/ethnicity,

education, household size, family poverty income ratio (PIR) and current pregnant and

breastfeeding status. Except pregnant and breastfeeding status, demographic data were

98

collected during household interviews in respondents’ homes, using the Sample Person

and Family Demographics questionnaires containing family-level and individual-level

questions. Participants who were 16 years and older and emancipated minors were

interviewed directly by interviewers. The variable of pregnancy status in the demographic

file was derived from two other variables – the self-reported current pregnancy status

assessed in the MEC, and the urine pregnancy test result from the laboratory results. The

current breastfeeding question was asked face-to-face in the MEC to female participants

who were pregnant before. Quality control included the use of NHANES computer-

assisted personal interview (CAPI) software program for data collection and error

checking, and reviewing and re-contacting a subset of household interviews and

participants.

The family poverty income ratio (PIR) was used to determine whether a

household is low-income or not, subsequently, low-income women were those whose

family PIR was less than or equal to 1.3.

Weight history

Self-reported weight 1-yr ago and currently self-reported weight were used to

calculate women’s weight change over one year. These questions were asked in the

Weight History section in household in-person interviews and were asked to persons 16

years and older. In NHANES 2005-2006, 207-2008, the data collection was assisted with

a computer-assisted personal interviewing (CAPI) system.

99

In the analysis, self-reported body weights were released in pounds in the dataset

and were converted to kilograms using the conversion factor 2.2046 pounds per kilogram.

In addition, current self-reported weight was compared with measured weight to estimate

the report bias of self-reported weight.

Except the mean value of 1-yr weight change, the study used 2.27 kg (5 lbs) and

4.54 kg (10 lbs) as cutoff points to generate two dummy variables indicating 1-yr weight

gain bigger than or equal to the two cutoff values. Studies have shown that weight gain is

associated with vitality and physical functioning declines [156]. For example, a long-term

weight gain of 5 kg or more is a risk factor for diseases like diabetes [157], stroke [158],

and coronary heart disease [76]; and the risks are increased by maintaining a 4.54 kg

weight gain in a single year [41]. The two cutoff points chosen here were for single-year

weight change, which can reach substantial weight gain if maintained for a long term;

additionally, these cutoff points have been proved to be associated with household food

security in a previous study of NHANES [41].

Household food security data

NHANES household food security section (FSQ) consisted of four sub-

components: 1) household food security, 2) individual food security, 3) household and

individual FSP benefits, and 4) household and individual Women, Infants and Children

(WIC) benefits. The sub-components analyzed in this study are the household food

security and household FSP benefits. The household food security status was measured

with the US FSSM questions responded by an adult in the household during the

100

household interview [3]. The US FSSM asked 18 questions to households with children,

and 10 questions to households without children. Household food security status was then

derived from these responses as follows: 1) high food secure (score: 0); 2) marginal food

secure (score: 1-2); 3) low food secure, (score: 3-7 in households with children; 3-5 in

households without children); 4) very low food secure (score: 8 and above in households

with children; 6 and above in households without children) [107]. Quality control system

when acquiring the household food security data included reviewing incoming data and

taped interviews, interviews observation, training and re-training sessions, and reviewing

for outliers and non-response.

In this study, households in both the categories of high food security and marginal

food security were considered as food secure households, and households of low food

security and very low food security were combined as food insecure households. In

addition, data of a subcomponent ―household FSP benefits‖ were used to define food

stamp recipients in the study: women who live in the households with at least one person

received food stamps in the last 12 months.

24-hour dietary recall data

The dietary intake data were collected using two different methods. In NHANES

1999-2000, the data were collected using the NHANES computer-assisted dietary

interview (CADI) system with built-in food probe features [159]. Databases like Quick

List food list, brand name food list, and food amount unit list were linked to this

automated data collection system. A multi-pass method ―quick list, time, occasion and

101

place, food details, and final review‖ was used in CADI to collect details on food and

beverage consumption. In this cycle, in-person interview was the primary interview

mode6.

In NHANES 2001-2002, two different computer-assisted systems were used in

dietary intake data collection. In 2001, data were collected using the same computer-

assisted dietary interview system (CADI) as in NHANES 1999-2000 [159]. In 2002, the

dietary intake survey in NHANES was integrated with another nationwide survey - the

Continuing Survey of Food Intakes by Individuals (CSFII). Due to this integration,

another fully computerized method: Automated Multiple Pass Method (AMPM) with the

assistance of five steps (i.e. quick list, forgotten foods, time and occasion, detail cycle,

and final probe) has been used in dietary intake interviews since 2002 [143]. Compared

to the CADI system, the AMPM is updated yearly and ―features automated routing of

questions based on previous answers‖ [143]. Collected 24-hour dietary intake data were

processed by USDA’s Food and Nutrient Database for Dietary Studies, 3.0 (FNDDS, 3.0)

to generate dietary intakes. Nutrient values in FNDDS 3.0 were derived from the USDA

National Nutrient database for Standard Reference, release 20 [160]. In-person interview

was still the primary interview mode in this cycle7.

6 Participants who were randomly assigned to be interviewed in afternoons or evenings were also

asked for a following telephone interview 4-10 days later; only a small part of these participants completed

the 2nd phone interview.

7 In 2002, dietary intake data also included data from a second-day telephone interview, but the

data were not publicly released due to confidentiality issues.

102

Starting from the cycle of 2003-2004, NHANES has two 24-hour dietary recall

interviews conducted to NHANES examinees. The first recall was an in-person interview

conducted in the Mobile Examination Centers (MECs). Measuring guides like glasses,

rulers, spoons were provided to help for estimation of food consumption amounts. A

second telephone dietary interview was conducted 3 to 10 days after the initial interview

but not on the same day of the week as the one in MEC [161]. To keep data consistent

among different cycles of NHANES, the study here used 24-hour dietary recall data from

the first in-person interview in all the cycles in NHANES.

To obtain high quality data, interviewers in the interviews and coders in the data

processing period received training and re-training. Other quality control methods like

interview monitoring, review of data, re-coding by another coder were applied.

Dietary intake of macronutrients (i.e. protein, fat, and carbohydrates) was released

as grams in NHANES. To generate ratios of protein/energy, fat/energy, and

carbohydrates/energy, macronutrient intakes were first converted to kcals -1 gram of fat

containing 9 kcal, 1 gram of carbohydrates and 1 gram of protein containing 4 kcal – and

were divided by total energy intake (kcal).

Anthropometric measurements

Participants’ weights and heights were measured by trained health technicians in

the NHANES mobile examination centers (MECs). Weight was measured in pounds by a

digital scale and was converted to kilograms. Standing height measurements were taken

to the nearest tenth of a centimeter on a fixed stadiometer. Measurements would be

103

verified before going to the next step. Body Mass Index (BMI, kg/m2) was calculated by

weight (in kilograms) divided by height squared (in meters square), and used to

categorize weight status by NIH recommended cut-off points normal weight (BMI 18.5-

24.9 kg/m2), overweight (BMI 25-29.9 kg/m

2), and obesity (BMI higher or equal to 30

kg/m2) [27].

4.2.3 Data Analysis

Public-available NHANES 1999-2008 from National Center for Health Statistics

(NCHS) website were downloaded and saved as appropriate files. Components from

different survey cycles were recoded, concatenated, and merged into one dataset.

