Dietary Intake of Children Aged 1 Year To

DIETARY INTAKE OF CHILDREN AGED 1 YEAR TO

5 YEARS AND THEIR ANTHROPOMETRIC

MEASURES IN KWENENG DISTRICT-BOTSWANA

WANANANI B JOROSI-TSHIAMO

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Dissertation Adviser: Dr. Linda C Lewin

Frances Payne Bolton School of Nursing

CASE WESTERN RESERVE UNIVERSITY

August, 2012

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Wananani B Jorosi–Tshiamo

candidate for the Doctor of Philosophy degree*.

(signed) Dr Linda C Lewin Committee Chair

Dr Camille B.Warner

Dr Jaclene Zauszniewski

Dr Hope Barkoukis

Dr Jill F Kilanowski

(date) May 1, 2012

*We also certify that written approval has been obtained for any proprietary material contained therein.

Dedication

This dissertation is dedicated to my loving parents, my mother Kesekile and my late father Jorosi, to my late grandmother Masole, to the memory of my brother Tamuhla, to my siblings Mable, Mbutjili, Boemo and Alphious and to my extended family. You taught me to persevere. Thank you for your love and support.

TABLE OF CONTENTS

DEDICATION...... i

TABLE OF CONTENTS...... ii

LIST OF TABLES...... vi

LIST OF FIGURES...... vii

ACKNOWLEDGEMENTS...... viii

ABSTRACT...... xi

Chapter 1 Introduction and Background...... 1

Purpose of the Study...... 2

Description of the Problem...... 3

Selection of the Theoretical Framework...... 5

Social Cognitive Theory...... 5

Social Ecological Framework...... 8

UNICEF Model...... 11

Historical Background...... 11

Description of Model...... 23

Key Model Parts...... 25

Research Questions...... 31

Significance of the Study...... 31

Chapter 2 Literature Review...... 33

Introduction...... 33

Theoretical Literature...... 34

Empirical Literature...... 35

Nutritional Status of Infants and Children...... 36

Food Consumption and Dietary Intakes of Young Children...... 40

Caregiver’s Role on Children’s Food Consumption………………………….. 45

Anthropometry /Growth Measures...... 47

Household Food Security...... 49

Chapter 3 Methods...... 57

Introduction...... 57

Research Design...... 57

Setting...... 58

Sample...... 60

Measurements...... 63

Recruitment...... 73

Procedures...... 74

Instrument Administration...... 75

Data Management and Cleaning...... 78

Data Analysis...... 79

Protection of Human Subjects...... 84

Chapter 4 Results...... 87

Introduction...... 87

Study Sites...... 87

Caregiver Demographic Data...... 88

Children’s Characteristics...... 94

Summary Description of the NDSR 2011 Software...... 104

iii

Research Question One...... 110

Research Question Two...... 114

Research Question Three...... 115

Research Question Four...... 121

Research Question Five...... 124

Chapter 5 Discussion...... 128

Introduction...... 128

Discussion...... 130

Core/main foods consumed (RQ 1)...... 130

Energy and protein intake (RQ 2)...... 133

Energy and protein intake and anthropometric measures (RQ 3)...... 138

Household food security (RQ 4)...... 140

Household food security and anthropometric measures (RQ 5)...... 143

Implications...... 145

Limitations...... 153

APPENDICES...... 158

Appendix A Case Western Reserve University IRB Approval...... 158

Appendix B. Ministry of Health IRB Approval...... 160

Appendix C Informed Consent –English...... 162

Appendix C (a) Informed Consent –Setswana...... 165

Appendix D. Recruitment Flyer- English……………………………………... 168

Appendix D (a) Recruitment Flyer- Setswana………………………………… 169

Appendix E Caregiver Demographic Data Form English…………………….. 170

Appendix E (a) Caregiver Demographic Data Form-Setswana………………. 174

Appendix F Child Anthropometric Measures and Health Habits Data 178

Form......

Appendix G Permission letter by MacIntyre...... 179

Appendix G (a) Child Food Frequency Questionnaire-English...... 180

Appendix G (b) Child Food Frequency Questionnaire- Setswana...... 190

Appendix H Permission Letter by Melgar-Quinonez...... 201

Appendix H (a) Household Food Security Scale-Latin American & Caribbean 202

English......

Appendix H (b) Household Food Security Scale-Latin American & 207

Caribbean –Setswana......

Appendix I List of Food Items Adjusted for Nutrient Values...... 211

References...... 213

LIST OF TABLES

Table 1 Model Evolution Summary...... 15

Table 2 Study Variables & Analysis Plan...... 83

Table 3 Sample Descriptive Statistics...... 92

Table 4 Children’s Characteristics...... 96

Table 5 Children’s Anthropometrics by Gender...... 98

Table 6 Caregivers’ Household Food Scores...... 103

Table 7 Summary Descriptive Statistics...... 110

Table 8 Core/main Food Items...... 111

Table 9 NDSR 2011 Averaged Food Group/subgroup...... 113

Table 10 Descriptive Data for Interval/ratio variables...... 116

Table 11 Correlation between Intakes and Anthropometric...... 116

Table 12 One-way ANOVA...... 118

Table 13 Multiple Comparisons for Energy Intake...... 120

Table 14 Multiple Comparisons for Protein Intake...... 120

Table 15 Independent Sample t-Test Results...... 124

Table 16 Correlation Matrix –HFSS...... 125

LIST OF FIGURES

Figure 1 Reciprocal Determinism...... 7

Figure 2 Environment Behavior Physiology Health Outcomes-Pathways…. 10

Figure 3 Extended Model of Care- UNICEF...... 28

Figure 4 Weight-for-Length/height...... 99

Figure 5 Weight-for-Age...... 100

Figure 6 Length/height-for-Age...... 101

Figure 7 BMI-for-Age...... 102

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ACKNOWLEDGMENTS

Various individuals have contributed to the development of this dissertation from

the proposal development stage to completion. I wish to acknowledge the warm support

of my academic advisor and chairperson for my dissertation committee, Dr. Linda C.

Lewin, who tirelessly and willingly reviewed my work throughout the stages. Her

guidance and encouragement were always inspiring, especially when I faced difficult

situations, I counted on her support, patience and understanding. I wish to express my

sincere and heartfelt gratitude to you.

I am grateful also, to my committee members for their contribution throughout the process, Dr. J.F Kilanowski, for the constructive criticism and for directing me to the many resources that shaped and strengthened the measurement piece early in my dissertation work. I also extend my thanks to Dr. C. Warner, who constantly expressed the merit of this study and provided ongoing critique from the quantitative perspective.

Dr. J. Zauszniewski, although joining the team at a later stage, her invaluable critique and

editorial contributions were the epitome of my learning experience. To Dr. H. Barkoukis,

who provided gentle consultation and guidance for the search of an appropriate software

program when I was in desperate need for help, I am deeply indebted.

This study was sponsored by the University of Botswana, I extend my special

thanks to them for their funding. The project described was supported by the National

Center for Research Resources and the National Center of Advancing Translational

Sciences, National Institutes of Health, through Grant UL1RR024989. The content is

solely the responsibility of the author and does not necessarily represent the official

views of the NIH. My gratitude also goes to the Bionutrition division of the Dahms

Clinical Research Unit (DCRU) at University Hospitals and Case Western Reserve

viii

University for assisting me with the software program for the nutrient analysis. I

sincerely appreciate Alicia Thomas, Registered Dietitian, Jacquelyn Leach, Dietetic

Technician, and Wanda Rhynes, Nutrition Assistant, who cheerfully tolerated my

presence for over five months at the Bionutrition center.

This work would not have been possible without the caregivers and children who devoted their time to participate in this study. The staff and management of Kweneng

District were very helpful in facilitating the data collection process, I would like to thank them all. I would also like to acknowledge Duduetsang Semele, Maikano Masole and Dr.

S. Maruapula who assisted with translation of my instruments. A special thank you goes

to Gabantshetsi Mogorosi for helping with back- translation of the instruments as well as

serving as my research assistant.

Many colleagues and friends have generously provided support and

encouragement throughout the years. In particular, the encouragement of my classmates,

Dr. N. Silo, Nokuthula Majingo and Relebohile Morojele, Mabedi Kgositau and Dineo

Mulwale and family was ever so present. To Louise and Edward McKinney, your

hospitality surpasses all imagination, I am grateful for knowing you.

In a special way, I wish to express my gratitude to my sister-in-law, Makamu and

my brother, Dr. B. N. Jorosi and their children, who graciously accepted my family and

the challenges of carrying all the responsibilities of maintaining the family united while I

was away for four and half years. Your unwavering support has indeed humbled me. “Ne

mangwana banunguna bangu”

Finally, a special acknowledgement goes to my children, Mbuya, Tiroyaone and

Botshelo who strongly believed that I needed to fulfill my dream. To my adorable

ix grandchildren, Watida, Tlotlo and Luzibo, I know you missed me, it was worth it, let us share the joys of achievement.

Dietary Intake of Children Aged 1Year to 5 Years and Their Anthropometric

Measures in Kweneng District- Botswana

Abstract

WANANANI B JOROSI-TSHIAMO

Background. The nutritional well-being of young children is positively and

negatively affected by the interaction between food intake, health and care. Most

important, inadequate intake of food, energy and nutrients remains highly prevalent in

developing countries. Dietary standards designed to provide guidelines on basic nutrients

for sufficient growth and health that are found in the developed countries do not exist or

are not accessible to many in the third world or developing countries.

Purpose. The major focus of this study was to describe and explore the relationship between the food and beverages consumed by children aged 1 to 5 years and their anthropometric measurements as well as to determine the relationships between the children’s food and beverage consumption, caregiver’s household food security and

children’s anthropometric measures.

Methods. A cross-sectional descriptive -correlational design was used. A

convenience sample including 99 pairs of caregivers and their children was recruited

from six clinical sites. Data collection employed face-to-face interviews and the

participants responded to three instruments. The instruments included the Caregiver

Demographic Data Form, Child Food Frequency Questionnaire and the Household Food

Security Scale. In addition, anthropomorphic measurement including heights and weights

were obtained from children. Data were analyzed by descriptive methods, the Pearson

product moment correlations, independent sample t-tests and one-way ANOVA.

Results. The caregivers were aged 18 to 65 years (M = 33.65, SD = 10.50) and

children’s ages ranged from12-56 months (M = 28.99, SD = 12.9). Five percent had

weight-for-height z-scores at -1, while 16.2% had HAZ that were below the -2 z-score

indicating stunted growth. Thirty-one percent of children were at risk for overweight. The

five core/main food items that were frequently consumed by the children were sorghum,

milk, sugar, tea/coffee and yoghurt. The mean energy intake was 1618.4 kcal/d, (SD =

713.4) and the mean protein intake was 45.9 g/d (SD = 22.1). Nineteen percent of

caregivers were food secure while 28.3% and 20.2% were moderately and severely food

insecure respectively. The independent samples t-test revealed statistically significant

differences among the household food security score means for caregivers with low and

high education, suggesting that caregivers with low education were more likely to be

food insecure than their counterparts with higher education. Children’s total energy and

protein intakes were statistically significant and moderately correlated with height and weight (r (97) =.35 p<.01 and r (97) =.32 p<.01) 2-tailed). The one-way ANOVA results were [F (2, 96) = 9.19, p<.05] (energy) and [F (2, 96) = 6.59, p<.05] (protein). These findings indicated that the average intake of energy and protein differed according to the age groups of children.

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Conclusion. Overall, the findings show that children in the study consumed a limited number of food items that may lead to inadequate intake of nutrients such as vitamins and minerals. In addition, the prevalence of household food insecurity, stunting of growth among children and the BMI suggestive of higher risk for overweight warrant further investigation. Future longitudinal studies should examine the associations between dietary patterns and child health and development to provide evidence needed to improve dietary advice given to parents of young children.

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CHAPTER 1

Introduction and Background

The nutritional well-being of infants and young children is positively and negatively affected by the interaction between food intake, health and care. Most important, inadequate intake of food, energy and nutrients remains highly prevalent in developing countries (Faber & Laubscher 2008; Nandy & Miranda, 2008; Petrou &

Kupek 2010; Schoeman, Hendricks, Hattingh, Benade, Laubscher & Dhansay, 2006).

Scarcity, the rising prices of food and other environmental factors such as weather cycles, instability of food transport have been linked to inadequate consumption of appropriate foods in children (Hendricks, 2010). In particular, lack of essential nutrients in the first two years of life may lead to irreversible damage including reduced height, increased risk of childhood and adult obesity, delayed cognitive development (Hendricks; Mannar,

2006; Willey, Cameron, Norris, Pettifor & Griffs, 2009) and disturbances in psychological functioning (Tomlinson & Landman, 2007). Undernutrition combined with poor dietary intake and infectious diseases form a vicious cycle, with insufficient intake leading to disease and vice versa (Ashworth, 2006; Katona & Katona-Apte, 2008).

Dietary intake determines to a large extent the health status of children (Nicklaus,

Chabanet, Boggio & Issanchou, 2005).

A United Nations International Children’s Fund (UNICEF) report shows that in

2008, an estimated 195 million children under the age of 5 years in developing countries had stunted growth as a result of chronic nutritional insufficiency that begins in the intrauterine period. On the other hand, underweight, an indication of acute nutritional deficiency, was estimated at 129 million of children in the same age group (UNICEF,

2009). In sub-Saharan Africa, overall child malnutrition was estimated at 12% and child mortality related to malnutrition at 4% (Pelletier & Frongillo, 2002). More recently, estimates suggest that stunting, wasting and intrauterine growth retardation accounts for

2.2 million deaths among children younger than 5 years (Victora, de Onis, Hallal,

Blossner & Shrimpton, 2010).

Based on the conceptual model by the UNICEF, there are many underlying and immediate factors associated with undernutrition in young children. These include the environment, household food security, care received by women and children and inadequate dietary intake and health (Engle, Lhotska & Armstrong, 1997; Katona &

Katona-Apte, 2008). Children require enough food with essential nutrients. Failure to get sufficient nutrients will result in malnutrition (Manu & Khetarpaul, 2006). The importance of reducing childhood undernutrition is embraced in the global Millennium

Development Goals (MDGs) with a goal of a 50% reduction of underweight children under -five years of age by 2015 (UN Millennium Development Project, 2005). This has served as an important target that many developing nations are currently working toward achieving.

Purpose of the Study

The study was designed to a) describe and explore the relationship between the food and beverages consumed by children aged 1 to 5 years and their anthropometric measurements and b) determine the relationship between children’s food and beverage consumption, caregiver’s household food security and children’s anthropometric measures.

Description of the Problem

Dietary standards designed to provide guidance on basic nutrients for sufficient growth and good health that exist for the developed countries (Grodner, Long &

Walkingshaw, 2007), do not exist or are not accessible to many in the third world countries. In the developing world, foods that are consumed by many young children are neither adequate nor suitable for the body’s maintenance of resistance against disease as well as physical development (Manu & Khetarpaul, 2006). Food consumption reflects dietary trends that impact the nutritional status of an individual. The consequences of an inadequate food intake in children include poor immunity, delayed growth and development, and increased morbidity and mortality (Martorell, 2010).

In young children, dietary intake is highly dependent on the caregiver who determines the type, quantity and quality of foods the child receives (Kourlaba, Kondaki,

Grammatikaki, Roma-Giannikou & Manis 2009). In their study, Kourlaba and colleagues compared maternal perception with the actual quality of the child’s diet and found that

86% of mothers overestimated the quality of their children’s diet despite the fact that the

diet was poor or required improvement. This has serious implications as the mother’s

perception is an important factor in determining their child’s food intake. In another study

that examined maternal dietary counseling in the first year of life, mothers who received

counseling had an improved diet quality score on the Healthy Eating Index of their

preschool aged children (Vitolo, Rauber, Dal Bo Campagnolo, Feldens & Hoffman,

2010). Clearly, this indicates that dietary counseling for mothers empowers them to make

better choices for selecting healthier foods for their children.

According to the 2001 census, Botswana’s population was about 1, 680, 863 and

children aged 0 -5 years accounted for 11.6% of the entire population (Central Statistics

Office, 2005). The preliminary results of the 2011 population and housing census

indicated that Botswana’s total population has since recorded 2, 038, 228 (Central

Statistics Office, 2011). However, the number of children aged between 0-5 years is yet to be known as the disaggregated census results are still to be published. Botswana endorsed the MDGs and is working toward achieving the goal that addresses eradication of extreme poverty and hunger including a reduction in the proportion of underweight children (UNICEF, WHO & Ministry of Health, 2009). In Botswana, the prevalence of stunting, underweight and wasting among children aged 0-5 years is estimated at 13.7%,

11.3% and 3.9% respectively (Nnyepi, 2006). Specifically, Botswana has also identified the need to promote optimal infant and child feeding as well as the promotion of appropriate diets and lifestyles (Ministry of Health, 2009; Ministry of Health, 2005). A

2008 government report questioned whether there was a relationship between the child nutritional status and regular attendance of health clinics such as the monthly child welfare clinic in Botswana (UNICEF, WHO & Ministry of Health, 2009). Despite the need to address the identified nutritional concerns for children, no known study has examined diet, food and beverage intake in children aged 1-5 years in Botswana. The study provides some descriptive data on core/main foods and beverages consumed by children 1-5 years in Kweneng region. Results from this study may have important implications for guiding nursing practice and health policy.

Selection of the Theoretical Framework

To facilitate establishment of a conceptual linkage for this study, two theories and a conceptual model were reviewed in order to select one that served as the organizing framework for the study. The selected framework provided concepts that were used to characterize dietary intake in children aged 1-5 years in Kweneng.

Social Cognitive Theory.

Originating from psychology as a social learning theory, the social cognitive theory (SCT) posits that human behavior is a result of three dynamic and reciprocal interactions among personal factors, behavior and the environment mediated by cognitive processes (Clark & Houle, 2009). As proposed by Bandura, (1977), the SCT asserts that people do not simply react to the external environment, but select and alter the stimuli that affect them. Bandura further argues that human behavior is reciprocal in nature. That is, human behavior and environmental determinants are intertwined. Both people and their environment have major influences on each other. Hence, in searching for explanations of human behavior, it is appropriate to examine human responsiveness to the environment. Although interdependent in many ways, people and environment exert varying degrees of influence on each other at different times (Bandura, 1977; Bandura,

1986).

The SCT also put forward that behavior is largely regulated through cognitive processes. According to the SCT, behavior emerges from one’s thoughts in response to the environmental stimuli (Bandura, 1977). Bandura further argues that an individual’s behavior is regulated by one’s cognitive ability to process and integrate previous conditions and outcomes that result in patterns of behavior. Of importance in the theory

are roles played by the symbolic, vicarious and self-regulating processes in influencing behavior. These are associated with learning that occurs through observing other people or events (Clark & Houle, 2009). Vicarious learning occurs when individuals replicate or model selected behaviors of others. In specific terms, the SCT explains modeling as a key method that is influential in children’s development of skills that translate into their own patterns of behavior. Children’s behaviors are viewed as resulting from observing modeled examples as well as knowing the consequences of behavior (Bandura). Young children’s imitation of what they observe is influenced by their adult models. When an adult model responds to the child’s imitative behavior by showing appreciation for the child, then the child’s behavior is reinforced. Social cognitive theory suggests that behaviors such as diet intake can be learned by observing the behavior of others. These can be reinforced or discouraged on the basis of observed outcomes. Hence, children may imitate dietary intake patterns of their adult caregivers.

According to the SCT, an individual’s behavior is uniquely determined by personal factors, environmental factors and behavior. Any of the factors in the SCT may exert varied levels of influence at different times. Some sources of influence may be stronger than others and may not occur at the same time. The strong emphasis on one’s cognitions suggests that thought is involved in performance of behavior. Cognitive control has been linked to behavioral self-management of dietary adherence (Bandura,

1986; Clark & Houle, 2009). The caregiver’s personal and environmental factors such as those in the psychosocial and socioeconomic areas serve as key determinants that are interrelated with behavior such as children’s dietary intakes.

The schematic diagram on Figure 1 depicts the interlocking determinants that reflect the reciprocal nature of behavior, other personal factors and environmental factors.

SCT emphasizes the triadic nature in reciprocal determinism. Reciprocal determinism is the central construct that explains the interaction and feedback between the person and the environment (Bandura, 1986; Owen, Splett & Owen, 1999). The double ended arrows

depict the bi-directional influence in- person behavior interaction that involves one’s

thoughts, emotions, biological properties and one’s actions (Bandura, 1977). For

instance, a person’s expectations, beliefs, goals and intentions may explain the way the

individual might respond to a given situation. Another bi-directional interaction occurs

between the environment and personal characteristics (Bandura). In this instance, an

individual’s expectations, beliefs and cognitive abilities are developed and modified by

social influences and physical factors found in the environment. The last interaction

occurs between behavior and the environment. Bandura proposed that people influence

the environment and they are also influenced by the environment to which they are

exposed. These three factors can highly influence each other.

Figure1: Reciprocal Determinism

Person/Personal Characteristics

Behavior Environmental

Adapted from Bandura (1977)

The SCT proposes an extremely complex causal model for behavior. Such a

theory would be relevant in research that aims at designing a health behavioral change

program (Glanz, Rimer & Lewis, 2002). The SCT may not be appropriate in guiding a

descriptive study such as this one that follows a cross-sectional design. The aim of the proposed study is to describe usual dietary intake of children as reported by the caregivers of children aged 1year to 5 years and to document their weight and height measurements. Because SCT recognizes cognitive abilities as important in behavioral responses, it may be more useful in research that focuses on behavioral change and intervention programs.

Social Ecological Framework.

The inadequate explanatory power of the cognitive model for behavior change led to the development of the social ecological framework (SEF) (Schneider & Stokols,

2009). The SEF emerged as a result of the realization that many public health problems were influenced by various factors that are found at different levels of analysis of the socio-environmental system. Originating in the United States, SEF was based on a multilevel, ecological systems’ approach to address diverse health issues such as tobacco use, poor nutrition, physical inactivity and alcohol consumption (Schneider & Stokols).

The SEF places more emphasis on the role played by environmental factors on the individual’s or group’s health and health-related behavior. The model suggests that behavior is influenced by a combination of factors found at the intrapersonal, interpersonal, organizational, community and public policy levels (Sallis, Owen & Fisher,

2008). Hence, interventions for behavior change have to address all aspects and levels of influence.

The model focused also on the impact of both the natural and the constructed

environments on health behavior. The constructed environment includes the physical features of a given environment such as residential, workplace, educational, health care and recreational facilities (Stokols, Grzywacz, McMahan & Phillips, 2003). Further, the model depicted processes in health and illness thereby embracing the social and ecological factors that influence human health (Schneider & Stokols, 2009). In a way, the

SEF addressed influences that result from the immediate as well as the distant environment. This indicated the dynamic and interdependence of the individual and the environment. The social-ecological approach examines the interaction between the environmental resources that are available and the health habits or life-style of people in a given area (Stokols, 1992). Rather than focusing exclusively on one category of the health-determining factors, the SEF emphasized the joint influence of behavior and environment on wellness at the individual level and collectively. The concept of wellness promotion places more emphasis on the role of individuals, groups and organizations as actively shaping health practices to promote their wellbeing (Stokols, 2000). Giving more attention to wellness and health promotion, the SEF stressed those health-promotive interventions that should be well coordinated to target individual and groups at various levels (Shneider & Stokols, 2009).

As a result of its ecological orientation, the framework has been considered helpful in identifying multiple environmental and behavioral factors that are interrelated and have an impact on health outcomes. It assumes that the health status and the well being of human beings is influenced by environmental factors as well as other personal

attributes related to genetic heritage, psychological make-up and behavior (Shneider &

Stokols, 2009).

The SEF’s basic assumptions are reflected on the pathway model below on Figure

2. The environment represents a key influence on the individual’s behavior. For instance, increasing the number of accessible physical activity programs within reach would result in regular exercise being part of an individual’s lifestyle. The change in behavior may lead to physiological changes such as improved cardiovascular function. The physiological effects from a change in lifestyle will eventually lead to better health outcomes. The effects of some environmental factors on health outcomes may be direct whereas other influences on health status may be mediated or moderated by behavioral factors. Behavior too, may have direct effects on health outcome while some behavioral impacts on health may be mediatory in nature (Schneider & Stokols, 2009). However, the model only reflects arrows showing a linear association that does not indicate the point at which mediation and moderation effects may occur. Also, linearity appears to reflect causality.

Figure 2: Environment Behavior Physiology Health Outcomes-Pathways

Health Environmen t Behavior Physiology Outcomes

Adapted: from: Schneider & Stokols, (2009) pg.97

Although the environment, behavior, physiology and health outcomes are

identified as the basic elements for the SEF, specifying their influence on personal and

specific indicators of well-being may be not easy. Hence, the SEF is limited in providing

conceptual, methodological and translational precision required for empirical testing

(Shneider & Stokols, 2009) of specific measures such as food consumption and dietary intake of children explored using a cross-sectional approach.

UNICEF Model-Extended Model of Care

The United Nations International Children’s Emergency Fund (UNICEF)

Extended Model of Care provided the conceptual framework for the study. The model is an extension of the conceptual framework formerly titled the Causes of Malnutrition and

Death (UNICEF, 1990).

Historical Background.

The Causes of Malnutrition and Death model emerged following the Convention on the Rights of the Child that was adopted by the United Nations General Assembly in

1989. This convention unified several rights that related to the development, survival and involvement of children. These included for instance, the right to combat disease and malnutrition, the right to access pre-natal and post-natal care for mothers and the right to maternity leave with pay or comparable social benefits. Part of the declaration required countries to ensure that measures to reduce infant and child mortality and malnutrition were put in place (UNICEF, 1990). As UNICEF has a mandate to support elimination of malnutrition across nations of the world, the organization was then responsible for developing strategies for improving nutrition of mothers and children in the developing world. Informed by its extensive work on nutrition programs across the world, UNICEF identified good nutrition as a basic human right (UNICEF, 1990). Improving nutrition became an important strategy aimed at reducing and eventually eliminating malnutrition in developing countries. The strategy focused on nutrition of mothers and children to improve child survival and development.

The Causes of Malnutrition and Death Model evolved to guide the theoretical

exploration of factors that were associated with the eminent problem of malnutrition

primarily in young children. The model identified three basic factors related to malnutrition. These include insufficient household food security, inadequate maternal and child care; and insufficient health services and unhealthy environment (UNICEF, 1990).

By 1992, the International Conference on Nutrition (ICN) held in Rome, urged governments to develop nutrition policies that targeted the reduction of hunger before the millennium. This conference contributed significantly to provide guidance to the improvement of nutrition in countries that signed the World Declaration on Nutrition

(ICN, 1992). By endorsing the treaty, governments were to establish priority areas for nutrition in their respective countries. Furthermore, this conference significantly added to the understanding of the concept of care as it relates to nutrition by developing themes that later formed the basis of caregiving behaviors. The concept of care as applied to nutrition broadly relates to the provision of time, attention and support to meet physical, mental and social needs of the growing child by household members (Engle, Menon &

Haddad, 1997).

According to UNICEF, improvement in food security and health services did not result in lower rates of malnutrition in some countries leading to a further examination of the model. By 1997, the model was modified and adapted. Consequently, it was renamed the UNICEF’s Conceptual Framework for Nutrition (Engle, Lhotska & Armstrong,

1997). At this stage, household practices were added to the concept of care for growth and development of young children. Thus, the Care for Nutrition model now identifies six kinds of care practices that affect the nutrition of women and children. Although the

three basic components of the model of the Causes of Malnutrition and Death were retained, the names of factors changed to exclude the prefix that implied a deficit model of inadequacy. The major change in the model underscores the role of care.

Additional conceptual refinement occurred during the same year in 1997, and the

UNICEF’s Conceptual Framework: Care for Nutrition was adapted to incorporate care to the caregiver and renamed the Extended UNICEF Model of Care (Engle, Lhotska &

Armstrong, 1997; Engle, Menon & Haddad, 1997). More importantly, six categories of resources required by caregivers were described. The six categories pertained to the human, economic and organizational resources that were specific components of the original model of the Causes of Malnutrition and Death (UNICEF, 1990). Care practices were explicitly referred as caregiving behaviors in this model to reflect the fundamental role played by caregivers in good child nutrition and overall health. The model maintained that the interrelationships among household food security, health services and healthy environment, and caregiving behaviors influence adequate dietary intake and health which directly impact child survival, growth and development. The Extended

UNICEF Model of Care also underscored that the child’s behavior has an important influence on the care the child receives (Engle, Menon & Haddad, 1997). The Extended

UNICEF Model of Care further incorporated six caregiving behaviors and six major categories of resources for care.

Other adaptations associated with the model occurred in 2004 when the World

Health Organization reviewed how the caregiver and child interaction influenced the survival and healthy development of young children (WHO, 2004). The term caregiver

was defined and contextualized. A brief summary of the evolution of the model is provided (Table 1).

Table: 1 Model Evolution Summary

Date Source Inception Key Concepts Comments & Changes 1978 Interna Right to be free Freedom from hunger Elimination of tional from hunger and and malnutrition malnutrition as Covenant on malnutrition WHO’s declaration Economic , on Health for all by Social and the year 2000. Cultural Rights 1982 UNICEF A policy strategy Growth monitoring, Global measures for improved oral rehydration emphasized in nutrition of therapy, breast- improving child mothers and feeding, survival. children. immunization, food supplementation, female education (GOBI-FF) strategy. 1987 Naivasha Reduction of the Child survival and Global measures (Kenya- prevalence of development, the emphasized in UNICEF staff malnutrition. situation of women. improving child meeting ) survival. 1989 The Combined all Combating disease, Concerns about Convention on rights related to malnutrition and infant and child the Rights of survival, provision of adequate mortality the Child. development, nutritious food. protection and participation of children. 1990 The UNICEF To control Assessment, analysis Targeting the Executive protein-energy and action. nutrition problem Board malnutrition and in the context of deficiency developing disorders. countries. 1990 The UNICEF To control A conceptual Indicators for Executive protein-energy framework for the monitoring, Board malnutrition and analysis of the causes underweight, deficiency of malnutrition in a wasting and disorders specific context stunting defined. 1992 International Governments Each participating Special attention Conference on reaffirmed their country required to given to the Nutrition(ICN- commitment to develop its own vulnerable Rome) ensure nutrition to national nutrition members of all. plans. society-women and children. 1997 Care for Third underlying Emphasis on care Care practices may nutrition determinant –care. practices and their determine the outlined in the Changes in relevance to nutrition. course of life of Care Initiative terminology a) children from birth 15

Renaming of causes to to 3 years. UNICEF of determinants b) Practices are Causes of dietary intake to dependent on Malnutrition adequate dietary caregivers. and Death to intake c) Terminology used UNICEF’s Manifestations to within the model Conceptual outcome becomes more Framework for general. Nutrition Care initiative Aims at providing Focuses on care Targets critical knowledge and practices directly or period of growth. skills on indirectly affecting Care initiative must assessing, nutrition. be adapted to the analyzing and Improvement of cultural context. taking action to nutrition. Identifies change care food, health and care practices. as all necessary for good nutrition. 1997 UNICEF’s Care for nutrition Identifies 6 aspects of Conceptual linked practices of care necessary for Framework : caregivers growth and Care for development of young Nutrition children: (a). Care for women (b) Breastfeeding and feeding practices (c) Psycho-social care (d) Food preparation (e) Hygiene practices (f) Home health practices 1997 Care and Links care to nutrition resources needed concepts and for caregivers. measurement Engle, Menon & Haddad (1997) 1997 Extended Identifies care 6 major categories of Main focus on model of care practices as resources needed by assessing ability caregiving caregivers are: and capacity of the behaviors and education knowledge caregiver and outlines 6 care /beliefs family to provide resources. health/nutritional care. status, mental health/lack of stress autonomy, control of resources, reasonable workloads and social support

1998 UNICEF(1998) Determinants of The state of world’s The triple A childhood children report approach to undernutrition emphasized the addressing multiple factors nutrition problems associated child under nutrition including micronutrient deficiencies 1999 Engle, Menon Concept of care Definition of care as UNICEF model & Haddad defined and applied to nutrition- used to assess (1999) measurement practices of caregivers capacity of explored. that affect nutrient caregiver to intake, health, provide care. cognitive and psychosocial development of children (Engle, Menon & Haddad (1999). 2004 WHO Caregiver was Caregiver for young defined as a children person who looks after infants and young children (WHO 2004).

UNICEF Model Concepts

Since the declaration of the freedom from hunger and malnutrition as a basic human right by the United Nations in 1948, (UNICEF, 1990), adequate household and national food supply have become important means of achieving this human right.

UNICEF’s mandate includes addressing hunger and improving nutrition of mothers and children in all countries of the world. There are several concepts that have evolved within the Extended Model of Care – UNICEF.

Nutrition. Based on a review of reports that examined evidence on nutrition strategies, UNICEF proposed a goal in nutrition that would address the global problem of malnutrition in the developing world (UNICEF, 1990). Food is the means of ensuring the

individual’s nourishment. Different types of foods are used primarily to provide nutrients

and prevent both hunger and malnutrition. The concept of nutrition focuses on digestion

and absorption of nutrients. A regular intake of the basic nutrients is required to support

the body’s structural and physiological functions (Dudek, 2006). In the absence of

malabsorption syndrome, physiologic malformation or enzymatic deficiencies, inability

to get the required amount and quality of nutrients brings about a state of malnutrition

(Grodner, Long & Walkingshaw, 2007).

Malnutrition. A problem characterized by inadequate intake of certain basic

nutrients, malnutrition is complex as it is associated with individual and societal factors.

In the model, malnutrition represents the ultimate problem associated with multiple

factors that occur in society such as poverty and unemployment (Oldewage-Theron,

Dicks & Napier, 2006; UNICEF, 1990). The complexities of malnutrition include basic,

underlying and immediate causes. Some of the basic causes of malnutrition are, food prices (Ministry of Health, 2005) and the high rate of migration from rural to peri-urban areas where there is high unemployment that influences food security (Oldewage-Theron, et.al). The immediate causes are inadequate dietary intake and infectious diseases

(UNICEF, 1990). Underlying determinants of malnutrition identified in the model include insufficient household food security, inadequate maternal and child care, insufficient health services, and unhealthy environment.

Insufficient Household Food Security. The concept of household food security was present in the model from its inception. It relates to the adequacy and access or

capacity to acquire safe food sources of sufficient quantity and quality. Improving

nutrition requires ensuring that various types or groups of food such as fruits, vegetables,

grains, fish and meats, are available and affordable to all people. In the model,

insufficient household food security is an underlying determinant of malnutrition that is

linked directly to inadequate dietary intake (UNICEF, 1990). Food security is defined in

the United States as access by all people at all times to enough food for active, healthy

life that includes ready availability of nutritionally adequate and safe food and ability to

acquire acceptable food in socially acceptable ways (Radimer, 2002).

Insufficient Health Services and Unhealthy Environment. An original concept in the model, insufficient health services and unhealthy environment are concerned about the condition of the physical environment and the health care delivery system. In the context of developing countries, insufficient health services may relate to lack of facilities, providers or inaccessible care (Schoeman, Hendricks, Hattingh, Benade,

Laubscher & Dhansay, 2006). Unhealthy environment may be a result of unsanitary conditions as indicated by poor refuse and sewage disposal and poor water supply and quality. Classified as underlying causes of malnutrition, insufficient health services and unhealthy environment are associated with occurrence of infectious diseases often leading to malnutrition (UNICEF, 1990).

Inadequate Dietary Intake. Pertaining to both amount and quality of regular food intake, dietary inadequacy is associated with insufficient supply of food and essential nutrients. The concept is considered a significant immediate cause of malnutrition in children. In young children, inadequate intake may be linked to many factors including low food security (Radimer, 2002; Schoeman, et al, 2006), poverty (Petrou & Kupek,

2010), and malabsorption associated with disease processes (Katona & Katona-Apte,

2008; UNICEF, 1990).