Variables of interest were kept in the final dataset. Here is a list of variables of interest:

Demographics - gender, age, race/ethnicity, education, household size, family

PIR, current pregnant status, current breastfeeding status

Body measures - measured weight (kg), measured height (cm), BMI

Food security - household food security category, anyone in the household

authorized for food stamps in last 12 months

Dietary intake - energy (kcal), protein (gm), protein/energy ratio (%),

carbohydrates (gm), carbohydrates/energy ratio (%), dietary fiber (gm), total fat (gm),

fat/energy ratio (%)

Weight history - currently self-reported weight (kg), self-reported weight 1-yr ago

(kg), 1-yr weight change (kg), 1-yr weight gain >= 2.27 kg (5 lbs), 1-yr weight gain >=

4.54 kg (10 lbs)

104

Statistical analysis

The comparison between food secure and food insecure women, between

overweight or obese women who were food secure and food insecure, and between food

insecure women who were food stamp recipients and non-recipients were assessed by t-

test analysis and chi-square test. All statistical analyses were performed using the

software package STATA [152]. The complex sample design in NHANES was

considered when running statistical analyses by using this statistical software.

Statistically significant differences were determined at p < 0.05, while p < 0.1 was

defined as marginally significant.

Sample weights

In order to gain a nationally representative sample, three types of sample weights

in NHANES were used in the data analysis: the interview weights (for interviewed

sample persons), the MEC weights (for the sample persons with MEC data), and the

dietary intake weights (for the sample persons with dietary intake data). Different sample

weights account for different interview settings. Data of demographics, breastfeeding

status, weight history, and household food security and food stamp participation were

collected at home, and the interview weights were applied to these data. The MEC

weights were applied to weight and height which were measured in the MEC. Although

the dietary intake data on Day 1 (in-person interview) were also collected in the MEC,

105

the MEC sample weights should not be used for dietary intake analysis. That is because

dietary intake interviews occurred more on weekends than on weekdays. Due to the high

day-to-day variation in dietary intake, use of the MEC weights would ―disproportionately

represent‖ intakes during weekends. Therefore, the study used the dietary intake (Day 1)

weights, which were constructed from the MEC weights but further adjusting for the

―additional non-response‖ and the ―differential allocation by day of the week‖ [155].

The original 2-year sample weights in each NHANES cycle were used to

construct 10-year sample weights for combined surveys, except in 1999-2000 and 2001-

2002. In these two cycles, the combined 4-year weights were used rather than the 2-year

weights to account for the two different population bases – the population estimates

before 2000 Census counts in NHANES 1999-2000 and after 2000 Census counts in

NHANES 2001-2002 and later 2-year cycles.

4.3 Results

Basic characteristics of food secure women and food insecure women were

examined in Table 4.1. Compared to food secure women, food insecure women had a

higher mean BMI (29.22 vs. 27.44, p<0.01), and weighed significantly more in measured

weight (78.95 vs. 74.28 kg, p<0.01) and self-reported weight (77.04 vs. 72.73 kg,

p<0.05). The report bias of women’s weight - calculated as measured weight minus self-

reported weight (not shown in Table 4.1) - was significant in both food secure women

106

(1.50 kg, 95% CI8: 1.28 - 1.72 kg) and food insecure women (1.86 kg, 95% CI: 1.23-2.49

kg). The prevalence of obesity (42.47% (95% CI: 34.9 - 50.03%) vs. 29.3% (95% CI:

26.74 - 31.87%)) was higher in food insecure women than food secure women;

comparatively, a lower prevalence of normal weight women (30.82% (95% CI: 24.29 -

37.36%) vs. 44.52% (95% CI: 41.27 - 47.78%)) was found in food insecure women. The

prevalence of overweight in food insecure women was not significantly different from

food insecure women.

No significant differences between the two groups of women were found in age,

household size, and measured height. Food secure women were wealthier: a higher

family poverty income ratio (PIR) was found in this group of women compared to food

insecure women (3.47 vs. 1.32, p<0.001). In addition, half of the food insecure women

(50%) participated in the FSP, compared to a much lower participation rate 7.75% among

food secure women.

The education status in women was categorized into five levels (Table 4.1).

Significant differences were observed in the distribution of education status. More than

half (55%) of food insecure women were in low education levels (i.e. less than 9th

grade,

9-11th

grade, 12th

grade with no diploma, and high school grad/GED or equivalent),

compared to a frequency of 30% in food secure women. On the other hand, more than

one third (35%) of food secure women had college degree or above, while less than 8%

food insecure women were in this level.

8 CI: Confidence Interval

107

Table 4.2 compared the same characteristics as Table 4.1 among low-income food

secure women and low-income food insecure women. No significant differences were

found in household size, family PIR, education level, BMI, prevalence of underweight,

normal weight, overweight, obesity, measured weight and self-reported weight between

the two groups, but food secure low-income women were younger (34.51 vs. 36.82 years,

p=0.022) and shorter (162.67 vs. 164.07 cm, p=0.037) than food insecure counterparts. A

significant difference between the two groups of women was found in the participation of

the FSP: about two thirds (64.22%) of food insecure low-income women were in the

households receiving food stamps in the last 12 months, compared to a participation rate

42.71% in food secure low-income women (p=0.0006).

Table 4.3 shows the comparison of 1-yr weight change and dietary intake (total

energy and macronutrient intakes) between food secure women and food insecure women.

A positive weight change (i.e. weight increase) was found in food insecure women

showed within last 12 months (1.24, 95% CI: 0.20 – 2.28 kg) with no significant

difference from food secure women. However, for food insecure women, the prevalence

of substantial 1-yr weight gain was significantly higher than food secure women (2.27 kg

or 5 lbs - 35.84% vs. 26.21%; 4.54 kg or 10 lbs - 24.45% vs. 15.28%). The total energy

intake (FS: 1915.49 vs. FIS: 1968.03 kcal) and fat intake (FS: 73.48 vs. FIS: 72.1 gm)

were not significantly different in food secure women and food insecure women.

However, food insecure women consumed less protein (63.46 vs. 71.65 gm, p=0.001),

less fiber (12.11 vs. 13.99 gm, p=0.017), but more carbohydrates (257.26 vs. 235.57 gm,

p=0.017) than food insecure women. Macronutrient energy ratios were also calculated

108

and compared in Table 4.3: food insecure women obtained 13.28% of energy from

protein, more than half from carbohydrates (52.7%), and about one third (32.69%) from

fat; comparatively, food secure women showed a higher protein/energy ratio (15.27%,

p<0.001) and a lower carbohydrates/energy ratio (49.77%, p=0.002). No significant

different was found when comparing fat/energy ratio in the two groups of women.

The macronutrient energy ratios were also compared with the Acceptable

Macronutrient Distribution Range (AMDR)9 range (carbohydrates: 45-65%, total fat: 20-

35%, protein and amino acids: 10-35%) [162], and both food secure women and food

insecure women were with within the AMDR range. But the percentages of energy from

fat were around the upper limit in both groups (FS: 33.98, FIS: 32.69 %), and the

protein/energy ratio was around the lower bound (FS: 15.27, FIS: 13.28 %). In addition,

the daily fiber intake was about half of the required fiber intake (Adequate Intake - AI: 25

gm/day) [162].

Both food secure women and food insecure women were then categorized into

two subgroups separately – overweight/obese group and normal weight (including

underweight) group. In Table 4.4 four women groups were analyzed: FS/ovob, FIS/ovob,

FS/norm, FIS/norm. Pair-wise comparisons of 1-yr weight change and dietary intake

between FIS/ovob women and the other three women groups were examined in Table 4.4.

FIS/ovob women were the only group showing a weight increase over one year (1.67,

95% CI: 0.07-3.27 kg). Furthermore, for FIS/ovob women, the prevalence of a 2.27 kg (5

9 AMDR is defined as ―the range of intake for a particular energy source that is associated with

reduced risk of chronic disease while providing intakes of essential nutrients‖ [Trumbo.etal2002].

109

lbs) weight gain over one year (37.41%) was significantly higher than FS/norm women

(18.94%); the prevalence of a 4.54 kg (10 lbs) weight gain over one year (28.81%) was

significantly higher than any other women group (FS/ovob: 21.83%, FS/norm:7.95%,

FIS/norm: 17.10%). No significant difference of total energy intake was found among the

four groups. FIS/ovob women showed a lower protein intake than FS/ovob women (65.26

vs. 71.74 gm, p=0.037) and FS/norm women (65.26 vs. 71.99 gm, p=0.026). And the

protein/energy ratio in FIS/ovob women was lower than in the two food secure groups of

women, but higher than in FIS/norm women. Consumption of carbohydrates in FIS/ovob

women was marginally higher than FS/ovob women (253.48 vs. 232.58 gm, p=0.062),

and the carbohydrates/energy ratio was significantly higher in FIS/ovob women than

FS/ovob women and FS/norm women. No significant differences were found in either fat

intake or fat/energy ratio. Significant differences of fiber intake were not found between

FIS/ovob women and FS/ovob women, but was found between FIS/ovob women and

FS/norm women (12.76 vs. 14.89 gm, p=0.044).

The TEI in FIS/ovob women calculated from the NHANES data (Table 4.4) were

also compared with that of the Ohio sample (Table 3.7a). Compared to the average TEI

(1944.35±77.90 kcal) from NHANES, FIS/ovob women in the Ohio sample had a

monthly decrease in TEI in Month 1, characterized by a higher energy intake at the

beginning of the month (2114.19±150.08 kcal) followed with a lower TEI at the end of

the month (1843.06±139.67 kcal). The TEIs in Month 2 and in Month 3 in the Ohio study

(Month 2: beginning- 2097.35±146.49, end- 2099.06±160.49; Month 3: beginning-

110

2108.26±209.53, end- 2112.96±164.07, kcal), regardless the beginning or the end of the

month, were higher than the average TEI in FIS/ovob women in U.S. (Figure 3).

Figure 3 The total energy intake (TEI) in food insecure and overweight/obese (FIS/ovob)

women in the Ohio study and in the NHANES study

Beg – beginning of the month, end – end of the month

Food insecure women were also analyzed by their food stamp recipient status

(Table 4.5). Characteristics of BMI, weight status, 1-yr weight change, and dietary intake

were compared between recipients and non-recipients among food insecure women. The

BMI of food stamp recipients was not significantly different from that of non-recipients

(30.09 vs. 28.72, p=0.23). Similarly, no significant differences were found in the

prevalence of normal weight or underweight (BMI<25) and overweight/obesity

(BMI>=25) between recipients and non-recipients. The 1-yr weight change was positive

in food stamp non-recipients (1.63, 95% CI: 0.11-3.16 kg), but neither the weight change

NHANES

111

nor the prevalence of a 2.27 kg (5 lbs) or a 4.54 kg (10 lbs) weight gain over one year

was significantly different from food stamps recipients. The comparison of total energy

intake and macronutrient intakes between recipients and non-recipients did not show

significant differences, except for the protein intake. It was found that recipients

consumed marginally less protein (60.49 vs. 68.41 gm, p=0.09) and showed a lower

protein/energy ratio (12.41% vs. 13.89%, p=0.036) compared to non-recipients.

4.4 Summary and Results of Hypothesis Testing

The study analyzed women sample from the NHANES 1999-2008 who were 22-

49 yr, non-Hispanic white, non-pregnant and non-lactating, to address the paradoxical

association in food insecurity and overweight/obesity in this women population on a

national level.

Food insecure women had a higher mean BMI (29.22 vs. 27.44), higher

prevalence of obesity (42.47% vs. 29.3%), a lower family poverty income ratio (PIR)

(1.32 vs. 3.47), a higher FSP participation rate (50% vs. 7.75%), and lower education

level than food secure women. However, no significant differences were found between

low-income food secure women and low-income food insecure women, except for the

higher percentages food stamp participation in food insecure low-income households

(64.22% vs. 42.71%). Therefore, the higher BMI in food insecure women than food

secure women in Hypothesis 1 is accepted, but the higher BMI in low-income food

insecure women than low-income food secure women is rejected.

112

In Hypothesis 2 it was hypothesized that FIS/ovob women have greater 1-yr body

weight increase, higher total energy intake (TEI), and higher percentage of energy from

fat, lower percentage of energy from protein and lower grams of fiber intake than to

FS/ovob women. Although no significant difference was observed in the values of 1-yr

body weight change, FIS/ovob women had a higher prevalence of a 4.54 kg (10 lbs) 1-yr

weight gain (28.81%) than FS/ovob women (21.83%). No significant differences were

found in TEI or in fat/energy ratio between the two groups. The protein/energy ratio was

higher in FS/ovob women (15.44) than in FIS/ovob women (13.79%). And the fiber

consumption in FIS/ovob women (12.76 gm) was not differed from that in FS/ovob

women (13.34 gm). Therefore, Hypothesis 2 is partly accepted.

Unlike Hypothesis 2, Hypothesis 3, the comparisons between food insecure food-

stamps-recipients and non-recipients, was rejected in most parts: except for the lower

protein/energy ratio (12.41% vs. 13.89%) in recipients, no significant differences were

found in BMI, prevalence of overweight/obesity, 1-yr weight change, TEI, and

macronutrient intakes.

The final hypothesis, Hypothesis 4 is only true in Month 1, where FIS/ovob

women in the NHANES study showed lower TEI (1944.35±77.90 kcal) than FIS/ovob

women in the Ohio study at the beginning of Month 1 (2114.19±150.08 kcal) , but higher

at the end of Month 1 (1843.06±139.67 kcal). Comparatively, the TEI in Month 2 and in

Month 3 in the Ohio study was higher than that in the NHANES study, regardless it was

at the beginning or the end of the month.

113

Characteristic FS

1 FIS

1 p

2

n=1816 n=275

Mean

(or %) [95% CI]

Mean

(or %) [95% CI]

BMI (kg/m2) 27.44 26.97 27.90 29.22 28.05 30.38 0.004

Weight status (%)

0.0012

Underweight (BMI < 18) 1.71 1.10 2.31 4.55 1.79 7.31

Normal (BMI >= 18 & < 25) 44.52 41.27 47.78 30.82 24.29 37.36

overweight (BMI >= 25 & < 30) 24.47 22.23 26.71 22.16 17.32 27.00

obesity (BMI>=30) 29.30 26.74 31.87 42.47 34.90 50.03

Age (years) 37.14 36.79 37.50 36.80 35.62 37.98 0.592

Household size 3.29 3.22 3.36 3.36 3.18 3.55 0.504

Family poverty income ratio (PIR) 3.47 3.34 3.59 1.49 1.32 1.66 <0.001

Food stamp participation3 (%) 7.75 5.72 9.78 50.00 41.93 58.07 <0.001

Continued

Table 4.1 Characteristics of women by household food security status 1- FS - food secure, FIS - food insecure;

2- p-values, from two-tailed t-test for continuous variables, and from chi-square test

for categorical/dummy variables; 3- food stamp participation was defined as at least one household member receiving food

stamps in the last 12 months

113

114

Table 4.1 continued

Characteristic FS

1 FIS

1 p

2

n=1816 n=275

Mean

(or %) [95% CI]

Mean

(or %) [95% CI]

Measured height (cm) 164.46 164.19 164.73 164.29 163.47 165.12 0.698

Measured weight (kg) 74.28 73.04 75.52 78.95 75.70 82.20 0.007

Self-reported weight (kg) 72.73 71.57 73.88 77.04 73.78 80.29 0.014

Self-reported weight 1-yr ago (kg) 72.48 71.27 73.69 75.61 72.49 78.73 0.054

Education (%)