Diseases. Commonly occurring infectious diseases are associated with problems that result with impaired food intake and utilization of nutrients (Nandy & Miranda,

2008). Young children are susceptible to many childhood illnesses. The interaction between nutrition and infectious diseases is well documented (Calder & Jackson, 2000;

Katona & Katona-Apte, 2008). The presence of an infection in a person’s body may result in impaired nutrient absorption as there is mucosal injury and alteration in the gastrointestinal tract function. These, in turn, cause reduced dietary intake. In addition, fever increases both energy and micronutrient demands, thus making the vicious cycle of disease and malnutrition to continue (Calder & Jackson; Shetty, 2002). Micronutrient deficiencies of vitamin A and zinc are associated with increased morbidity and mortality in children (Sudfeld, Navar & Halsey, 2010). Access to health services such as immunizations and essential drugs, vitamin A supplementation and environmental health conditions including safe water, sanitation and housing have an influence on the occurrence of disease and nutrition.

Care Practices. In addition to the basic model concepts, the UNICEF’s

Conceptual Framework: Care for Nutrition explicitly identifies six care practices under the umbrella concept of care for women and children, an element of the original model.

The focus on care as the core determinant of malnutrition has led to the delineation of specific household activities that impact the course of life for young children. The six specific practices represent activities performed at the household level that impact the child’s survival, growth and development (Engle, Lhotska & Armstrong, 1997). The following is a brief description of each of the six care practices:

Care for Women/ Maternal and Child Care. This concept highlights the importance of women’s overall health, welfare and social responsibility toward young

children’s nutrition, growth and development (Engle, Lhotska & Armstrong, 1997). In

many societies, mothers or female caregivers are the critical human resource for the

nurturance of young children. Therefore, the woman’s condition directly affects that of

the young child.

The concept of maternal and child care emphases the importance of the

economically vulnerable and socially disadvantaged groups in society, such as women

and children. In many parts of the developing world, women and children have limited

influence over resource allocation both at the household level and higher levels of

society. In addition, women’s involvement on labor intensive agricultural work limits

their time to effectively provide care to children (UNICEF & the World Bank, 2002). To

ensure adequate nutrition for women and children, services at the family, community and

national levels need to be well coordinated. For instance, concerns about environmental

sanitation like safe drinking water may be resolved by having the three levels working together to address the problem. According to the original model, the maternal and child care element includes conditions such as women’s time available for child care and services that are important to ensure better nutritional outcomes for both women and

children (UNICEF, 1990). Inadequate maternal and child care may relate to lack of

resources and services for women involved in rearing children, a primary objective for

the model. Hence, maternal child health care is interrelated with basic, underlying and

immediate causes of malnutrition within the model (UNICEF).

Breastfeeding/Feeding Practices. Breastfeeding and feeding practices as outlined

under the care practices directly influencing the nutritional status of infants and young

children. The practices include measures of breastfeeding, complementary feeding, and

adaptation to the family diet to meet the needs of the growing child (Engle, Lhotska &

Armstrong, 1997). As such, most care practices are under the control of the caregivers who are predominantly women. For this reason, caregivers’ breastfeeding/feeding practices have proximate impact on the nutrition and health of infants and young children.

Psycho-social Care. The concept relates to the caregiver responsiveness to children’s behavior. The caregivers’ attention, affection and involvement are associated with better child intake and optimal nutritional status (Engle, Lhotska & Armstrong,

1997).

Food Preparation. Involving hygiene, processing, and storage and cooking, food preparation directly affects child nutrition. Preparation and storage of food for infants and young children requires special attention to prevent contamination and occurrence of disease.

Hygiene Practices. The model identifies basic hygiene practices that include hand washing, making water safe, keeping the child clean, and as well as the maintenance of a sanitary environment (Engle, Lhotska & Armstrong, 1997). The caregiver behaviors are important in meeting the required sanitary standard for good health.

Home Health Practices. These are described as practices that prevent illnesses and promote health in children. Practices cover home management of illness, utilization

of health services and making the home environment safer (Engle, Lhotska & Armstrong,

1997).

Caregiver. In the model, UNICEF’s Conceptual Framework: Care for Nutrition, the term caregiver refers to individuals primarily responsible for providing care to women and children (Engle, Lhotska & Armstrong, 1997). The term caregiver is further redefined as the person who looks after infants and young children (WHO, 2004).

Typically, women make up the majority of caregivers who are responsible for food intake and feeding of young children. The caregiver may include any of the biological parents, an adult sibling, grandparent or a community member hired to provide care for the child

(Engle, Lhotska & Armstrong; WHO).

Growth and Development. The Conceptual Framework for Nutrition (Engle,

Lhotska & Armstrong, 1997) identifies child survival, growth and development as the ultimate outcome for the nutrition framework. In order to assess and monitor care practices, indices of child growth and development have to be used to evaluate if the outcome is being met.

Description of the Model

The Extended Model of Care -UNICEF recognizes that the underlying determinants of malnutrition require a multifaceted approach in addressing them. The model of Care- UNICEF was developed as a strategy to address the complex multilevel nature of nutrition of children and women in developing countries (Engle, Lhotska &

Armstrong, 1997). The model serves as a guide that may help to identify concurrent factors associated with the nutritional concerns in a particular country. To this end, the model emphasizes assessment of circumstances of children and women as related to

nutritional needs. The model assumes factors that are relational and interactive and are

linked to the macro and micro- systems that control available resources. Further, it is

assumed that there is a distinction between physiological concept of nutrition and the

concept that broadly covers the economic, social, dietary intake, and cultural factors

associated with the nutrition problem (Engle, Lhotska & Armstrong, 1997; Engle, Menon

& Haddad, 1997). The model recognizes that the context in which a nutrition problem

occurs changes over time, thus requiring continuous monitoring and revision of

interventions, for this reason, the model embraces the pragmatic triple A approach for assessment, analysis and action in any given situation. In this model, various factors

linked to the social, political and economic aspects are considered to have a major impact on the lives of caregivers and children as they are associated with the nutrition outcomes in young children.

Relevance of Model to the Study. The UNICEF model has been selected because it depicts interactions and relationships of young children and their caregivers (World

Health Organization 2004). According to this model, the quality of nutrition, care and caregiving behaviors highly influence the overall intake of food and have a significant role in the survival and development of young children (Brown, Creed-Kanashiro &

Dewey, 1995).

An improved understanding of the factors involved in the nutritional wellbeing of

children requires concrete measures for assessing food consumption and dietary intake of

infants and young children and their growth. In young children, inadequate intake is often manifested by weight loss, growth faltering and lowered immunity (Katona & Katona-

Apte, 2008; UNICEF, 1998). Accurate measurement of weight and height in relation to

growth may provide indicators of the overall nutritional status of young children (Joosten

& Hulst, 2011).

The UNICEF model recognizes the important role of caregivers in influencing the child outcomes as well as acknowledging the young children’s responses to the caregiving process (WHO, 2004). The model recognizes that caregivers’ and children’s interactions are dynamic and reciprocal thus affecting each other. While the model does not determine causality, it is extremely valuable because it provides descriptive information on practices within the family, not bound by home but rather the context that impacts directly on the young child’s health outcomes (Brown, Creed-Kanashiro &

Dewey, 1995). Using the triple A approach of assessment, analysis and action, (Engle,

Lhotska & Armstrong, 1997), underlying and immediate determinants that impact food consumption, adequate dietary intake and health of children can be identified and appropriate interventions described.

Key Parts of the Model.

The model has its central focus on the caregiver behaviors that influence the quality of environment for young children’s nutrient intake, health and survival. Six specific care practices identified earlier represent common activities performed by the caregivers. The practices include, a) breast feeding and feeding of young children; b) psycho-social and cognitive stimulation, c) food preparation and storage practices; d) hygiene practices; e) home health practices; and f) care for women are the caregivers’ behaviors which are used in meeting nutritional needs for a child’s growth and development (Engle, Lhotska & Armstrong, 1997). All caregiver behaviors found in the center of figure 3, depict care practices that form the major constructs of the model. The

model assumes that it is not only the caregiver practices that play a role but also the

manner in which the practices are performed that is critical to children’s growth and

development. Since caregivers need other important resources, there are two elements to

the caregiving behaviors: health care and healthy environment. These are proximal to the

caregiving behaviors reflecting the strong link between health care, healthy environment and the caregiver’s behaviors. Health care and healthy environment are underlying determinants of health that interact with adequate dietary intake to influence the child’s growth (Engle, Menon & Haddad, 1999). Even though the care practices may be performed well, if the health care environment is not conducive, children may not reach their optimum levels of growth and development.

Household food security is superimposed on the caregiver behaviors. Household food security is a key factor in caregivers’ provision of food because of the immediate influence on the quality and quantity of nutrients that can be afforded to the young child.

Accessibility of household food is closely linked to food consumption and adequate dietary intake. In the model, there is a direct linkage between the household food security element and adequate dietary intake. The two are also linked commonly through caregiving behaviors. The common linkage helps to explain the role of caregiver behavior in ensuring that household food is translated into adequate nutrient intake by children.

The essential elements of health care and healthy environment are linked directly

to health status. Caregiving behaviors have a shared interaction with the health care and

healthy environment to influence overall health. The bi-directional relationship between

caregiving behaviors and child survival, growth and development represent the overall

interchange between the caregiver and child interaction. Further, adequate nutrient intake

and health are interrelated in the impact on the overall health of children. The linkages

among household food security and nutrient intake represent the major focus the study.

The main elements interact with each other in an indirect way thus reflecting the complex nature of various determinants of child growth, development and overall health outcome.

Other elements in the model including food /economic resources and health resources are basic factors that influence availability and control of food at community and national levels. They are community and national components that contribute to child survival and nutritional health. If food production and health resources are impaired, then these macro-system elements can directly affect families, but if not, then elements at family level become the primary focus for understanding influences on young children’s nutrition and health (Engle, Lhotska & Armstrong, 1997). The focus of the study is to examine the relationship of dietary intake and the growth of young children. Thus, the influence of food production and resources is beyond the scope of the current study.

Figure 3: The Extended Model of Care- UNICEF

Child survival/ Growth Development Adequateintake Health nutrient

Health care and Caregiving behaviors (e.g. feeding hygiene Household food security healthy environment behaviors, food prep & storage)

Availability of resources Food/economic Health resources Caregiving resources (e.g. knowledge/beliefs Resources (values of child care) health nutritional status, (water supply /food/production control of resources/autonomy) sanitation, health care availability)

Cultural Political, Social Context (urban, rural)

Adapted from, WHO, 2004

For the study, concepts from the Extended Model of Care-UNICEF were used.

The aims of the study were to: (1) describe and explore the relationship between food and beverage consumed by children aged between 1-5 years and their anthropometric measurements, and (2) determine the household food security among caregivers of these children. Examining these factors will improve the understanding of dietary intake and related problems of young children in Botswana.

On the model, adequate nutrient intake found at the immediate level of causes represents one important concept that interacts with the health of the child to contribute to

overall child survival and development (UNICEF, 1990). Adequacy of nutrients is related to the quantity of food consumed, quality of the overall diet with respect to both macronutrients and micronutrients as well as the frequency with which food is consumed

(Reis, 2012).The bidirectional arrows indicate that adequate nutrient intake and the health of a child are interrelated. For instance, sufficient nutrient intake may influence maintainance of good health of children. Similarly, health influences how the child may be able to consume and absorb nutrients in order to maintain an adequate intake

(Weisstaub, Aray, Hill & Uauy, 2008). In this study, the dietary intake variable will be operationalized by the consumption of food and beverages using the child food frequency questionnaire.

At the undelying level of factors, household food security interacts with caregiving behaviors and health care and healthy environment. The three factors are overlapping implying that they are related to each other in complex ways (UNICEF

&The World Bank, 2002). For instance, household food security, feeding behaviors or healthy environment may impact nutrient intake. If there is insufficient food at the household level both quantity and quality of food will be compromised impacting adequate nutrient intake. Likewise, poor feeding may result with insufficient nutrient intake and access to health care both for primary care to monitor health and acute care during illness may also affect nutrient sufficiency. Because caregivers are responsible for the quality and quantity of food and beverages consumed by young children, (Pelletier &

Frongillo, 2002; UNICEF; UNICEF &The World Bank) caregivers’ household food security indirectly influences children’s dietary intake. Thus, the study variables are linked to the immediate and underlying factors of the UNICEF’s model of care. The

association between the factors is represented by arrows that imply the direction of

influence in the model and impacts children’s nutritional status, the ultimate outcome of

the model. Adequate nutrient intake and household food security (indicated with shading in Fig 3) are linked to the study variables.

Major problems caused by undernutrition are potentially preventable through

timely nutrition interventions that ensure appropriate macro and micronutrients intake by

infants and young children (UNICEF, 1998; Wardlaw & Smith, 2009). The selection of

the study’s research questions was influenced by the need to explore the explicit factors

found in the model such as adequate intake and household food security as well as to

understand influences beyond the obvious determinants. Thus, in addition to questions

that explored factors directly linked to some parts on the UNICEF’s conceptual model,

the study explored the relationship between household food security and children’s

anthropometry because height-for- age is an indicator for nutritional adequacy

(Weisstaub, Aray, Hill & Uauy, 2008). In the developed world, household food insecurity

has been negatively associated with children’s body mass indexes (Matheson, Varady,

Varandy & Killen, 2000). Moreover, household food security is directly related to

children’s home food consumption which may impact anthropometric indexes. This has

prompted the investigator to examine the relationship between household food security

and anthropometric measures of children.

Research Questions

Specifically, the study addressed the following research questions:

1. What are the main or core foods and beverages consumed by children aged 1 year to 5 years in Kweneng?

2. What is the average and range of energy (calories) and protein in food consumed by children aged 1 year to 5 years in Kweneng?

3. What is the relationship between caloric and protein intake of children aged 1 year to 5 years and their anthropometric measures?

4. What is the level of household food security among caregivers of children 1 year to 5 years?

5. What is the relationship between household food security and children’s anthropometric measures?

Significance of the Study

Nursing Practice. The goal of health promotion is to increase the level of health of individuals, families, groups and communities. Nurses are the major health professionals who implement health promotion strategies that focus on development of positive health behaviors (Grodner, Long & Walkingshaw 2007). The study identified the relationships of current food and beverage consumption, food security and anthropometric measures among young children. The contribution of the study will help in the future to design interventions such as dietary counseling that may improve current practices that impact children’s nutrition.

Nursing Research. New information about the influences of dietary intake, food security and growth measurement is essential. Investigating food consumption is

critically important to understand how change may be effected to correct problems of undernutrition (Quandt, 2006). Findings from the study will provide initial evidence- based knowledge for future studies regarding the significant influences on child growth

and development in Kweneng.

Individuals, families, groups and community. Knowledge from this study may

lead to improvement and modification of dietary influences for children. Changes in

dietary intake and food security for young children may influence their health status and indirectly affecting families and communities.

Summary

The chapter introduced issues related to the role of nutrition, food intake, growth and the nutritional status of young children. The social cognitive theory and the social ecological framework were examined for usefulness in guiding the study. The social cognitive theory was found to be more useful in explaining behavior change (Clark &

Houle, 2009), while the social ecological framework emphasized system processes in health and illness (Schneider & Stokols 2009). The Extended Model of Care -UNICEF was selected as the framework for guiding the study as it provided concepts that describe and explain relationships between young children’s diet intake, growth and development and health status (WHO, 2004). The model depicts important factors in the understanding of the nutrition problem and young children’s health outcomes (UNICEF, 1998). A brief historical background of the model and the evolution of concepts were provided. The chapter concluded with the five research questions that were addressed in the study.

CHAPTER II

LITERATURE REVIEW

Introduction

The literature review presents and discusses the findings of studies on nutrition, food consumption and dietary intake of young children younger than five years. The

review of the literature also includes the role of these elements in the growth and health status of young children. In addition, the review includes applications from developing countries with special reference to Botswana. Inadequate consumption of appropriate foods remains a major problem for young children, particularly because they experience rapid growth and development, are vulnerable to illness and there is evidence that feeding practices are suboptimal in most developing countries (Ministry of Health, 2005;

Ogunba, 2010).

Young children aged 0 to 5 years constitute a vulnerable segment of the population of the developing countries. The nutritional status of young children is considered a critical indicator of the population’s health and nutrition (Agbon, Okeke &

Omotayo, 2010). Investigations on the food consumption patterns and dietary intake for young children have demonstrated the effect of inadequate nutrient on brain growth, cognitive development and subsequent learning abilities (Dodds & Laraia, 2005). In addition, inappropriate dietary intake may be associated with malnutrition and increased risks of both infectious and chronic diseases (Ruel, 2003; Vereecken, Hybrechts, Maena

& De Henauw, 2008). The Extended Model of Care- UNICEF provided a framework for the study therefore, a theoretical review of some major concepts of the model is given.

Theoretical Literature

The Extended Model of Care- UNICEF

The model provides the foundation of conceptual issues for understanding factors

linked to child growth and malnutrition. The conceptual framework incorporates various

biological and social factors linked to the nutrition problem. Factors that influence child

nutrition are found at three different levels within the model. The three levels are:

immediate determinants, underlying determinants, and basic determinants (Engle, Menon

& Haddad, 1997). At the immediate level of determinants lies inadequate dietary intake and disease which exert the most profound influence on child growth and development.

Inadequate intake of food and the occurrence of disease, especially infectious diseases, are the most significant causes of malnutrition since they affect food consumption and nutrient utilization (UNICEF, 1990). In the model, inadequate intake includes insufficient food intake, a diet with poor nutrient content, and food that is not safe. Similarly, infectious diseases may lead to undernutrition through inadequate intake caused by anorexia, inappropriate feeding, and increased metabolic demands leading to increased utilization of nutrients, reduced nutrient digestion or absorption (Baqui & Black, 2002).

Underlying determinants consist of three main clusters: household food security, maternal and child care and health services, and healthy environment. Household food security and maternal and child care are necessary conditions for adequate dietary intake and support of a healthy immune system necessary in the control of common infectious diseases among children.

The study examined a limited number of factors from the model including dietary intake and household food security. In the model, dietary intake encompass meal

frequency, amount of food per meal, energy and nutrient density of the food; and

biological utilization (UNICEF, 1990). Household food security refers to access by all

people at all times to enough food for active, healthy life that includes ready availability

of nutritionally adequate and safe food and ability to acquire acceptable food in socially

acceptable ways (Radimer, 2002). Child survival and growth and development are the

overall health outcomes for basic, underlying and immediate determinants of

malnutrition.

Empirical Literature

Food and beverage consumption are characteristics of an important aspect of the

dietary intake. In young children, inadequate intake food is linked to low levels of

essential nutrients, fiber and energy (Grodner, Long & Walkingshaw 2007). Extensive

research conducted on the impact of quantity and quality of specific nutrient intake also

indicate negative long term health outcomes for children (Abrams, et al., 2003; Beaton,

Martorell, L’Abbe, Emonston, McCabe, Ross et al., 1993; Kranz, Mitchell, Siega-Riz &

Smicklas-Wright, 2005). In sub-Saharan Africa, dietary intake has shown to be inadequate in terms of both energy intake and nutrient density (Tomkins, 2000). As such, poor dietary intake remains a major concern for public health that needs to be addressed.

The imbalance between nutrient intake and caloric requirements has resulted in coexistence of overnutrition and undernutrition among young children (Bowley, Pentz-

Kluyts, Bourne & Marino, 2007). Moreover, childhood malnutrition and maternal obesity have been found to co-exist in the same household and community (Faber, 2010).

Vulnerable communities generally consume a diet constituting mainly starch based staple

foods with insufficient animal products and seasonal fruits and vegetables intake

variations (Arimond & Ruel, 2004; Faber). Consequently, low consumption of animal source foods and fruits and vegetables predisposes children to deficiencies in both macronutrients and micronutrients (Lutter & Rivera, 2003). However, despite the existing body of knowledge, food and beverage consumption and dietary intake of young children has not been characterized in many developing countries such as Botswana.

Nutritional Status of Infants and Children

The individual child’s nutritional status is influenced mainly by food, care and health (Engle, Menon & Haddad, 1997). Scientific evidence has shown that children who are well-fed, healthy and do not have any environmental constraints on growth have similar growth patterns even though they may come from different geographic and ethnic backgrounds (Arimond & Ruel, 2004; Owusu, Lartey, de Onis, Onyango & Frongillo,

2004). The WHO Multicenter Growth Reference Study (2006) based on care practices including breastfeeding/complementary feeding practices and health practices demonstrated how food, responsive feeding practices and safe hygiene, among others can contribute to infants and young children’s nutritional status (WHO Multicentre Growth

Reference Study Group, 2006a). This global study provided the important data that were used to develop growth reference tools for assessing children’s growth. This longitudinal study used a pooled sample from Brazil, Ghana, India, Norway, Oman and the USA to represent differing geographic, cultural settings and ethnic backgrounds. Findings from this study revealed similarities in growth during early childhood with only a 3% variability in length attributed to between-sites differences (WHO Multicentre Growth

Reference Study Group). The results suggest that children who are breastfed, given adequate nutrients and have optimal environmental conditions are likely to achieve

similar growth potential regardless of their place of origin (WHO Multicentre Growth

Reference Study Group).

In contrast, poor nutrition in infants and young children has been linked to both

short and long term problems such as nutrient deficiencies, susceptibility to infections,

poor growth; risks of obesity, diabetes, cardiovascular diseases, certain types of cancers,

poor academic performance and emotional difficulties (Knol, Haughton & Fitzhugh,

2004; Kranz, Mitchell, Siega-Riz & Smicklas-Wright, 2005; Kruger & Gericke 2003;

Lutter & Rivera, 2003, Onyayngo, Koski & Tucker 1998). Proper infant and young child feeding is a key determinant to improved child nutrition. Good nutrition improves the body’s ability to resist infection and even when infection occurs, good nutrition reduces the severity of illness and speeds up recovery. Analytic reports from scientific studies suggest that adding vitamin A to boost immunity and zinc to treat diarrheal disease improves lives of young children. Supplementation with vitamin A can reduce child mortality from all causes by 23 percent. Similarly, zinc supplementation can reduce the prevalence of diarrhea in children by 27 percent as it decreases the severity and duration of the disease (Beaton, et al, 1993; UNICEF, 2009). Meta-analysis of 369 studies

conducted in 76 developing countries and countries in transition (Africa, Asia and South

America) revealed that nutritional status of school-aged children including younger

children in these regions was inadequate (Best, Neufingerl, van Geel, van den Briel &

Osendarp, 2010).

Malnutrition. Malnutrition is characterized by growth failure or inappropriate

weight gain due to an imbalance of nutrient intake and utilization (Grodner, Long &

DeYoung, 2004). The term undernutrition has been used to describe a state of dietary

energy deficiency whereby an individual is unable to maintain good health or a desirable

level of physical functioning. The terms undernutrition and malnutrition may be used

interchangeably in reference to inadequate intake of protein and total calories or energy

(Rice, Hyder, Caulfield, Stoltzfus, Fishman, Frangakis et al., 2002). The study focused on

the main foods and beverages that are consumed by children in the context of a

developing country. The occurrence of malnutrition in infants and young children is

associated with a variety of factors including food, health and care practices at the

individual and family levels (UNICEF, 1990). Malnutrition has many adverse effects on

children ranging from impaired physical growth and cognitive development, morbidity

and premature death. Severe malnutrition in children commonly coexists with

gastroenteritis, pneumonia and other infections (Ashworth, 2006; Pelletier & Frongillo,

2002; UNICEF, 1998). Malnutrition can also be a vicious cycle, with acute infections

leading to malnutrition in previously healthy children (Katona & Katona-Apte, 2008).

Malnutrition contributes to more than half of all the under-five childhood deaths throughout the developing world (WHO, 2007). Three classic forms of malnutrition have long been identified as stunting, wasting and underweight (WHO Working Group, 1986).

Stunting signifies a slowing in skeletal growth characterized by a height-for-age that is below international reference value. Wasting indicates a deficit in tissues and fat mass relative to the amount for a child with similar height or length, while underweight denotes weight–for-age below international reference value (WHO Working Group).

A review of child nutrition in sub-Saharan Africa revealed that malnutrition was

linked to nutrient inadequacies and infections. Many infants and young children survive

on a monotonous diet consisting of gruel and porridge that is consumed with vegetables

and legumes. Such a diet is also associated with a diminished appetite (Onyango, 2003)

thus affecting food intake. In a cross-sectional survey of a nationally representative

sample of 2,894 children in South Africa, one in five children was stunted; and one in 10

children was underweight. Furthermore, 26% of children aged 1 to 3 years did not meet

the daily energy needs (Labadarios, Steyn, Maunder, MacIntyre, Gericke, Swart et al.,

2005). This survey also revealed that children commonly consumed maize, sugar, tea and

brown bread and 52% of the household had experienced hunger while 23% were at risk

of hunger. Another study in Lesotho, revealed prevalence of stunting at 21% in the first

year of life and a close to 30% underweight during the third year of life in children

(Jooste et al., 1997). In Botswana, a recent study that examined the nutritional status of

children aged 0 to 5 years reported stunting and underweight at 13.7%, and 11.3%

(Nnyepi, 2006). Other micronutrient related deficiencies such as iron deficiency anemia

were found in 38% of children. Similarly, vitamin A deficiency affected 35% of the

children (Ministry of Health, 2005).

Major problems caused by malnutrition are preventable through timely nutrition

interventions that provide appropriate macro and micronutrients to infants and young

children (UNICEF, 1998; Wardlaw & Smith, 2009). The estimated benefit-cost ratios for interventions to reduce hunger and malnutrition indicate that there is considerable potential for improving growth and productivity by investing in young children’s nutrition (Behrman, 2009). Hence, engaging in low cost preventive intervention measures for child malnutrition has the potential to translate into substantial benefits at the population level later as children become healthier and more productive adults.

Young children, particularly infants, are prone to malnutrition because the introduction of complementary foods is associated with complicated micronutrient requirements. An appropriately balanced diet often requires both understanding and appropriate utilization of nutritional information on the part of the caregiver. Lutter and

Rivera, (2003) and Engle, (P. Engle, personal communication, February, 2010) have argued that there is no single set of nutritional requirements for infants and children.

Also, Lutter and Rivera have suggested harmonization of nutrient requirement for infants and young children, as the methods of estimating quantities of food and nutrient requirement vary and therefore too complicated for caregivers to use. The end result may be that caregivers may provide too much or too little of essential nutrients to children at the introduction of complementary feeding.

Food Consumption and Dietary Intakes of Young Children

In children, food consumption is influenced by various factors that include meal frequency, amount of food per meal, nutrient density of the food, food diversity, food variety, nutrient intake, and child characteristics (UNICEF, 1990).

Meal frequency and energy intake. The common classification of meal frequency in many societies is the traditional three including breakfast, lunch and dinner. Meal frequency may influence how the body responds to intake. One study that specified the number of eating episodes for pre-school children indicated that children had four meals and four snacks in a day (Sepp, Lennernas & Abrahamsson, 2006). Earlier studies that have investigated the relationship between meal frequency and energy levels suggest that children adjust their energy needs to compensate for higher or lower intakes (Birch, 1996;

Birch et al., 1991). However, a more recent study based on structural equation modeling

reported a 20% variability in young children’s intake at different meal times (Hanley &

Hutcheon 2010). This suggests that children’s meal frequency and energy intake at one meal may not be strongly correlated with a subsequent one. Thus, the meal frequency and energy levels for children may be influenced by other factors such as the amount served and the quality of foods (Hanley & Hutcheon) making it difficult to relate the energy levels with meal frequency. Regarding intake of specific foods, one USA study reported inconsistent effects on total energy intake following consumption of chocolate -flavored milk with high levels of energy, aspartame flavored milk with moderate energy and plain milk with lower energy. The study showed that young children who drank more milk sweetened and flavored with chocolate but did not reduce intake of other food items, suggesting that offering high-energy drinks with meals might predispose the child to chronic over-eating (Wilson, 2000).

Amount of food per meal or portion sizes. Portion sizes give an estimate of the energy and nutrient content consumed from a meal. Portion sizes may also fulfill the physiological need for food or satisfy hunger. While accurate measurement of food intake requires complex and labor intensive techniques (Hudson 1995, Huybrechts, et al., 2008), the cognitive abilities in younger children are limited for a task that requires recognition and description of amounts in terms of proportions or wholes of food items consumed

(Burrows, Martin & Collins 2010). But estimating portion sizes using household measures and food models has been used successfully in some studies investigating children’s intake (Colapinto, Fitzgerald, Taper & Veugelers, 2007; Nicklaus, Chabanet,

Boggio & Issanchou, 2005; Vereecken, Hybrechts, Maes & De Hanauw, 2008) where parents were proxies for young children. In the developed world, concerns over the risk

of excess body weight in children has led to studies that focus on assessing preference for

larger portion sizes (Colapinto, Fitzgerald, Taper & Veugelers), and the assessment of the role played by food environment on children’s meal intake (Gubbels, Kremer, Stafleu,

Dagnelie, de Vries & Thijs, 2009). These studies have reported significant changes in serving sizes especially those involving commercial eating places. However, little is

known about the amount of food per meal and portion sizes for children raised in the

developing countries such as Botswana where feeding practices are suboptimal (Ministry

of Health, 2005). Other means of gathering information on the amount of foods

consumed that have been used in controlled clinical trials include observation at meal

times, keeping food intake records and the weighed food intake record including plate-

waste (Baik & Lee, 2009).

Other factors related to food intake in children are the individual child’s appetite

and taste acuity. Children with higher taste acuity may be more prone to food neophobia

(Baik & Lee, 2009). Neophobia may limit the child’s willingness to try new foods, which

affects food consumption. Also, food preferences have been associated with increased

intake of the liked foods; while intake of disliked foods may be lower (Skinner, Carruth,

Bounds & Ziegler, 2002). On the other hand, a good appetite eases the feeding and eating

process and increases food and energy intake.

Energy and nutrient density of foods. Various foods provide nutrients as well as

different amounts of energy. Foods that contain carbohydrates and proteins produce 4

kcal per gram while lipids contain double the amount of energy (Grodner, Long &

Walkingshaw, 2007). Availability of food may influence the quantity and quality of

nutrient intake. In addition, biologic factors may affect the body’s ability to absorb and

use nutrients resulting in nutrient deficiencies (Brown, et al., 2008). A South African study that compared dietary intakes for stunted and non-stunted children in a rural and an urban setting revealed that stunted children had lower mean energy intakes than non- stunted children. Although, poorly nourished children consumed a diet similar in amounts of food to well–nourished children, their mean intakes were substantially lower (Theron,

Amissah, Kleynhans, Albertse & MacIntyre, 2006). This indicates that children who are already malnourished are likely to deteriorate as their energy intakes continue to be suboptimal.

Food diversity. A score computed for the number of food groups consumed over a period of 24 hours is defined as the food diversity score (Steyn, Nel, Nantel, Kennedy &

Labadarios, 2005). Food diversity has been associated with improved nutrient adequacy in children (Arimond & Ruel, 2004). In addition, food diversity ensures adequate supply of essential nutrients (Onyango, 2003) as dietary counseling based on food groups is more practical for many people in developing countries where illiteracy is still a problem

(Lutter & Rivera, 2003).

Food variety. The score computed for the number of food items consumed over a

24 hour period from all possible total items in a given food environment or context indicates a food variety score (Steyn, et al., 2005). Intake of a variety of foods may influence energy and nutrient adequacy because consuming a combination of foods ensures that one obtains the needed nutrients (Brown, et al., 2008).

Individual food/nutrient intake. Studies on individual nutrient intakes have evaluated consumption of dietary fiber, fruit and vegetables and trace elements such as iron and calcium (Abrams, et al., 2003; Bere & Klepp, 2005; Kranz, Mitchell, Siega-Riz

& Smiciklas-Wright, 2005). Although the consumption of nutrient and energy dense foods meets the dietary reference intakes, (Butte et al, 2010), a US study found that most children eat fewer fruits and vegetables than recommended (Kranz, et al.; Lorson,

Melgar-Quinonez, & Taylor, 2009) and intake of dietary fiber is low. Seasonal variation

in food supplies and the prevalence of diarrheal diseases was reported to have some

influences on macronutrient and micronutrient intake in children especially among the

rural populations in developing countries (Marin, et al., 1996). Fruits and vegetables may

not be easy to get or tend to be more expensive, thus affecting their intake. For instance,

after-harvest and dry-season food intakes can be remarkably different in quantity, variety

and quality (van Staveren & Ocke, 2001). Food diversity and variety may help address

the deficit. As noted, Theron et al., (2006) revealed that children who consumed

insufficient dairy products, fruits and vegetables had low micronutrient levels such as

calcium and iron. Therefore, a diet composed of different food sources may provide

various macro and micronutrients.

Children’s characteristics. The individual child’s characteristics may influence the overall food consumption and nutrient intake. In a study that examined growth,

energy and meal intake in five-year old children, it was found that children who were

considered poor eaters were on average lighter and shorter than those who ate well

(Saarilehto, Lapinlemu, Keskinen, Helenius, Talvia & Simell, 2004). Children’s body

frame such as a small stature may be associated with a parental perception that the child

is not getting enough food leading to pressuring the child to eat more. In contrast, a child

who appears overweight may be regarded as over indulging on food and the tendency

may be to withhold energy dense foods.

Caregiver’s Role on Children’s Food Consumption

In young children, dietary intake is highly influenced by the parent or caregiver.

The caregiver has a unique opportunity to determine which foods are available, how foods are prepared, and the quantity or quality of food consumed by young children. As a result, caregivers are gatekeepers, controlling the availability of foods in their children’s environments (Ball, Benjamin & Ward, 2007; Wardlaw & Smith, 2009; Wardle, Carnel

& Cooke 2005). Caregivers also need to understand that children require stimulation and attention to ensure maximum dietary intake (Wardlaw & Smith). For instance, caregivers have to be responsive to the child’s physiological as well as psychological need for food by picking up cues for the need for food and satiation. Moreover, it is equally important that the caregiver is able to determine if the child is getting sufficient nutrients from the various food sources. For this reason, it is necessary that the caregiver monitors the frequency of food intake as well as the quality and quantity consumed by child.

Several factors have been suggested as influencing the caregiver’s role on young children’s food consumption and eating patterns (Faber, 2010; Nicklaus, et al., 2005;

Vereecken, Rovner & Maes, 2010). Among these, accessibility, parenting styles, parental feeding practices and children’s characteristics have been isolated (Faber; Vereecken et al.). The quantities and the nature of foods made available to young children are highly influenced by the caregivers’ socioeconomic status, knowledge, and availability of time and cultural factors (Bere & Klepp, 2005; Onyango, 2003; Theron, et al., 2006). These factors affect individual children and families in different and unique ways. Parents and caregivers are also role models for children and thus their behavior has a significant influence on the child. In addition, parents may use styles that indicate authoritarian

parenting practices that include attempts to command the child’s food consumption and the use of pressure or coercion to eat. Other parents may use permissive food-related parenting styles allowing their children to choose what they want (Vereecken, Keukelier

& Maes, 2004).

Parental feeding practices are described as strategies used by parents to get their children to eat. These may involve parent-centered or child-centered strategies

(Vereecken, Rovner & Maes, 2010). Each of the styles may influence the child’s food intake in a different way. Parent-centered practices have been associated with negative vegetable intake outcomes while child-centered practices had a positive result

(Vereecken, et al.). Other findings from studies on pressuring children to eat more or eat selected foods indicated that these strategies were correlated with lower weight and a higher intake of fruits and vegetables (Fisher & Birch, 2000; Francis, Hofer, & Birch,

2001, Kroller & Warschburger, 2009). But using food as reward to the child for eating the food he/she dislikes was found to promote preference for the unhealthy food thus decreasing the consumption of the target food. Notably, studies have proposed that using sweet foods as reward for eating healthy foods may alter taste preferences in young children (Campbell, Crawford & Ball, 2006; Musher-Eizenman & Holub, 2007).