<0.0001

< 9th Grade 1.10 0.61 1.59 3.85 1.58 6.12

9-11th Grade, 12th grade with no

diploma 7.44 5.68 9.19 17.50 12.40 22.60

High School Grad/GED or equivalent 22.11 19.91 24.31 33.24 26.44 40.03

Some College or AA degree 34.51 32.30 36.73 37.72 32.02 43.42

College Graduate or above 34.84 31.27 38.41 7.69 3.68 11.70

114

115

Characteristic FS/lowinc

1 FIS/lowinc

1 p

2

n=279 n=176

Mean (or %) [95% CI] Mean (or %) [95% CI]

BMI (kg/m2) 28.84 27.97 29.71 28.83 27.40 30.26 0.992

Weight status (%)

0.5553

Underweight (BMI < 18) 2.50 0.52 4.49 4.66 1.11 8.20

Normal (BMI >= 18 & < 25) 39.90 34.77 45.03 35.31 25.78 44.84

overweight (BMI >= 25 & < 30) 19.53 14.14 24.91 17.21 12.11 22.32

obesity (BMI>=30) 38.07 31.74 44.41 42.82 33.22 52.43

Age (years) 34.51 33.41 35.61 36.82 35.36 38.27 0.022

Household size 3.50 3.27 3.73 3.45 3.18 3.72 0.791

Family poverty income ratio (PIR) 0.79 0.73 0.85 0.82 0.76 0.88 0.492

Food stamp participation3 (%) 42.71 34.38 51.03 64.22 56.08 72.37 <0.001

Continued

Table 4.2 Characteristics of low-income women by household food security status 1: FS - food secure, FIS - food insecure, lowinc - low-income, defined as family poverty income ratio (PIR) <= 130;

2- p-values,

from two-tailed t-test for continuous variables, and from chi-square test for categorical/dummy variables; 3- food stamp

participation was defined as at least one household member receiving food stamps in the last 12 months

115

116

Table 4.2 continued

Characteristic FS/lowinc

1 FIS/lowinc

1 p

2

n=279 n=176

Mean (or %) [95% CI] Mean (or %) [95% CI]

Measured height (cm) 162.67 161.92 163.41 164.07 162.87 165.26 0.037

Measured weight (kg) 76.65 74.25 79.04 77.68 73.81 81.55 0.649

Self-reported weight (kg) 74.91 72.43 77.39 75.58 71.78 79.37 0.761

Self-reported weight 1-yr ago (kg) 73.85 71.27 76.43 73.48 69.81 77.15 0.844

Education (%)

0.166

< 9th Grade 3.53 1.26 5.81 4.61 1.98 7.24

9-11th Grade, 12th grade with no diploma 15.35 10.03 20.67 23.22 15.53 30.92

High School Grad/GED or equivalent 32.39 26.39 38.38 33.74 24.06 43.42

Some College or AA degree 37.01 31.29 42.72 30.94 23.67 38.22

College Graduate or above 11.72 7.18 16.27 7.49 2.57 12.41

116

117

FS1 FIS

1 p

2

n=1816 n=275

Mean (or %) [95% CI] Mean (or %) [95% CI]

1-yr weight change (kg)3 0.19 -0.19 0.57 1.24 0.20 2.28 0.073

1-yr weight gain (%)

>= 2.27 kg (5 lbs) 26.21 24.00 28.42 35.84 30.35 41.32 0.002

>= 4.54 kg (10 lbs) 15.28 13.38 17.19 24.45 19.59 29.31 0.001

Energy intake (kcal) 1915.49 1874.75 1956.23 1968.03 1835.77 2100.28 0.443

Protein intake (gm) 71.65 69.96 73.33 63.46 58.97 67.96 0.001

Protein/energy ratio (%) 15.27 14.97 15.57 13.28 12.60 13.96 <0.001

Carbohydrates (gm) 235.57 230.27 240.87 257.26 239.97 274.55 0.017

Carbohydrates/energy ratio (%) 49.77 49.03 50.51 52.70 50.99 54.41 0.002

Fat (gm) 73.48 71.36 75.61 72.10 66.13 78.07 0.658

Fat/energy ratio (%) 33.98 33.44 34.51 32.69 31.26 34.12 0.102

Fiber (gm) 13.99 13.36 14.62 12.11 10.58 13.64 0.017

Table 4.3 Comparison of 1-yr weight change and macronutrient intakes by food security status 1- FS - food secure, FIS - food insecure;

2- p-values from two-tailed t-test;

3- 1-yr weight change was calculated as self-reported

weight minus self-reported weight 1-yr ago

117

118

Continued

Table 4.4 Comparison of 1-yr weight change and macronutrient intakes by weight status and food insecurity 1- FS- food secure, FIS- food insecure, ovob- overweight/obese (BMI >= 25), norm- normal weight (BMI < 25);

2,3,4- p-values of

pair-wise comparisons calculated from ANOVA, 2- comparisons between FIS/ovob and FS/ovob women,

3- comparisons

between FIS/ovob and FS/norm women, 4- comparisons between FIS/ovob and FIS/norm women;

5- 1-yr weight change was

calculated as self-reported weight minus self-reported weight 1-yr ago

mean [95% CI] mean [95% CI]

FIS/ovob1 (n=177) Referent FS/ovob

1 (n=960) p

2

1-yr weight change (kg)5 1.67 0.07 3.27 0.51 -0.09 1.12 0.178

1-yr weight gain (%)

>= 2.27 kg (5 lbs) 37.41 30.14 44.68 32.75 29.75 35.75 0.214

>= 4.54 kg (10 lbs) 28.81 22.73 34.89 21.83 19.08 24.57 0.037

Energy intake (kcal) 1944.35 1789.09 2099.61 1896.85 1843.39 1950.32 0.553

Protein intake (gm) 65.26 59.70 70.82 71.74 69.31 74.18 0.037

Protein/energy ratio (%) 13.79 13.01 14.57 15.44 14.99 15.89 0.001

Carbohydrates (gm) 253.48 232.47 274.50 232.58 225.12 240.04 0.062

Carbohydrates/energy ratio (%) 52.33 50.46 54.19 49.48 48.50 50.46 0.01

Fat (gm) 72.27 65.35 79.19 74.00 71.43 76.57 0.644

Fat/energy ratio (%) 33.49 31.77 35.21 34.60 33.88 35.31 0.221

Fiber (gm) 12.76 10.79 14.72 13.34 12.67 14.00 0.565

118

119

Table 4.4 continued

mean [95% CI] mean [95% CI]

FS/norm1 (n=836) p

3 FIS/norm

1 (n=95) p

4

1-yr weight change (kg)5 -0.10 -0.42 0.23 0.039 0.44 -0.83 1.71 0.282

1-yr weight gain (%)

>= 2.27 kg (5 lbs) 18.94 16.31 21.57 <0.001 32.36 20.51 44.22 0.519

>= 4.54 kg (10 lbs) 7.95 6.31 9.59 <0.001 17.10 7.89 26.30 0.042

Energy intake (kcal) 1947.33 1879.41 2015.24 0.972 2018.38 1795.22 2241.54 0.576

Protein intake (gm) 71.99 69.43 74.54 0.026 60.48 53.98 66.99 0.242

Protein/energy ratio (%) 15.10 14.66 15.55 0.004 12.36 11.19 13.53 0.043

Carbohydrates (gm) 240.02 230.89 249.14 0.263 264.91 230.97 298.84 0.586

Carbohydrates/energy ratio (%) 49.99 49.01 50.96 0.027 53.31 49.76 56.86 0.631

Fat (gm) 73.37 69.85 76.89 0.764 72.19 63.39 81.00 0.988

Fat/energy ratio (%) 33.35 32.62 34.09 0.887 31.34 29.04 33.63 0.129

Fiber (gm) 14.89 13.99 15.80 0.044 11.08 9.34 12.81 0.158

119

120

FIS/nonrp1 FIS/rp

1 p

2

n 107 117

mean [95% CI] mean [95% CI]

BMI (kg/m2) 28.72 26.90 30.53 30.09 28.58 31.61 0.23

Weight status (%)

0.3619 underweight or normal (BMI < 25) 37.97 27.16 48.77 30.95 20.67 41.23

overweight/obesity (BMI >= 25) 62.03 51.23 72.84 69.05 58.77 79.33

1-yr weight change (kg)3 1.63 0.11 3.16 0.72 -1.28 2.71 0.482

1-yr weight gain >= 2.27 kg (5 lbs) (%) 32.85 23.98 41.71 37.81 28.04 47.58 0.472

1-yr weight gain >= 4.54 kg (10 lbs) (%) 22.57 14.41 30.73 24.48 16.27 32.69 0.746

Energy intake (kcal) 2003.75 1823.37 2184.12 1994.62 1760.03 2229.21 0.948

Protein intake (gm) 68.41 61.55 75.28 60.49 53.52 67.46 0.09

Protein/energy ratio (%) 13.89 12.88 14.89 12.41 11.33 13.49 0.036

Carbohydrates (gm) 258.10 231.91 284.28 265.11 231.53 298.69 0.745

Carbohydrates/energy ratio (%) 52.07 49.69 54.45 53.90 50.93 56.88 0.333

Fat (gm) 72.16 64.67 79.64 71.60 61.05 82.15 0.925

Fat/energy ratio (%) 32.49 30.25 34.72 31.53 29.32 33.74 0.518

Fiber (gm) 13.08 11.34 14.82 12.45 9.80 15.09 0.656

Table 4.5 Comparison of 1-yr weight change and macronutrient intakes in food insecure women by food stamp participation 1- FIS- food insecure, nonrp - women with no household members receiving food stamps in last 12 months, rp- women with at

least one household member receiving food stamps in last 12 months; 2- p-values, from two-tailed t-test for continuous variables,

and from chi-square test for categorical/dummy variables; 3- 1-yr weight change was calculated as self-reported weight minus

self-reported weight 1-yr ago

120

121

CHAPTER 5

DISCUSSION AND CONCLUSIONS

The paradoxical association in food insecurity and obesity has been a public

health concern for about twenty years [44], [45], [46], [47], [48], [49], [50]. However, to

the best of our knowledge, studies in this dissertation, for the first time, addressed the

episodic pattern of food supply and food intake in the population of high vulnerability:

women dealing with both food insecurity and overweight/obesity [51] [97] [98] [99].

Two studies were included in this dissertation. The Ohio study is a prospective and

regional study; it showed a possible monthly cycle of food abundance and food shortage

among a convenience sample of FIS/ovob women in Ohio in three continuous months.

Further analysis in the Ohio study focused on explaining this monthly cycle with Food

Stamp Program (FSP) participation and disordered eating. The second study, the

NHANES study was designed to use the cross-sectional and nationally representative

survey data to explain the association in food insecurity and obesity by examining the

associations of dietary intake and FSP participation with food insecurity and

overweight/obesity in women. Both studies confirmed the association in food insecurity

and overweight/obesity among U.S. women who were aged 22-49 yr, non-Hispanic

white, non-pregnant and non-lactating. The findings from the two studies were integrated

and are discussed as follows.

122

5.1 Discussion

5.1.1 Study Populations and TEIs Comparison

Although both the Ohio study and the NHANES study showed significantly

higher BMI in women living in food insecure households compared to women in food

secure households, the mean BMIs in the Ohio study were higher than those in the

NHANES study (food secure: 30.67 vs.27.44; food insecure: 33.86 vs. 29.22). This may

be explained by the high percentages of low-income women in Ohio study (Table 3.2).

However, when analyzing the BMI in low-income women in the NHANES study, it was

found that the BMI in the Ohio sample was higher than that in the NHANES sample, in

both food secure women (OARDC: 30.67 vs. NHANES: 28.84) and food insecure

women (OARDC: 33.86 vs. NHANES: 28.83). Furthermore, the OARDC respondents

showed a higher prevalence of overweight (62.5%) than the NHANES low-income

women (39.20%). Therefore, the two populations of FIS/ovob women examined in the

Ohio study and in the NHANES study may not be the same. While the NHANES study

was designed to describe nationally representative populations, the Ohio study was to

purposively testify the monthly cycle hypothesis in low-income women by recruiting

such women using a convenience sampling method.

One of the purposes of the NHANES study was to calculate the TEI for FIS/ovob

women in U.S. on average to provide a reference value for the Ohio study. It is true that

the FIS/ovob population from the Ohio study was not a nationally-representative

population as the one from the NHANES study. The reason to compare the TEIs from the

123

two populations was to provide some implications for the potential local impacts in the

sample in Ohio. The comparison results (Figure 3) in Month 1 suggest the group of low-

income FIS/ovob women in Ohio may consume more energy at the beginning of the

month, but less at the end, when compared to the average energy intake in U.S. FIS/ovob

women. Such a TEI pattern may indicate the existence a monthly cycle of food

abundance and food shortage in these women. However, due to the lack of physical

activity measure in Ohio study, women’s energy requirements could not be estimated;

thus one could not conclude that FIS/ovob women in the Ohio sample had energy over-

consumption at the beginning followed by an energy under-consumption at the end in

Month 1. The lack of variation in TEI in Month 2 and Month 3 could be a consequence of

high percentages of holiday interviews in these two months, as later discussed in this

chapter.

5.1.2 Energy Requirements and Over-buying Behaviors

In Ohio study, the list of declined individual food items in FIS/ovob women

included food items from almost every food group. Food items which are more

affordable, like white bread, soda pop, and candy were also included in the list. One

possible explanation to this wide-range monthly decline is that these food insecure

households may have episodic over-buying behaviors at the beginning of the months

when food sources are available after a lack of resources for a period of time. Such kind

of behaviors may lead to subsequent over-eating and weight gain in these women.

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Based on this theory, FIS/ovob women should have enough food to meet their

energy requirements, even at the end of the months. The energy requirements for these

women have been estimated in Ohio study: FIS/ovob women were 35 years old (mean

age) and 163cm tall (mean height); assuming physical activity level as sedentary, the

Estimated Energy Requirements (EERs) for 35 years old women who are 165cm and

have BMI of 24.99 kg/m2 is 1947 kcal [162]. Compared to the mean TEIs in the three

months (Month 1: beginning- 2114.19±937.26, end- 1843.06±872.23; Month 2:

beginning- 2097.35±802.36, end- 2099.06±879.06; Month 3: beginning-

2108.26±1088.73, end- 2112.96±854.1, kcal), more energy than needed was consumed

by FIS/ovob women in Month 2, Month 3, and the beginning of Month 1.

However, the Ohio study is limited by its high percentage of holiday interviews

and large estimation errors resulted from the small sample size. To find out whether the

energy requirement is met in FIS/ovob women in U.S., women’s TEI was also compared

with their EER in the NHANES study, where women were 37 years old and 164 cm tall

on average. Assuming physical activity level as sedentary, the EER for 37 years old

women who are 165cm and have BMI of 24.99 kg/m2 is 1933 kcal [162]. This EER was

lower than the mean TEI of FIS/ovob women in the NHANES study (1944.35±77.90

kcal), but was still within the 95% CI (1896.85, 95% CI: 1843.39, 1950.32, kcal). In

addition, dietary analysis results showed no significant difference in TEI in FIS/ovob

women compared to FS/norm, FS/ovob, FIS/norm women. Thus, on average, FIS/ovob

women in U.S. may have enough food sources to meet their energy requirements.