Specific to children’s diet intake, Beydoun and Wang (2009) found that there was moderate resemblance in dietary intake patterns between parents and their children. As a result of children’s observation of their parent’s eating patterns, children are more likely to consume what their parents eat. However, the moderate resemblance in intake patterns for children and parents may indicate that, in addition to individual and household factors, there may be other factors influencing children’s dietary intakes. It may

therefore, not be assumed that children’s dietary intakes will be closely related to those of

their parents or caregivers. Literature on dietary intake of young children shows an

inadequate intake of certain essential nutrient is associated with poor weaning practices

such as providing a limited variety of foods (Kruger & Gericke, 2003). Kruger & Gericke

also found that caregivers and mothers had inadequate nutrition knowledge that was

associated with unsuitable feeding practices. Food choices were not made on nutritional

value; instead, foods were selected for reasons of hunger and satiety. Hence, most of the

weaning foods comprised starchy meals that were also overcooked (Kruger & Gericke).

Anthropometry/Growth Measures

Growth and development of the human body depends on an adequate supply of various nutrients. As growth is most rapid in the first year of life, with infants doubling birth weight in the first six months of life, there is greatest nutrient requirement to support these rapid physical changes (Lartey, 2008; Wardlaw & Smith, 2009). Growth is a basic indicator of health and wellness in young children. It is regarded as the single best indicator of a child’s nutritional status (Wardlaw & Smith). Regular assessment of growth in young children facilitates early identification and timely intervention for emerging health problems (Maleta, Virtanen, Espo, Kulmala & Ashorn, 2003).

Anthropometric measurements are commonly used for assessing physiologic growth and nutritional status of children (Onyango, 2003, WHO, 1986; WHO, 2006a;

WHO, 2011). Anthropometry is compared with recognized reference to assess if growth is optimal. Currently, the WHO reference charts based on the growth pattern of breastfed

children that reflects physiological growth (Vignerova & Lhotska, 2006) is the reference

standard used internationally (Onyango; WHO, 2011). Anthropometric measurements

commonly used to assess attained growth include weight-for-age (WA), weight-for-

height (WH) and height-for-age (HA) (Onyango, WHO, 2006b). An individual’s weight

or height is compared with the reference median of the same age and sex, if the attained

weight or height is below -2 standard deviations (SD) from the median reference, then

one is classified as underweight using WA, stunted using HA, or wasted, using WH

(WHO, 2011). Although not specific for any particular cause of malnutrition, anthropometric indicators are considered sensitive to immediate and underlying causes of malnutrition. In young children growth failure is synonymous with malnutrition; hence the measures are used to evaluate both nutritional well-being and malnutrition

(Mandleco, 2004; Moore & Roche, 1987).

Several studies have used anthropometric measurements that include weight, height and age to assess growth (Colapinto, et al., 2007; Fisher, Liu, Birch & Rolls, 2007;

Nicklaus, Chabanet, Boggio & Issanychou, 2005; Onyango, et al., 1998; Steyn, Nel,

Nantel, Kennedy & Labadarios, 2005). The Multicentre Growth Reference Study developed the new standards using exclusively breastfed children’s anthropometric measurements that included both longitudinal and cross-sectional data (WHO, 2006a).

These tools (growth reference charts) provide guidance on effective measurement and evaluation of normal growth in children aged 0 to 5 years.

A Kenyan randomized controlled study on feeding showed that both meat and milk supplementation had beneficial outcomes for children’s growth. School children who received animal source protein in the form of meat had a greater rate of weight gain than the control group. Similarly, children who were younger than 6 years who received milk revealed a significant gain in height than other children (Neumann, Murphy, Gewa,

Grillenberger & Bwibo, 2007). Still in Kenya, an earlier study of 154 rural children aged

between 1-3 years, found that the number of different foods consumed was strongly,

consistently and positively related to the five anthropometric measures including height,

weight, age, triceps skin folds and mid-upper arm circumference that were examined

(Onyango, et al., 1998). These studies indicate that nutrient intake and protein improves

children’s growth. Additionally, iron-fortified milk has been associated with improved

indices of weight-for-age and height-for-age in preschool children (Silva, Dias, Ferreira,

Franceschini & Costa, 2008) and fortification of other beverages has been associated with

an improved nutritional status (Abrams, et al., 2003). Several studies on energy and

protein intake in pre-term babies have also revealed a consistent positive influence on

growth (Collins, Gibson, Miller, McPhee, Willson, Smithers, et al., 2008; Collins, Chua,

Rajadurai, McPhee Miller, Gibson, et al., 2010).

Household Food Security

Household food security relates to the access and adequacy or capacity to acquire safe food sources of sufficient quantity and quality (UNICEF, 1990). Household food security is defined as “a state in which all people at all times have both physical and economic access to sufficient food to meet their dietary needs for a productive and healthy life” (Coates, Swindale & Bilinsky, 2007, p.1). Household food security is essential to ensure dietary intake for vulnerable groups such as young children.

Improving nutrition requires ensuring that a variety of foods are available in the right quantity, quality and affordable to all people within the household. Household food insecurity is an underlying determinant of malnutrition that is linked directly to inadequate dietary intake (UNICEF).

Recent figures on global food insecurity indicate that about 1.02 billion people do

not have sufficient food to eat (Food and Agriculture Organization of the United Nation,

2009). As households experience the burden of food shortage, poor people reduce their dietary diversity leading to nutrient deficiencies. Therefore, food insecurity increases the risks of malnutrition and illness especially for groups such as children and the elderly.

Food insecure families tend to rely on energy dense foods with low intake of fruits, vegetable and meat (Melgar-Quinonez & Hackett, 2008).

The Food and Nutrition Technical Assistance (FANTA) project has devised indicators that guide evaluation of food insecurity within households (Coates, Swindale

& Bilinsky, 2007). Some scales measure household food insecurity on three aspects including perception that the quantity and quality of food is insufficient, coping strategies and the experience of hunger due to lack of resources (Oldewage-Theron, Dicks &

Napier, 2006; Rosas et al., 2009,). However, the presence of food insecurity may not

necessarily meet all three criteria as it may be subtle. For instance, households may adjust

the quality and quantity of their food intake and not acknowledge the social and

psychological stress associated with food insufficiency (Melgar-Quinonez & Hackett,

2008).

Food security indicators can be used to understand the nutritional status of

children and their communities. Studies on household food security have revealed that

caregivers in many parts of the developing world are confronted by either seasonal or

continuous food insecurity. The presence of stunting in children in any given community

is an indirect indication of household food insecurity since stunted growth is a result of

long-term inadequate food intake (Gray, Cossman & Powers, 2006; Oldewage-Theron, et

al., 2006). Seasonal variation of food insecurity occurs in many areas due to the harvest

times of various crops (Gray, Cossman & Powers). In contrast, food insecurity may be a

constant threat in places where there is extreme poverty (Oldewage-Theron, et al.) and in

parts of the world where people are displaced due to political instability or natural

disasters.

In developed countries, household food insecurity may be prevalent among

immigrants and minority groups. For instance, a bi-national study conducted in the

United States (California) and Mexico among children of Mexican descent revealed that

food insecurity was associated with a higher intake of fat and saturated fat. In

comparison, food insecure children in Mexico consumed a higher total of carbohydrates,

dairy and fruits than for those who were in California (Rosas et al., 2009). Thus, food

insecurity is associated with an inappropriate dietary intake that may encourage

childhood overweight associated with the intake of lower nutrient density foods that are

less expensive. Food insecurity may have varying levels of severity. For instance, at the

mildest level there is food insecurity without hunger. The household may worry about the

diminishing supply and make adjustment that involve quantity and quality of food. When

food insecurity is also accompanied by moderate hunger, adults develop some strategies

to be able to provide for children. Measures may include parents skipping meals or

cutting adult portion sizes to increase the intake by their children. In the severest form of

food insecurity, all members in the household experience hunger (Egeland, Pacey, Cao &

Sobol, 2010; Melgar-Quinonez & Kaiser, 2004; Oldewage-Theron, et al., 2006). Other issues related to food insecurity have led women as the largest group of caregivers, to seek more jobs to supplement their income. Inadvertently, poor health care results, as

working mothers are less likely to seek health care for themselves and their children

(Food and Agriculture Organization of the United Nations, 2009). All the various levels

of household food insecurity and the coping strategies have an influence on children’s

dietary intake, eventually affecting the nutritional status.

Despite being a middle-income country with government supplementary nutrition programs for infants and young children, Botswana’s, food insecurity is estimated at 26% of households (Food and Agriculture Organization of the United Nations, 2009). Also, there are limited data on food consumption patterns and complimentary foods for young children. The typical diets are based on starchy cereals that have a low nutrient density and young children are fed less than the recommended five times a day (Ministry of

Health, 2005). However, under-nutrition is an important consideration for vulnerable households. It is therefore pertinent to include household food security in a study that examines children’s dietary intakes because household food supply impacts the nutritional status of children.

Measurement of food consumption and dietary intake in young children. Dietary intake assessment incorporates various methods of collecting data regarding actual food consumption and habitual dietary intake (Grodner, Long & DeYoung, 2004). The assessment of food and beverage consumption in young children requires the help of a parent/caregiver. Bollella and colleagues have noted the need for the development of valid and reliable assessment methods for dietary intake in young children (Bollella, et al., 1999). Most dietary intake assessment methods for young children rely on retrospective reports from parents or caregivers in the form of 24-hour diet recalls, diet

records, weighed food intake and food frequency questionnaires (Ball, Benjamin &

Ward, 2007).

Dietary records may use weighed food record where study participants are taught

to weigh and record the food before eating and also weigh the plate-waste. Alternatively,

participants may keep records of portion sizes using household utensils (van Staveren &

Ocke, 2001). On an average, three to four days’ record may be required to ensure reliable

estimates. Twenty-four-hour diet or food recall (24 hour diet recall) may also be used to

collect data on the participant’s food and beverage intake of the immediate past 24 hours.

Food models and measuring cups are used to assist the participant in estimating the

portion sizes of foods consumed (Grodner, Long & Walkingshaw, 2007). The use of food

models may reduce the respondent’s cognitive burden of recalling and describing the

intake. Estimates of how frequently certain foods are consumed may be assessed by using

the food frequency method. Food frequency questionnaires may be used as a measure of

the usual intake for individuals and are less labor-intensive than food records. The food list may represent the total diet or food items high on certain nutrients such as iron or

calcium (van Staveren & Ocke). A more reliable method of determining dietary intake

such as direct observation may be impractical and intrusive in situations where children

are still under the care of the family. Moreover, direct observation requires well trained personnel to make proper estimates of the food portion consumed (Ball, Benjamin &

Ward, 2007).

Dietary intake has also been evaluated using the Healthy Eating Index (HEI) to estimate the overall diet quality. The HEI uses food groups and certain specific nutrients considered to have important influence on health outcomes such as the accumulation of

saturated fat, sodium and dairy products (Beydoun & Wang, 2009; Kennedy, Ohls,

Carlson & Fleming, 1995). The HEI approach of assessing dietary intake is more convenient as it captures intake of a variety of food groups. Food based dietary guidelines have an advantage over individual nutrient intakes because the guidelines take into consideration dietary diversity which is quantified by the number of food groups consumed over a period of time (Beydoun &Wang; Clausen, Chalton, Gobotswang &

Holmboe-Ottesen, 2005; Steyn, et al., 2005; Vereecken & Maes, 2010). In addition, assessing overall dietary intake reduces the risk of day-to-day measurement errors that result from monitoring of individual nutrient intake.

Another issue related to dietary intake in young children is the caregiver’s ability to estimate the amount of food consumed constituting a complex cognitive task. Many adults experience considerable difficulty in visually estimating quantity of food intakes

(Livingstone, Robson & Wallace, 2004). Therefore, the actual dietary intake is often based on rough estimation that may include under or over-reporting of the food consumed. The extremes in under- or over-reporting may impact proper assessment of dietary intake.

Parents who are overweight tend to underestimate the amount of food consumed by their children (Livingstone, Robson & Wallace, 2004; McGloin et al, 2002). The perception that the child has higher than normal weight has also been linked to underestimation of food intakes. On the other hand, parents who perceive their children as lean are likely to provide correct estimates of the dietary intakes (McGloin et al.) The methods used for dietary survey have also been associated with patterns of underreporting dietary intakes, for instance using recall methods, parents or caregivers may not

remember everything that was consumed. Besides, parents/caregivers may not be able to report food intake that occurs outside the home (Livingstone & Robson, 2000).

Summary

Although there has been significant improvement in the nutritional status of children globally, a review of the literature indicates that many infants and young children continue to face actual and potential nutritional problems that impact their growth (Oldewage-Theron, Dicks & Napier, 2006; Vesel, et.al., 2010). Malnutrition remains a significant condition with long-term consequences on growth and development of young children. The predominance of malnutrition in the developing world remains a health and socioeconomic priority for such nations (Maleta, et al., 2003; UNICEF, 2009).

In sub-Saharan Africa, both macronutrient and micronutrient deficiencies and the high burden of infectious disease are a major problem in young children (Kulwa, Kinabo &

Modest, 2006; Lartey, 2008). In Botswana, stunted growth was estimated at 29%

(UNICEF, 2009). Other forms of malnutrition including underweight and wasting remain at 11.3 % and 3.9% respectively (Nnyepi, 2006) in some districts while the average national underweight recorded 4.6% (Ministry of Finance and Development Planning

2008).

The growth of a child reflects nutritional adequacy, health status and other environmental influences on the family (Oldewage-Theron, Dicks & Napier, 2006).

Regular growth monitoring is a key component of health care for infants and young children. The overall growth pattern is important as deviation may indicate development of serious health problems (Brown, et al; 2008). Caregivers play an important role of responding to the infant’s needs for care and nutrition by providing appropriate food and

care necessary for good nutrition and health. Serving as gatekeepers, the caregiver may influence food availability and intake in young children environment (Ball, Benjamin &

Ward, 2007). In addition, dietary intake may be linked to household food security and these ultimately influence the caregiver’s decisions related to care and nutrition (Melgar-

Quinonez & Hackett, 2008). Investigating food and beverage consumption in young

children may provide important evidence on the type of dietary improvements that are

needed in targeted nutrition education, counseling and nutrition promotion activities.

The national infant and young child practice in Botswana involves a child welfare

program known as the Growth Monitoring and Promotion Program including monthly

growth monitoring for children from birth until age of five years. But the trends of

underweight in children in some parts of the country remain higher than 11% (Ministry of

Finance Development Planning, 2008). The Botswana’s 2009 National Policy on Infant

and Young Child Feeding reports that the feeding practices of young children are suboptimal (Ministry of Health, 2009b). Other than government reports, there are limited data regarding the under five children’s growth. Therefore, the study addressed this gap by exploring the children’s food and beverage consumption, caregiver’s household food security and their relationship to children’s anthropometric indicators.

CHAPTER III

METHODS

Introduction

In this chapter, the methods for the study that include the design, sampling methods, measurement procedures, data management and analysis, and the protection of human subjects are described. Included is a description of the food and beverage intake of young children and assessment of their anthropometric indicators. Specifically, the study sought to answer the following research questions:

1. What are the main or core foods and beverages consumed by children aged 1 year to 5 years in Kweneng?

2. What is the average and range of energy (calories) and protein in food consumed by children aged 1 year to 5 years in Kweneng?

3. What is the relationship between caloric and protein intake of children aged 1 year to 5 years and their anthropometric measures?

4. What is the level of household food security among caregivers of children 1 year to 5 years?

5. What is the relationship between household food security and children’s anthropometric measures?

Research Design

A cross-sectional descriptive correlational design was used. The study explored the food and beverage intake of young children and examined the children’s anthropometric measurements. In addition, household food security among caregivers of children was examined. The design allowed for participant input in their natural setting at

one point in time (Brink & Wood 1998; Cottrell & McKenzie, 2011). The major advantage of a cross-sectional descriptive-correlational design was that it was more practical, feasible, and had no attrition since data was collected at a single point in time

(Hulley, Cummings, Browner, Grady & Newman, 2007; Marston, 2010). The cross- sectional design facilitated collection of data from caregivers on the dietary intake associated with their children. The method was appropriate for the study as it enabled the researcher to provide extensive description of the phenomena as they naturally occurred

(Burns & Grove, 2009; Polit & Beck, 2004). The design was most suitable for the study as there was limited data about the phenomena (LoBiondo & Haber, 2006) occurring in

Botswana where much remains unknown about food and beverage consumption among children aged 1-5 years.

Setting

The Botswana Ministry of Health is responsible for the delivery of both preventive and curative health care in the country. Botswana is divided into 24 health districts for administrative purposes (Central Statistics Office, 2005). The annual rate of malnutrition among children who are under the age of five years ranged from 1.2% in

Ngamiland to 10.0% in Kgalagadi North district and growth failure was 1.6% and 13.2% respectively (Ministry of Finance, 2009). Kgalagadi north, a remote area with the highest malnutrition rates, has the lowest population density in the country. Conducting a study in such an area would have been both costly and time consuming therefore, the area was inaccessible for the study. In addition, the sparse population would not have supported the recruitment of a sufficient number of participants in a reasonable time frame. Thus, the setting for this study was purposively selected as Kweneng East (South West

Botswana). This region represented a health district with an average prevalence of

malnutrition and growth failure rate at 4.8% and 5.7% respectively (Ministry of Finance).

Each health district has several facilities ranging from mobile clinics, clinics providing maternity services, primary hospitals, and district hospitals. All clinics provided monthly

child welfare services, hence the program was targeted for the recruitment of study

participants. This was a government sponsored program that was provided to all children

aged 0 to 5 years and free of charge.

Selection of the health facilities.

According to information provided by the Public Health Specialist Office, there

were forty-four (44) health facilities including clinics and health-posts in Kweneng east district region (N. Majingo, personal communication, September 9, 2010). Clinics provided a variety of services including follow-up care for well children, immunizations, prenatal care and preventive and curative services for all individuals. Health-posts were

found in smaller villages or farming areas and provided limited services in contrast to

those offered at the clinic level. At the level of a health-post, the facility may not operate

throughout the week as it may depend on limited health personnel. Hence, the study

excluded health-posts.

All 18 clinics in the Kweneng east health region formed the sampling frame for the health facilities. Clinics were further grouped into two sub-districts namely south-east

and north-central, and hence, equal representation was ensured. The clinics for the study

were randomly selected from the sampling frame by assigning each clinic a number. A

random selection of the sites for data collection ensured a representative sample of all

clinics to enable making inferences to the population in these areas as well as the

elimination of sampling bias (Cottrell & McKenzie, 2011; LoBiondo-Wood & Haber,

2006). The investigator prepared an equivalent number of paper slips to the number of

clinics. The slips were similar in shape and size. Each number corresponding to the name

of a clinic was written on the piece of paper that was then folded. The folded pieces of paper were placed in a bag and mixed or shaken well to ensure that the slips of paper did

not lie in any order. Then, a single piece of paper was drawn from the bag and the

number was recorded on a notebook. After noting the number, the slip was placed back

into the bag. The process was repeated several times until there was a total of six randomly selected health facilities. The method of selection with replacement provided exactly equal chances for each element to be selected (Burns & Grove, 2009).The selected health clinics were: Nkoyaphiri, Mankgodi and Thamaga in the south area and

Lentsweletau, Phuthadikobo and Borakalalo in the north central region of Kweneng.

Sample

The sample for the study included caregivers and children who were selected

according to the inclusion criteria below.

Inclusion criteria. Adult caregivers aged 18 years and above with children 1 to 5

years, who were residents of Kweneng East areas of Botswana, were recruited. To be

classified as a caregiver, the adult person primarily provided direct care to a child aged

between 1 to 5 years for at least six hours per day for a minimum of four days in a week.

Caregivers were parents, grandparents or persons who were closely related to the child or

were paid to provide care to the young child. In most societies (Engle, Lhotska &

Armstrong, 1997) and many African cultures (WHO, 2004) caregivers of young children

were commonly females. Consistent with this view, only female caregivers were eligible

for participation. The caregiver’s participation was limited to only one child meeting the

required inclusion criteria. Children were single and full-term birth, healthy, without any

significant morbidity or condition as per the child welfare clinic card record, and were

not identified under reasons for special care category as per classification provided in the

Botswana child welfare clinic record (Ministry of Health, 2007). If the caregiver had

more than one qualifying child, the investigator used random numbers to select the child

who was to participate in the study. Both the caregiver and the child had to meet the

inclusion criteria in order to participate.

Exclusion criteria. The study excluded caregivers and children enrolled in special

government supplementary feeding programs such as the food-basket, because the

government subsidy was atypical of the average food consumption practices with

provision of unique food supplies. The study excluded children institutionalized for social

services as those children would not have regular caregivers. Additionally, children who

had chronic debilitating health problems, metabolic disorders or those diagnosed with any

form of malnutrition and acute illnesses that required specific therapeutic diets or had

suffered gastroenteritis in the past two weeks were not included. Children who attended

pre-school were also excluded from the study as their food consumption was likely to be different from the usual patterns in households.

Sampling.

For this study, it was not possible to know all the prospective participants who formed the sample frame to properly and effectively use probability sampling. Thus, non- probability sampling was used. A non-probability method was appropriate as potential

participants had an unknown chance of being selected (Polit & Beck, 2004). The

sampling frame included caregivers and their young children aged 1 to 5 years who

resided in Kweneng East region. The investigator selected participants who were readily

accessible and met the inclusion criteria (Cottrell & McKenzie, 2011).

An adequate sample size was considered critical because it provided a basis for

the estimation of effect size of the study, which facilitated understanding of the

magnitude of the difference that existed between the study variables in the population

(Hulley, Cummings, Browner, Grady & Newman, 2007). Effect size is a measure of the strength of the relationship between the variables of interest and relies on the sample size

(Cohen, 1988). The effect size was calculated based on prior studies that examined similar concepts (Burns & Grove, 2009; Hulley, et al. 2007). According to Cohen an

effect size may be chosen conventionally, meaning that an estimation can be done

according to established rules. For instance, effect size can be small, medium or large

(Cohen). Using the G* Power 3, a medium effect size of r = .30 between the predictor

variable, (children food and beverage consumption), and the outcome variable (children’s

anthropometric measures), at an alpha of .05 and a power of .80, the required sample size

was 82 participants (Faul, Erdfelder, Lang & Buchner, 2007). An additional 18

participants were included to provide an adequate sample size in case of subject

withdrawal thus, the total sample size was 100 caregivers and 100 children. Previous

studies of children’s food intake in the literature were descriptive. One study revealed

correlations (r = .24 to .54) between children’s food intake according to the type of food

classified as unhealthy or healthy and maternal feeding strategy (Kroller &

Warschburger, 2009).

Since statistical power derives from an adequate sample size, a small sample

would have been insufficient to detect the existence of the phenomena among variables.

However, if the sample was too large, it would be too costly to conduct the study (Burns

& Grove, 2009). The sample size for the study allowed descriptive statistics and the use of the Pearson product moment correlation coefficient and ANOVA for testing relationships among variables.

Measurements

The study examined children’s food and beverage consumption using a child food frequency questionnaire to document the usual dietary intake. Anthropometric measurements including weight-for-age (WA), height-for-weight (WH) and height-for- age (HA) were obtained for each participating child to assess attained growth and nutritional status. The caregivers for these children responded to the household food security scale. In all, four instruments were used for data collection in this study. These included the caregiver demographic data form (Appendix E), the child anthropometric measures and health habits data form (Appendix F) the child food frequency questionnaire (CFFQ- Appendix G), and the household food security scale (HFSS-

ELCSA, Appendix H). The demographic data and child anthropometric measures and

health habits data forms were developed specifically for this study and the other two

instruments were adapted from previous studies to ensure conceptual understanding of

items for the target population, culture and the local situation of the present study

(Gjersing, Caplehorn & Clausen, 2010). A description of the measures follows.

Caregiver Demographic Data Form. The purpose of collecting demographic

variables was to describe the sample and assess other factors that had a potential

influence on the variables of interest (Creswell, 2009). The investigator developed a data

form that was used to gather specific demographic characteristics of participating

caregivers and children. The measure is divided into two sections. Section A consists of a

total of 13 items that are close-ended and open-ended. Two questions required the caregivers to provide the number of people in the household who were below and above

18 years to classify adults and children accordingly. The remaining questions were close- ended items which required participants to choose from the possible answers provided.

Section B required demographic data of the target child for the study. The structured demographic questions provided several advantages including easy completion for the respondent and greater chance to answer sensitive questions such as income along with easy coding and analysis (Cottrell & McKenzie, 2011). The demographic data form included the caregivers’ age, educational level, type of employment, source of income and marital status. Information on the number of people living with the caregiver and the participating child’s age and gender were recorded as well. An open-ended item to specify the relationship of the caregiver to child was included. These factors were important to report so that their influence on the study’s major variables could be assessed during data analysis. The demographic data information were collected as enumerated on the form labeled Appendix E. In addition to demographic data, information on the household energy source, water supply, refuse disposal, and type of toilet facility were collected as some of these basic social amenities were previously associated with undernutrition (Nnyepi, 2007) and wasting (Tharakan & Suchindran,

1999) in children at birth to 5 years. Some of these factors may have had significant influences on the study variables.

Child Food Frequency Questionnaire (CFFQ). In this study, dietary intake was operationally defined as various types of food items and beverages usually consumed by children as recorded in the child food frequency questionnaire. The reference period for this study was seven days. A food frequency questionnaire can be a reliable and accurate method for describing average dietary intake (Rockett, Wolf & Colditz, 1995).

Investigators that examined dietary patterns in Botswana focused on the adult population and used checklists that had few food items (Clausen, Chalton, Gobotswang & Holmboe-

Ottesen, 2005; Maruapula & Chapman-Novakofski, 2007). These checklists were inadequate for this study. Therefore, a food frequency questionnaire was adapted and modified for the pediatric population in this study. A food frequency questionnaire originally developed for adults may overestimate dietary intake in children (Kobayashi,

Tanaka, Toji, Shinohara, Kamimura, Okamoto et al., 2010). A study that compared two methods of assessing dietary intake in children also reported some discrepancies in intake estimates of energy and macronutrients between the Block 98 FFQ, a food frequency instrument originally used for adults and a 3-day diet record. The correlations between nutrients calculated from the Block 98 and the 3-day food records ranged between r = 0.4 to 0.55 (Wilson & Lewis, 2004). Food frequency questionnaires have been shown to provide valid and reliable estimates of usual intake in a variety of populations

(McPherson, Kohl, Garcia, Nichaman & Hanis, 1995; Rutishauser & Black, 2002). In addition, food frequency questionnaires are easy to administer and can be adjusted to include cultural specific foods (Kobayashi, et al). The only study that examined the role of multi-nutrient-fortified foods in improving the nutritional status of children in

Botswana used an experimental design that limited intake of food items to only those that

were of primary interest for the investigators (Abrams, Mushi, Hilmers, Griffin, Davila &

Allen, 2003). To date, no food frequency questionnaire developed or tested in children in

Botswana was found.

A food frequency questionnaire that was originally used in a South African study of the adult population has been adapted by the investigator and experts in nutrition to

meet the needs for the target population for this study. The original questionnaire was

developed by MacIntyre (1998) as part of a study for doctoral requirements.

Subsequently, the MacIntyre’s questionnaire was used in other studies to collect dietary intake information from adolescents and adults. This food frequency questionnaire listed a variety of over 70 food items and beverages that were consumed by both adults and

children in South Africa and were similar to Botswana’s foods. Moreover, food items

used in Botswana are imported from South Africa with the exception of a few items that

were unique to the culture. Included in the original MacIntyre dietary intake guide were

questions that explored food procurement and storage measures. The response scale used

amount of food consumed and the frequency of eating choices per day, per week, per

month and seldom. The instrument is comprehensive and also includes the methods of

preparing various food dishes. Permission to adapt and use the instrument was received

from the author (U.E. MacIntyre, personal communication, January 25, 2011 see

appendix G).

In order for the instrument to meet the needs of the study, local foods had to be

added and those that are not consumed by children were removed. Also, items that were

culturally unique to the South African context were not included in the adapted

questionnaire. Similarly, items on food procurement and storage were excluded. Instead

of using the six month recall period, the adapted questionnaire recall period was

shortened to one week (seven days).

Other food frequency checklists from previous studies (Clausen, Chalton,

Gobotswang & Holmboe-Ottesen 2005; Maruapula & Chapman-Novakofski, 2007) were

also examined for similarities with the MacIntyre (1998) food frequency questionnaire.

Although these checklists were used in studies that were conducted in Botswana, the food

items were few, not quantified and did not include methods of food preparation.

However, the checklists identified foods according to groups of primary interest

including grains, meat, fruits and vegetables, milk and fat as in the MacIntyre

questionnaire. One of these studies conducted a factor analysis of dietary patterns of the

elderly (Maruapula & Chapman- Novakofski) and identified a five-factor component of

the diet consumed in Botswana. The factor components that emerged were beer pattern,

meat and fruit pattern, vegetable and bread pattern, and seasonal produce pattern, and

milk, tea and candy pattern. Although the factor components provided useful

classification of food groups, the beer pattern was not included for this study of young children’s food consumption. The adapted questionnaire was translated into the national language (Setswana) for non-English speaking participants. Also back-translation was

done to ensure that construct validity was maintained. As a measure of validating this

instrument, the questionnaire was given to two local experts in nutrition who reviewed it

for face and content validity. The discrepancies identified by experts were addressed and

modifications were made before pre-testing was done. Five caregivers of children aged 1

to 5 years completed the questionnaire to provide their perception and understanding

about food and beverage consumption as well as portion sizes for children. Pre-testing

was done with caregivers in a different health clinic than the ones selected for the study.

The investigator then reviewed subjects’ responses to determine if each question was clearly understood. Minor corrections for clarity and meaning were made accordingly.

The CFFQ had over 70 food items that reflected the foods that were commonly consumed by Botswana’s children. The instrument included a descriptive aspect that identified the food, amount and a range of frequencies of consumption of the specific food items. The food frequency response format required the caregiver to recall the child’s food intake during the past week. The food intake frequencies were classified into five categories: N/A = does not eat, 0 = less than once a week, 1 = one to two times a week, 2 = three to five times a week, 3 = six or more times a week. The constrained response format allowed respondents to order answers according to the required options and enhanced the respondent’s task of answering questions (Fowler, 2009). Higher scores indicated more intake of the particular food item. To ensure better estimates of food intake, typical food model portion sizes of various foods were presented to respondents to help them estimate the amount that represented intake by the child. In some instances, common household utensils were used to provide estimates (Croker, Sweetman & Cooke

2009; Fisher, Rolls & Birch, 2003). A copy of the food frequency questionnaire was appended as Appendix G (a). Foods were converted into nutrient values using the

FoodFinder (Medical Research Council, 2010) food composition software to calculate the nutrient quantity and quality. The CFFQ could be easily understood by a person at the grade 8 level of education in the Junior Certificate in Botswana’s education system. The instrument was administered by the researcher and the duration of the interview was approximately 45 minutes to an hour.

Anthropometry.Growth depends on sufficient supply of amino acids that are required to make the necessary proteins to support muscle and tissue formation (Grodner,

Long & DeYoung, 2004). Anthropometry is considered the single most portable,

inexpensive, noninvasive and universally applicable method of assessing human growth

(de Onis & Habicht, 1996). Growth standards reflect desirable norms that can be used for

comparison to check if an optimum target has been achieved (Duggan, 2010). Growth is

considered a basic indicator of nutritional and health status and was measured by selected

anthropometric indicators. In the study, child anthropometric indices included weight-for-

height, height-for-age and weight-for age according to the gender of each participating

child. The weights of children were measured with a portable scale (Seca 354 Dual

Purpose Baby Scale) calibrated to the nearest 0. 01kg. A Seca 213 Portable Stadiometer

was used to measure heights of children. Findings were interpreted according to the

WHO child growth standards that were developed from an international sample of

healthy breastfed infants and young children across the world (WHO, 2006a). The Child

Anthropometric Measures and Health Habits Data Form labeled Appendix F was used to

record findings. In addition to anthropometric measures, 7 items were used to capture

data about the child’s immunization status, vitamin A supplementation, the use of other

vitamins, and feeding and eating habits. Information on immunizations and vitamin A

was obtained from the child welfare card record (MH 1040/REV.07, Ministry of Health

2007). Information on feeding and eating habits of the child was gathered using 3 open-

ended questions.

Household Food Security. Household food security was measured by the Latin

American and Caribbean Household Food Security Scale, English version also known as

ELCSA-Latinoamericana y Caribena de Seguridad Alimentaria (Spanish translation).

This questionnaire measures experiences of food insecurity within households. According to the corresponding author, the ELCSA is linked to the scale currently used in the United

States, the household food security survey module (HFSSM) (Melgar-Quinonez,

Alverez-Uribe, Amoroso, Ballard, Ortega, Perez.Escamilla, et al., 2010 see appendix H).

The ELCSA consisted of 15 items designed to assess the experiences related to

household food insecurity in the past 3 months. The items listed closed-ended responses

that required the participant to either affirm (Yes) or negate (No) their state. If the

response was affirmative, then a follow-up question required the subject to state the

frequency with which that experience occurred. A higher score indicated a greater severity of the food insecurity (Hackett, Melgar-Quinonez & Alvarez, 2009). If the initial response was negative, the respondent had to proceed to the next item. The instrument had 2 subscales, for the first part, 1-8 items involved the adult related food experiences,

while 9-15 were on the situation of the child involved in the research. Permission to use the ELCSA as well as to translate it into the national language, (Setswana) was sought

and granted by the author (H.R. Melgar-Quinonez, personal communication, July 2,

2010).

Although the ELCSA had been used in projects in many parts of the world such

as China, India and Kenya, precise information about its psychometric performance has

not yet been established (H.R. Melgar-Quinonez, personal communication, July 2, 2010).

However, this instrument is conceptually similar to a modified US-household food security survey module (US-HFSSM) that was validated in Campinas, Brazil and renamed Campinas household food security scale module (Campinas HFSSM), (Melgar-

Quinonez, Nord, Perez-Escamilla & Segall-Corea, 2008, Perez-Escamilla, Segall-Corea,

Maranha, Sampaio, Marin-Leon & Panigassi, 2004). The US- HFSSM has also been used as a proxy for measuring poverty in a variety of developing countries (Melgar-Quinonez,

Zubieta, MkNelly, Nteziyaremye, Gerardo & Dunford, 2006). Universally, poverty is associated with the presence of household food insecurity. The ELCSA, like the

Campinas-HFSSM includes 15 items that assess food insecurity experiences of various degrees based on the previous three months period. On the Campinas-HFSSM, 9 questions address the adult’s experience while 6 items describe conditions that relate to children in the household (Melgar-Quinonez, et al, 2008). Items in Campinas-HFSSM are structured in the same way as in the ELCSA instrument, that is, participants who respond affirmatively to any of the preceding questions, have a follow-up question that assesses the frequency of the experience within the household. When subjects responded to the stem question with a No answer, the interviewer moved on to the next main question.

In the Campinas -HFSSM, the main questions are dichotomous (Yes/No) responses just like the ELCSA. The Campinas -HFSSM was validated using the logistic

Rasch model (Melgar-Quinonez, et al, 2008). The Rasch model originated in educational testing, but has been used in the health sciences research. The Rasch model can be used to analyze the psychometric properties of health scales which assess a unidimensional construct (Hagquist, Bruce & Gustavsson, 2009). The Rasch is an item response theory model that uses a single item as an indicator of a latent trait. Its major assumption is based on the understanding that the probability of a correct response to an item by a research participant depends on the latent trait as well as the difficulty of the item (Spiel

& Gluck, 1998). The Rasch model has been used particularly in validating various

adapted household food security instruments (Hackett, Melgar-Quinonez & Uribe, 2008;

Gulliford, Mahabir & Rocke 2004). Therefore, using the Rasch analysis, items in the

Campinas-HFSSM (which is very similar to the ELCSA) showed strong similarities with the US-HFSSM on item severities of food insecurity indicating that the two survey instruments measure the same phenomenon (Melgar-Quinonez, Nord, Perez-Escamilla &

Segall-Corea, 2008). The model generates statistics that measure the difference between the expected and actual responses of each item. The acceptable value score ranges from

0.7-1.3 (Hackett et al). Findings of the item infit values for the Campinas and US household food security scales ranged between 0.80 -1.20 indicating that the two instruments measure a common phenomenon. The infit values are used to assess how well each item and each household relate to the assumption of the model (Melgar-

Quinonez, et al).

All items in the ELCSA instrument were adapted with minimal re-phrasing. The responses were Yes = 1 and No = 0. If the response to the stem item was Yes, a follow-up question on frequency of occurrence which assessed how often the condition occurred and were coded as: 1= Rarely, 2 = Sometimes and 3 = Frequently. The sum of all responses provided four categories of food security scores with a range of points as follows: 0 =Food Secure Households and food insecure households were 1-5 = Mildly

Insecure, 6-10 = Moderately Insecure and 11-15 = Severely Insecure. This scoring approach was guided by the corresponding author of the scale (Melgar-Quinonez,

Alverez-Uribe, Amoroso, Ballard, Ortega, Perez.Escamilla, et al., 2010).

Pre-testing.

All instruments developed in different cultural contexts were tested for equivalence between the original and the adapted versions (Reichenheim & Moreas,

2007). The Child Food Frequency Questionnaire, Household Food Security Scale

ELCSA and the Caregiver Demographic Data Form were all translated into Setswana

(National language) and checked for reliability and face and content validity by conducting a pre-test. Three experts, 1 with doctoral preparation, another one a Masters holder and the last one a nutritionist with a focus on clinical nutrition, who were also familiar with both English and Setswana languages, reviewed the translated instruments.

Upon receiving input from the experts, items that were ambiguous were revised and modified accordingly. The instruments were pre-tested using a sample of five caregivers from Mmopane Clinic, a different health facility than the ones that were used for the study. Items that were unclear or had diverse responses were refined prior to data collection. Pre-testing helped to clarify the equivalence of language (Reicheheim &

Moraes,) and in estimating the time that was required for completing the adapted instruments (Hulley, Cummings, Browner, Grady & Newman, 2007).

Recruitment

Approval of the Institutional Review Board (IRB) at Case Western Reserve

University and the Botswana Ministry of Health’s Research and Development Division was obtained. The Public Health Specialist in the Kweneng district council who served as the director for the district health team and Senior Nursing Officer responsible for the nursing services at the district level were briefed about the purposes of the study and data collection procedures. Both the Public Health Specialist and the Principal Nursing Officer

facilitated communication linking the investigator to the district office and the six data

collection sites by sending memoranda. The investigator then, arranged meetings with the

respective participating clinics’ charge-nurses to discuss the study and the data collection plan.

In each of the sites, the investigator distributed recruitment flyers (see Appendix

D) introducing the study and inviting participation from the caregivers when they arrived and registered at the reception desk. This allowed the caregiver time to make a decision regarding participation while they waited to be served. The waiting period for clinic health service visits ranged from 30-45 minutes. After each caregiver had been served, the investigator inquired if she was interested in participating in the study. Caregivers who showed interest were then taken to a private office to determine if both the caregiver and child met the inclusion criteria. The pairs who met all the conditions were given detailed explanation of the purpose of the study and the informed consent document.

Because the study was expected to have minimal risks, informed consent was obtained concurrent with the enrollment of participants.

Procedures

Participants were recruited from the health facilities that had been randomly selected including Borakalalo, Lentsweletau, Nkoyaphiri, Phuthadikobo, Thamaga and

Scottish Livingstone clinics. An average range of 12-19 subjects were recruited per site to achieve the required sample size. Those meeting the criteria were asked to read a few lines out loud of the consent document. If participants were unable to read or write, the investigator read and explained the informed consent document. The investigator explained that children who met the requirements for the study would have their weight

and height measured. Caregivers were requested to answer questions about their demographic data and children’s food and beverage intake. In addition, caregivers were requested to respond to questions on the status of their household food security. It was also explained that participants were not required to participate and had the right to refuse or discontinue participation without fear of any negative consequences. If the caregiver agreed to participate in the study, she was asked to sign the informed consent document in her preferred language. Caregivers who were unable to write were asked to indicate agreement marking an X and placing her right thumb on an ink pad then making a mark next to the X sign.

Instrument Administration

The Caregiver Demographic Data Form, and the other three measurements, Child

Food Frequency Questionnaire (CFFQ); Household Food Security Scale-ELCSA and the

Child Anthropometric Measures and Health Habits data forms were used to collect relevant data for the study in the following sequence:

Caregiver Demographic Data.The investigator conducted face-to-face interviews in the language preferred by each caregiver, this was either English or Setswana, and invariably participants chose Setswana. The investigator asked the caregiver to respond to questions as outlined in the demographic data form and all responses were documented accordingly.

Household Food Security Scale-ELCSA.This instrument was used to assess the caregiver’s level of food security in the past three months preceding the study. The 15- item instrument was administered in person by the investigator. Participants’ response to

each item were recorded as Yes or No, with affirmative statement indicating food insecure experience while a negative response represented a food secure state.

Affirmative responses had follow-up questions that assessed the degree of food insecurity.

Child Food Frequency Questionnaire (CFFQ). The investigator obtained information about the participating child’s health from the child welfare clinic card (MH

1040/Rev.07, 2007). The caregiver was asked to confirm the child’s date of birth. The

CFFQ was administered carefully to ensure that there were no omissions and all items were answered. To estimate amount of food and beverages consumed by children, caregivers were presented a variety of food models and or household utensils to assist them to estimate the amount of intake. In some instances, probing questions were used to help caregivers to recall food and beverage items that had been consumed including the methods of food preparation. Social desirability including either underreporting or reporting exaggerated amounts of the food consumption (Buzzard, 1998) was a concern.

To help mitigate this potential bias during data collection, the investigator explained that there were no right or wrong foods. Therefore, caregivers were encouraged to provide information to the best of their knowledge. The participants’ responses were recorded according to the type, amount and the number of times the child ate a specific food item or beverage.

Anthropometric measurements.The investigator who is a registered nurse (RN) and pediatric nurse practitioner (PNP) and a research assistant (RA) also an RN conducted all anthropometric measurements. To ensure precision and fidelity of measurement, the investigator and research assistant reviewed a video by the WHO on

standardization of anthropometric measures for children, prior to data collection. The

standardized training reduced technical measurement error (de Onis, et.al, 2004). For

weight assessment, children were weighed without shoes or heavy clothing and

measurements were recorded to the nearest 0.1kg. The same electronic scale (Seca 354

Dual Purpose Baby Scale calibrated to the nearest 0. 01kg) was used consistently for all

measurements in the data collection process. Using a Seca 213 Portable Stadiometer,

recumbent length was measured for children between 1 and 2 years while standing height

was checked for older children. For those young children who were not able to stand still,

supine length was obtained with the child lying on a flat level firm board. To obtain

children’s weights, the scale was calibrated each morning before weighing began. The

same instrument was used consistently throughout the study. Age was calculated based

on the date obtained from the child welfare card and confirmed with the caregiver.

Using the WHOAnthro 2011 software (WHO, 2011), raw anthropometric

measurements were transformed and interpreted as Z-scores. Measures were reported in comparison with the median line. Weight-for-age below the median line at -2 =

Underweight; and -3 = Severely underweight according to gender Z-scores. Height-for- age above -3 = Obese, above -2 = Overweight, above 1 = Possible risk of overweight,

Weight-for-height, below -2 = Wasted and below -3 = Severely wasted (WHO Child

Growth Standards -The Multicentre Growth Reference Study Training Course and other tools 2010, WHO, 2011). In addition, weight and height measurements were used to derive a body mass index (BMI) for age. BMI can be used as an indicator for both obesity and thinness (Duggan, 2010).

Data Management and Cleaning

A data management plan was developed because it was considered critical for the

empirical phase of the study. The plan served as a blue print for all decisions that were

made during the data collection and later in the analysis phase of the research project

(Polit & Beck, 2006). There were several steps that had to be completed before data analysis began. Some of the steps ran concurrently, while others such as data collection

and data entry had to be strictly sequential. Polit and Beck (2008) have suggested that the researcher should start with the pre-analysis phase. During this period, the researcher mostly engaged in administrative tasks that included logging in data forms to check and review the raw data for completeness. The paper and pencil method of data collection was used. All forms used in data collection were reviewed for completeness. Each caregiver was assigned a code. The caregiver code number was used on all data collection forms linked to the particular caregiver and child. A codebook was designed to capture all variables used in the study. Variables were assigned an abbreviated name limited to 6-8 characters and a descriptive variable label. Coding helped to transform the collected information into symbols that were compatible with the selected computer software (Polit and Beck, 2008). In addition to the codebook, a hard cover log book was kept for documenting all decisions that related to data entry and data cleaning processes to prevent relying on one’s memory (Polit and Beck).

Various software packages including the IBM SPSS Statistics 20 software package, Medical Research Council FoodFinder3 Application software, Nutrition Data

System for Research (NDSR 2011) (NCC Food Nutrient Database 2011) and the WHO

Anthro- software (WHO Version 3.2.2, January 2011) and macros (2011) were used for

data analysis. Data security was ensured by keeping all the paper forms in a locked

cabinet. The cabinet was stored in the investigator’s office that was also locked except

when in use by the primary investigator. Once data were transferred into the computer, a

password or personal identification number was used to access the data. A back-up of all data files was encrypted before transferring data from the primary study site to the secure data site at Case Western Reserve University. The primary investigator, the responsible investigator and the dissertation committee members had access to the password for computerized encrypted data files. Data were assessed to identify the type, amount and patterns of missing data on the main variables. Missing data was minimal because the investigator conducted in-person interviews.

Data Analysis

Once data were entered into the IBM SPSS Statistics 20 software package,

cleaning followed and descriptive statistical analyses of all variables in the caregiver

demographic data and the child anthropometric measures data forms were done.

Descriptive statistical analysis included frequencies, summary statistics for each variable

on measures of central tendency and dispersion such as means, median, standard

deviations (SD) and range of scores where appropriate (Burns & Grove, 2009).

Frequencies were run several times to identify any outliers and missing data. Analysis

from the caregiver demographic data form provided a summary of the descriptive

characteristics of those caregivers of children aged 1 to 5 years and children who

participated in the study.

Other software packages including the MRC FoodFinder3, NDSR 2011 and the

WHO Anthro- software were used to analyze data from the CFFQ and the child

anthropometric measures instrument. To examine relationships, preliminary data analysis

and testing for the assumptions of the Pearson correlation coefficient were done

(Marston, 2010). Each of the research questions were analyzed as follows:

Question 1: What are the main or core foods and beverages consumed by

children aged 1 year to 5 years in Kweneng? An assessment of the child food frequency

questionnaire provided detailed description of the food items and beverages consumed by children in the sample reflecting the usual dietary intake of children in the district. Foods with frequencies of consumption occurring more than three times per week represented main or core foods. The main types of food commonly eaten were thus characterized and this enabled an assessment of the dietary quantity as well as quality of the food. In

addition, examining the types of food consumed allowed the investigator to evaluate the

variety and diversity of the dietary intake for children. The data were reported as the

percentage of the top most frequently consumed food items according to the CFFQ

intake. The core/main foods and beverages were those that had the highest frequency of

consumption in a week’s period. That is, a frequency of consumption of a food item with

three or more times per week was classified as main or core food.

Question 2 What is the average and range of energy (calories) and protein in food

consumed by children aged 1 year to 5 years in Kweneng? To answer this question,

nutrient analyses of the food items on the CFFQ were conducted. Initially, the MRC

FoodFinder3 Dietary Analysis Program was going to be used to perform the analysis.

This software analysis system was developed by the South African Medical Research

Council based on locally available food and nutrient values obtained from international tables (MacIntyre, Venter, Vorster & Steyn, 2000). However, during analysis, it was

discovered that the MRC Foodfinder3 had several limitations. For instance, it could only

show analysis of serving amounts not food items. The software was not easy to use, it

could hang-up as the analysis was in progress and technical support was not easily

accessible. The limitations of the MRC Foodfinder3 led to a search for an alternative

software package. The Nutrition Data System for Research 2011 (NDSR) was found

suitable for the greater part of the analysis required for the study. The Foodfinder3 was

used only to extract nutrients per 100 g of food items listed in the CFFQ. Seventy-one

(71) food items from the Foodfinder3 were matched with those in NDSR 2011 software

for calories, protein, carbohydrate and fat nutrient composition. Twenty-six (26) food items did not match 1:1. These were corrected to match at least 85% nutrient values. Two food items needed to be added as recipes due to their uniqueness. The calories, carbohydrate and protein averages were compared to the dietary reference intake standard values of nutrients provided in the NDSR software. Data on energy and protein values were extracted and interpreted accordingly from the output.

Question 3 What is the relationship between caloric and protein intake of children aged 1 year to 5 years and their anthropometric measures? In order to address the question, data from the CFFQ and the children’s weight and height were used to test for existing relationships. The strength and direction of the relationship of the correlation coefficient were reported according to range from -1 to +1 (Leedy & Ormrod, 2005).

Question 4 What is the level of household food security among caregivers of children aged 1 year to 5 years? Information generated from the household food security questionnaire was used to determine the levels of household food security. Households were categorized into: food secure, mildly food insecure, moderately food insecure and

severely food insecure according to their scores (Hackett, Melga-Quinonez & Alvarez,

2009). Household scores per category were reported as a percentage of the sample.

Question 5 What is the relationship between household food security and children’s anthropometric measures? To elicit relationships between household food security and children’s anthropometric measures, the food insecurity scores were used to compute correlations for household food security and weight measures for children in the respective households. The relationship between household food security and the anthropometric measures of children data were examined using the Pearson product moment correlation statistic. The strength and direction of the relationship that existed among the variables was reported. A summary of the analysis plan is shown Table 2.

Table 2 Study variables and analysis plan

Research Question Variable Empirical Indicator Instrument Statistical Method Caregivers and Descriptive statistics were Characteristics of caregivers of children children’s Caregiver Demographic used and included Demographic data aged 1-5 years characteristics data form frequencies and mean /demographic data distributions Question 1: What are the main or core foods and beverages consumed by Food and beverages Child Food Frequency Descriptive analysis of food Dietary intake children aged 1year to 5 years in categories Questionnaire (CFFQ) items. Kweneng? Question 2. What is the average and The software revealed the range of energy (calories) and protein in calories and nutrient values Dietary Intake Calories and proteins NDSR 2011 food consumed by children aged 1 year of the foods consumed by to 5 years in Kweneng? participants

Question 3 What is the relationship CFFQ scores and Dietary Intake and CFFQ and children’s SPSS was used to compute between caloric and protein intake of results of anthropometric anthropometric Pearson product moment children aged 1 year to 5 years and their anthropometric measures measurement. correlation coefficients anthropometric measures? measures.

Question 4: What is the level of Descriptive analysis of household food security among Household food Descriptive information Household food security responses from the caregivers of children aged 1 year- 5 security by caregivers questionnaire questionnaire years?

Question5: What is the relationship Household food SPSS was used to compute Household food security between household food security security, and Correlation coefficients Pearson product moment questionnaire children’s anthropometric measures? height, weight correlation coefficients

Protection of Human Subjects

The study protocol was submitted and approved by the Institutional Review

Boards at the Case Western Reserve University and the Health Research Unit of the

Ministry of Health (Botswana) for approval of the plan to protect human subjects. In

addition, the investigator asked individual caregivers for a written consent if they agreed

to participate in the study. Those who indicated interest in participating were then

evaluated according to the inclusion criteria. If criteria were met, the investigator

explained the informed consent process and then enrolled participants.

Benefits and risks. It was explained that participating in the study had no direct benefits but caregivers had an opportunity to share information about their food consumption practices and experiences regarding food security in their homes. Although, there was no guarantee that caregivers and children benefited directly from the study, participants were afforded individualized attention by the investigator and referred to the

Social Services if their situation was considered needy. Participants whose household food security score fell within severely food insecure category qualified for referral. It was anticipated that the study would have minimal psychological risks. There was a possibility that caregivers could experience some psychological discomfort related to the questions on household food security as questions may have highlighted their socioeconomic status. If there was an indication of discomfort such as reluctance to answer questions or a change in the demeanor, the investigator offered emotional support and the option of being referred to the local social welfare office for follow-up care.

Caregivers who required referral were provided with information on accessing the relevant office. Some participating children experienced slight emotional discomfort

when weight and height measurements were done by two people that they did not know.

Children were given time to ease their discomfort.

Confidentiality and voluntary participation. To ensure confidentiality, all questionnaires received random number identifier codes. Instead, participant’s forms were assigned codes and the names were used only on the consent document. The consent documents were kept separately from all data collection forms. Participants were assured of their right to privacy and confidentiality. The investigator informed participants that the information they provided would be kept confidential and used only for the purposes of the study. The coded data was kept in cabinets in a locked office and only the principal investigator had access to the data. Data forms and encrypted electronic data files were air lifted to Case Western University by the investigator. Upon completion of the data analysis, data were stored according to the university’s IRB requirements and will be destroyed 3 years following the completion of the study and acceptance of potential data-based manuscripts.

Participants were informed that their involvement in the study was voluntary, and they had the right to withdraw participation without fear of being disadvantaged in their future encounters with nurses or denied services or any future relationship with Case

Western Reserve University or health clinics in Botswana. As children who participated in the study were unable to give informed consent, their participation depended on the caregiver’s consent. A second line on the consent form had the adult participant sign for the minor child as children in the study were too young to assent.

Caregivers and children spent extra time at the clinic to participate in the research.

Upon completion of all data collection, caregivers were given a P30.00 an equivalent of

$3. 90 (in the year 2011) for a meal.

Summary

The chapter discussed the methods and procedures that were used to obtain data.

The design, sample and setting for the study were described. The instruments, data collection procedures including human subjects approval by both the Case Western

Reserve University and Botswana Ministry of Health institutional review boards and the informed consent document were presented. The chapter concluded with a data analysis plan for the study.

CHAPTER 1V

RESULTS

Introduction

The purpose of the study was to describe and explore the relationship between the food consumed by children 1-5 years and their anthropometric measurements, and

determine the relationship between children’s food and beverage consumption,

caregiver’s household food security and children’s anthropometric measures. This

chapter describes the data collection procedures, the analysis and findings. Data

collection involved face-to-face interviews with caregivers at the local health facilities

and their response to the survey questionnaire contained: three instruments including the

caregiver demographic data form, the household food security scale and a child food

frequency questionnaire adapted from MacIntyre (1998).

Study Sites

The study participants were recruited from six health facilities in the Kweneng

east district of Botswana. The sites were Borakalalo, Lentsweletau, Nkoyaphiri,

Phuthadikobo, Scottish Livingstone, and Thamaga clinics. The pool of participants was

caregivers who brought children for their routine monthly child welfare visit. The initial

plan was to recruit sixteen caregivers and their children from each of the selected

facilities. However, the actual sample consisted of 10 more participants than planned

from Phuthadikobo (n = 26) caregivers because the clinic served a comparatively larger

population. Another site, Borakalalo, had fewer participants (n = 7) caregivers related to

serving a smaller population. Other sites had participants ranging from 12 to19. The

numbers of participants according to each site are detailed in Table 3.

Caregiver Demographic Data

The sample participants consisted of 100 pairs of female caregivers and their children aged 12 to 56 months. One pair was excluded from the analysis because the child associated with one caregiver was above the age limit for the study’s inclusion criteria resulting in a final sample of 99 pairs. The mean age of the caregivers was 33.65 years with an age range of 18 to 65 years. Most of the caregivers were in the 21-30 years age group (41.4%) with 28.3% in the 31-40 year age group, 16.2% in the 41-50 years,

8.1% in 51- 60 years and 2% of the participants in the 61-65 year age group; only 4% were younger than 20 years (see Table 3).

More than half of the caregivers were single (57.6%), 19.2% were married, 10.1% were widowed and 12.1 % cohabited. Only 8.1% of the caregivers were employed, 58.6% were unemployed, 1% were students, another 1% pensioners and information on the employment status of 31 caregivers (31.3%) indicated that they were either irregularly employed on the government drought relief project (Namola leuba) or engaged in subsistence farming. Of those who were employed, 7% were sales persons while the rest were doing lower paying jobs including packing hair braids 3%, cooking 4% and custodial work 2%. Thirty-six percent (36.4%) of the caregivers had a monthly income less than P699.00 ($100.00). Overall, the monthly income for caregivers ranged widely from P699.00 ($100) to P5, 100.00 ($675.00). Notably, 32.3% of the sample relied on remittances that were not quantifiable on a monthly basis. According to a Central

Intelligence Agency (CIA) report in 2010, the average monthly family income in

Botswana was about $1,166.66 ranking it one of the top five countries in Africa and number 84 world-wide (CIA- The World FactBook, 2012) (Refer to Table 3).

Nearly half of the caregivers had secondary school education (47.5%), while

37.4% attained primary school level; 5.1% and 7.1% attained technical and university level education respectively. Two caregivers had never attended school. However, one of the two had attained non-formal education. The Botswana National Literacy Program commonly known as non-formal education focuses on the teaching of reading and writing skills only. The non-formal education program is a government initiative to increase literacy of the adult population who had missed the opportunity of schooling (Maruatana,

2007). Adult educators follow a basic learner- focused instructional program that is flexible and allows learners to attend lessons according to their needs. Individuals who successfully complete this program become proficient in reading and writing in the

Setswana language. Those who perform well may proceed to the formal educational program through the distance learning program. Only one caregiver was unable to read or write and needed to have the informed consent document read to her in Setswana.

The majority of the respondents were biological mothers (75.8%) of children participating in the study and the remainder were either grandmothers (16.2%) or close relatives (8.1%). Approximately, 52.5% of caregivers lived with one adult in the same household and 39.3% had at least two adults living in the same household while 7% of the caregivers had three adults and only 1% had four adults, which represented the highest number per household for the sample. Six-eight percent (68.7%) of the sample caregivers indicated that they were caring for another child below 18 years in addition the one participating in the study. About 24.2 % of the caregivers had two children while 6.1

% had three children who were below 18 years. The number of dependents for the

caregivers may influence the quantity and quality of food received by the young child

(Engle, Lhotska & Armstrong, 1997). All respondents were Batswana (See Table 3).

Source of Water Supply. In the village settings various sources of water supply are found. These include public or communal stand-pipes, family owned stand-pipes or running water. Both communal and family/household owned stand-pipes are connected to a water reticulation system managed by a corporate body that ensures that the water is treated and safe for consumption. Communal stand-pipes are located at different sites that involve walking distances ranging from 1 to 2 km (0.5-1.5miles). This distance has implications for caregivers with young children who have to make time to fetch the water as well as prepare the meal for the child. Although, the water supply is usually reliable in most sites that were used for the study, Molepolole has often experienced interruptions.

The alternative source of running water depends on whether households can afford the water installation costs. Nearly sixty-six percent (65.7%) of the caregivers owned standpipes that were within the household premises, 19.2 % had running water in the house, 10.1% depended on the community standpipes and 5% used other sources such a neighbor’s standpipe or natural ground water (See Table 3).

Waste Disposal. The majority of households (53.5%) used a pit within the compound to dispose their refuse, 28.3% relied on the public district waste management services. In big villages, the district waste management system ensures weekly collection of refuse, but in medium sized villages, collection may be every two weeks. Although, it is a crime to dispose refuse at communal dumpsites, 11% of the households reported using this method. Communal dumpsites are commonly found outside the residential areas because they are used illegally, therefore, individuals using this form of refuse

disposal travel 2-3 km from the village. Eight percent reported other or indiscriminate disposal which may involve other illegal disposal methods such as burning the refuse.

Household refuse that is disposed indiscriminately can be a public health concern for the community because activities such as burning may result with air pollution.

Toilet Facilities. Considering the toilet facilities, 74.7% of the sample used pit latrines, 15.2% had water system toilets, 7% used both types, i.e. pit latrine and water system and 3% did not have any form of toilet facility, but were using their neighbor’s.

Table 3 includes details of both sewage and refuse waste disposal.

Energy Source. Caregivers used a combination of energy sources ranging from firewood, paraffin (kerosene), domestic gas to electricity. About eighteen caregivers

(18.2%) depended on firewood compared to 11.1% who used electricity. The type of fuel used may impact food preparation and consumption as firewood involves labor- intensive work of harvesting. More than half of the sample reported using a combination of energy sources as reflected below on Table 3.

Table 3. Sample Descriptive Statistics

Sample Characteristics (N = 99) N % Mean (Range) Villages Lentsweletau 18 18.5 Mmankgodi 12 12.1 Mogoditshane 17 17.2 Molepolole 33 33.3 Thamaga 19 19.2 Health facilities Borakalalo Clinic 7 7.1 Lentsweletau Clinic 18 18.2 Nkoyaphiri Clinic 17 17.2 Phuthadikobo Clinic 26 26.2 Scottish Livingstone Clinic 12 12.1 Thamaga Clinic 19 19.2 Age of caregivers 99 100 (18-65 years) (M = 33.65, SD = 10.50) Number of adults per household 1 52 2.5 2 39 39.3 3 7 7.2 4 1 1.0 Number of children below 18 years (excluding those in the study) 0 1 1.0 2 68 68.7 3 24 24.2 4 6 6.1 Ethnicity Motswana 99 100 Caregivers’ relationship to child Mother 75 75.8 Grandmother 16 16.2 Other 8 8.1 Marital Status Single 57 57.6 Married 19 19.2 Divorced 1 1.0 Widowed 10 10.1 Cohabiting 12 12.1 Education Never attended school 2 2.0 Primary school 37 37.4 Secondary school 47 47.5 Technical or trades 5 5.1 University or tertiary 7 7.1 Non-formal 1 1.0 Employment Status 92

Student 1 1.0 Employed 8 8.1 Unemployed 58 58.6 Other 31 31.3 Pensioner 1 1.0 Monthly Income P699.00 or below 17 17.2 P700-P1,500 11 11.1 P3,000- P5, 000 2 2.0 P5,100 1 1.0 Remittances 32 32.4 No income 36 36.4 Water Supply Community standpipe 10 10.1 Own standpipe 65 65.7 Running water in the house 19 19.2 Other 5 5.1 Refuse Disposal Uses pit in the compound 53 53.5 Collected by the district council 28 28.3 Disposes at communal site 10 10.1 Other 8 8.1 Type of Toilet Facility Pit latrine 74 74.7 Water system 15 15.2 Both 7 7.1 Uses neighbor’s 3 3.0 Energy Source Firewood and paraffin 11 11.1 Firewood, paraffin and domestic 5 5.1 gas Domestic gas and electricity 12 12.1 Firewood, electricity and domestic 9 9.1 gas Firewood and domestic gas 11 11.1 Electricity 11 11.1 Domestic gas 8 8.1 Firewood and electricity 13 13.1 Paraffin and domestic gas 1 1.0 Firewood 18 18.2

Children’s Characteristics

The number of children recruited from each site matched that of the caregivers

already reported. A total of 99 children between 12 to 56 months participated in this

study. Children’s sex and age were obtained from the child welfare card record and

confirmed with caregivers. Weight and height were checked by the investigator and the

research assistant as previously described in the methods chapter.

Children who participated in this study were nearly gender balanced (n = 46

males, n = 53 females). The age of children ranged from 12-56 months (M = 28.99, SD =

12.9). The height had a range of 68.9-107.3 cm (M = 85.6, SD = 9.48), while the weight

ranged from 8-18 kg (M = 12.8, SD =2.32). Using the WHO (2011), anthropomorphic

classifying system to present z-scores or standard deviation (SD) scores, the height and

weight indicators were compared to the 0 median standard measure to derive summary

statistics that describe the children’s nutritional status. Approximately 17.4 % of the boys

had a weight-for-age below the -1 z-score compared to 17% of girls at the same z-score.

Forty-five percent boys had a length/height-for-age at -1 z-score, 6.2% and 8.7% were

below the -2 and -3 z-scores respectively, while 30.9%, 13.2% and 3.8% girls fell within

the same categories. For the weight-for-length/height measurements, 7.5% of girls fell

below -1 z-score compared to only 2 .2% boys with the same score. Interestingly, both

boys (26.1%) and girls (35.8%) had body mass indexes that were higher than +1 z-score of the median index, suggesting BMI that is at risk for overweight, Table 4 outlines z- scores and the gender differences. Information obtained from the child welfare clinic card

(Ministry of Health, 2007) showed that virtually all (N = 99) of the children received all immunizations according to the Botswana Expanded Program Immunization schedule

and only one child (1%) had not taken the Bacillus Calmette-Guerin (BCG) vaccine to prevent tuberculosis. Similarly, 99% of the children received vitamin A supplementation except 1% who had not. More than half of the children (60.6%) had been breastfed and

8.1% were currently breastfeeding, while 31.3% had never breastfed. Of the children who were breastfed, more than half (n = 32) were breastfed for over 12 months and only 9% were breastfed for less a than 6 months period. Only 2% of the children had once eaten at a restaurant the week preceding data collection. Descriptive data are indicated on Table 4.

Table 4 Children’s characteristics ages 1-5 years

Sample Characteristics (N = (N) (%) Mean (Range) 99) Gender M = 46 46.5 F = 53 53.5 Age (Months) (M =28.99, SD = 12.9) (12- 56) Weight for-age (kg) (M =.12.28, SD = 2.32) (8 - 18.7) Length/height for age (cm) (M = 85.60, SD = 9.48) (68.9-107.3) Did the child receive all immunization? Yes 98 99.9 No 1 1.1 Did the child receive vitamin A supplementation? Yes 98 99.9 No 1 1.1 Is the child being breastfed at the present? Yes 8 8.1 No 91 91.9

Ninety-nine children associated with each of the participating caregivers had their height and weight obtained physically by the investigator and the research assistant.

About 37.4% children had a HAZ at -1z-score of the median while 16.2% fell below the -

2 z-score mark indicating that these children were shorter than the WHO reference group.

Z-scores below the -2 score cut-off point indicate stunting (WHO, 2006a). Thus, 7.1% of children were below the -3 z-score point, which is the cut-off for severe stunting. About 96

seventeen percent (17.1%) of the children had WAZ at -1 z-score suggesting that they were at risk for underweight for age and only 3% were underweight as evidenced by -2 z-

scores. Thirty-one percent of the children had a BAZ at +1 z- score for age, suggesting

that these children were at risk for overweight, 3 % were overweight as reflected by BAZ

scores at +2 or more while 1% was obese. The variables on Table 5 were also graphically

presented on Figures 4 to 7. The graphs enable a visual comparison between children in

the current study in relation to the WHO standard reference curves (WHO, 2011).

Table 5 Children’s anthropometry by gender

Male (%) Female (%) Total (%) Variable n =46 n= 53 N = 99 WHZ +3 z-score 1 2.2 0 0.0 1 1.0 +2 z-score 1 2.2 1 1.9 2 2.0 +1 z-score 9 19.5 17 32.1 26 26.3 -1 z-score 1 2.2 4 7.5 5 5.1 HAZ +3 z-score 0 0.0 0 0.0 0.0 0.0 +1 z-score 3 6.2 2 3.8 5 5.1 -1 z-score 21 5.7 16 30.9 37 37.4 -2 z-score 3 6.2 8 15.1 11 11.1 -3 z-score 4 8.7 1 1.9 5 5.1 WAZ +3 z-score 0 0.0 0 0.0 0 0.0 +2 z-score 1 2.3 0 0.0 1 1.0 +1 z-score 3 6.2 3 5.7 6 6.1 -1 z-score 8 17.4 9 17.0 17 17.1 -2 z-score 1 2.2 2 3.8 3 3.0 BAZ +3 z-score 1 2.3 0 0.0 1 1.0 +2 z-score 2 4.3 1 1.9 3 3.0 +1 z-score 12 26.1 19 35.8 31 31.3 -1 z-score 1 2.2 4 7.5 5 5.1 Legend: WHZ=Weight-for-height, HAZ= Height-for-age, WAZ=Weight-for-age, BAZ= Body Mass Index- for-age

Figure 4 Weight-for-Length/height

Figure 5 Weight-for-Age

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Figure 6 Length/height-for-Age

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Figure 7 BMI-for-Age

Household Food Security Scale. The food security status of households linked to

the caregivers and children in the study was assessed using an adapted Household Food

Security Scale the Latin American and Caribbean Household Food Security Scale,

English version, also known as ELCSA-Latinoamericana y Caribena de Seguridad

Alimentaria (Spanish translation (H. R. Melgar-Quinonez, personal communication, July

2, 2010). As this was an adapted instrument, it was tested for reliability and the

Cronbach’s alpha was .92. Each caregiver represented a household and responded to the

15 item questionnaire. Caregivers were asked to indicate their observations and experiences regarding food availability in their homes in the three months preceding the

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date of interview. The instrument composed of two subscales, the first 8- items assessed

the situation of adults in the household while 7-items focused on children. Households

were classified into four categories (Zero point = food secure household, 1-5 points =

mildly food insecure household, 6-10 moderately food insecure household and 11-15 =

severely food insecure household), based on the scores earned by the participant (Melgar-

Quinonez, Alverez-Uribe, Amoroso, Ballard, Ortega, Perez.Escamilla, et al., 2010).

Overall, 19.2% of the households were food secure, 32.3% were mildly food insecure,

28.3% and 20.2% exhibited moderate and severe food insecurity respectively. Table 6

presents findings on the overall food security status of participants.

Table 6 Caregiver household food scores

Category N %

Food secure 19 19.2

Mildly food insecure 32 32.3

Moderately food insecure 28 28.3

Severely food insecure 20 20.2

Legend: Food secure = 0, Mildly food insecure = 1-5, Moderately food insecure = 6-10, Severely food insecure = 11-15

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Summary Descrption of the NDSR 2011 Software

In order to determine dietary intake, a software program for analyzing food items linked to the standard food composition table with foods consumed in a particular country or region is necessary. For this study, it was planned that the Medical Research

Council (MRC) software, the FoodFinder3 would be used for the analysis of the Child

Food Frequency Questionnaire (CFFQ) data. Botswana does not have a food composition table, therefore, the selection of this software was based on the foods usually consumed in Botswana which are imported from South Africa (SA). The FoodFinder3 software designed in SA is linked to the food composition table of that country. In addition, the adapted CFFQ that was used in this study was developed and has been tested in various studies in the same country (MacIntyre personal communication, 25 January, 2011).

Attempts to use the software for data entry and analysis showed that as data were entered, options to allow selection of unit of measurement for the food item such as the volume, gram amount or household unit (i.e. tablespoon) required that the food item to be moved from the navigation bar of the software to a plain screen. However, if the exercise was repeated for subsequent food items consumed by the same participant, the program did not allow execution of the command. Subsequently, a comparative analysis of three additional software programs did not find a suitable replacement. Generally, the three programs had multiple disadvantages including the inability to analyze food items that were originally not included in the database, lack of technical support, limited reports that could be generated from the data and a prohibitive cost of the software.

An alternative software program, the NDSR 2011 was identified to be suitable for the analyses of the food items that had been consumed by the study participants. The

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NDSR 2011 software is designed to perform comprehensive analysis of raw data on food

group counts, serving counts and nutrient analysis from 24-hour diet recalls, food

records, record assisted and 30-day dietary intake information and allows addition of

food recipes. The NDSR 2011 is linked to two separate databases, the Nutrition

Coordination Center (NCC) and Nutrient Database which allows it to be used for a wide

variety of food and nutrient analyses. The NDSR 2011 has over 18,000 foods and 168

nutrients associated with 9 food groups and nutrient ratios derived from the United State

Department of Agriculture (USDA), food manufacturers and foreign food composition

tables (NDSR 2011, Manual). The NDSR is commonly used for educational purposes, in clinical settings and in nutrition research, therefore it serves as the gold standard for nutritional analysis (Miller, Mitchell, Harala, Pettit, Smicklas-Wright, etal., 2010; Willett,

1998).