Accordingly, the monthly decline of food items in every food group in the women sample

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in Ohio might be a result from the episodic over-buying behaviors in FIS/ovob women’s

households. Nevertheless, the dissertation has very little power to prove whether

FIS/ovob women met their energy requirements or not throughout the month. the

NHANES study generated TEI from a single-day 24-hour recall for FIS/ovob women,

while the Ohio study only measured TEI in two days of the month in a small group of

convenience sample; none of the studies associated women’s physical activity levels with

EERs. Therefore, multiple-day 24-hour dietary recalls, a more representative sample with

larger sample size than Ohio study, and valid measures of physical activity levels are

required to address this issue in the future.

5.1.3 Food Insecurity and Dietary Intake Patterns (TEI and Macronutrient Intake)

Lower diet diversity [10], less consumption of fruits/vegetables [11], [12] and

animal-source products [12], and lower nutrient intakes [11], [12], [13], [14] have been

reported in food insecure people compared to food secure people. The Ohio study tried to

find a monthly variation in dietary intake patterns (like the yo-yo dieting) to explain the

high prevalence of obesity in food insecure population. The study observed a monthly

decline in TEI and fat intake in FIS/ovob women in Month 1, but no significant

differences were found in other macronutrient intakes.

The NHANES study could not identify such a monthly variation due to lack of

necessary information; instead, the NHANES study presented a picture of the dietary

intake patterns on average in food insecure women, and particularly, in FIS/ovob women

compared to women who were food secure or who were not overweight/obese. In this

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picture, the TEI in FIS/ovob women constituted a higher portion from carbohydrates, a

lower portion from protein, and a similar portion from fat, compared to FS/norm women

and FS/ovob women; when compared to FIS/norm women, FIS/ovob women took more

energy from protein.

The findings, that food insecure women consumed less protein and fiber than food

secure women, have confirmed results from previous studies [13] [14] as expected. But

the findings of total energy intake (TEI) and fat intake agree only in part with previous

studies. The TEI results in the dissertation are consistent with some of the previous

studies in women using NHANES III [12] and NHANES 1999-2002 [81]. However, in

Canada, a country facing similar paradoxical situation as in U.S., studies have found

lower energy intake in food insecure women receiving emergency food assistance [13],

and in women from the 2004 Canadian Community Health Survey (marginal difference,

p<0.1) [14]. Although the results were inconsistent, none of the studies has reported

higher energy intake in food insecure women compared to food secure women. Thus, the

association in food insecurity and overweight/obesity may not result from a higher daily

energy intake in food insecure women.

If the regular TEI in women does not differ by their food security status, the

macronutrient intakes may have different patterns in food secure women and food

insecure women. As mentioned in Chapter 2, Dietz [47] proposed that food insecure

individuals may over-consume fat and become obese. In addition, Townsend et al. [38]

proposed ―fat, % energy‖ as one of the factors mediating food insecurity and obesity.

However, the overconsumption of fat in food insecure people has not been confirmed.

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One study reported higher fat intake in women in food insecurity with hunger compared

to fully food secure women aged 18-60 yr [81], while another study found that fat intake

was lower in food insecure women who were 31-50 yr [14]. Our study showed no

significant difference in fat intake between food secure women and food insecure

women, a finding similar to some of the previous studies [12] [13].

While no differences were found in fat intake, the NHANES study demonstrated

higher carbohydrates/energy ratio in FIS/ovob women compared to FS/norm women, and

even FS/ovob women. To the best of our knowledge, only one study has reported

significantly higher carbohydrates intake in food insecure women (31-50 yr) in a

bivariate model; the difference became marginal (p < 0.1) after controlling for variables

including income, education, immigrant status, current daily smoking status, and

household size [14]. In addition, it has been proposed that obesity can be a consequence

of excessive consumption of carbohydrates [163] [164]. Moreover, a cost-benefit study in

371 low-income women enrolled in the Expanded Food and Nutrition Education Program

(EFNEP) reported a higher carbohydrate intake related with a monthly saving in family

food expenditure due to the enrollment of EFNEP [165]. Thus, overconsumption of

carbohydrates could be a reason that results in weight gain in food insecure women.

The inconsistency in dietary findings from different studies should be treated

carefully when the results of dietary intake and food insecurity were interpreted. The

inconsistency can resulted from many reasons. Different target populations can be the

first reason. The studies can sample people from different countries, or from different age

ranges, or from participants of particular food assistance programs, or from men and

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women, or just from women. Furthermore, studies may use different instruments to

evaluate the food insecurity/insufficiency level. Other possible explanations include

differences in survey and sampling design, analytical methods and statistical models, and

different dietary assessment methods.

5.1.4 Food Insecurity and Disordered Eating

It is believed that the high psychosocial stress related with food insecurity may

alter the dietary behaviors related and change women’s weight status [8] [42], [52] [166]

[11]. Studies have been done about the eating frequency [167], energy per meal in adult

women [81], and disordered eating [52]. It is suggested the indirect effects of food

insecurity on obesity is mediated by disordered eating in women [52]; the study used a 4-

item scale with questions relevant to binge eating behaviors. In the Ohio Study, the

dissertation tested disordered eating behavior by EDE-Q in FIS/ovob women.

Community norms of EDE-Q for young women in the general population have

been developed [132]. FIS/ovob women in the Ohio study showed higher subscale scores

(Table 3.9a) in Eating Concern, Weight Concern and Shape Concern, and a higher global

score than any of the age group in the population (18–22, 23–27, 28–32, 33–37, and 38–

42 yr); comparatively, the subscale Restraint was within the range of norms [132]. In

addition, the frequency of overeating episodes in Ohio FIS/ovob women (36.11%) was

higher than the norms (< 30% in each age group) [132].

The higher subscale scores of Weight Concern and Shape Concern may be a

result of high BMIs in FIS/ovob women, but since FS/ovob women had a Eating Concern

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score (0.87) within the norms’ range, the higher Eating Concern score in FIS/ovob

women may be caused by the food insecurity. The results of the correlation test (Table

3.10) also proved it. Within subscale Eating Concern, food insecure women in the Ohio

sample showed more severe ―preoccupation with food/eating/calories‖ and ―fear of losing

control over eating‖ than food secure women. However, the former item asked the

frequency in the past 28 days of ―thinking about food, eating or calories made it very

difficult to concentrate on things you are interested in‖, which makes it hard to be

interpreted for food insecure participants, as the preoccupation with food in these women

can be caused by food shortage rather than worrying about eating too much, or can be

caused by both. Therefore, the findings of the subscale Eating Concern in EDE-Q in the

Ohio study could not provide enough evidence to support the mediating effects, but the

high frequency of overeating episodes in FIS/ovob may indicate some potential impacts.

Studies with larger sample size and more non-obese subjects are needed in the future to

address this issue.

5.1.5 The Mediating Effects of Food Stamp Program (FSP) Participation

Another possible explanation for the association in food insecurity and obesity

can be the monthly abundance and shortage cycle related to food stamps benefits. Since

the FSP distributes benefits monthly and is at the beginning of every month, according to

the tested hypothesis, food stamp recipients may at high risk of experiencing such a

monthly cycle of food abundance and food shortage. The Ohio study exhibited that

among food insecure women, food stamp recipients had more food items decreased from

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the beginning to the end of the months in the three months in total than non-recipients

(61.58 vs. 8.22, p<0.01). The results indicate an uneven distribution of household food

stores in food insecure recipients throughout the month, compared to more stable

monthly food allocation in both the food insecure non-recipients and food secure women.