In summary, the NDSR 2011, software is capable of generating records on food and nutrients such as the food report which can indicate frequency of food consumption, nutrient totals, nutrient per food item and recommended dietary allowances/adequate intake reports. The recommended dietary allowances were based on the United State

Department of Agriculture standards (NDSR, 2011). Depending on the analyses required, numerous data outputs can be obtained. Also based on each project or study, the software can provide output on averaged nutrient totals and averaged food group serving and multiple other combinations. For the purposes of this study, the food intake per participant and food reports, average food group totals, energy and protein were selected for analyses.

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Before the NDSR 2011 could be used, the names of CFFQ food items were checked against those of the NDSR 2011 and FoodFinder3 programs because the two were based on different food composition tables. The investigator of the study and a nutrition assistant at BioNutrition center of the Dahms Clinical Research Unit (DCRU) affiliated with the University Hospitals and the Case Western Reserve University examined all the food items to confirm if foods were similarly named or described. If food names were different, the item was checked online to find a visual drawing or picture of the food item. For instance this was done with food items such ketchup which in the FoodFinder3 is called “tomato sauce” but an equivalent in the NDSR 2011 is ketchup. After all naming was done, the food items were checked for equivalence on the basis of macronutrients. To check for equivalence, all macronutrients including calories, proteins, carbohydrates and fats in all food items in the CFFQ were each assessed using a unit of analysis of 100 g. For instance, macronutrient information on 100 g of macaroni was used to get the nutrient values from the FoodFinder3 and was matched with 100 g of the same food in the NDSR 2011. If the food did not match in terms of calories, protein, carbohydrates and fats, the items were adjusted. For instance, if the calories, protein, or fats were low, the gram amount of the food item was increased so that the macronutrient could increase.

Twenty-six foods were mathematically corrected to match least 85% of the macronutrients (See Appendix I). The correction was necessary to enable proper estimation of the macronutrients on those food items that were affected. For instance, when a food item in the NDSR 2011 had less macronutrient than the one that had been consumed by participants, it was necessary to adjust the amounts to make the calories,

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protein, carbohydrates and fats to match what was consumed. Once corrected, macronutrients would match the food item consumed by participants. Out of 71 food items that were in the CFFQ, 26 required (Appendix I) adjustments for the macronutrients, and this was done with the assistance of the registered dietician, who is also the director of the Bionutrition center at the (DCRU). The corrections for equivalence were made by the registered dietician due the software expertise that was required. Two indigenous foods were not included in the NDSR. The investigator referenced food recipes from Botswana to enter food items according to the user recipes as provided in the NDSR 2011 program. The two food items were then added into the

NDSR as user-recipe.

The investigator had entered all the data into a Microsoft Excel Spread sheet following data collection at the Botswana site of the study. Upon return from Botswana to the United States, the investigator entered all the raw data into the Bionutrition center’s

NDSR software database. This involved identifying all food items, amount and frequencies of consumption that were reported in the CFFQ. The measurements that were used consisted of food models (e.g. oatmeal, green peas, banana, and bread models), volumes and measures, (e.g. 120 ml milk, juice, cup or tablespoon) and sizes (small, medium or large for items like oranges or apples). After completing the initial data entry, the investigator worked with a registered dietetic technician to check if all the data had been entered correctly by going over the record properties of each participant’s food entry list on the NDSR 2011 comparing the entries to the raw data from the Excel spread sheet.

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Thereafter, data entry was checked for consistency by the registered dietician at

the center. The registered dietician performed the required conversions of food items that

had been entered using volume, household food items or portion sizes to gram amount. It

should be noted that the investigator was not a member of University Hospital

department and had limited access to the equipment. For instance a computer had to be

shared with the director, the registered dietician at the Bionutrition center, and there were certain files that were password protected. The NDSR output files can facilitate analysis at component/ ingredient level, food level, meal level and record total level for daily dietary intake (NDSR, 2011). Food items on the CFFQ for each participant were analyzed to give descriptive statistics based on food item frequencies and amounts for all foods that were consumed, nutrient totals, averaged food group serving count and the recommended dietary allowances/adequate intake report (RDA/AI). The reports used for analyses included the total energy and protein.

All caregivers were verbally interviewed to obtain the CFFQ information and their responses were recorded on the FFQ before they were entered into an Excel program. Information from the Excel program was then entered into the NDSR software to compute the energy and protein intake averages for children. The results were used to conduct the SPSS analyses of energy, protein variables and the heights/weight of children including the household food security. During the interviews by the investigator, the caregivers responded to the CFFQ questions regarding typical food and beverage intakes over the past week based on their recall. Three dimensional food models and portion size pictures were used to increase the reporting accuracy by the caregivers.

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Prior to inferential statistical analyses, data were examined to determine if the study variables were measured at the appropriate levels suitable for Pearson’s r testing for correlations. Other assumptions for the Pearson’s r include random sampling and normal distribution of variables (Polit, 2010). Accordingly, it was essential for all the assumptions to be met in order for the selected statistical test to be valid. The sampling method was previously fully described in the method’s chapter. Variables including weight, height, household food security score, energy, and protein intakes were all measured at interval or ratio scales. To test for normality of distribution, histograms of variables were examined. The histograms revealed that weight was highly peaked and slightly positively skewed while height was slightly negatively skewed. The assumption of normal distribution of variables is robust to violation if the sample size is larger than

50 (Corty, 2007; Kline, 1994). Household food security was normally distributed. Since the assumptions were all met, the Pearson’s r statistical test was done. Variables on the

CFFQ were normally distributed in the data as indicated by a skewness value below 8 and kurtosis below 3 (Field, 2005; Kline, 2005). Summary descriptive statistics of all interval/ratio variables are presented on Table 7. The analyses were performed using IBM

SPSS Statistic version 20.

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Table 7 Summary Descriptive Statistics

HFSS Weight (kg) Height (cm) Energy (kcal) Total protein (g) Mean 5.85 12.3 85.61 1618.37 45.89 Median 5 11.7 84.4 1532.9 40.9 Std. Deviation 4.76 2.32 9.48 713.38 22.13 Skewness 0.45 0.39 0.22 0.99 1.05 Std. E. Skewness 0.24 0.24 0.24 0.24 0.24 Kurtosis -0.88 -0.58 -1.09 1.17 1.08 Std. E. Kurtosis 0.4 0.48 0.48 0.48 0.48 Range 15 10.67 38.4 3835.5 112.33 Minimum 0 8.03 68.9 336.44 7.24 Maximum 15 18.7 107.3 4171.96 119.57 Legend: HFSS=household food security score

Question 1 What are the main or core foods and beverages consumed by children

aged 1 year to 5 years in Kweneng? This question was addressed by examining the

frequency of consumption of all foods and beverages. All food items and beverages were

recorded according to the caregivers’ reports. Staple foods are characterized by greater

and frequent consumption by the population in a given area. For instance, cassava is

considered the staple for nations such as Nigeria and Kenya in Africa because the

population relies on consumption of large amounts of cassava (Stephenson, et al., 2010).

In this study, core/main food items were those staple foods and beverages that had the

highest frequency of a weekly intake and had been consumed for an average intake of

three times in a week. This cut-off point was determined by the investigator based on a similar nutrition study in Botswana using a sample of elders (Maruapula & Chapman-

Novakofski, 2007). The nationwide cross-sectional survey that identified the dietary patterns of the 60-99 year old elderly in Botswana (N=1086) used the percentage of participants consuming food items at least three times in a week as the cut-off- point for identifying widely consumed food items among the elderly (Maruapula & Chapman- 110

Novakofski). Although the same study identified commonly consumed food items by

Botswana’s elderly, some of the food items such as the beer group are not nutritionally

and culturally relevant for the current study’s population.

The criterion that was used in the Maraupula & Chapman-Novafofski (2007) was

found to be explicit and consistent with the objective of identifying core/main foods that

were consumed by children over a week’s duration, thus it was adopted. Therefore, to

classify food items as core/main food, the particular food item should have been

consumed at least three times per week. According to the CFFQ records intake, only five

food items met the core/main foods criteria. Some of the core/main food items that had

the highest frequency of consumption included, sorghum porridge (5.5), tea (5), sugar

(5.8), milk (5), yoghurt (3.6), Table 8 indicates all core/main food items with the

frequency by percentage of participants. Milk was consumed by all participants, and tea and coffee (92.9%) were the only three beverages commonly used by the participants meeting the criteria.

Table 8 Core/main food items consumed per week

Food Item (N) (%) Frequency of Consumption

Milk 99 100 5.00

Sorghum 99 100 5.50

Sugar 99 100 5.80

Tea/regular/herbal/coffee 92 92.9 5.00

Yoghurt 36 36.4 3.60

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The NDSR also yielded another useful comparative way of classifying food items according to the nine food groups and sub/groups used in the USDA nutrient database

(NDSR, 2011) and providing the average serving count per group (not counts for individual food items). These figures represent the averaged food group serving counts and subgroups of all records that were reported in the current study. Since individual food items were recorded for a seven day period, the average food group count reflects the same period. The food groups reflect the Nutrition Coordinating Center’s classification including fruits, grains, meat/fish/poultry, vegetables, sweets and fats. The grains had the highest total average serving count at 16.2, indicating that on average 16.2 servings of grain were consumed during the week of the study. The food groups/subgroups that followed were sweets with 2.73 serving counts, and meat fish and poultry1.94, vegetables, 1.17 and fats 1.24 serving counts. Details of all the average serving count totals for the study are presented on Table 9.

The CFFQ also included a qualitative item that required caregivers to report the types of foods that were not permissible or acceptable for the participant. Some foods were not permissible for cultural or health reasons. A few children were not allowed to consume the following selected food items. For instance, mopani worms were restricted for five children (5), pork (4), donkey meat (3), eggs (2), while melon porridge, spicy

(hot) foods, pumpkin, game meat, fish, beans and cabbage were each restrict acceptable for one participant. Ovreall, 21.2% of the participants had some food restrictions. The only food item that was restricted on religious grounds was pork. Other foods were restricted because the children were intolerant or exhibited unusual symptom following consumption of the food item.

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Table 9 NDSR 2011 Averaged food group/sub/group serving count total reported

Food Group/Sub/group Servings

Fruits, Total Serving 0.93 Citrus juice 0.15 Fruit juice excluding citrus juice 0.00 Citrus fruit 0.47 Fruit excluding citrus 0.30 Vegetables, Total Servings 1.17 Dark-green vegetables 0.05 Deep-yellow vegetables 0.06 Tomato 0.04 White potatoes 0.13 Fried potatoes 0.05 Other starchy vegetables 0.05 Legumes (cooked dried beans) 0.59 Other vegetables 0.20 Grains, Total Servings 16.27 Grains, flour and dry mixes-whole grain 11.23 Grains, flour and dry mixes-refined grain 3.05 Loaf-type bread and plain rolls-some whole grain 0.27 Loaf-type bread and plain rolls-some- refined grain 0.53 Other breads (quick breads, corn muffins, tortillas)–refined grain 0.24 Crackers- refined grain 0.11 Pasta- refined grain 0.28 Ready-to-eat cereal (not presweetened)-refined grain 0.08 Cakes, cookies, pies, pastries, Danish, doughnuts & cobblers–refined grain 0.27 Snack chips-refined grain 0.21 Meat, fish, poultry, eggs, nuts and seeds, Total Servings 1.94 Beef 0.60 Lean beef 0.00 Poultry 0.38 Lean poultry 0.07 Lean fish-fresh and smoked 0.06 Cold cuts and sausage 0.35 Organ meats 0.16 Eggs 0.12 Nuts and seeds 0.05 Nuts and seed butters 0.15 Dairy and nondairy alternatives, Total Servings 0.56 Milk-whole 0.42 Milk reduced fat 0.02 Cheese-full fat 0.00 Yogurt-sweetened low fat 0.10 Frozen dairy dessert 0.01 Pudding and other dairy dessert 0.00 Fats, Total Servings 1.24 Margarine- regular 0.40 Margarine- reduced fat 0.16 Oil 0.01 Shortening 0.03 Butter and other animal fats –regular 0.29 Salad dressing-regular 0.18 113

Vegetable-based savory snack 0.04 Cream-nondairy 0.04 Sweet, Total Servings 2.73 Syrup, honey, jam, jelly, preserves 0.00 Chocolate candy 0.00 Non-chocolate candy 0.14 Beverages, Total Servings 0.65 Sweetened soft drinks 0.10 Sweetened fruit drinks 0.00 Sweetened tea 0.13 Unsweetened tea 0.16 Unsweetened coffee 0.25 Miscellaneous Foods, Total Serving 0.28 Gravy- regular 1.13 Sauces and condiments-reduced fat 0.14 Soup broth 0.01

Question 2 What is the average and range of energy (calories) and protein in food consumed by children aged 1 year to 5 years in Kweneng? To compute the nutrient intake, the reported amount and frequency of consumption for each food item were multiplied, and then the amount was divided by seven to get an average daily intake. The

NDSR 2011 nutrient profile was extracted from the average gram amount of food consumed by the participant. Overall, there were five units of analyses per participant representing the total nutrient total grams of food items consumed, energy calories (kcal), total fat, total carbohydrate and total protein. All were measured in gram amount with the exception of energy. Although, the other average daily intake totals were mentioned above, the current question focused on energy and protein intake totals only. The results revealed an energy intake ranging from a low 336.4 to 4171.9 kcal (M = 1618.4, SD =

713.4). The range of protein intake for participants was from 7.2 g and the upper limit was 119.6 g (M = 45.9, SD = 22.13). It was noted that 8 participants were breastfed and their intake from breast milk was not accounted for in the total daily energy and protein.

Hence, the mean intakes may be slightly under-representative of the average daily consumption for the participants who were breastfeeding. According to the recommended 114

dietary allowances/adequate intake report generated from the NDSR, children in the life stage group age 1-3 years had 9.7% of energy from their protein and the figure is within the recommended intake of 5-20%. Similarly, the 4-5 year life stage group received 9.7% of their energy from protein. However, for this stage of development the recommended intake 10-30%. Thus, the latter group was slightly below the recommended/adequate intake (NDSR 2011, IOM (2006).

Question 3 What is the relationship between caloric and protein intake of children aged 1 year to 5 years and their anthropometric measures? To answer the research question, Pearson r correlations between energy, protein and the anthropometric variables were conducted. Although, a P value of 0.05 had been set as the threshold for significance, the Pearson r correlations results indicated significance at the 0.01 level (2- tailed). Moderate correlations of r (97) = .35 p < .01) and r (97) = .32 p < .01) (2-tailed) were found between children’ heights and energy calorie intake, total protein and height respectively. The results revealed that a linear relationship existed between the study children’s heights, energy and protein intakes. Further, moderate correlations of r (97)

=.28 p<.01 and r (97) =.31 p<.01 (2 tailed) were found between total protein, energy intake and weight of children respectively. These findings suggest that a linear relationship exists between children’s caloric, protein intake and their height and weight.

Table 10 shows the mean and standard deviation for all variables that were correlated and findings on all correlations are displayed on Tables 11.

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Table 10 Descriptive statistics for interval/ratio variables

Variable Mean Std. Deviation N = 99

Weight of child (kg) 12.29 2.32

Height of child (cm) 85.61 9.48

Energy of child (kcal) 1618.37 713.38

Total protein of child (g) 45.90 22.13

Table 11 Correlations between Intakes and Anthropometric Measures of Children

(n = 99)

Variables Weight (kg) Height (cm) Energy (kcal) Protein

Weight (kg) 1 .91** .31** .28**

Height (cm) .91 1 .35** .32**

Energy (kcal) .31** .35** 1 .97**

Protein .28** .32** .97** 1

**p< 0.01 (2-tailed)

Data were further explored to understand any differences in means that existed

among age groups of children for energy and proteins. In order to understand if there

were observed group differences in average protein and energy intake of children, the two

variables were treated as dependent and the age- categories were the quasi-independent variables or factor (Gravetter & Wallnau, 2007). Quasi-independent variables cannot be manipulated by the investigator but may be useful when performing an analysis of 116

variance. On the basis of the nature of the variables, that is when one dependent variable

was measured at interval/ratio and the other independent variable categorically measured,

the appropriate statistical test was a one-way Analysis of Variance (One-way ANOVA)

(Field, 2005; Heavey, 2011; Polit, 2010).

The data were examined for the basic assumptions of a one-way ANOVA test statistic including random selection of the groups, normal distribution of the dependent variable in the population and estimating variance within the population. The overall goal was to determine the amount in the dependent variable (energy and protein) that could be explained by the independent variable, age groups of participants. Children were grouped according to their chronological stages so as to reflect similar developmental needs. Data were examined and it was observed that the groups were unequal sizes. The age group of

12-23 months had 42 participants, 24-35 months with 25 participants, while the 36-56 months group had 32 participants but none of the groups was 1.5 times larger than the others. Even though, the variances were not homogenous within the population, the group size and homogeneity are robust to violation if the groups have more than 20 participants and if variance is not more than three times the variance in another group (Polit, 2010).

The non-robust assumptions were met and because the groups were large, the one-way

ANOVA was conducted.

It was hypothesized that there would be no relationship between age group of children and the energy and protein intake. In other words, it was posited that the population means would be equal and that there will be no difference in the population means among the three age subgroups of participants. The exploratory hypothesis was formally stated as:

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H0:µ1 =µ2=µ3

The results of the one-way ANOVA were: F (2, 96) = 9.19, p < .05 (energy) and F

(2, 96) = 6.59, p < .05 (protein) indicating that the null hypothesis was rejected. The results indicated that the sample means were statistically different from each other. In the current study that described the average energy and protein intake of children from food items reported in CFFQ and further sought to assess any differences in the average intake of energy/calories and protein according to specific age groups of children, the F statistic was larger than the p-value therefore, the null hypotheses were rejected, suggesting that the three group means were probably unequal. Table 12 shows the one-way ANOVA summary results.

As the results did not specify which groups had unequal means, post-hoc tests were conducted to help determine the inequality of means among age groups. The

Scheffe and Tukey’s post-hoc tests were chosen because both tests are capable of lowering the probability of Type 1error rate (Fields, 2005; Ott & Longnecker, 2010;

Polit, 2010).

Table 12 One-way ANOVA for Energy and Protein Intakes of Children Variable df F η p Between Subjects Energy (kcal) 2 9.19 .16 .000 Protein (g) 2 6.59 .12 .002 Within Subjects Energy (kcal) 96 9.19 .16 .000 Protein (g) 96 6.59 .12 .002 * p<0.05 level.

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On the basis of the results of the one-way ANOVA, the investigator concluded that the age of a child explained the amount of energy calories used by the child. Two age groups had means that were statistically significantly different from each other. Age group 24-35 months had a statically significant different mean (M =1748.00, SD =

631.89) and the age group 36- 56 months was also statically different (M = 1938.14, SD

= 840.96). Table 13 shows the result of the Scheffe post hoc test for energy intake

indicating specific age group mean differences. According to the Dietary Guidelines for

Americans 2010, (DGA) the overall mean kcal per day for children aged 24 months and

above is estimated at 2,157 kcal. Although Botswana does not have established energy

intake rates, the intake for the study sample exceeded the recommended average intake

by United States standards of 1,000-1,400 kcal for the age group 24-36 months and

1,000-1800 kcal for 48-96 months old (U.S. Department of Agriculture & U.S.

Department of Health& Human Services, 2010). For the current study the overall mean

intake was 1618.4 kcal, (SD = 713.40 ) for all sample participants aged 12-56 months

year old, however the mean intake for 12-23 months group was 1297.6 kcal (SD

=502.46) while the mean intake for 24-35 months and the 36 months and older children were 1748 kcal (SD = 631.89) and 1938.1 kcal (SD = 840.96) respectively indicating intakes above the recommended average by US standards (U.S. Department of

Agriculture & U.S. Department of Health & Human Services, 2010).

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Table 13 Multiple comparisons for energy intake

(I) Age group of child (J) Age group of child Mean difference Std E Sig 95% CI

I- J Lower Upper

12-23 months 24-35 months -450.44* 166.79 . 030 [-865.18-35.70]

36 months and over -640.58* 54.94 .000 [-1025.83-55.32]

12-23 months 450.44* 166.79 .030 [35.70-865.17]

Scheffe

24-35 months 36 months and over -190.14 176.26 .561 [-628.39-248.11]

36 months and over 12 -23 months 640.58* 154.94 .000 [55.32-1025.83]

24 -35 months 190.14 176.26 .561 [-248.11- 628.39]

*p< 0.05 level

Table 14 Multiple comparisons for average protein intake

(I) Age group of child (J) Age group of child Mean difference Std E Sig 95% CI I-J Lower Upper 12-23 months 24-35 months -9.88 5.29 .181 [-23.05 3.28] 36 months and over -17.71* 4.91 .002 [-29.94 -5.48] 12-23 months 9.88 5.29 .181 [-3.28 23.05] Scheffe 24-35 months 36 months and over -7.83 5.59 .380 [-21.74 6.09] 12 -23 months 17.71* 4.92 .000 [5.47 29.94] 36 months and over 24 -35 months 7.83 5.59 .380 [-6.09 21.74]

*p< 0.05 level

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The results of the one-way ANOVA for the mean protein intake indicated that the

means were statistically significantly different for ages 12-23 months and 36-56 months

indicating a difference in means between the age groups. The mean difference was 17.7g,

(SD = 4.92). The protein intake is calculated as a proportion of the ranges for percentages of calories that should be derived from all macronutrients including carbohydrates and fat. The recommended intake for 12-36 months is 5-20% and 10-30% for children 37-56 months (4 years and older) (U.S. Department of Agriculture & U.S. Department of

Health& Human Services, 2010). According to the NDSR results, the percentage of energy that was derived from the consumption of protein by all participants was 9.7%.

All macronutrients consumed by individuals contribute a proportionate amount of the total energy. In addition, the recommended average safe level of protein intake for children aged 12 months is 1.57 gram protein/kg body weight. The intake slightly decreases with age and at 24, 36, and 48 months the average is about 1.17, 1.13, and 1.09 g protein/kg body weight in that order (WHO/FAO/UNU Technical Report, 2007).

Question 4: What is the level of household food security among caregivers of children aged 1 year- 5 years? Households were categorized into four levels of food security based on the scores obtained on the household food security scale. The instrument particularly focuses on the effects of limited resources on food and examines the experiences suggestive of lack or inadequate food consumption at the household level. The instrument includes a subscale that addresses the situation of children regarding the household food security status. It was found that only 19.2% of the caregivers were food secure, approximately 32.3 % were mildly food insecure, 28.3% moderately food insecure and 20.2% were severely food insecure. Details of the

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household food security scores are found on Table 6. The level of food security was

measured on the basis of the caregivers’ experiences and perceptions regarding food

availability in their households. The reference period was three months preceding the

date of interview.

To further explore the implications of the household food security scores for the

caregivers, the background characteristics of the caregivers were reviewed. Based on

previous reviews and studies that associated the level of education with food insecurity

(Hackett, Melgar-Quinonez, Taylor & Uribe, 2010; Mason, 2003, Mmopelwa, Nnyepi &

Codjia, 2011) it was prudent to examine the same variables in the current study. The

household food security scores of caregivers were used to explore differences between

the population means of caregivers with low and high level education. The appropriate

test statistic for examining a relationship in a dichotomous variable or two independent

groups of participants with one continuous variable was the T-test (Gravetter & Wallnau,

2007; Heavey, 2011). The decision to conduct an independent-T-test was based on the number of groups that could provide sound interpretation of the existing data.

The assumptions of the independent-measures t- statistic include an independent variable that is measured dichotomously (Polit, 2010) as in the case of caregivers’ level of education, caregivers were classified into two groups of low or high level education.

Low education included caregivers (n = 40) with no, non-formal, and primary education, while the group with high education included caregivers (n = 59) with secondary, tertiary and university education. The sample of caregivers was randomly selected. The dependent variable, household food security score was assumed to be normally distributed within the two groups. In addition, the assumption of normality is robust to

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violation when the sample size is large for low (n = 40) and for high level education (n =

59). Polit also asserts that a t- test can provide accurate results even with severe departure from normality. The last assumption of the homogeneity of variance was assessed by further examining the group sizes. One condition for variance to be assumed equal is that none of the group should be 1.5 times larger than the other, (Corty, 2007; Polit). As all assumptions were met, the null hypothesis to be tested was that the two sample means were equal to zero (H0: µ1 - µ2 = 0).

The independent samples t-test was performed and the results were statistically significant (t = 4.53, p = .000), (M = 8, 25, SD = 4.72) for the low education group compared to (M = 4.22, SD = 4.08) for the high education group. In other words, the group with low education had significantly higher household food insecurity than the group with high education implying that caregivers with low education were more likely to be food insecure than those who had higher level education. Table 15 outlines the results of the independent -t-test

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Table 15 Independent samples t-test between caregivers with low and high education

Levene’s test for Test for equality of means Equality of variance F Sig t df Sig Mean Std E 95%CI (2-tailed) Difference Lower Upper Household Equal variances 1.342 .250 4.53 97 .000 .03 .89 [2.27 .80]

Food assumed

Security Equal variances

not assumed 4.40 75.43 .000 4. 03 .92 [2.21 .85]

Question 5 What is the relationship between household food security and children’s anthropometric measures? To determine if there was a relationship between household food security and children’s anthropometric measures, a Pearson’s product moment correlation was performed to test the relationship between the participants’ weight, height and the household food security score. Weak correlations that were not statistically significant were found (r (97) = .05, p ˃ .01) and (r (97) = .10, p ˃ .01) (2-

tailed) between weight, height and household food security score. The results of the

correlation matrix are indicated on Table 16. The correlations between household food

security and weight and height were not significant but height and weight were

significantly correlated. It can be inferred that the evidence was not sufficient to

determine if household food security was related to both weight and height of children in

the study.

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Table 16 Correlation matrix - Household Food Security Scores, Weight and Height

Variable HFSS (raw) Weight (kg) Height (cm)

HFSS (raw) 1 .05 .10

Weight (kg) .05 1 .91**

Height (cm) .10 .91** 1

**.p> 0.01 level (2-tailed).

In a review that examined measures used to explore hunger and malnutrition,

Mason (2003) reported that anthropometry may not be a suitable indicator of household

food insecurity as changes in anthropometric indicators may be sensitive to other factors

such as illness which may result in growth failure. Moreover, adults in households

experiencing food insecurity may adapt by adjusting portion sizes or skipping meals to

increase the intake by children (Egeland, Pacey, Cao & Sobol, 2010; Oldewage-Theron, et al., 2006). Thus, such coping stratgies may delay the effects of food insecurity on children.

Summary

The study results were derived from data that came from (N= 99) caregivers and reports of their (N = 99) children’s food and beverage consumption drawn from a CFFQ.

Data on food items that were consumed by the participating children were analyzed using the NDSR 2011 software program. The program was capable of generating informative reports on the quantity and quality of food items that were consumed by the participants.

Five food items that were consumed most frequently by children were categorized as the core/main foods. The food items classified as core /main were milk, sorghum, sugar, tea/ 125

coffee and yoghurt. All foods items that were reported in the study met the nine food

group/subgroups classification used by USDA (2010) and the NDSR 2011.

According to the averaged food group sub/ group criteria, the grains had the

highest serving count. With respect to the serving count output, the NDSR provides all

food items that were consumed by participants according to food grouped or subgroup

used. On the basis of the data that was entered, the food items represented the food

groups that are linked to NCC database used to classify foods into nine food groups and

subgroups. On the other hand, core foods were individual food items that were consumed

by the participants for three or more times per week. Therefore, from the food

group/subgroups, participants consumed an estimated average of 16.3 serving counts of

grain during the week preceding the study. The grains were followed by sweets with 2.7

serving counts, while the groups with the lowest food group serving counts were the

beverages at .65 serving counts and dairy and nondairy alternatives with .56 serving counts. The mean intakes for energy and protein were 1618.4 kcal, (SD = 713.40) and

45.9 grams (SD = 22.10) respectively.

The one-way ANOVA revealed statically significant differences in the means of

energy and protein intakes for children across age groups of children. Using the life stage

group for 1-3 years, children in this group with 9.7% met the recommended adequate

intake (A/I) for 5-20% of energy from protein classification used by the Dietary

Guidelines for Americans 2010. However the 4- 5 years group were slightly below the

adequate intake percentage which was also 9.7% of energy from protein instead of the recommended 10-30% (U.S. Department of Agriculture & U.S. Department of Health &

Human Services, 2010).

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Caregivers also responded to a household food security scale that measured perceived availability of food. The instrument particularly focused on the effect of limited resources on food and examined the experiences suggestive of lack or inadequate food consumption at the household level. The time frame for the food security was the last three months before the date of interview. Data were used to categorize caregivers’ level of household food security. The results indicated that only 19.2% of households were food secure with the rest of the caregivers reporting various degrees of food insecurity. There was a significant difference between low and highly educated caregivers. The results suggest that caregivers with higher education were more likely to be food securethan those with low level education.

Other findings revealed statistically significant correlations between energy, protein intakes and children’s anthropometric measures including height and weight. One notable and unexpected finding was the non-significant correlations between children’s anthropometric measurements and the household food security scores despite the high level of food insecurity reported by caregivers. In the sample children, stunting was far higher than underweight, but there was no evidence of wasting.

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CHAPTER V

DISCUSSION AND RECOMMENDATIONS

Introduction

This chapter presents a discussion of the major findings, limitations of the study,

recommendations for practice and conclusions. The purposes of the study were to

describe and explore the relationship between the core/main foods and beverages

consumed by children aged 1 to 5 years and their anthropometric measurements and to

determine the relationship between children’s energy, protein intake, caregivers’

household food security and children’s anthropometric measures. The study used a cross-

sectional design and a convenience sample to collect pertinent data.

The conceptual framework underlying this study was adapted from the United

Nations Children’s Fund’s (UNICEF) framework for the Causes of Child Malnutrition

(UNICEF, 1990) that has since undergone several revisions and subsequently renamed

UNICEF’s Conceptual Framework: Care for Nutrition (Engle, Menon & Haddad, 1997).

Although at the beginning, the framework was used to identify causes of malnutrition in children, it is still relevant in assessing factors that influence child nutritional status. The framework recognizes three levels of factors associated with children’s nutrition. The levels include immediate, underlying and basic determinants of child nutritional status

(UNICEF).

The immediate determinants of child nutritional status are manifest at the level of the individual and these include dietary intake and health status and are interrelated. For

instance, a child with inadequate dietary intake is more susceptible to compromised

immunity. On the other hand, a child who is ill may have inhibited consumption and

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absorption of food and nutrients (Levitt, Pelletier & Pell 2009, UNICEF, 1990). The immediate determinants of child nutritional status are in turn influenced by three underlying factors manifesting themselves at the household level. These include food security, adequate care for mothers and children, and a healthy environment and access to health services. Finally, the underlying determinants are influenced by the basic determinants. The basic determinants comprise resources available at the community or country level that influence the operation of the underlying and immediate determinants.

Economic, cultural, political and social factors influence the utilization of resources for access to food, care and health (UNICEF).

The model provided for identifying variables for analysis for the current study.

The investigator examined children’s food and beverage consumption including anthropometric measurements and household food security among caregivers of children aged 1 year to 5 years. Face-to-face interviews of caregivers for the 1-5 years children were conducted to elicit responses for the child food frequency questionnaire that measured food and beverage intake. In total, 71 food items were included in the CFFQ.

The nutrient calculations were based on the frequency of consumption reported in the questionnaire multiplied by the portion sizes of food divided by 7 to get the average intake per day. The NDSR 2011 software based on the USDA Nutrient Database was used for the analyses of macronutrients and food group /subgroup classification of all foods that were consumed by the participants (NDRS, 2011). Anthropometry was measured by two observers, the investigator and a research assistant, using the same equipment throughout the data collection process to ensure consistency and reliability in measurements. In addition, the scale was calibrated before the beginning of each data

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collection session. Caregivers of the children responded to a household food security questionnaire that examined their experiences regarding availability of resources and food in their households using a three months reference period.

Discussion

Although, the overall malnutrition rates have declined in Botswana, severe

stunting remains a problem for children aged 0-5 years (Ministry of Health, 2009). Of the

few studies that investigated the nutritional status of young children (Abrams, Mushi,

Hilmers, Griffin, Davila, & Allen, 2003; Ramolefhe, Nnyepi, Chimbari & Ama, 2011;

Tharakan & Suchindran, 1999) none of them examined food and beverage consumption

in Botswana’s 1-5 years children. Food and beverage consumption were measured by a

child food frequency questionnaire that relied on food models to assist caregivers to

estimate portion sizes of the intake by their children. This study was important to the

nutrition of children in Botswana because understanding young children’s dietary intake

can promote nutrition education and counseling of caregivers which can guide

appropriate feeding and eating patterns for children. The average total protein and energy

caloric intakes for children were analyzed. The study results will serve as baseline for

future investigations.

Core/main foods consumed (RQ 1)

The Ramolefhe, et al., (2011) study that evaluated feeding practices, feeding

environment, and growth status of children in Botswana, assessed children’s diet by using

proportions of food groups represented in a 24 hour diet recall. The food groups that were

represented in the intake were then reported according to the total percentage of children

who had consumed the foods in the 24 hour-diet recall. It emerged that cereals (96.7%),

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milk products (56.7%), oils and fats (50.0%), and tea/coffee (43.3%) had the highest

percentages of consumption while meat was consumed by only 26.7% children. In the

current study, the core/main foods were those food items that were consumed at least

three or more times per week (Maruapula & Chapman-Novakofski 2007) and these included sorghum, sugar, tea/ coffee, milk, and yoghurt. This classification of core/main foods was based on the highest frequency of food items consumed in a week’s period not food group per se. Compared to those food items that represented specific food groups in the Ramolefhe et al. study, there is a close match with foods that were consumed in the two studies. The cereal group consumed by 96.7% children in the Ramolefhe, et al., study

closely matches the grain group consumed by all participants in the current study. The

tea/coffee consumption by 92.9% of participant in the current study was almost double

the percentage of the 43.3% participant reported by Ramolefhe et al. The reasons for the

disparities in the tea/coffee intake may be in the environment of the two studies. While,

the current study was done in major villages associated with urban lifestyles, the other

study was done on small rural farming villages. The eating habits in the two settings tend

to differ. In farming villages there may be more reliance on farm proceeds such as fresh

cow’s milk instead of tea/coffee beverages.

The NDSR 2011 software program that was used for data analysis in the current

study facilitated for the classification of food group/subgroups that had the highest

average serving count suggestive of frequent consumption among the study participants

also indicating a frequent intake of the grain group identified the study by Ramolefhe et

al.( 2011). The food group/subgroups that ranked highest in serving counts were 16.3

grains, followed by sweets with 2.7 counts, meat 1.9, and fats 1.2 serving count. In this

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study however, whole grains 11.2 servings provided almost four times the number of

serving counts compared to refined grain with 3.1 serving counts that were reported.

The food group/subgroup classification has highlighted foods that are similar to

those reported in Ramolefhe et al. (2011). These food items are comparatively

inexpensive and readily available sources that are the staples for Botswana. To an extent,

the refined grains and sweets that were most frequently consumed are associated with

high energy density (Wardlaw, Hampl & DiSilvestro, 2004). These foods have a high

yield of energy which can negatively affect children’s health in the long-term if

consumed excessively (Colapinto, Fitzgerald, Taper & Veugelers, 2007; Otten, Hellwig

& Meyers, 2006). From the list of core/main food items that were consumed by the

participants, the diet comprised a limited variety of food items. This is not unique to this

study, other studies on the diet of adults in Southern Africa, have reported similar

findings of diets that are comprised of predominantly starchy foods that are monotonous and limited in variety (Maruapula & Chapman-Novakofski 2007; Oldewage-Theron,

Dicks & Napier, 2006; Rose, Chotard, Oliveira, Mock & Libombo, 2008).

Further, studies in South Africa that investigated the nutritional status of preschool children revealed that children’s diets consisted of mainly maize-meal porridge consumed three times a day, milk taken once or twice daily and tea and coffee also taken once daily (Dannhauser, Bester, Joubert, Badenhorst, Slabber, Badenhorst, et al., 2000;

Theron, Amissah, Albertse & MacIntyre, 2006). Theron et al., also indicated that in their study children aged 12-24 months consumed 200-366 g of maize-meal daily, which is a carbohydrate-rich food item. This has health implications because overconsumption of energy dense foods such as refined maize-meal have been associated with overweight

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(Corvalan, Kain, Weisstaub & Uauy, 2009). It is important to consume a variety of foods

each day. By eating a variety of food items, children are better able to meet essential

micronutrients requirement for their needs (Wardlaw, Hampl, DiSilvestro, 2004). Food

items such as fruits and vegetables provide good sources of vitamins and minerals.

Milk consumption for children in this age group is particularly important as milk

represent a food source in the children’s diet that provides protein, zinc and calcium

needed for the strength and growth of bones (Wardlaw & Smith, 2009). However, the

average serving counts that were reported in this study indicate lower servings compared

to the recommended serving counts for children in the same groups who should be

getting 2 to 21/2 cups of milk per day (MyPyramid.gov. Available at:

http://www.choosemyplate.gov/foodgroups/d. Accessed on 3/10/2012). For children in

the current study, milk and yoghurt were the only two food items that were widely

consumed from the dairy and nondairy alternatives food group/subgroup range. Although

yoghurt met the classification criteria for frequently consumed food items, only 36.4% of

the children consumed this food item. Therefore, the majority of children may have relied

on the milk intake for their consumption of dairy and nondairy products which recorded

low serving counts, thus suggesting inadequate intake.

Energy and protein consumption (RQ 2)

In developing countries, energy and protein intake have received special attention

because of the high incidence of protein-energy malnutrition among young children (Lin, et al., 2011). In this study, the estimated total energy and protein intakes were derived from a food frequency consumption data and analyzed using the NDSR 2011. The results

indicated that children consumed various sources of energy foods and proteins. The mean

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daily total energy intake for the sample was 1618.4 kcal/d, (SD = 713.4) while protein was 45.9 g/d, (SD = 22.1) with ranges of 336.4-4172 kcal and 7.2 -119.6 g/d for energy and proteins respectively. Due to the lack of information on total energy and protein intake in Botswana’s children, findings from this study are compared to those available from other countries in Southern African and elsewhere.

A South African cross-sectional baseline study that examined the nutritional status of preschool children (N = 171) from two informal settlements, revealed that energy and protein intake for children in the two sites varied. The energy and protein intakes derived from a 24-hour dietary recall used caregivers of children as informants.

Children from a site that was later designated as an experimental group, the Joe Slovo site had a mean energy and protein intakes of 818.12 kcal/d, (SD = 356.12) and 29.6 g, (SD =

14.6) respectively for children who were aged 2- 3.9 years, while children in a similar age group but in the group that was from a different site, JB Mafora, later a control group had mean intakes were 1021.51 kcal/d, (SD = 394.83) and 34.1 g, (SD = 15.4) for energy and protein. For the older children aged 4-5.9 years at the Joe Slovo site, the energy and proteins means were 905.83 kcal/d, (SD = 385.27) and 31.7 g, (SD = 16.1), and the JB

Mafora reported higher averages at 1047.57 kcal/d, (SD = 373.08,) and33.9 g, (SD =

15.4) respectively (Dannhauser, Bester, Joubert, Badenhorst, Slabber, Badenhorst et al.,

2000). These differences were not statistically confirmed, however the results provide for useful information for comparison with this Botswana study.

Clearly, children in the current study had higher average intakes of both energy and protein than those from the two South African study sites, the Joe Slovo and JB

Mafora settlements. Possible reasons for the differences may be in the socioeconomic

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status of households in the two countries. The South African children came from informal settlements located in the outskirts of cities and towns. The settlements are generally poorer than a major village in Botswana. In addition, in terms of household resources, almost 88% of the South African sample households used paraffin (kerosene) stoves as main sources of fuel for food preparation compared to only 5% households using a similar mode in the current study. Also, the anthropometric measures in the South

African study indicate higher rates of stunting of 21-29%, including wasting (6.5%) while the Botswana study found 16.1% stunting and only 2% wasting. In terms of educational achievement of the children’s caregivers, in the South African study, it was reported that most caregivers had completed primary school (7-8 years of elementary school) level of education compared to over 60% caregivers who had secondary or higher level education equivalent to 12 years and higher.

In another more recent South African study that measured dietary intakes of young children aged 12-24 months, the energy and protein intakes for the study participants who were classified according to rural and urban settings revealed that intakes were similar across settings (Theron, Amissah, Albertse & MacIntyre, 2006). The rural group (n= 58) had mean energy and protein intakes of 1149.38 kcal/d, (SD =

554.49) and 33 g/d, (SD = 17) and the urban group (n = 74) had energy of 1260.76 kcal/d, (SD 595.84) and a protein intake of 40 g/d, (SD = 21). Considering the age ranges for children in this South African study, on average the mean intake of protein for both the urban and rural samples was slightly higher than the former South African study

(Dannhouser, et al., 2000) and the Botswana study.

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In a recent Belgian study by Lin and colleagues (2011), total energy and protein

intakes of 1408.4 to 1474.4 (kcal/d), and 54.8 to 56.4 (g/d) were reported among

preschool children in the ages 2 to 5 years, while Vereecken & Maes, (2010) reported an

average energy intake of 1362 kcal/d also in preschool children. Compared to the study

by Lin et al., higher intakes of energy were reported from the present study while the total

protein intake was slightly lower. Several possible explanations may account for the

differences in the findings of the two studies. The Belgian study estimated the dietary

intakes from a 3 day non-consecutive record diaries completed by parents compared to

the food frequency questionnaire that was used in the current study. Food frequencies

questionnaires are particularly prone to misreporting (Otten, Hellwig & Meyers, 2006)

because participants may suffer memory lapses (Willet, 1998). In addition social

desirability may have influenced the findings as the study involved face-to-face interviews (LoBiondo-Wood & Haber, 2006). Hence food frequency questionnaires may be more liable to error that may result with over or underreporting of food intakes.

Wilson & Lewis (2004) reported higher energy and macronutrient intakes measured by the Block98 FFQ than when measured by the 3 day diet record. However, the use of models and household utensils in the current study facilitated better estimates of food intakes by the caregivers. Another reason for the higher average energy intake that was found in the current study may be the result of the high frequency in consumption of the core food items such as sorghum and tea with added sugar. The long term effects of overconsumption of energy dense foods may lead to overweight (Otten, Hellwig &

Meyers).

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Evidence from the sample showed that 16.1% of the children were stunted as

reflected by their height-for-age z-score that were at the -2 z-score cut-off point or more

(WHO, 2006a). In addition, 31.3% of children in this study were at risk for overweight with body mass indexes (BAZ) that were at +1 z-score cut-off point, 3% of children were overweight as reflected by BAZ below the +2 z-score cut-off point for overweight. This is not unusual because China and Thailand have reported co-existence of both underweight and overweight among children (Willows, Barbarich, Wang, Olstad, &

Clandinin, 2011; Firestone, Punpung, Peterson, Acevedo-Garcia & Gortmaker, 2011).

When countries such as Botswana experience demographic and epidemiological transition (Maruapula & Chapman-Novakofski, 2007), such as a shift from communicable to chronic non-communicable diseases associated with lifestyle changes

(Ministry of Finance& Development Planning, 2003) then under- and overweight can co- exist in the same society. Problems associated with lifestyles include high fat, high cholesterol diets often associated with obesity. Children in the current study consumed energy dense food such as tea/coffee with added sugars. Also the diet had a limited variety of food as reflected by only five foods that were consumed more than three times per week.

The Institute of Medicine (IOM, 2006) has established ranges of percentages of calories that should come from protein, carbohydrate and fat. The acceptable macronutrient distribution range (AMDR) for protein is 5-20% for children aged 1-3 years, and 10-30% for children aged 4-8 years. According to this criterion, children aged

1-3 years in this study met the requirement as reflected by 9.7% of energy from protein.

However, those in the 4-5 years range were slightly below their 10-30% goal as the total

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energy from protein was also 9.7%. According to these results, children generally met the

adequate intake level for protein-energy requirements. The adequate intake level is based on observed estimates of nutrient intake by a group or groups of healthy people (Murphy

& Poos, 2002).

A comparative analysis of the protein consumption for the 16.1% of stunted children and the non-stunted children (83.8%) showed that mean intakes were similar

45.9 gm but the standard deviations were slightly different. The stunted children had

(SD=21.00), while the non-stunted were (SD=22.5). Although the group sizes for stunted and non-stunted children were not the same, the mean intake was the same, suggesting that the stunted group may have overreported their protein comsumption.

Energy and protein consumption and anthropometric measures (RQ 3)

Using the Pearson r correlation, it was found that height and weight were

significantly positively correlated with total energy and protein intake, suggesting energy

and protein are associated with growth of children. However, the nature of the existing

correlation cannot be causal as correlations indicate presence, strength, and direction of

an existing linear relationship among variables (Polit, 2010). In this case, the correlations

that were found between energy, protein intake total mean intakes and height/weight of

children merely indicate a positive association among these variables. The reported

correlations were moderately and positively strong with r = .32 and r =.35 for energy,

protein and height variables and also moderately strong correlations of r = .28 and r.31 for energy, protein and weight variables. These correlations suggest a medium effect size of ≈.30 (Corty, 2007; Field, 2005).

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The existence of statistically significant correlations between energy, protein and

height is a plausible finding because protein and energy are required for various

metabolic processes that include the production of essential amino acids and cellular

synthesis (Lin, et al, 2011; Wardlaw & Smith, 2009, Wardlaw, Hampl & DiSilvestro,

2004). The relationship between energy balance and protein utilization is an important

one (Rodriguez, 2005). A WHO longitudinal and cross-sectional (n= 8440) study that

was used to derive the WHO child growth standards was based on a sample of healthy

young children who were raised in environments that minimized constraint of growth

such as poor diets and infection. The study demonstrated that a sample of international

children from (Brazil, Pelotas; Ghana, Accra; India, South Delhi; Norway, Oslo; Oman ,

Muscat; and USA-California, Davis), aged 0-5 years with similar nutritional status had a strong similarity in linear growth among children from these different countries (WHO,

2006a) suggesting that nutrient intake supports growth. In the current study both protein and energy intake are positively correlated to height and weight of children indicating the

existence of a positive association, suggesting that adequate intake of energy and is

important for the physiological growth in young children. Height is an important

indicator for identifying the presence and duration of undernutrition associated with

inadequate intake of protein and energy in young children (de Onis & Habicht, 1996).

Evidence indicating that inadequate intake of protein and energy can lead to reduced

height in young children has been documented (Grantham-McGregor & Baker-

Henningham, 2005; Park & Choue, 2011; Willows, et al 2011). In a similar study that examined dietary intake, anthropometry and symptoms of nutritional deficiencies in children 0-5 years, stunting is a sign that signifies slowing of the skeletal growth, stunting

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was found in 51% of the study children and was associated with inadequate consumption

of protein and energy (Singh, Fotedar, Lakshminarayana & Anand, 2006). In the present

study, 16.1% of the children had a height that was below the -2 z-score cut-off point that indicated stunting (WHO, 2006a). Linear growth can be affected by environmental factors such as diet and infection (WHO, 2006c). Despite the positive moderate correlations between the children’s energy, protein intake and height and weight in this study, 16.1% children had stunted growth. Stunting has been associated with a diet high in energy but low in essential nutreints associated with linear growth (Fernald & Neufeld,

2007). There are no national surveys that have examined nutritional status of young 1-5

years children in Botswana. The one regional study that focused on this age group using a

smaller sample size (n = 75) than the current study, reported that 19% of children were

underweight, 6% stunted while 22% were wasted (Ramolefhe, et al., 2011). This finding

of stunted growth in the current study sample requires further investigation, such as the

anthropometric measures of parents to provide comparison that can help to determine the

role that may be played by other factors such as genetic predisposition.

Household food security (RQ 4)

Despite being a middle-upper income country (CIA- The World FactBook, 2012),

many households in Botswana are still faced with food insecurity. Household food

security refers to“a state in which all people at all times have both physical and economic

access to sufficient food to meet their dietary needs for a productive and healthy life”

(Coates, Swindale & Bilinsky, 2007, p.1). Thus, food insecurity is associated with a

perceived lack of access to food. On the other hand, food insecurity includes concerns

over obtaining enough food as well as measures of coping with inadequate amount and

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quality of food consumed (Hackett, Melgar-Quninonez, Taylor & Uribe, 2010). In this study, almost 80% of the households were reported by the caregivers as having experienced varying degrees of food insecurity. Thirty-two percent of the households were mildly food insecure, 28.3% moderate and 20.2% were severely food insecure. This trend of household food insecurity may be explained by a number of factors. For instance, many caregivers had no income or if they had any, it was below the official poverty line. A total of 36% caregivers had no income, thus, their perceptions of food security may have reflected the uncertainty related to lack of income but not the actual lack of food. Moreover, food insecurity may not be absolute as it may exist in various degrees. For instance, food might be available but not adequate to meet all perceived nutritional needs (Mason, 2003). In such cases, caregivers may even rely on foods that meet the basic need for hunger but being of a low nutritional quality.

More than 50% of the sample caregivers were single. Evidence has shown that single and female headed families are at higher risk for food insecurity than the average family (Bowman & Harris, 2003; Stevens, 2010). Similar observations on socioeconomic conditions and household food insecurity have been reported by other researchers

(Edelstein, 2011; Hackett, Melgar-Quinonez, Taylor & Uribe, 2010, Mmopelwa, Nnyepi

& Codjia, 2011). Furthermore, household food insecurity commonly affects people in rural areas where living standards are lower than in urban settings (Ministry of Finance &

Development Planning, 2008).

Education is another factor that is linked to household food insecurity because the less educated have a greater occurrence of unemployment and poorer living conditions

(Hackett et al.; 2010; Stevens, 2010). In the present study, 47% of the caregivers had

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twelve years of education, 37% had less than 12 years, and only 7% had achieved

university level of education. The results of an independent sample t- test conducted in

this study revealed that caregivers with low education were more likely to have higher

scores on the household food security scale than caregivers with higher education. As

higher scores implied the degree of food insecurity, it can be inferred that the level of

education influenced one’s food security status. In support of the view that education

impacts household food security, a Botswana study reported that education significantly

reduced food insecurity among households that were headed by individuals with higher

education than those who had lower level education (Mmopelwa, et al., 2011).

Household food insecurity has implications for children’s dietary intake. A

number of studies found that household food insecurity had immediate and long term

effects on the nutrition and overall health status of children (Hackett, Melgar-Quinonez &

Alvarez, 2009; Mukhopadhyay & Biswas, 2011; Oldewage-Theron, Dicks & Napier,

2006). Children in food insecure households were stunted, underweight and wasted

(Mukhopadhyay & Biswas) and the long-term effects of poor nutrition on young children are well documented (Knol, Haughton & Fitzhugh, 2004; Kranz, Mitchell, Siega-Riz &

Smicklas-Wright, 2005; Kruger & Gericke 2003; Lutter & Rivera, 2003, Onyayngo,

Koski & Tucker 1998). While the long-term outcomes of household food insecurity may not be predicted from the results of this cross-sectional study, it can be indicated that children from the moderately and severely food insecure households may be at higher risk of the negative effects of food insecurity such as stunted growth and underweight.

Although the current study focused on young children age 1-5 years, it may be important to note that non-nutritional effects of food insecurity in older children that

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include poor educational performance, behavioral and psychological problems such as

suicidal tendencies were cited (Holben, 2010). In some cases, household food insecurity

was associated with child overweight (Bowman & Harris, 2003; Holben; Stevens, 2010)

because with limited resources, adaptive measures determine the type and quality of food

that is procured (Oldewage-Theron, et.al 2006). Food insecure households are likely to

reduce the food intake and even alter their eating patterns as a result of lack of money or

resources for food. Changes may involve using the limited money on food that may

satisfy hunger instead of nutrients and such foods tend to be energy dense (Edelstein,

2011). Access to sufficient and nutrient rich as opposed to nutrient deficient foods like

sweets is a priority for households as this is essential for the consumption of good quality

food by young children in respective households.

Household food security and anthropometric measures. (RQ 5)

Findings about the correlation between household security and the children’s

anthropometric measures revealed a non-significant relationship, suggesting that there was not sufficient evidence to conclude that there was a linear relationship between the variables (Corty, 2007). Non-significant correlations of (r (97) = 05, p ˃ .01) and (r (97)

= .10, p ˃ .01) that supported a weak or lack of association or relationship between household food security score and the children’s height and weight indicated a small effect size (Heavey, 2011) requiring a larger sample size (Ott & Longnecker, 2010). In this study however, the sample size was calculated and it met the required size for the type of statistical analysis that was used (Faul, Erdfelder, Lang & Buchner, 2007). The existence of a non-significant correlation may be that in the present sample, the variables were not related.

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The lack of association between household food insecurity and children’s

anthropometric measure was an unexpected result. However, Mason (2003) has argued

that the time reference period of food insecurity and the occurrence of hunger may

influence anthropometry. Hence, while anthropometry can determine physical

malnutrition, it may be less sensitive to household food insecurity. A recent study that

was done in Botswana found that household food insecurity scores and children’s height-

for-age and weight-for- age z-scores were negatively correlated (Mmopelwa, Nnyepi &

Codjia, 2011). In addition, previous studies on household food insecurity and children’s

nutritional outcomes assessed by measurement of anthropometry indicated that in

households that experienced severe food insecurity, children were at higher risk for poor

nutritional and health outcomes (Egeland, Pacey. Cao & Sobol, 2010; Gray, Cossman &

Powers, 2006; Hackett, Melgar-Quinonez & Alvarez, 2009; Mukhopadhyay & Biswas,

2011; Oldewage-Theron, et al., 2006). Even though there was a non-significant

correlation between the household food security score and children’s weight and height

scores, 10.1% children had length/height-for-age z-scores that were below the -2 z-scores

suggesting moderate growth stunting in these children and 6% were severely stunted at -3

z-score of the median scores.

One possible explanation for this finding may be that the effects of household

food insecurity can be associated with the consumption of nutrient- poor but energy dense diet contributing inadequate growth coupled with excessive weight gain (Jehn &

Brewis, 2009). Secondly, as this was a cross-sectional study, the reported household food insecurity may have not been long enough to be associated with negative effects on the weight and length/height of children because caregivers may develop strategies that

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ensure their children do not go hungry (Stevens, 2010). In a South African study, caregivers were also reportedly using a variety of coping strategies that included eating less preferred foods, skipping meals and reducing portions sizes to ensure that children had more food (Oldwage-Theron, Dicks & Napier). Lastly, it may be that caregivers who are more attuned to the importance of health and regular consumers of health care services via the clinics would be more likely to provide nutrition to their children as the greatest priority, in spite of food insecurity of the household.

Implications

Implications for future knowledge development

Dietary intake for young children is a complex area that is not easily understood.

There are several influences that determine the food consumption patterns that are eventually adopted by caregivers and families for young children. The UNICEF framework for the causes of child malnutrition that was used to examine variables for this study recognizes that there are multiple influences on dietary intake of young children

(UNICEF, 1990). The model links the influences of child nutrition to different social and organizational levels. For instance, the immediate factors affect individuals like the case of foods consumed by children, the underlying factors are linked to the family as in the case of access to food and care of children by their parents and caregivers. Basic factors are related to the community and the nation but indirectly impact the children’s nutritional status. The model emphases the importance of a descriptive rather than a prescriptive approach in addressing problems related to nutrition. Thus, according to this framework, to address any nutrition related problem, the assessment, analysis and action process (triple A approach) needs to be followed so as to fully understand how the

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nutrition problems impact a given community. Such understanding will enable the

development of appropriate interventions (UNICEF). The UNICEF’s framework

provided for useful assessment of the household food security among the caregivers

responsible for the provision of food as well as the feeding of children. The framework

was particularly useful because it was developed and evolved with international input and

applicable to the context of Botswana’s children. The model was useful in suggesting

interrelated dynamics for testing. Although findings suggested prevelance of household food insecurity among caregivers, the results of children’s anthropometric measurement s did not support any association. Since the model does not propose explicit relationships thus, concepts must be operationalized and empirically tested to explain assumed relationships. Thus, the relationships between the concepts are hypothesized in the model as it is currently depicted. As the model evolved from a perspective of determinants of malnutrition such as insufficient food intake, it does not seem to be congruent with findings of the current study which imply inappropriate consumption by quantity and quality of food in the sample by children.

The results of this study indicate that children aged 1-5 years in Kweneng commonly consumed food items and beverages including sorghum, sugar, milk and tea/coffee and yoghurt. Also, considering the food groups represented by the food items that were consumed in this study, it is apparent that children consumed a limited variety of foods. A low variety in foods consumed is associated with inadequate nutrient intake especially essential micronutrients (Beydoun & Wang, 2009; Clausen, et al., 2005;

Dannhauser, et al., 2000). In addition, 16.1% of children were stunted, 31.3% children had a body mass index at the +1 z-score of median suggesting a higher risk for

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overweight. About 20% caregivers reported severe food insecurity within their

households. Caregivers with low education were more likely to report food insecurity

than those with higher education. These findings have important implications for teaching

caregivers about the need to include a variety of foods in the children’s diet.

Further investigation of the variables related to the food consumption patterns of

Botswana’s children is required. Longitudinal studies and quasi-experimental studies may provide a better understanding of the food consumption patterns that can promote optimum nutrition and health for children. Such studies may also provide knowledge on adequacy of consumption of energy and proteins and other nutrients that can support proper growth and health of children thereby contributing to the nutrition guidelines needed in the country. The study would benefit from replication to confirm the core/main food items consumed by a larger number of children and to assess both the quantity and quality of the diet. Also testing the same instruments that were used in this study in a larger sample would ensure reliability of the measurement procedures (Meyer, 2010). As the present study was a descriptive and cross-sectional one, triangulation of methods for future studies may broaden current knowledge. Both qualitative and longitudinal studies may further increase the knowledge required in this area.

Implications for practice

This is one of the first studies to describe and explore the relationship between food and beverage consumption and children’s anthropometric measures and using food frequencies to determine the core food items consumed by young children in Kweneng

region. Currently, Botswana does not have national nutritional guidelines for young

children and this study provides important information on the commonly consumed foods

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by children in Kweneng district. Understanding the dietary intake of children would help in determining appropriate interventions that can address food and beverage consumption as well as the nutritional needs for proper growth and health. Results of this study indicate that young children frequently consumed only five food items, and of the food items that were widely eaten, three of them are energy dense and nutrient sparse.

Indications are that some children in this study had stunted growth and some were at risk for overweight. Both malnutrition and the risk for overweight need interventions that focus on improving linear growth for children in this stage of development (Uauy, Kain,

Mericq, Rojas & Carvalan, 2008). Therefore, special attention should be paid to the energy calories given to children to ensure that they consume the required amount in

order to avoid undesirable weight gain while preventing under-nutrition. This empirical

evidence will serve to guide public policy and health education on the need to improve

the quality of the diet for young children.

The results indicate the need for primary prevention through a basic nutrition

program that can equip parents with facts related to healthy food consumption patterns.

Nurses can ensure the provision of simple age appropriate educational and nutritional

materials for children. These can be used by caregivers or parents of young children.

Nurses can look for appropriate opportunities to teach women about nutrition. For

instance, nurses working in the maternal and child services can sensitize women about

the importance of nutrition even before they plan pregnancy as maternal nutritional status

influences both fetal survival and growth and development and later in postnatal period

and through infancy and early childhood (Shetty, 2002). Also, maternal height influences

birth-weight and postnatal growth (WHO, 2006c).

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Several studies have also demonstrated that caregivers impact young children’s nutrition the most (Faber, 2010; Nicklaus, et al., 2005; Nicklas & Hayes, 2008;

Vereecken, Povner & Maes, 2010). Invariably, caregivers are the gatekeepers, controlling the quality and availability of foods in their children’s environments (Ball, Benjamin &

Ward, 2007; Fisk, Crozier, Inskip, Godfrey, Cooper, et al; 2011; Robinson, et.al 2007;

Slusser, Prelip, Kinsler, Erausquin, Thai, et al., 2011; Wardlaw & Smith, 2009; Wardle,

Carnel & Cooke 2005). Moreover, eating habits are established at an early age and persist into adult life (Fisk, et al.). Caregivers operate as models thus providing opportunities for children to observe their dietary patterns (Slusser, et al,). Therefore, nutrition education provided to caregivers of young children is of primary importance. Caregivers can also make quantitative and qualitative decisions regarding the food consumption patterns of children. Empowering caregivers would help them make informed decisions about the types of food as well as the serving amount desired for healthy eating.

Studies examining influences of young children’s nutrition have demonstrated that in general the level of maternal education influences children’s food environment

(Fisk, et al., 2010; Robinson, et al., 2007; Slusser, et al., 2011; Vereecken & Maes, 2010,

Vereecken, Keukelier & Maes, 2004). Interventions to improve childhood nutrition should focus on public information and media directed at mothers to ensure that early appropriate food consumption practices are adopted to enhance overall growth and prevent the risks of dietary related health problems both in the early and later lives of children (Nicklas & Hayes, 2008). Consumption of healthy food can reduce health risks of preventable chronic diseases such as malnutrition, poor immunity, overweight, diabetes mellitus (type 2), coronary heart diseases and some types of cancer (Hassink,

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2009; Institute of Medicine, 2005, 2006; Hassink, 2009; Nicklas & Hayes; Wardlaw,

Hampl & DiSilvestro, 2004).

According to a recent media article, a cross-country survey that explored core welfare indicators in Botswana revealed that 21% households in rural areas sometimes did not have food for a whole day, suggesting severe food insecurity (Moeng, 2011). The current study revealed that 20% of the households in the sample were severely food insecure, a similar finding to the general population. Household food insecurity impacts both quantity and quality of the nutrient intake for children (Holben, 2010; Mmopelwa et al., 2011). The finding has major policy implications. While food insecurity alleviation programs such as the food rations delivered through health clinics and the food basket exists in Botswana (Ministry of Finance & Development Planning, 2008), innovative ways to promote and support self-sufficiency are urgently needed. The existing programs should be closely monitored and evaluated to ensure that they remain relevant to the current needs of children and caregivers.

The results of the study will assist nurses and other health workers to understand the linkages between children’s food consumption and household food security conditions as well as the energy and protein needs for a healthy diet that may prevent risks associated with suboptimal consumption and excessive intakes. Nurses involved in the monthly growth monitoring program of children may use a broader assessment approach that includes the individual child, the family and access to resources that relate to nutrition and food security. Nurses in the primary care settings are in a unique position to help caregivers to be aware of food consumption patterns that are associated with diet related health problems.

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Implications for future research

This study has raised several questions that require further investigation. That the study used face-to-face interviews to elicit data from caregivers may have influenced the results. Using public institutions such as clinics for data collection may have influenced caregivers to respond in a manner suggesting social desirability. Clinics are the usual places where caregivers receive education in the form of health talks from nurses and there are poster materials that reflect key messages on various public health issues. The health educational talks that are given by nurses and other healthcare providers normally form part of usual services. The posters found in clinics communicate key messages on a range of topics such as immunization schedules for children and information related complementary foods. Therefore, interviewing caregivers in the clinic environment may have triggered an expectation of providing the investigator with the ideal information or knowledge. There is need to examine measures that can elicit more reliable data. Using data collection methods such as the 3-day diet record to document food consumption may be an option for future research. Three day diet records may involve more intensive instruction on the type of record required so it would reduce the influence of an interviewer.

Basic dietary intake guidelines are needed for Botswana. This can serve as a standard for comparison. Given the core/main foods that were consumed by children in this study, there is need to determine the nutrients and intake levels derived from the unique food consumption pattern by these children as well as determine the quality of indigenous foods items that are readily accessible and available to the families.

Understanding the nutrition needs of caregivers with young children could help in

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tailoring nutrition education and counseling including discussions on how to make

healthy meals using limited resources. In addition, there is need to examine the caregiver

behaviors that influence food choices as well as cultural practices that are related to food

consumption. Qualitative studies may be more relevant in exploring the cultural ways

that influence food patterns for children. Efforts for developing better tools for

monitoring child nutritional status including reporting children’s body mass indexes as

part of regular monthly monitoring are needed. Tools need to be sensitive enough to

identify children at risk for food insecurity.

Implications for health policy

The commendable efforts of exclusive breastfeeding for the first six-months enshrined in the Botswana’s Infant and Young Child Feeding policy (Ministry of Health,

2009) should continue to be encouraged by nurses working in the maternal child health units as it lays the foundation for proper acquisition of nutrients. Nonetheless, further research is needed to develop specific dietary recommendations and guidelines for children aged 1-5 years in Botswana and the entire population.

Reliable food composition data are important in studies that relate to food consumption. To date, Botswana does not have national food composition data. The present study has revealed the importance of developing food composition tables for food analysis. The food composition tables and databases for Botswana can provide relevant nutritional guidelines for everyday use as well as address needs for diet related diseases that are associated with lifestyle changes now found in the general population in the country (Ministry of Finance and Development Planning, 2003). Lack of locally derived nutrition information can impact efforts of dietary diversification that relies on the

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nutrient content of the most commonly consumed foods (van Heerden & Schonfeldt,

2004). Policymakers can provide guidance and direction on the development of the food composition tables and databases needed for the country.

Further exploration and policy initiatives are needed to examine innovative measures that include assessment of household food security in caregivers of young children receiving care in primary health care settings. The current study has shown that

20.2% of households reported severe food insecurity and 36% of the caregivers for children did not have income. These indicators suggest inadequate access to good quality food for health. It is therefore important to develop sustainable strategies that can alleviate such economic deprivation.

Much remains unknown regarding local foods that are less expensive but consisting high quality nutrients. The National Food Technology Research Centre

(NFTRC) in Botswana that is mandated to develop educational materials about food quality and safety in the country may provide such essential knowledge. This effort can guide research and the production of local foods that are nutrient dense. This can stimulate the production of foods that have appropriate nutrients as well as enhance access for caregivers to gain stable food security.

Limitations

Reliable food composition data are important for studies investigating food consumption and nutrient data. Considerable efforts were taken to ensure that data for the study were analyzed using the FoodFinder3, a software program based on South African food composition tables. All food items that were listed in the food frequency questionnaire were compared to those that are in the FoodFinder3 database. The lack of

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local food composition tables as well as research experience with the measurement of

dietary intake for both children and adults in countries undergoing nutritional transition

such as Botswana has been documented (Popkin, Lu & Zhai 2002).

Since the study used a cross-sectional design, rather than a baseline-repeated measures design, causal inferences cannot be made. Further, information obtained from a food-frequency may not be representative of the typical consumption patterns of the children in Botswana because there may have been food recall reporting errors by caregivers or parents who responded to questions based on their recall of the food consumed over a one week period. In addition, respondents may have been prone to providing socially acceptable descriptions of what they believed would impress the investigator regarding the children’s food consumption. Social desirability may include either underreporting or reporting exaggerated amounts of the food consumption

(Buzzard, 1998). However, reporting errors of food intake is not unique to this study: similar problems have been reported by others (Faber, Jogessar & Benade, 2001).

The sample was comprised of caregivers and children who were already using healthcare facilities, so the results may not be generalized to all caregivers and children in the age groups. In addition, the study was confined to Kweneng region and therefore, findings cannot be generalized to children and caregivers in other regions of Botswana as there may be differences in the food and beverage consumption patterns. The other limitation was that indigenous food items such as mopani worms were not included in the analysis because there was no other equivalent food in the NDSR that was found. Only four participants had consumed the mopani food item. Moreover, this food item is seasonal and available for less than four months of the year hence only have limited

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effect on usual dietary patterns. Therefore, this food item is not likely to have affected the

means of the sample.

Lack of published research on food and beverage consumption of children has

resulted in a limited understanding of the core/main foods, protein and energy consumed

by Botswana’s children. The nutritional value of young children’s diet has immediate and

long- term consequences on their health, growth and development (de, Pee et al., 2010).

Limited research in the area has contributed to the lack of knowledge and understanding

of a dietary intakes recommended for young children in Botswana’s context. Future

research can help to identify both gaps and good practices in food consumption patterns

for young children. Therefore, it is recommended that research should be scaled-up and program evaluation be instituted in order to inform policy.

This study provides important data that will guide future research on core/main food items consumed by young children in Botswana. The study combined three measures that included the child food frequency questionnaire, children’s anthropometric measurements and the caregiver household food security scale to derive descriptive and correlational data that were used to draw conclusions. The estimation of protein and energy intake of children based on three- dimensional models that enhanced measurements by caregivers. These baseline findings will inform future studies on this important subject. The study has added knowledge on average energy and protein intake by children aged 1-5 years in Botswana.

The results of this study have significance to the public health of young children.

The study revealed that limited food items were commonly used for children. These may not provide all the essential nutrients that are required to support growth and

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development. Limited food items suggest that children may be getting enough to satisfy

hunger but not nutrients required for proper nourishment. Replication of this study aimed

at understanding food and beverage consumption in various regions of Botswana is needed. These data may guide the development of nutrition guidelines and interventions

that can be used to help caregivers to select foods that meet recommended nutrient

intakes.

Summary

The study investigated core/main food and beverages consumed by children aged

1-5 years. Energy and proteins were estimated from the food and beverages that had been

consumed in the previous 7 day period before data collection. Children’s weight and

height were measured and caregivers responded to an instrument that assessed household

food security level. Findings revealed that only five food items dominated the food intake

giving a picture of low variety. The weight and height of children indicated that 16.2% of

the children had stunted growth while none of them were wasted. More than 50% of the

caregivers were single and did not have regular income. Household food insecurity was

common.

In conclusion, prevalence of stunting among children in this study was noted,

suggesting chronic malnutrition. Further investigations may provide an understanding of

the prevalent household food insecurity reported by caregiver in this study. In addition, there were indications of overconsumption of energy dense food, highlighting the importance of interventions needed to monitor children with BMI at risk for overweight before the problems can reach epidemic proportions. These findings are of significant importance in developing appropriate strategies to improve the nutritional status of

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children. Nurses can play the important role of investigating and addressing clinical problems.