The associations of FSP participation with food insecurity and overweight/obesity

haven’t been cleared yet. Positive associations have been reported by both cross-sectional

and longitudinal studies. Townsend et al. [38] examined 9451 women from the 1994–

1996 Continuing Study of Food Intake of Individuals (CSFII). After controlling for

potential confounders (including food insecurity), they reported women FSP recipients

had a 38% increased odds of being overweight (classified as BMI> 27.3 kg/m2) compared

to non-recipients. In the study in Ohio, significantly higher BMI was observed among

food insecure FSP recipients compared to food insecure non-recipients (38.24 vs. 30.94,

p<0.01), indicating an increased risk of getting obese when participating FSP. Likewise, a

longitudinal study in low-income women reported that a 5-yr participation in FSP was

associated with increased probability of obesity, holding all available confounders; but

women’s food insecurity level was not analyzed in this study [83]. Another longitudinal

study investigated the effects of FSP participation on weight change in a sample of 5303

food insecure women from 1999 to 2001 [53]. Their study found that a full participation

in FSP would result in a weight increase only among ―persistently food insecure‖ women,

while in women who were ―became food secure or food insecure‖, or ―persistently food

secure‖; food insecurity was associated with a weight decrease [53].

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However, contradictory results were found in the NHANES study: no differences

in BMI or 1-yr weight change between food insecure recipients and non-recipients were

observed. Several reasons can explain this inconsistency. 1) The definition of FSP

participation was different in these studies. The NHANES study defined FSP

participation as ―at least one member received food stamps in the last 12 months‖; while

in Ohio study, current FSP participation was directly asked to the respondents. One study

used the amounts of food stamps benefits received during the research time to calculate

FSP participation [53] and another study used both the FSP participation and the amounts

of benefits received in the previous calendar year [83]. It is possible that the indicator

used to identify FSP participation in the NHANES study affected the ability to reveal the

effects of FSP participation on women’s weight status. 2) The simultaneous nature of

cross-sectional study design in the NHANES study. Due to the dynamic nature of FSP

participation and food insecurity status, a cross-sectional study design may not be able to

exactly reflect the variation of weight change in response to the dynamic processes.

Although the CSFII study [38] and the Ohio study10

were cross-sectional design as well,

the former one used a much larger sample that was not restricted to only non-Hispanic

White women, while the latter one selected sample from a population with very BMI. 3)

The 1-yr period in the NHAENS study may not be long enough for researchers to observe

any significant change in weight resulted from FSP participation in food insecure women.

4) The socioeconomic characteristics like age, and ethnic group (non-Hispanic White vs.

10 The Ohio study measured food security status, weight status, and FSP participation at the

beginning of the study.

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multiple ethnic groups) were different in the NHANES study from other studies [53]

[83].

Therefore, it is possible that a full FSP participation can alter the shopping

strategies and dietary behaviors in women suffering long-term food insecurity, and lead

to weight gain over long periods of time. In addition, FSP participation may also lead to a

change in dietary intake patterns [83], such as the lower protein/energy ratio in food

insecure recipients found in the NHANES study.

One argument to the proxy role of FSP participation in obesity-food insecurity

association is the absence of potentially long-term confounders in most of the studies

assessing FSP and obesity, such as poverty history, health history, long-term social

program participation or other long-term resources. These variables can interact with FSP

participation to affect individuals’ weight status [83].

5.1.6 Other Issues

Food Insecurity Levels - The effects of the severity in food insecurity on dietary

intake behaviors and weight status has not been analyzed in this dissertation. The Ohio

study could not separate the effects in food insecurity on overweight/obesity by the

severity levels, because most of the participants had very high BMI: high food secure

(HFS) women – BMI = 27.16, marginal food security (MFS) women – BMI = 34.76, low

food security (LFS) women – BMI = 36.37, very low food security (VLFS) women –

BMI = 35.77. In addition, the comparisons of the monthly variations in TEI and total

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household food stores (data not shown) showed that there were no significant differences

among women of different household food security status.

However, evidence has been found to support the association of less severe food

insecurity and overweight/obesity, since severe food insecurity may restrict food intake

involuntarily and lead to weight loss rather than weight gain [52] [38] [168]. Some

studies have shown significant association in food insecurity and BMI exist only in

households of less severe food insecurity [8] [38]. Townsend et al. [38] reported lower

prevalence of overweight in severely food insecure women compared to mildly and

moderately counterpart; but the sample size in this group was too small (n=11) to support

a statistically significant comparison. Frongillo et al. [52] proposed that food insecurity

would lead to both weight gain and weight loss; while weight gain was mediated by

disordered eating caused by mild food insecurity, weight loss was predominant in

severely food insecure households. In addition, among ―persistently food insecure‖

women, food insecurity was associated with a weight loss while a full participation in the

FSP would result in weight gain in these women [53]. Therefore, when interpreting the

effects of the monthly cycle of food abundance and food shortage mediated by FSP or the

eating order behaviors, one should keep in mind the possible opposite effects of severe

food insecurity on weight status.

The Monthly Stability of Baby Food - The consistent decreasing patterns in

household food stores among FIS/ovob women in the Ohio study are consistent with

previous studies on low-income households: depletion of food supply happened at the

end of the months in these households [169], [170], [171], [172], [97]. However, the

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relatively stable stores of baby food were reported the first time. The stability can be

explained by the Women, Infants and Children (WIC) program participation: women in

the study may run out of food from other sources like food stamps at the end of the

month, but still have enough food items from WIC. In addition, children living in food

insecure households may be protected from hunger by their parents or other adults in

these households.

Holiday Season Interviews – The Ohio study showed that FIS/ovob women had

monthly TEI decrease in Month 1, not in Month 2 or Month 3. This could be explained

by the high percentage of holiday interviews in the other two months. For example, the

sample size of FIS/ovob women interviewed in December were five (12.82%) in Month

1, eleven (36.67%) in Month 2 and seven (25.93%) in Month 3. This could affect the

results of dietary intake: the non-significant monthly variation patterns of TEI in

FIS/ovob women in Month 2 and Month 3 could be partly explained by the high

percentages of holiday interviews, particularly interviews conducted in December. Since

the whole month of December is within the holiday season, dietary intake measured in

December may affect the final results. Data from the 1989–1991 Continuing Survey of

Food Intakes by Individuals (CSFII) found that energy intake significantly dropped from

the first week to the last week in ―infrequent shoppers‖ (households doing grocery

shopping once per month or less) [51]. Therefore, these FIS/ovob women might be

―infrequent shoppers‖ in Month 1, but turned to be frequent shoppers in holiday seasons,

particularly in December.

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Food insecurity and 1-yr Weight Change – It has been suggested that individuals in

food insecurity may be inclined to gain weight [45]. In the NHANES study, though the 1-

yr weight change was positive in food insecure women and in FIS/ovob women as well,

differences of the weight change were not significant until this continuous variable was

categorized by weight gain equal or bigger than specific values (2.27 kg and 4.54 kg

here). Further analysis found that food secure women had a much wider range of weight

change (data not shown) due to the larger sample size in this population compared to

food insecure women in NHANES. In addition, large weight gain and weight loss can be

canceled out when calculating mean values; thus, only comparing the mean values of

weight change may not be able to uncover the more frequent weight gain in vulnerable

populations. The higher percentages of gaining weight equal to or more than 2.27 kg (i.e.

5 lbs) or 4.54 kg (i.e. 10 lbs) in food insecure women confirmed the results from a

previous study using NHANES 1999-2002 [41]. Furthermore, the findings that FIS/ovob

women had a higher percentage of 1-yr weight gain >= 4.54 kg than FS/ovob women,

indicate that FIS/ovob women, may have a different cause to gain weight compared to

FS/ovob women.

The Report Bias of Women’s weight – The NHANES study calculated the 1-yr

weight change by comparing the self-reported current weight with the self-reported

weight 1-yr ago. The quality of the weight change variable should be more accurate when

comparing self-reported weight 1-yr ago with the currently self-reported weight rather

than the measured weight, because respondents have a tendency to underreport their

weight [173]; thus, the reporting bias is systematic errors and can be cancelled out, at

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least partly, by comparing the two self-reported weight (i.e. current weight vs. weight 1-

yr ago). In this study, a 1.5 kg-report bias was found in food secure women and a bias of

1.86 kg was found in food insecure women. These underreporting bias are similar to

those reported by other studies [173], [174].