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APPENDICES APPENDIX A- CWRU IRB Approval Case Western Reserve University Institutional Review Board NOTICE OF ADDENDUM APPROVAL Responsible Investigator: Linda C. Lewin Department: Nursing - General IRB Protocol Number: 20110432 Addendum Number: 1, changes in ICDs and data collection instruments Title: Dietary Intake of Children Aged 1 - 5 Years of Age and Their Anthropometric Measures in Kweneng District - Botswana Co-Investigator: Wananani Tshiamo Addendum Approval Date: July 7, 2011 The Institutional Review Board (IRB) has APPROVED the submitted addendum for the above protocol. As an investigator of human subjects, your responsibilities include the following (see full description of responsibilities at our website): Report all adverse events involving human subjects to the Institutional Review Board (IRB) within three (3) business days of the occurrence. Submit any further changes to an approved protocol or consent form to the IRB, and receive approval from the IRB, before implementation of the change. Keep all research data and original consent documents in your possession for at least three (3) years after the study is terminated. 4. Please use the consent forms attached. Feel free to use copies of these forms as long as they are identical to what was originally IRB approved. If you wish to change the forms or any other part of the study, you must submit an addendum request with a revised copy of the relevant document(s) and wait for an approval before a modification can be implemented Questions? Please visit our website: http://ora.ra.cwru.edu/orc_humansubjects_CWRU_IRB.asp

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OR contact our administrative office… Isabel Sanchez-Cummings, IRB Director 216.368.6993 Maureen Dore-Arshenovitz, IRB Assistant 216.368.6925 Fax: 216.368.3737 or CASE Institutional Review Board Office of Research Compliance Sears Building 657 Cleveland OH 44106

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Recruitment Script –Appendix-D

 You are asked to participate in a research study on food and beverage consumption of children aged 1 to 5 years.  You will be asked questions about the food and drink your child usually consumes.  You will also be asked to explain about food availability in your home in the past three months.  If you are interested in participating in this study, the researcher, Wananani Tshiamo, a pediatric nurse, will invite you at the end of your clinic visit today.  Participating involves approximately 30 minutes interview including 10 minutes of taking your child’s weight and height measurements; these will be done by the nurse researcher.  The information you provide will assist us in understanding the nutrition concerns of your community.  The information you share will be kept confidential and will not be shared with people not involved in this study  Be informed that your participation in this study is entirely voluntary and declining to take part in the study will not affect you or the services you will receive from the clinic, the Ministry of Health or Case Western Reserve University either now or in the future. Thank you

Wananani Tshiamo

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APPENDIX D (a)

Mokgwa wa go ja le seemo sa sekale mo baneng ba ba ngwaga e le nngwe go fitlha dingwaga tse tlhano mo kgaolong ya Kweneng mo Botswana

 O kopiwa go tsenelela patlisiso e e remeletseng mo go se bana ba ba ngwaga e le nngwe go fitlha dingwaga tse tlhano ba se jang.

 Mo patlisisong e, o tla botswa dipotso ka se ngwana wa gago a se jang le go se nwa ka tlwaelo.

 Gape, o kopiwa go tlhalosa ka seemo sa dijo mo lapeng la gago mo kgweding tse tharo tse di fitileng

 Fa o na le kgatlhego ya go tsenelela patlisiso e, motlhotlhomisi, Wananani Tshiamo, yo e leng mooki wa bana, o tla go laletsa, morago ga o sena go thuswa mo kokelwaneng tsatsi jeno.

 Dipotso tsotlhe di tsaya metsotso e e masome a mararo, mme nako e, e akaretsa go kala ngwana le go tlhotlhomisa boleele jwa gagwe. Tse tsotlhe di tlaabo di dirwa ke motlhotlhomisi yo e leng mooki.

 Dikarabo tsa gago di a go re thusa go tlhaloganya seemo sa dikotla mo kgaolong ya gago.

 Dikarabo tsa gago e ya gonna sephiri, mme ga di na go newa ope yo o se nang karolo/seabe mo ditlhotlhomisong tse.

 Itse fa o sa patelediwe go tsenelela patlisiso e, mme e bile go sa tsenelele patlisiso e ga go na go go kgoreletsa go newa ditlamelo ke ba lephata la botsogo kgotsa ba Case Western University.

Ke a leboga

Wananani Tshiamo

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APPENDIX E

Caregiver Demographic Data Form

Subject Code:

Date of Birth:____/____/___ Interviewer:______Day /Month /Year Health District: ______Place/Village: ______

Health Facility______Date of Interview: ____/___/____ Day /Month /Year 1. Which of the following best represents your age? a) 20 years or below b) 21 – 30 years c) 31- 40 years d) 41- 50 years e) 51 – 60 years f) 6 1 years and above

2. How many adults aged 18 years or older currently live in your household?

Include your self

Number_____

3. How many children aged less than 18 years currently live in your household?

Excluding the target child for this study

Number______

4 .What is your ethnic/racial group? a) Motswana b) Other African (specify)

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c) Non African (specify)

5. What is your relationship to the child? a) Mother b) Grandmother c) Other( please specify)______

6. What is your marital status? (Circle the letter for one answer only) a) Single b) Married c) Divorced d) Separated e) Widowed f) Cohabiting

7. What is your highest level of schooling? (Circle the letter beside one answer only and explain if response is (f)) a) Never attended school b) Primary school c) Secondary school d) Technical or trade school e) University or tertiary f) Other (specify)______

8. How do you earn your living? (Circle the letter for one answer only) a) Employed b) Unemployed

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c) Student d) Other (please specify)______

9. What is your occupation? (Please specify)------.

10. Which of the following best represents your monthly income? a) P699.00 and below b) P700 – P1, 500 c) P1,501- P3,000 d) P3,001 – P5,000 e) P5,100 +

11. What is your source of water supply? a) Own standpipe b) There is running water in the house c) Community standpipe d) Other(specify)______

12. How do you dispose refuse? a) Use pit in the compound b) Collected by district council c) Dispose at communal site d) Other specify______

13. Which type of toilet facility do you use? a) Water system b) Pit latrine c) Other specify______

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14. What is your source of energy? a) Firewood b) Paraffin c) Domestic gas d) Electricity e) Other specify______

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APPENDIX E (a)

FOROMO YA MOTLHOKOMEDI WA NGWANA

Nomoro ya motsayakarolo

Matsalo_____/_____/______

Letsatsi Kgwedi Ngwaga Mokanoki______.

Kgaolo ya tsa botsogo ______Motse ______

Kokelwana ______Letsatsi la potsoloso______/______/______

Letsatsi Kgwedi Ngwaga

1. Ke efe ya tse di latelang ee akaretsang digwaga tsa gago? a) Masome mabedi kgotsa kwa tlase. (20 years and below) b) Masome mabedi le bongwe go ya kwa go masome mararo (21- 30 yrs) c) Masome mararo le bongwe go ya go masome mane (31 – 40 yrs) d) Masome mane le bongwe go ya go masome matlhano (41 – 50 yrs) e) Masome matlhano le bongwe go ya go masome marataro (51-60 yrs) f) Masome maratato le bongwe le go feta.(61years and above)

2. Go na le bagolo ba le kae ba dingwaga tse di lesome le borobabobedi kgotsa go feta

ba ba nnang mo lwapeng? O ipalele mo teng. Palo ______.

3. Go na le bana ba le kae ba digwaga tse di kwa tlase ga lesome le borobabobedi ba

ba nnang mo lwapeng, o ntshe ngwana yo o tsayang karolo mo teng. Palo______.

4. O motho wa letso lefe? a) Motswana b) MoAferika e se Motswana? (tlhalosa) ______174

c) E se MoAferika (tlhalosa)______

5. O tsalana jang le ngwana?

a) Mmaagwe

b) Nkuku

c) Mongwe fela (tlhalosa) ______

6. A o nyetswe? (Agelela thaka ya karabo e e supang seemo sa nyalo) a) Ga ke a nyalwa b) Ke nyetswe c) Ke tlhadile/tlhadilwe d) Ke kgaogane le mopati e) Ke motlholagadi/moswagadi f) Ke nna le mokapelo

7. O tsene sekolo go ema kae? (Agelela thaka e e supang karabo ya seemo sa thuto ya gago. O bo o tlhalose fa karabo e le (f). a) Ga ke a tsena sekolo gotlelele b) Ke tsene sekolo se sebotlana c) Ke tsene sekolo se segolwane d) Ke tsene sekolo sa ithutelo tiro ya diatla e) Ke tsene kwa mmadikolo kgotsa tsa ithutelo ditiro tse dikgolo. f) Tse dingwe (Tlhalosa) ______

8. O itshetsa jang? (Agelalela thaka e e supang karabo ya seemo sa itshetso ya gago). a) Ke a dira (beraka) b) Ga ke dire (bereke) c) Ke moithuti/ke tsena sekolo

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d) Tse dingwe (Tlhalosa) ______

9. Mo tirong ya gago, o dira o le eng? ______

10. O amogela bokae? a. Makgolo a marataro, masome a a ferang bognwe le metso e ferabongwe kana ko tlase. (P699.00 and below) b. Makgolo a supang go ya go sekete le makgolo matlhano.(P700.00 – P1,500) c. Sekete, makgolo a matlhano le motso go ya go dikete tse tharo.(P1,501 – P3,000) d. Dikete tse tharo le motso go ya go dikete tse tlhano.(P3,000 –P5, 000)

e. Dikete tse tlhano le lekgolo le go feta.(P5,100 +)

11. Ke efe mo dikarabong tse di latelang e e supang kwa o nwang/gelelang metsi teng?

a) Pompo mo lwapeng

b) Metsi a goketswe mo ntlong

c) Pompo ya morafe/setshaba

d) Gongwe fela kwa o tsayang metsi teng (Tlhalosa) ______

12. O latlha matlakala jang?

a) Ke dirisa lehuti le le epilweng mo lwapeng

b) Matlakala a tsewa ke khansele

c) Ke latlhela kwa go latlhelwang teng mo motseng

d) Mekgwa e mengwe (Tlhalosa) ______

13. O dirisa ntlwana ya boitiketso e e ntseng jang?

a) E e dirisang metsi

b) Ntlwana ya lehuti

c) Tse di ngwe (Tlhalosa) ______

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14. O dirisa eng go gotetsa?

a) Dikgong

b) Parafine

c) Gase

d) Motlakase

e) Tse dingwe (Tlhalosa) ______

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APPENDIX F

Child Anthropometric Measures and Health Habits Data Form

Clinic: ______Participant ID______

DOB

Age in Months:

Gender: Female___ Male____

Q1. Measurements Weight: ______Height______Length______BMI______.

Q2. Child Immunization status_____ TB___ DPT___ Polio____ Measles____

Q3. Vitamin A Supplementation No Dosages____

Q4. Other Vitamins (specify) ______

Q5. Is the child being breastfed at the present? Yes __/ NO ___

Q6. Has the child been breastfed as a baby? Yes__ / No__ If yes, for how long?___

Q7. In the last week, did the child eat at a restaurant? Yes___ / No___ If yes, how many times?___

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APPENDIX G

[email protected]

Dear Wananani

The instrument that was used in Theron's was specifically developed for the North West Province, it was also very long (possibly too long). I have since shortened it considerably for a study I did in Venda. You will need to modify the questionnaire for your target population e.g. add local foods, remove foods not used and test it before using it in your research study. Also the questionnaire was designed to be used with a food photograph book to assist with portion size estimations. Although the period of recall was 6 months, I suggest that for children you shorten the period to a month or less.

I am attaching the questionnaire, but I must stress again thta you CANNOT use it as it is - you must test in your target population.

The reference for the questionnaire is: MACINTYRE UE. 1998 Dietary intakes of Africans in transition in North West Province. PhD Thesis. University of Potchefstroom for Christian Higher Education. .

Sincerely

Una MacIntyre

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APPENDIX G (a)

Child Food Frequency Questionnaire

Subject Code:

Health District______Village______. Interviewer______.

Health Facility:______Age in Month_____/____ Gender_ M/F

Date of Interview

INTRODUCTION

I would like to find out what children living in this area eat and drink. I will be asking you to

think carefully about the food and drink your child/child under your care

consumed in the past

week. I will go through

a list of foods and ask you:

• If the child eats the food.

• How you cook the food.

• How much the child eats at a time.

• How often the child eats it.

To help you describe the amount of food you eat I will show you pictures of different amounts of food. I will ask you to say which picture is most like the food you usually give to the child.

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There is no right or wrong answer.

Everything you tell me is confidential.

Do you want to ask anything now?

I will start by asking about porridge. Are you ready to start? Yes__ No__

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Food Description Amount Times Eaten Per Week Code Total Amount Does Less 1 – 2 3 -5 6 or not eat than 1 times times more Energy giving foods Sorghum Stiff porridge Soft Sour Maize meal Stiff porridge: Soft Sour Tsabana Enriched cereal Oats Breakfast Names: cereals

Do you add milk on the child’s porridge /cereal? YES NO If NO, go to next question on sugar. If YES, what type of milk: fresh, sour, milk powder (Nespray), skim milk powder (name)? If yes, how much milk? Do you add sugar on the child’s porridge/cereal? YES NO If no, go to ‘samp’ If yes, how much sugar? Samp Samp and beans Samp and peanuts Rice Maize rice Macaroni / Spaghetti

Bread: White bread Brown bread Whole wheat

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Food Description Amount Times Eaten Per Week Code Total Amount Does Less 1 – 2 3 -5 6 or not eat than 1 times times more bread Dumplings Fat cakes Roasted bread (homemade buns) Other types of bread:

Do you spread anything on the bread? YES NO If no, go to next section (chicken, meat, fish) IF YES, What do you spread and how much?

Margarine Hard Tub Peanut butter Butter Jam Fish/meat paste Cheese Other spreads: specify You are being very helpful. Can I now ask you about Chicken, Meat and Fish? Chicken Boiled, nothing added Fried (not coated) Fried: in batter/crumb s (Kentucky) Roasted (Nandos) Stewed with vegetables Chicken feet (boiled or fried) Chicken

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Food Description Amount Times Eaten Per Week Code Total Amount Does Less 1 – 2 3 -5 6 or not eat than 1 times times more heads (boiled or fried) Chicken offal (boiled or fried) Other specify Red meat Fried Stewed- without bones Roasted Mince Other describe Intestines Boiled nothing added Stewed with vegetables Liver Boiled or fried Other organ meats Specify Sausage Boiled - fried-roasted Polony Tinned meat (corned meat) Other: specify Fish Pilchards in tomato/ chilli Fried fish With batter/crumb s Without batter/crumb s, boiled Eggs Boiled 184

Food Description Amount Times Eaten Per Week Code Total Amount Does Less 1 – 2 3 -5 6 or not eat than 1 times times more Scrambled Fried Omelet Meat alternative s Dried beans Ground nuts Lentils Others (specify) Vegetables Dried bean Boiled with leaves fat/with melon nuts Cabbage Boiled, nothing added

Boiled with potato and onion and fat Fried, nothing added Boiled then fried with potato, onion Cole slow Other specify: Spinach/Ra Boiled, pe /other nothing green leafy added (specify) Boiled fat added Fried, nothing added

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Food Description Amount Times Eaten Per Week Code Total Amount Does Less 1 – 2 3 -5 6 or not eat than 1 times times more Boiled then fried with onion/tomat o/potato and fat With peanuts Other: specify Soup Stewed tomato and onion (gravy) Other (specify) Pumpkin Cooked in fat & sugar

Boiled, little sugar & fat Other describe Carrots Boiled with sugar, & fat With potato/onion Raw, salad Other specify Potatoes Boiled Mashed French fries (Slap Chips) Other specify Sweet Boiled potatoes Other specify Beetroot salad Salad Raw tomato vegetables Lettuce Other vegetables 186

Food Description Amount Times Eaten Per Week Code Total Amount Does Less 1 – 2 3 -5 6 or not eat than 1 times times more (specify) Fruit

Apples Bananas Oranges Naartjies Paw paw Mangoes Peaches Grapes Guavas Other (specify) Wild fruit/ berries Beverages (Hot or warm) Tea Rooibos tea Coffee Other (specify) Do you add milk to the child tea? If yes, which of the following? Fresh milk Nespray Skim milk powder (Name) Cremora (coffee creamers) Condensed milk Do you add sugar to the child tea? If yes,

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Food Description Amount Times Eaten Per Week Code Total Amount Does Less 1 – 2 3 -5 6 or not eat than 1 times times more indicate amount Cold drinks Fizzy Other drinks (Coke, Fanta etc) Fruit juice Type: (Fresh/ Liquifruit) Tropical Oros Sweeto/ Kool aid Mageu (starchy drink) Other (specify) Potato crisps (Simba) Cheese curls, niknaks, etc Peanuts Sweets and Specify: Chocolates Biscuits, rusks Cakes Other: (specify) Puddings

Jelly Custard Ice cream Yoghurt Other (specify) Sauces

Tomato Sauce 188

Food Description Amount Times Eaten Per Week Code Total Amount Does Less 1 – 2 3 -5 6 or not eat than 1 times times more Mayonnais e Packet soups Other specify: Savory Snacks Pie Russian Sandwich Insects Mopani worms Locusts Other (specify) Are there any other foods/drinks used more than once a week which we have not been included? Specify 1. 2 3. 4.

Are there any available foods which you do not allow the child to eat? (For example for health, religious or cultural reasons). YES ___ NO___

IF YES, please describe the food and give the reason/s why you do not eat it. List them below

Food Reason for not eating 1 2. 3 4 Adapted and modified with permission from MacIntyre (1998)

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APPENDIX G (b)

Child Food Frequency Questionnaire (C-FFQ) (Thanodi ya Setswana)

Nomoro ya motsayakarolo

Kgaolo ya Botsogo______Motse______. Mokanoki______

Kokelwana ______Matsalo ka dikgwedi_____/____ Bong_ M/F

Letsatsi la potsoloso

MATSENO

Ke eletsa go itse se bana ba kgaolo eno ba se jang kana ba se nwang. Ke kopa o ikgakolole ka kelotlhoko dijo le dino tse ngwana wa gago/ ngwana yo mo tlhokomelong ya ga go a di jeleng kana a di noleng mo bekeng e e fetileng?

Ke tla balolola dijo, ke bo ke botsa gore:

• a ngwana o etle a je sejo sengwe • se apailwe jang • o jele selekanyo se se kae • o se jele ga kae Go go thusa go tlhalosa selekanyo sa dijo, ke tla a go bontsha ditshwantsho tsa dilekanyo tsa dijo tse di farologanyeng go tlhopha selekanyo se ka gale o se fang ngwana.

Gololesega go araba, ka go sena karabo e e siameng le e e sa siamang.

Sengwe le sengwe se o se mpolelelang ke sephiri.

A o batla go botsa sengwe?

A o ipaakanyeditse go simolola? Ee___/Nnyaa____ Fa o ipaakantse, Ke tla a simolola go botsa ka bogobe.

190

Sejo Tlhaloso ya sone Seleka Se jewa ga kae mo Code Tsho nyo bekeng boko ya Selek anyo

in g/ml)

-5 6 Dijo tse di fang matla (Cereal foods) Bogobe jwa -jo bo loileng mabele (ting/mosoko) - motogo (ting/mosoko) phaletshe -e e loileng (ting/mosoko) - motogo (ting/mosoko) Tsabana Dijo tsa bana tse di okeditsweng dikotla Thopi/Bogobe jwa lerotse (melon porridge) Oats Breakfast cereals Maina a tsone: (e.g. cornflakes, rice crispis, bran flakes, etc) A ka gale o tshela Fa o a tshela mashi mo dijong mashi ke mefuta tsa ngwana? efe? Sekai, Ee__/Nnyaa__ lebese, madila, mashi a bopi.Tlatsa mela e e ka fa mojeng jaaka o ntse o dira ka dijo tse di fa godimo A ka gale o tsenya Fa o e tsenya sukuri mo dijong tlatsa mela e e ka

191

tsa ngwana? fa mojeng Ee__/Nnyaa__ Setampa Dikgobe Setampa le manoko (Setampa se se tswakilweng ka manoko) Raese Mmele-raese Macaroni/spaghet ti Borotho: -jo bosweu -jwa rantlhasi -jo bo nang le moroko -madombi magwinya/many onyomane -diphaphatha -mapakiwa (homemade buns) -mefuta e mengwe ya borotho (tlhalosa): A ka gale o tshasa sengwe mo Fa o etle o tshase sengwe, o tshasa eng, go le kae? (Tlatsa borothong? mela e e latelang fa tlase jaaka o ntse o dira ka dijo tse di fa godimo) Ee__/Nnyaa__ Margarine Hard Tub Peanut butter Botoro (butter) Jeme (jam) Fish/meat paste Cheese Tsedi dingwe (Tlhalosa); …………

192

Ke lebogela thuso ya gago. A ke ka tswelela ke botsa ka “dinama”? Ee__/Nnyaa Nama ya koko e bedisitswe ka metsi e gadikilwe (e sa tshasiwa sepe) e gadikilwe, mme e tshasitswe botoro/mafofora a borotho e besitswe e apeilwe ka digwete jaaka carrot kana tapole Menoto e bedisitswe ka metsi e gadikilwe Ditlhogo di bedisitswe ka metsi di gadikilwe Mala, le dintshu di bedisitswe ka metsi di gadikilwe Mefuta e mengwe (tlhalosa):……. Nama e khubidu e besitswe (ya kgomo, pudi, e gadikilwe nku kana kolobe) e kgabakgabisitswe, mme e sena marapo Nama e e sidilweng (Mince meat) Other meats (tlhalosa): Mala le mogodu a bedisitswe ka (tripe/offals) metsi, -a apeilwe ka digwete (jaaka carrots kana ditapole) Sebete se bedisitswe se gadikilwe

193

Mateng a mangwe: Tlhalosa dirwe jaaka diphilo (kidney), pelo (heart), makgwafo (lungs), lebete (spleen), le tse dingwe Boroso e bedisitswe e gadikilwe E besitswe Polony Nama e e mo “thining” (jaaka corned beef) Mefuta e mengwe (tlhalosa): ………. Tlhapi ya pilchard -mo tamati sosong e e mo “thining” -e e babang (chili) Tlhapi E bedisitswe E gadikilwe mafura fela e gadikilwe ka botoro/mafofora a borotho Mae -a bedisitswe -a go tlhakatlhakantswe borobe le “egg- white” (scrambled) -a a gadikilwe

-Omelet

Dijo tse di nang le dikotla tsa nama (Meat alternatives) Dinawa Ditloo Letlhodi (lentils)

194

Tse dingwe (Tlhalosa) Merogo (Vegetables) Morogo wa dinawa o bedisitswe (dried bean leaves) o na le mafura o bedisitswe o tsentswe dithotse tsa marotse o tsentswe marotse, o bedisitswe, o na le mafura Morogo wa lephutshe o bedisitswe, o (pumpkin leaves) na le mafura Rothwe/thepe/monya o bedisitswe o ku (wild vegetables) na le mafura Cabbage e bedisitswe, e sa tsenngwa sepe e bedisitswe, e na le mafura e gadikilwe, e sa tsenngwa sepe e bedisitswe e bo e gadikiwa ka tapole, kwiii, tamati le mafura -e sa butswa, e le salad -e sa butswa e kopantswe le moretwa le mayonnaise -e sa butswa e le coleslaw salad (cabbage, carrots le mayonnaise) e apeilwe ka mefuta e mengwe (tlhalosa): ………… Spinach/rape/chomoli se bedisitswe, se a sa tsenngwa sepe 195

se bedisitswe se na le mafura se gadikilwe, se sa tsenngwa sepe se bedisitswe se bo se gadikiwa ka kwii/ tamati/tapole, le mafura se tsentswe manoko se apeilwe ka mefuta e mengwe (tlhalosa): ……..…… Merogo e mengwe (tlhalosa): Mero (Soup) Wa nama e apeilwe ka tamati le kwii Stewed tomato and onion (gravy Mefuta e mengwe(tlhalosa Lephutshe le apeilwe ka mafura le sukuri le bedisitswe ka metsi, le latlhetse sukuri le mafura le apeilwe ka mefuta e mengwe (tlhalosa): . Carrots e bedisitswe ka sukuri le mafura -e na le tapole/kwii -e sa butswa (salad) -mefuta e mengwe (tlhalosa): 196

Ditapole -di bedisitswe -di ritilwe -e le “ma-fresh” (“dichipisi”/fresh fries) -di apeilwe ka mefuta e mengwe ya kapei (tlhalosa): Dipotata (sweet -di bedisitswe potato) - mefuta e mengwe ya kapei (tlhalosa): Beetroot -salad ya beetroot Merogo e e sa apewang (salads) Tamate Lettuce -salad ya lettuce le tamati E mengwe (tlhalosa) Maungo Selekanyo/ size: (Small/ Medium/ Large) Apole Banana Namoni Swirinamoni (Lemon) Naraki Pawpaw

Mengo (Mango) Perekisi (peach) Moretlwa wa sekgoa (grapes) Guava Mangwe (tlhalosa) Maungo a naga (tlhalosa): Dino (beverages)

197

Dino tse di molelo/bothitho (hot/warm drinks) Tee ( jaake 5-roses) (roibos tea,) Kofi Tse dingwe (tlhalosa) A ka gale o tshela Fa o etle o a thele, o thela afe, a le kae? (Tlatsa mela e e mashi mo teeng ya latelang fa tlase jaaka o ntse o dira ka dijo/dino tse di fa godimo) ngwana? Ee__/Nnyaa__ Fresh milk Nespray Skimmed milk powder: Coffee creamers (jaaka Cremora, Ellis Brown, etc) Kontase/ mashi a a bonyepo (condensed milk) A ka gale o tsenya Fa o e tsenya sukuri mo dinong tlatsa mela e e ka (tee/kofi) tsa fa mojeng ngwana? Ee__/Nnyaa__ Dino tsididi (cold drinks) Fizzy drink (e.g. Tlhalosa gore coke, fanta, etc) efe? Matute a maungo Mefuta (type): Fruit juice e.g. fresh/liquifruit, etc Tropica Oros (orange squash Sweet aid/kool aid Maheu Dino tse dingwe (tlhalosa): Potato crisps (e.g. “Simba” chips, ) Digaugau, Mabudula 198

Manoko (Peanuts) Dimonamonane/dileke Tlhalosa: re (Sweets & Chocolates) Biscuits, rusk,cake Tse dingwe (tlhalosa) Puddings Jelly Custard Yoghurt Ice ream Tse dingwe (tlhalosa): Savoury snacks Pie Russian Sandwich Dimota melomo (Sauces) Tamati soso Mayonnaise Moro wa pakete tse dingwe (tlhalosa): Insects/worms Phane (mophane worms) Ditsiane/ (locusts) Tse dingwe (tlhalosa): A go na le dijo/dino tse di sa buiwang tse ngwana a di jang (tlhalosa fa tlase): 1. 2. 3. 4. 5.

A go na le dijo dingwe tse o itsang ngwana go di ja? (Sekai: Ka ntlha ya botsogo, tumedi kana mabaka a ngwao) Ee ___ /Nnyaa ____

199

Fa di le teng ke dife, o fe mabaka a gore ke eng ngwana a sa di je. Dirisa lomati (table) lo lo latelang go araba: Sejo Lebaka la go itsa ngwana sejo seo 1. 2. 3. 4. Adapted and modified with permission from MacIntyre (1998)

200

APPENDIX H

On Fri, Jul 2, 2010 at 10:37 AM, Hugo Melgar-Quinonez wrote:

Dear Wanani,

We have been working with a food security scale for Latin America for several years now. The Latin American and Caribbean Food Security Scale (ELCSA - Escala Latinoamericana y Caribeña de Seguridad Alimentaria) is been applied throughout the region and has its roots in the scale currently used in the US. At this point we also have some projects ongoing in Kenya, China and India, but we don't have precise information about its performance. Nevertheless, with the support of FAO we intend to convert this tool into a global instrument.

Attached please find the English version of ELCSA.

May I ask you what your target population is?

Sincerely,

Hugo Melgar-Quinonez

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APPENDIX H (a)

Household Food Security Scale

Subject Code:

Health District ______Village______.

Health Facility______Interviewer:______.

Date of Interview

Introduction: I am going to request you to answer questions regarding food availability in your household in the past three months.

1. During the last three months, were you worried that your household would run out of food because of lack of money or other resources to obtain food? a. Yes b. No (go to question 2) 1a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 2. During the last three months, did your household run out of food because of lack of money or other resources to obtain food? a. Yes b. No (go to question 3) 2a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 3. During the last three months, did your household lack money or other resources to obtain a nutritious and varied diet? a. Yes

202

b. No (go to question 4) 3a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 4. During the last three months, did you or any adult in your household have to consume just one or two kinds of food because of lack of money or other resources to obtain food? a. Yes b. No (go to question 5) 4a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 5. During the last three months, did you or any adult in your household not eat breakfast, lunch or dinner because of lack of money or other resources to obtain food? a. Yes b. No (go to question 6) 5a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 6. During the last three months, did you or any adult in your household eat less than you thought you should because of lack of money or other resources to obtain food? a. Yes b. No (go to question 7) 6a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days)

203

7. During the last three months, did you or any adult in your household feel hungry but couldn’t eat because there was no food nor any way to obtain it? a. Yes b. No (go to question 8) 7a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 8. During the last three months, did you or any adult in your household go without eating for a whole day because there was no food nor any way to obtain it? a. Yes b. No (go to question 9) 8a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days)

The following questions are about children up to age 18 living in your household.

9. During the last three months, did any child in your household not receive a nutritious and varied diet because of lack of money or other resources to obtain food? a. Yes b. No (go to question 10) 9. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days)

204

10. During the last three months, did any child in your household have to consume just a few types of food because of lack of money or other resources to obtain food? a. Yes b. No (go to question 11) 10a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 11. During the last three months, any child in your household eat less than you thought he/she should because of lack of money or other resources to obtain food? a. Yes b. No (go to question 12) 11a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 12. During the last three months, did you have to serve less food to any child in your household because of lack of money or other resources to obtain food? a. Yes b. No (go to question 13) 12a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days)

205

13. During the last three months, did any child in your household feel hungry but you could not get more food because of lack of money or other resources to obtain food? a. Yes b. No (go to question 14) 13a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 14. During the last three months, did any child in your household go to bed hungry because of lack of money or other resources to obtain food? a. Yes b. No (go to question 15) 14a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days) 15. During the last three months, did any child in your household go without eating for a whole day there was no food nor you had the possibility of obtain it? a. Yes b. No 15a. How often did this happen? a. Frequently (almost every day) b. Sometimes (some days but not every day) c. Rarely (on only 1 or 2 days)

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APPENDIX G (b) Sekale sa Dijo mo Lwapeng – Household Food Scale Nomoro ya motsayakarolo

Kgaolo ya Botsogo______Motse______.

Kokelwana ______Mokanoki______.

Letsatsi la potsoloso

Matseno. Ke tlaa go botsa dipotso mabapi le go nna teng ga dijo mo lwapeng la gago mo dikgweding tse tharo tse di fetileng.

1. Mo dikgweding tse tharo tse di fetileng a o kile wa tshwenyega ka gore lo tlaa felelwa ke dijo mo lwapeng ka ntlha ya go tlhoka madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (yaa ko potsong ee latelang)

1.a. Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

2. Mo dikgweding tse tharo tse di fetileng a lelwapa la gago le ne la felelwa ke dijo ka ntlha ya letlhoko la madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (yaa ko potsong ee latelang)

2.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

3. Mo dikgweding tse tharo tse di fetileng a lelwapa la gago le ne la tlhoka madi kana metswedi mengwe ya go bona dijo tse di farologaneng gape di na le dikotla? a) Ee b) Nyaa (yaa ko potsong ee latelang)

3.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

207

4. Mo kgweding tse tharo tse di fetileng, a wena kgotsa mogolo, mongwe mo lwapeng o ne a patelesega go ja mofuta o le mongwe kgotsa e mebedi fela ya dijo ka mabaka a go tlhoka madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (yaa ko potsong ee latelang)

4.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

5. Mo dikgweding tse tharo tse di fetileng a wena kgotsa mogolo mongwe mo lwapeng o ne a tlhoka go fitlhola, go ja motshegare le go lalela ka ntlha ya go tlhoka madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (yaa ko potsong ee latelang)

5.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

6. Mo dikgweding tse tharo tse di fetileng a wena kgotsa mogolo mongwe mo lwapeng o kile a ja mo go sa kgotsogatseng ka ntlha ya letlhoko la madi kgotsa metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (fetela kwa potsong ee latelang)

6.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

7. Mo dikgweding tse tharo tsedi fetileng a wena kgotsa mogolo mongwe mo lwapeng o kile a tshwarwa ke tlala mme a tlhoka se a se jang ka go ne go sena dijo gape go sena gore di ka bonwa jang? a) Ee b) Nyaa (yaa ko potsong ee latelang)

7.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

208

8. Mo dikgweding tse tharo tse di fetileng a wena kgotsa mogolo mongwe mo lwapeng one a tlhoka go ja letsatsi lotlhe ka gonne go sena dijo gape go sena gore di ka bonwa jang? a) Ee b) Nyaa (fetela ko potsong ee latelang)

8.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

Dipotso tse di latelang ke ka ga bana go ya go ba ba dingwaga tse di lesome le bofera bobedi ba ba nnang mo lapeng la gago.

9. Mo dikgweding tse tharo tse di fetileng a ngwana mongwe mo lwapeng la gago gaa kgona go bona dijo tse di farologaneng gape di na le dikotla ka ntlha ya letlhoko la madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (fetela ko potsong ee latelang)

9.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

10. Mo dikgweding tse tharo tse di fetileng a ngwana mongwe mo lwapeng o jele mefuta ya dijo e sa lekana ka ntlha ya go tlhoka madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (fetela ko potsong ee latelang)

10.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

11. Mo dikgweding tse tharo tse di fetileng a ngwana mongwe mo lwapeng o jele dijo tse di kwa tlase ga tse a tshwanetseng go di ja ka ntlha ya go tlhoka madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (fetela ko potsong ee latelang)

11.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe)

209

c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

12. Mo dikgweding tse tharo tse di fetileng, a o ne wa tsholela ngwana mongwe dijo tse dinnye mo lwapeng ka ntlha ya go tlhoka madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (fetela ko potsong ee latelang)

12.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

13. Mo dikgweding tse tharo tse di fetileng a ngwana mongwe mo lwapeng o ne a tshwarwa ke tlala mme wa tlhaela go mo fa dijo ka ntlha ya go tlhoka madi kana metswedi mengwe ya go bona dijo? a) Ee b) Nyaa (fetela ko potsong ee latelang)

13.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

14. Mo dikgweding tse tharo tse di fetileng a ngwana mongwe mo lwapeng la gago one a lala ka tlala ka ntlha ya go tlhoka madi kana metswedi mengwe ya go bona dijo? Ee a) Nyaa (fetela ko potsong ee latelang) 14.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

15. Mo dikgweding tse tharo tse di fetileng a ngwana mongwe mo lwapeng o kile a tlhola letsatsi lotlhe go sena se a se jang ka gore go ne go sena dijo gape go sena gore o ka di bona jang? a) Ee b) Nyaa

15.a Fa karabo ele ee go diragetse ga kae? a) Gantsi (e batlile e le malatsi otlhe) b) Nako tse dingwe (malatsi mangwe mme eseng otlhe) c) Ka sewelo (e ka na letsatsi kgotsa malatsi a mabedi)

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APPENDIX - I

List of Food Items Adjusted for Nutrient Values - 26 food items that did not

match at 1:1

SA Food Item SA Gram US Food Item Equivalent US Gram %

Weight Gram Weight

Weight Matched

Bean leaves 100 Collard Greens 25 400

Mabella/Sorghum 100 Flour Sorghum 30 116.7

Beef Spleen 100 Beef/Organ Spleen 80 125.0

Beef Kidney 100 Beef/Organ Kidney 85 117.5

Macaroni/Spaghetti 100 Macaroni/Spaghetti 90 111.1

Mealie Meal 100 Rice, White, Regular 80 125.0

Bean Dried 100 Bean, White 110 90.9

Chicken Feet 100 Chicken Feet 93 107.5

Peanut Butter 100 Peanut Butter, Reg 106 94.3

Margarine 100 Margarine, Reg, Stick 102 98.0

Apple 100 Apple, Fresh 130 76.9

Rice 100 Rice 130 76.9

Cheese 100 American Cheese Spread 95 105.3

Chicken 100 Chicken Dark Unknown 90 111.1

Chicken Head 100 Chicken Feet 60 166.7

Canned Beef 100 Beef Corned Canned 85 117.6

211

Carrot 100 Carrots, Boiled 110 90.9

Butter 100 Butter, Reg, Unsalted 101.5 98.5

Cabbage 100 Cabbage, Green, cooked 105 95.2

Corn flakes 100 Corn Flakes, UNSW 106 94.3

Pawpaw 100 Cantaloupe, Fresh 120 83.3

Maphakiwa 100 Bun, White Bread 110 90.9

White Bread 100 Rolls/White 90 111.1

Coleslaw 100 Coleslaw/mayo 65 153.8

Samp 100 Black-Eyed Peas 110 90.9

Russian Pie 100 Pot Pie Beef 175 57.1

212

References

Abrams, S. A., Mushi, A., Hilmers, D. C., Griffin, I. J.; Davila, P., & Allen, L.