5.2 Limitations and Future Studies

5.2.1 Limitations

The Ohio study was supported by a seed grant, to explore the possible existence

of the monthly cycle of food abundance and food shortage in FIS/ovob women. Due to

the limited resources of the seed grant, the study results presented in this dissertation are

more exploratory and do not allow confirmative conclusions of any causality or

mediating process. One of the weaknesses in the study is that women’s sample sizes were

not large enough, particularly for those who were food secure or normal weight. Because

the study was designed to compare food stores and food intake in FIS/ovob women with

those in other groups of women: FS/norm, FS/ovob, and FIS/norm, small sample sizes in

these groups would make the comparison results lack of significance. However, the

sample size of FIS/ovob women - the target population in the study - was enough to

demonstrate the monthly TEI and food variations in this group of women. Likewise, the

examination in the NHANES study is also limited by the small numbers when stratifying

food insecure respondents by their FSP participation, or by severity of their household

food insecurity. It is necessary to oversampling these sub-population groups in future

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survey studies is necessary to describe the pathways leading food insecure women to

obesity.

The high percentages of overweight/obese women and food insecure women in

the OARDC sample may affect the study’s generalization ability. Participants in the Ohio

study had a very high mean BMI (> 30), and 70% of them were from food insecure

households. Therefore, the results from the Ohio study may not be applicable to the

general population. Researchers put a lot of effort in recruiting normal weight women and

food secure women, but it was very difficult to recruit these women from a low-income

population; the hard economic status during the study time deteriorated the subject

recruitment. In future studies for comparisons among women of different weight status

and food security status, I would suggest recruit women sample from both low-income

and normal income women.

Another limitation of the Ohio study is that some of the interviews were taken

during holiday seasons. As was mentioned before, assessing dietary intake and household

food stores during holiday seasons would bias the results. However, data from the

household food stores found that FIS/ovob women showed significant decreases in food

stores in three continuous months, even when including data gathered during holidays.

The cross-sectional nature in the NHANES study restricts its ability to capture the

dynamics in food insecurity on the change of dietary intakes and weigh status. Although

the Ohio study measured dietary intake in three continuous months, the study assessed

the food insecurity experience and weight status at the first interview, which in some

sense could also be considered as a cross-sectional study when assessing the associations

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of these two. Another limitation relevant to the cross-sectional study design is that the

dietary intakes measured by the 24-hour recalls may not reflect the exact dietary intake

patterns under food insecurity. In the NHANES study, food security measurements asked

the experience in the past 12 months while the dietary assessment encompassed only one

24-hour recall. Similarly, the Ohio study used a single 24-hour recall at the beginning and

the end of each month to assess dietary intake, and classified food insecurity status based

on the experience in the past month before the initial interview. Therefore, one cannot be

certain the change of dietary intake patterns and dietary behavior occurred when

individuals were really food insecure.

One more consideration is that both the Ohio study and the NHANES study used

dietary intake data from 24-hour dietary recalls. Since it is a self-reported method, under-

reporting of diets has been found and is more common in obese respondents [134].

Consequently, the results may underestimate the true energy intake and fat intake in

FIS/ovob women and hence the effects in food insecurity on weight status.

5.2.2 Future studies

Based on these results and limitations mentioned before, additional research

questions are inspired by this research. In order to capture the monthly variations of

dietary intake more precisely, it would be of great importance for future studies to collect

multiple-day dietary intake data within one time period (i.e. the first or last ten days of

the months in this study), including both weekdays and weekends. If one is interested in

the variations of micronutrient intake, more recall days are needed. In Ohio study,

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significant declines of several micronutrients in FIS/ovob women were observed only in

Month 1. This could be explained by the high day-to-day variation of micronutrient

intakes among free-living individuals, and the high percentage of holiday-season

interviews in Month 2 and 3 as well. Thus, in future studies holiday seasons should be

avoided for interviews or be analyzed separately with enough sample size, depending on

the research purposes.

One of the major findings in this dissertation is the monthly variations of TEI in

FIS/ovob women in Month 1. It has been hypothesized that such kind of variations are

partly caused by binge eating at the beginning of months and restricted eating at the end.

However, this hypothesis could not be testified in the study since no energy requirements

of these women were assessed. Future studies should be more specific in assessing the

physical activity level to assess the energy requirements and further verify the monthly

cycle of TEI as a cause of obesity in these women.

Two possible mediating factors for the obesity-food insecurity association were

discussed in the dissertation: FSP participation and disordered eating. The FSP

participation was associated with higher BMI and more severe monthly food declines in

food insecure women in Ohio study, but not in the NHANES study. One of the reasons

for this consistency could be that the women sub-populations in the two studies may not

be the same. In addition, the severity of household food insecurity has been discussed to

be related to weight change (weight gain or weight loss). Therefore, as part of future

studies, I would suggest examine the effects of FSP participation on weight status and on

the monthly variations by stratifying food insecure women with their BMI and the

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severity of their household food insecurity; enough sample size is needed when doing so.

After stratification, further analysis could use food insecure food-stamps recipients as the

target population, and food insecure non-recipients, food secure recipients and food

secure non-recipients as control groups. The exploratory analysis of disordered eating in

the Ohio study has revealed the potential role of Eating Concern subscale in relating food

insecurity with obesity. However, it is hard to determine whether the eating concern is

due to food shortage or worrying about eating too much, or both. Therefore, future

studies are necessary to address the conceptual process of eating concern in women

experiencing both food insecurity and obesity. Multiplevariate models should be

considered when assessing the mediating effects of both FSP participation and eating

disorders, controlling for other confounders.

Another direction for future studies would be a longitudinal study. In this

dissertation, the Ohio study was designed to examine the variations in dietary intakes and

household food stores in three continuous months. This time period may not be long

enough to demonstrate any significant change in weight, and could not account for

changes in weight, household food insecurity, and FSP participation. With a longitudinal

study (for example, a two-year study), the effects of the monthly food abundance and

food shortage cycle on women’s weight change can be assessed, and it allows to testify

the influence of the periodicity in food insecurity status and FSP participation [53]. In

addition, a longitudinal study with a larger sample size is helpful to investigate the

influence of seasonal factors on food insecurity [4] and dietary intake [140] [137]. A two-

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way ANOVA assessing the effects of beginning/end and specific month(s) and the

interaction between the two can be applied.

5.3 Conclusions

Taken together, this dissertation suggests that long-lasting status in food

insecurity may alter the shopping strategies and dietary behaviors, characterized by a

monthly cycle of food abundance and food shortage in women; carbohydrate intake may

increase, and daily energy intake and fat intake may fluctuate in response to the monthly

cycle and result in gradual weight gain over long periods of time. This process may be

mediated by a full participation in FSP.

From a policy point of view, these findings provide information for policy makers

to make changes in federally funded food assistance programs, such as the Food Stamp

Program, which assigns its benefits on a monthly basis; policy changes to better distribute

food assistance benefits (like food stamps) throughout the month may be helpful to

prevent high prevalence of obesity in food insecure women. In order to avoid the monthly

variation of energy intake and reduce the incidence of potentially episodic overeating

behaviors, nutrition education integrating with community-based intervention programs

and efforts from private sectors like food providers are needed in food insecure

households, emphasizing more even monthly household food source distribution and

more smart grocery shopping strategies.

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APPENDIX A

Family Record Questionnaire

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APPENDIX B

Modified Household Food Security Survey Module (HFSSM)

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168

169

170

171

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APPENDIX C

Shelf Food-Inventory Survey

173

174

175

176

177

178

179

180

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APPENDIX D

Eating Disorders Examination Questionnaire (EDE-Q)

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