(2003). A multi-nutrient-fortified beverage enhances the nutritional status of children in

Botswana. Journal of Nutrition, 133, 1834-1840.

Agbon, C., Okeke, E., & Omottayo, A. (2010). Nutrient intake of rural preschool

children in Southwest Nigeria. Infant, Child & Adolescent Nutrition, 2(6), 336-339.

Arimond, M., & Ruel, M. T. (2004). Dietary diversity is associated with child

nutritional status: Evidence on of children and women in developing countries from 11

demographic and health surveys. Journal of Nutrition, 134, 2579-2585.

Ashworth, A. (2006). Efficacy of community-based treatment of severe

malnutrition. Food & Nutrition Bulletin, 27(3), S24-S48.

Baik, J .Y., & Lee, H. (2009). Habitual plate-waste of 6- to-9-year-olds may not be associated with lower nutritional needs or taste acuity, but undesirable dietary factors.

Nutrition Research, 29, 831-838.

Ball, S.C., Benjamin, S. E., & Ward, D. S. (2007). Development of reliability of an observation method to assess food intake of young children in child care. Journal of the American Dietetic Association, 107, 651-661.

Bandura, A. (1977). Social learning theory. New Jersey. Prentice-Hall, Inc.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. New Jersey. Prentice-Hall, Inc.

Baqui, A. H., & Black, R. E. (2002). Childhood infectious diseases and their contribution to undernutrition. In: R. E. Black & K. F. Michaelsen (Eds.), Public health

213

issues in infant and child nutrition (pp 19-51). Philadelphia: Lippincott Williams &

Wilkins.

Beaton, G.H., Martorell, R., L’Abbe, K. A., Edmonston, B., McCabe, G., Ross,

A.C. et al., (1993). Effectiveness of vitamin A supplementation in the control of young

child morbidity and mortality in developing countries. Nutrition Policy Discussion Paper,

13, 4-6.

Behrman, J. R. (2009). Early life nutrition and subsequent education, wage and

intergenerational effects. In: Health and growth: Commission on growth and

development edited by Spence, M & Lewis, M. Washington, D.C: World Bank.

Bere, E., & Klepp, K. I. (2005). Changes in accessibility and preferences predict

children’s future fruit and vegetable intake. International Journal of Behavioral Nutrition

and Physical Activity, 2(15). Available from: http://www.ijbnpa.org/content/2/1/15.

Best, C., Neufingerl, N., van Geel, L., van den Briel, T., & Osendarp, S. (2010).

The nutritional status of school-aged children: Why should we care? Food and Nutrition

Bulletin, 31(3), 400-417.

Beydoun, M. A., & Wang, Y. (2009). Parent-child intake resemblance in the

United States: Evidence from a large representative survey. Social Science & Medicine,

69, 2137-2144.

Birch, L. L. (1996). Children’s food acceptance patterns. Nutrition Today, 31,

234-240.

Birch, L. L., Johnson, S.L., Anderson, G., Peters, J.C., & Schulte, M. C. (1991).

The variability of young children’s energy intake. New England Journal of Medicine,

324, 232-235.

214

Bollela, M. C., Boccia, L. A., Nicklas, T. A., Lefkowitz, K. B., Pittman, B.P.,

Zang, E. A. et al. (1999). Assessing dietary intake in preschool children: The healthy start

project- New York. Nutrition Research, 19(1), 37-48.

Bowley, N. A., Pentz-Kluyts, Bourne, L. T., & Marino, L. V. (2007). Feeding the

1 to 7-year-old. A support paper for the South African paediatric food-based dietary

guidelines. Maternal and Child Nutrition, 3, 281-291.

Bowman, S. A., & Harris, E. W. (2003). Food security, dietary choices, and

television-viewing status of preschool-aged children living in single-parent or two-parent

households. Family Economic & Nutrition Review, 15(2) 29-34.

Brink, P. J., & Wood, M. J. (1998). Advanced design in nursing research.

London: Sage Publication.

Brown, J. E., Isaac, J. S., Krinkle, U. B., Murtaugh, M.A., Sharbaugh, C., Stang,

J., et al., (2008). Nutrition through the life cycle (3rd ed.). Australia: Thomson

Wadsworth.

Brown, K. H., Creed-Kanashiro, H., & Dewey, K. G. (1995). Optimal

complementary feeding practices to prevent childhood malnutrition in developing

countries. Food and Nutrition Bulletin, 16, 4. Accessed 10/23/2009 from

http://www.unu.edu/unupress/food/8F164e/8F164E07.htm

Burns, N., & Grove, S. K. (2009). The practice of nursing research: Appraisal, synthesis, and generation of evidence (6th ed.). St. Louis: Saunders.

Burrows, T. L., Martin, J., & Collins, C. E. (2010). A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labeled water. Journal of American Dietetic Association, 110, 1501-1510.

215

Butte, N. F., Fox, M. K., Briefel, R. E., Siega-Riz, A. M., Dwyer, J. T., Deming,

D. M., et al. (2010). Nutrient intakes of US infants, toddlers, and preschoolers meet or

exceed dietary reference intakes. The Journal of American Dietetic Association, 110, S27

–S37.

Buzzard, M., 24-Hour dietary recall and food record methods In: Willett, W eds.

(1998). p50-3. Nutritional Epidemiology (2nd ed.). New York: Oxford.

Calder, P.C., & Jackson, A. A. (2000). Undernutrition, infection and immune function. Nutrition Research Reviews, 13, 3-29.

Campbell, K. J., Crawford, D. A., & Ball, K. (2006). Family food environment

and dietary behaviors likely to promote fatness in 5-6 year –old children. International

Journal of Obesity, 30, 1272-1280.

Central Intelligence Agency - The World FactBook 2012 Retrieved January 15,

2012, from CIA- The World FactBook, 2012 Retrieved January 15, 2012, from

https://www.cia.gov/library/publications/the-world-factbook/geos/bc.html

Central Statistics Office. (2005). Census demographic indicators; Botswana.

Gaborone: Department of Printing & Publishing Services.

Central Statistics Office. (2011). 2011 Population and Housing Census

Preliminary Rescults Brief. Botswana. Gaborone: Department of Printing & Publishing

Services.

Clark, N. M. & Houle, C. R. (2009). Theoretical models and strategies for

improving disease management by patients. In: S.A. Shumaker., J. K. Ockene, & K. A

Riekert, (Eds.), The handbook of health behavior change (pp 19-37). New York: Springer

Publishing Company.

216

Clausen, T., Charlton, K. E., Gobotswang, K. S., & Holmboe-Ottesen, G. (2005).

Predictor of food variety and dietary diversity among older persons in Botswana.

Nutrition, 21, 86-95.

Coates, J., Swindale, A., & Bilinsky, P. (2007). Household Food Insecurity

Access Scale (HFIAS) for Measurement of Household Food Access: Indicator Guide (v.3)

Washington DC, Food and Nutrition Technical Assistance Project, Academy for

Educational Development.

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd

.ed.) Hillsdale, New Jersey: Lawrence Erlbaum Associates Publishers.

Colapinto, C. K., Fitzgerald, A, Taper, L. J., & Veugelers, P. J. (2007). Children’s

preference for large portions: Prevalence, determinants, and consequences. Journal of

American Dietetic Association, 107, 1183-1190.

Collins, C. T., Gibson, R. A., Miller, J., McPhee, A. J., Willson, K. Smithers, L.

G. et al., (2008).Carbohydrate intake is the main determinant of growth in infants born

<33 weeks’ gestation when protein intake is adequate. Nutrition, 24, 451-457.

Collins, C. T., Chua, M. C., Rajadurai, V. S., McPhee, A. J., Miller, L. N.,

Gibson, R. A. et al., (2010). Higher protein and energy intake is associated with increased weight gain in pre-term infants. Journal of Paeditrics & Child Health, 46, 96-102.

Corty, E. W. (2007). Using and interpreting statistics: A practical text for the

health, behavioral, and behavioral social sciences. St. Louise: Mosby Elsevier.

Cottrell, R. R., & McKenzie, J. F. (2011). Health promotion & education research methods: Using the five-chapter thesis/dissertation model (2nd ed.). Boston:

Jones & Bartlett Publishers.

217

Corvalan, C; Kain, J., Weisstaub, G., & Uauy, R. (2009). Impact of growth pattern and early diet and obesity and cardiovascular risk factors in young children from developing countries. Proceedings of the Nutrition Society, 68, 337-337.

Creswell, J. W. (2009). Research design qualitative, quantitative and mixed methods approaches. (3rd.ed). Los Angeles: Sage.

Croker, H., Sweetman, C., & Cooke, L. (2009), Mothers’ views on portion sizes for children. Journal of Human Nutrition and Dietetics, 22, 437-443.

Dannhauser, A., Bester, C. J., Joubert, G., Badenhorst, P. N., Slabber, M.,

Badenhorst, A.M. et al. (2000). Nutritional status of preschool in informal settlement areas near Bloemfontein, South Africa. Nutrition Society, 3(3), 303-312.

de Onis, M., & Habicht, J. P. (1996). Anthropometric reference data for international use: recommendations from a World Health Organization Expert

Committee. American Journal of Clinical Nutrition, 64, 650-658.

de Onis, M., Onyango, A. W., Jan Van den Broeck, W., Chumlea, C., &

Martorell, R. (2004). Measurement and standardization protocols for anthropometry used in the construction of a new international growth reference. Food and Nutrition Bulletin,

25(1), s27–s36.

de Pee, S., Brinkman, H., Webb, P., Godfrey, S., Darnton- Hill, I., Alderman, H., at al, (2010). How to ensure nutrition security in the global economic crisis to protect and enhance development of young children and our common future. The Journal of

Nutrition 140, 138S-142S.

218

Dodds, J. & Laraia, B. (2005). Issues in maternal and child health nutrition. In: J.

B. Kotch (Eds.), Maternal and child health programs, problems, and policy in public

health (pp 417-462). Boston: Jones & Bartlett Publishers.

Dudek, S. G. (2006). Nutrition essentials for nursing practice. Philadelphia:

Lippincott Williams & Wilkins.

Duggan, M. B. (2010). Anthropometry as a tool for measuring malnutrition:

impact of the new WHO growth standards and reference. Annals of Tropical Pediatrics,

30, 1-17.

Edelstein, S. (2011). Nutrition in public health: A handbook for developing

programs and services (3rd ed.) Sudbury: Jones & Bartlett Learning.

Egeland, G. M., Pacey, A., Cao, Z., & Sobol, I. (2010). Food insecurity among

Innuit preschoolers: Nunavut Inuit child health survey, 2007-2008. Canadian Medical

Association Journal, 182(3), 243-248.

Engle, P. L., Lhotska, L., & Armstrong, H. (1997). The care initiative:

Assessment, analysis and action to improve care for nutrition. New York: UNICEF.

Engle, P. L., Menon, P., & Haddad, L. (1997). Care and nutrition: concepts and measurement. Washington, D.C.: International Food Policy Research Institute.

Engle, P.L., Menon, P., & Haddad, L. (1999). Care and nutrition concepts and measurement. World Development, 27(8), 1309-1337.

Faber, M., Jogessar, V.B., & Benade, A. J. (2001). Nutritional and dietary intakes of children 2 -5 years and their caregivers in rural South African community.

International Journal of Food Science & Nutrition, 52, 401-411.

219

Faber, M., & Laubscher, R. (2008). Seasonal availability and dietary intake of β-

carotene–rich vegetables and fruit of 2 years–old to 5 year–old children in a rural South

African setting growing these crops at household level. International Journal of Food

Sciences and Nutrition, 59(1), 46-60.

Faber, M. (2010). Nutrition in vulnerable communities in economically

marginalized societies. Livestock Science, 130, 110-114.

Faul, F., Erdfelder, E., Lang, A. -G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191.

Fernald, L. C. & Neufeld, L. M. (2007). Overweight with concurrent stunting in very young children from rural Mexico: Prevalence and associated factors. European

Journal of Clinical Nutrition, 61, 623-632.

Field, A. (2005). Discovering statistics using spss (2nd ed.) London: Sages.

Firestone, R., Punpuing, S., Peterson, K. E., Acevelo-Garcia, D., & Gortmarker,

S. L. (2011). Child overweight and undernutrition in Thailand: Is there an urban effect?

Social Science Medicine, 72, 1420-1428.

Fisher, J. O., & Birch, L. L. (2000). Parent’s restrictive feeding practices are

associated with young girl’s negative self-regulation of eating. Journal of American

Dietetic Association, 100(11), 1341-1346.

Fisher, J. O., Rolls, B.J., & Birch, L. L. (2003). Children’s bite size and intake of an entrée are greater with large portions than with age- appropriate or self –selected portions. American Journal of Clinical Nutrition, 77, 1164-1170.

220

Fisher, J. O., Liu, Y., Birch, L. L., & Rolls, B. J. (2007). Effects of portion size

and energy density on young children’s intake at a meal. American Journal of Clinical

Nutrition, 86(1), 174-179.

Fisk, C. M., Crozier, S. R., Inskip, H. M., Godfrey, K. M., Cooper, C., &

Robinson, S. M. (2011). Influence on the quality of young children’s diets: the

importance of maternal choices. British Journal of Nutrition 105, 287-296.

Food and Agriculture Organization of the United Nations. (2009). The state of food insecurity in the world: economic-crisis-impacts and lessons learned. Rome.

Fowler, F. J. (2009). Survey research methods (4th ed.). Los Angeles: Sage.

Francis, L. A., Hofer, S. M., & Birch, L. L. (2001). Predictors of maternal child- feeding style: Maternal and child characteristics. Appetite, 37, 231-243.

Gjersing, L., Caplehorn, J. R. M., & Clausen, T. (2010). Cross-cultural adaptation of research instruments: language, setting, time and statistical considerations. B M C

Medical Research Methodology, 10, 13. Available from: http://www.biomedcentral.com/1471-2288/10/13 Accessed on 29 January, 2011.

Glanz, K., Rimer, B. K., & Lewis, F. M. (2002). Health behavior and health

education theory, research, and practice. San Francisco: Josssey-Bass Wiley.

Grantham – McGregor, S., & Baker-Henningham, H. (2005). Review of the evidence linking protein and energy to mental development. Public Health Nutrition,

8(7A), 1191-1201.

Gravetter, F. J., & Wallnau, L. B. (2007). Statistics for the behavioral sciences

(7th ed.). Thomson Wadsworth: USA.

221

Gray, V. B., Cossman, J. S., & Powers, E. L. (2006). Stunted growth is associated with physical indicators of malnutrition but not food insecurity among rural school children in Honduras. Nutrition Research, 26, 549-555.

Grodner, M., Long, S., & DeYoung, S. (2004). Foundations and clinical applications of nutrition: A nursing approach (3rd ed.). St Louis: Mosby.

Grodner, M., Long, S., & Walkingshaw, B. C. (2007). Foundations and clinical applications of nutrition: A nursing approach (4th ed.). St Louis: Mosby.

Gubbels, J. S., Kremers, S. P. J., Stafleu, A., Dagnellie, P. C., de Vries, N. K., &

Thijs, C. (2009). Child-care environment and dietary intake of 2-and 3-year-old children.

Journal of Human Nutrition and Dietetics, 23, 97-101.

Gulliford, M.C., Mahabir, D., & Rocke, B. (2004). Reliability and validity of a short form household food security scale in a Caribbean community. B M C Public

Health, 4, 22-29.

Hackett, M., Melgar-Quinonez, H., Taylor, C.A., & Uribe, M. C. A. (2010).

Factors associated with household food security of participants of the MANA food supplement program in Colombia. Archives of Latinoamerican Nutrition, 60(1), 42-47.

Hackett, M., Melgar-Quinonez, H., & Alvarez, M. C. (2009). Household food insecurity associated with stunting and underweight among preschool children in

Antioquia, Colombia. Pan American Journal of Public Health, 25(6), 506-510.

Hackett, M., Melgar-Quinonez, H., & Uribe, M. C. A. (2008). Internal validity of a household food security scale is consistent among diverse populations participating in a food supplement program in Colombia. B M C Public Health, 8, 175-183.

222

Hagquist, C., Bruce, M., & Gusravsson, J. P. (2009). Using the Rasch model in nursing research: an introduction and illustrative example. International Journal of

Nursing Studies, 46, 380-393.

Hanley, J. A., & Hutcheon, J. A. (2010). Does children’s energy intake at one meal influence their intake at subsequent meals? Or do we just think it does? Paediatric

& Perinatal Epidemiology, 24, 240-248.

Hassink, S. G. (2009). Weighing risk: The expert committee’s recommendations in practice. Seminars in Pediatric Surgery, 18, 159-162.

Heavey, E. (2011). Statistics for nursing: A practical approach. Jones & Bartlett

Learning: United States.

Hendricks, K. M. (2010). Ready-to- use therapeutic food for prevention of childhood undernutrition. Nutrition Reviews, 68(7), 429-435.

Holben, D. H. (2010). The position of the American Dietetic Association: Food insecurity in the United States. Journal of the American Dietetic Association, 110, 1386-

1377.

Hudson, G. J. (1995). Food intake in a West African village. Estimation of food intake from a shared bowl. British Journal of Nutrition, 73, 551-569.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D. G., & Newman, T. B.

(2007). Designing Clinical Research (3rd ed.). Philadelphia: Wolters Kluwer.

Huybrechts, I., De Bacquer, D., Cox, B., Temme, E. H., Van Oyen, H., De

Backer, G. et al., (2008). Variation in energy and nutrient intakes among preschool children: implications for study design. European Journal of Public Health, 18(5), 509-

516.

223

IBM SPSS Statistics 20 (version 2011). Software package

ICN (International Conference on Nutrition) (1992). Plan of Action for Nutrition.

ICN, Rome.

Institute of Medicine. (2005). Dietary Reference Intakes. Washington, DC: The

National Academies Press

Institute of Medicine. (2006). Dietary Reference Intakes. Washington, DC: The

National Academies Press.

Jehn, M., & Brewis, A. (2009). Paradoxical malnutrition in mother-child pairs:

Untangling the phenomenon of over- and under-nutrition in underdeveloped economies.

Economics & Human Biology, 7, 28 -35.

Jooste, P. L., Longenhoven, M. L., Kriek, J. A., Kunneke, E., Nyaphisi, M., &

Sharp, B. (1997). Nutritional status of children in the Lesotho Highlands. East African

Medical Journal, 74(11), 680-689.

Joosten, K. F.M. & Hulst, J. (2011). Malnutrition in pediatric hospital patients:

Current issues. Nutrition, 27, 133-137.

Kaiser, L. L. et al. (2003). Food insecurity and food supplies in Latino households with young children. Journal of Nutrition Education and Behavior, 35(3), 148-153.

Katona, P., & Katona-Apte, J. (2008). The interaction between nutrition and infection. Clinical Practice, 46, 1582-588.

Kennedy, E. T., Ohls, J., Carlson, S., & Fleming, K. (1995). The Healthy Eating

Index: design and applications. Journal of American Dietetic Association, 10, 1103-108.

Kline, P. (1994). An easy guide to factor analysis. Routledge: New York.

224

Kline, R. B. (2005). Principles & practice of structural equation modeling (2nd ed.) The Guilford Press: New York.

Knol, L. L., Haughton, B., & Fitzhugh, E. C. (2004). Food insufficiency is not related to the overall variety of foods consumed by young children in low –income families. Journal of the American Dietetic Association, 1004, 640-644.

Kobayashi, T., Tanaka, S., Toji, C., Shinohar, H., Kamimura, M, Okamoto, N. et al. (2010). Development of a food frequency questionnaire to estimate habitual dietary intake in Japanese children. Nutrition Journal, 9(17) http://www.nutritionj.com/content/9/1/17.

Kourlaba, G., Kondaki, K., Grammatikaki, E., Roma-Giannikou, E & Manios,

Y.(2009). Diet quality of preschool children and maternal perceptions/misperceptions:

The Genesis study. Public Health, 123, 738-742.

Kranz, S., Mitchell, D. C., Siega-Riz, A. M., & Smiciklas-Wright, H. (2005).

Dietary fiber intake by American preschoolers is associated with more nutrient-dense diets. Journal of the American Dietetic Association, 105, 221-225.

Kroller, K., & Warschburger, P. (2009). Maternal feeding strategies and child’s food intake: considering weight and demographic influences using structural equation modeling. International Journal of Behavioral Nutrition and Physical Activity, 678 doi:10.1186/1479-5868-6-78.

Kruger, R., & Gericke, G. J. (2003). A qualitative exploration of rural feeding and weaning practices, knowledge and attitudes on nutrition. Public Health Nutrition, 6(2),

217-223.

225

Kulwa, K. B., Kinabo, L. D., & Modest, B. (2006). Constraints on good child-care practices and nutritional status in urban Dar-es-Salaam, Tanzania. Food and Nutrition

Bulletin, 27, 236 -244.

Labadarios, D., Steyn, N. P., Maunder, E., MacIntryre, U., Gericke, G., Swart, R. et al., (2005). The National Food Consumption Survey (NFCS): South African, 1999.

Public Health Nutrition, 8(5), 533-543.

Lartey, A. (2008). Maternal and child nutrition in Sub-Saharan Africa: challenges and interventions. Proceedings of the Nutrition Society, 67, 105-108.

Leed, P. D., & Ormrod, J. E. (2005). Practical research planning and design (8th ed.). New Jersey: Pearson Merrill Prentice Hall.

Levitt, E. J., Pelletier, D. L., & Pell, A. N. (2009). Revisiting the UNICEF malnutrition framework to foster agriculture and health sector collaboration to reduce malnutrition: A comparison of stakeholder priorities for action in Afghanistan. Food

Policy ,34, 156-165.

Lin, Y., Bolca, S., Vandevijvere, S., Van Oyen, H.,Van Camp, J., et al., (2011)

Dietary sources of animal and plant protein intakes among Flemish preschool children and the association with socioeconomic and lifestyle related factors. Nutrition Journal,

10, (97) Retrieved from: http://www.nutritionj.com/content/10/1/97.

Livingstone, M. B., & Robson, P. J. (2000). Measurement of dietary intake in children. Proceedings of the Nutrition Society, 59, 279-293.

Livingstone, M. B., Robson, P. J., & Wallace, J. M. (2004). Issues in dietary assessment of children and adolescents. British Journal of Nutrition, S2, S213-S222.

226

LoBiondo-Wood, G., & Haber, J. (2006). Nursing research, methods and critical

appraisal for evidence-based practice.(6th ed.). Elsevier: Mosby.

Lorson, B. A., Melgar-Quinonez, H. R., & Taylor, C. A. (2009). Correlates of fruit and vegetable intakes in US children. Journal of the American Dietetic Association,

109, 474-478.

Lutter, C. K., & Rivera, J. A. (2003). Nutritional status of infants and young children and characteristics of their diets. Journal of Nutrition, 133, 2941S-2949S.

MacIntyre, U. E. (1998). Dietary intakes of Africans in transition in North West

Province. Unpublished doctoral dissertation, University of Potchefstroom for Christian

Higher Education. Potchefstroom. South Africa

MacIntyre, U. E., Venter, C. S., & Vorster, H. H. (2000). A culture-sensitive quantitative food frequency questionnaire used in an African population: 2. Relative validation by 7-day weighed records and biomarkers. Public Health Nutrition, 4(1), 63-

71.

Maleta, K., Virtanen, S., Espo, M., Kulmala, T., & Ashorn, P. (2003). Timing of

growth faltering in rural Malawi. Archives of Diseases in Childhood, 88, 574-578.

Mandleco, B.L. (2004).Growth and development handbook: Newborn through adolescence. Clifton Park New York: Thomson /Delmer Learning.

Mannar, M.G.V., (2006). Successful food-based programmes, supplementation and fortification. Journal of Pediatric Gastroenterology and Nutrition 43 S47-S53.

Manu, & Khetarpaul, N. (2006). Food consumption pattern of Indian rural preschool children (four to five years). British Food Journal, 108(2), 127-140.

227

Marin, C. M., Segura, J. L., Bern, C., Freedman, D. S., Lescano, A.G., Benavente,

L. E., et al., (1996). Seasonal change in nutritional status among young children in an

urban shanty town in Peru. Transactions of the Royal Society of Tropical Medicine and

Hygiene, 90, 442-445.

Marston, L. (2010). Introductory statistics for health and nursing using spss. Los

Angeles: Sage.

Martorell, R. (2010). Physical growth and development of the malnourished child:

Contributions from 50 years of research at INCAP. Food and Nutrition Bulletin, 31(1),

68-82.

Maruapula, S. D., & Chapman-Novakofski, K. M. (2007). Health and dietary

patterns of the elderly in Botswana. Nutrition Education & Behavior, 39, 311-319.

Maruatona, T. (2007). Country profile for the Education for All Global

Monitoring Report 2008 for All by 2015: Will we make it? Botswana Non-formal

Education. Retrieved from: http://unesco.org/images/0015/001555/15557e.pdf Accessed

on April 28, 2012.

Mason, J.B. (2003). Measuring hunger and malnutrition. Retrieved February 12,

2012 from: http://www.fao.org/DOCREP/005/Y4249E/y4249.

Matheson, D. M., Varady, J., Varady, A., & Killen, J. D. (2002). Household food

security and nutritional status of Hispanic children in the fifth grade1-3 . American Journal

of Clinical Nutrition, 76, 210-217.

McGloin, A. F., Livingstone, M. B., Greene, L. C., Webb, S. C., Gibson, J. M.,

Jebb, S. A. et al. (2002). Energy and fat intake in obese and lean children at varying risk

of obesity. International Journal of Obesity, 26, 200-207.

228

McPherson, R. S., Kohl, H. W., Garcia, G., Nichaman, M. Z., & Hanis, C. L.

(1995). Food frequency questionnaire validation among Mexican-Americans: Starr

County, Texas. AEP, 5(5), 378-385.

Medical Research Council (2010). FoodFinder3 (version 2002).Cape Town:

South Africa.

Melgar-Quinonez, H, Alverez-Uribe, M. C., Amoroso, L, Ballard, T.,Ortega, J.,

Perez.Escamilla, R., et al., (2010). Informe sobre el Taller Regional: Armonización de la

Escala Latinoamericana y Caribeña de Seguridad Alimentaria – ELCSA. Available at: http://www.foodsec.org/fileadmin/user_upload/eufao-fsi4dm/docs/ELCSA_report.pdf

Melgar-Quinonez, H., & Hackett, M. (2008). Measuring household food security:

the global experience. Review Nutrition, 21S, 27s-37s.

Melgar-Quinonez, H. R., & Kaiser, L. L. (2004) Relationship of child-feeding

practices to overweight in low -income Mexican-American preschool-aged children.

Journal of the American Dietetic Association, 104(7), 1110-1119.

Melgar-Quinonez, H.R., Nord, M., Perez-Escamilla, R., & Segall-Correa, A.M.

(2008). Psychometric properties of a modified US-household food security survey module in Campinas, Brazil. European Journal of Clinical Nutrition, 62, 665-673.

Melgar-Quinonez, H.R., Zubieta, A.C., MkNelly, B., Nteziyaremye, A., Gerardo,

M. F., & Dunford, C. (2006). Household food insecurity and food expenditure in Bolivia,

Burkina Faso, and the Philippines. The Journal of Nutrition, 136, 1431S-1437S.

Meyer, J. P. (2010) Reliability. Oxford: University Press.

Miller, P.E., Mitchell, D.C., Harala, P. L., Pettit, J. M., Smiciklas-Wright, &

Hartman, T. J. (2010). Development and evaluation of a method for calculating the

229

Healthy Eating Index-2005 using Nutrition Data System for Research. Public Health

Nutrition , 14(2), 306-313.

Ministry of Finance & Development Planning. (2003). National Development

Plan 9 2003/04-2008/07. Gaborone: Botswana.

Ministry of Finance & Development Planning. (2008). Annual poverty monitoring report: 2007/08. Gaborone: Botswana.

Ministry of Finance & Development Planning. (2009). Draft annual poverty monitoring report: 2007 –08 (Unpublished report). Gaborone:

Ministry of Health. (2005). National Plan of Action for Nutrition 2005-2010.

Gaborone: Botswana.

Ministry of Health (2007). Child Welfare Clinic Card. Gaborone: Food &

Nutrition Unit, Department of Public Health, Gaborone: Botswana.

Ministry of Health (2009b). The Botswana National Policy on Infant and Young

Child Feeding. (March 2009). Gaborone: Botswana.

Mmopelwa, D., Nnyepi, M., & Codjia, P. (2011). Predictors of household food insecurity & implications for child nutrition (13-18). In Maundeni, T. & Nnyepi, M. S.

(eds.): Thari ya bana: reflections on children in Botswana. Gaborone: UNICEF 2011.

Moeng, G. (2011, December 5). 21% are hungry- BCWIS. The Monitor.

Retrieved February 5, 2012, from http://www.mmegi.bw/index.

Moore, W. M., & Roche, A. F. (1987). Pediatric Anthropometry (3rd ed.).

Columbus: Ross Growth & Development Program.

230

Mukhopadhyay, D. K., & Biswas, A.B. (2011). Food security and anthropometric failure among tribal children in Bankura, West Bengal. Indian Pediatrics, 48(17), 311-

314.

Murphy, S. P., & Poos, M. I. (2002). Dietary reference intakes: summary of

applications in dietary assessment. Public Health Nutrition, 5(6A), 843-849.

Musher-Eizenman, D., & Holub, S. (2007). Comprehensive feeding practices

questionnaire: Validation of a new parental feeding practices. Journal of Pediatric

Psychology, 32(8), 960-972.

MyPyramid.gov. Available at: http://www.choosemyplate.gov/foodgroups/d.

Accessed on 3/10/2012).

Nandy, S., & Miranda, J. J. (2008). Overlooking undernutrition? Using a

composite index of anthropometric failure to assess how underweight misses and

misleads the assessment of undernutrition in young children. Social Science Medicine,

66, 1963-1966.

Neumann, C. G., Murphy, S. P., Gewa, C., Grillenberger, M. & Bwibo, N. O.

(2007). Meat supplementation improves growth, cognitive and behavioral outcomes in

Kenyan children. The Journal of Nutrition, 137, 1119-1123.

Nicklaus, S., Chabanet, C., Boggio, V., & Issanychou, S. (2005). Food choices at

lunch during the third year of life; Increase in energy but decrease in variety. Acta

Paediatrica, 94, 1023-1029.

Nicklaus, T. A., & Hayes, D. (2008). Position of the American Dietetic

Association: Nutrition guidance for healthy children ages 2 to 11 years. Journal of the

American Dietetic Association, 108, 1038-1047.

231

Nnyepi, M. S. (2006). Dietary and nutrition screening for children seeking

curative care in health facilities in Botswana. African Journal of Food Agriculture

Nutrition and Development, 6(2), 1-16.

Nnyepi, M. S. (2007). Household factors are strong indicators of children’s

nutritional status in children with access to primary health care in the greater Gaborone

area. Scientific Research & Essay, 2(2), 055-061.

Nutrition Data System for Research (Version 2011). (2011). University of

Minnesota: Regents of the University of Minnesota.

Ogunba, B. O. (2010). Diet diversity in complementary feeding and nutritional

status of children aged 0 to 24 months in Osun state, Nigeria A comparison of the urban

and rural communities. Infant, Child & Adolescent Nutrition, 2(6), 330-335.

Oldewage-Theron, W. H., Dicks, E. G., & Napier, C. E. (2006). Poverty,

household food security and nutrition: Coping strategies in an informal settlement in the

Vaal Triangle in South Africa. Journal of the Royal Institute of Public Health, 120, 795-

804.

Onyango, A. W. (2003). Dietary diversity child nutrition and health in contemporary African communities. Comparative Biochemistry and Physiology Part A

136, 61-69.

Onyango, A., Koski, K. G., & Tucker, K. L. (1998). Food diversity versus breastfeeding choice in determining anthropometric status in Kenyan rural toddlers.

International Epidemiological Association, 27, 484-489.

Ott, R. L. & Longnecker, M. (2010). An introduction to statistical methods and data analysis (6th ed.). Brooks/Cole Cengage Learining: Belmont

232

Otten, J. J., Hellwig, J. P., & Meyers, L. D. (2006). Dietary Reference Intakes.

The essential guide to nutrient requirements. Institute of Medicine of the National

Academies. Washington: D.C.

Owen, A.L., Splett, P. L., & Owen, G. M. (1999). Nutrition in the community:

The art and science of delivering services (4th ed.). Boston: McGraw-Hill.

Owusu, W. B., Lartey, A., de Onis, M., Onyango, A.W., & Frongillo, E. A.

(2004). Factors associated with unconstrained growth among affluent Ghanaian children.

Acta Paediatrica, 93, 1115-1119.

Park, J., & Choue, R. (2011). Nutritional status of 8- to 12- year-old children with

height below or at 25th percentile associated with height in Seoul metropolitan area.

International Journal of Vitamin & Nutrition Research, 81(41), 225-235.

Pelletier, D. L., & Frongillo, E. A. (2002). Changes in child survival are strongly associated with changes in malnutrition in developing countries. Food and Nutrition

Technical Assistance Project. Washington, D.C: Academy for Educational Development.

Perez-Escamilla, R., Segall-Correa, A.M., Maranha, L. K., Sampaio, M. F.A.,

Marin-Leon, L., & Panigassi, G. (2004). An Adapted Version of the U.S. Department of

Agriculture Food Insecurity Module is a valid tool for assessing household food

insecurity in Campinas, Brazil. Journal of Nutrition, 134, 1923-1928.

Petrou, S., & Kupek, E. (2010). Poverty and childhood undernutrition in

developing countries: A multi-national cohort study. Social Science & Medicine, 71,

1366-1373.

Polit, D. F. (2010). Statistics and data analysis for nursing research (2nd ed.).

Upper Saddle River, NJ: Pearson Education Inc.

233

Polit, D. F., & Beck, C. T. (2004). Nursing research, principles and methods (7th ed.) Philadelphia: Lippincott, Williams & Wilkins.

Polit, D. F., & Beck, C. T. (2006). Essentials of Nursing Research Methods, appraisal, and utilization. (6th ed.). Philadelphia: Lippincott, Williams & Wilkins.

Polit, D. F., & Beck, C. T. (2008). Nursing Research Generating and assessing evidence for nursing practice. (8th ed.). Philadelphia: Lippincott, Williams & Wilkins.

Popkin, B.M., Lu, B. & Zhai, F. (2002) Understanding the nutrition transition: measuring rapid dietary changes in transitional countries. Public Health Nutrition, 5(6A),

947-953.

Quandt, S. A. (2006). Social and cultural influences on food consumption and nutritional status. In: M. E. Shils., M. Shike., A. C. Ross., B. Caballero., R. J. Cousins,

(Eds.), Modern nutrition in health and disease (pp1741-1751). Philadelphia: Lippincott

Williams & Wilkins.

Radimer, K. L. (2002). Measurement of household food security in the USA and other industrialized countries. Public Health Nutrition, 5(6A), 859-864.

Ramolefhe, G., Nnyepi, M., Chimbari, M. J., & Ama, N.O.C. (2010). Feeding practices, feeding environment & growth status of children (2 -5 years) in Tubu, Shorobe

& Xobe Molapo farm villages in Botswana (p.7-12). In: Maundeni, T. & Nnyepi, M. S.

(eds.): Thari ya bana: reflections on children in Botswana. Gaborone: UNICEF 2011.

Reichenheim, M. E., & Moreas, C. L. (2007). Operationalizing the cross-cultural adaption of epidemiological measurement instruments. Rev Saude Publica 41(4)

Available from: http://www.scielo.br/pdf/rsp/v41n4/en_6294.pd Accessed on 30 January,

2011.

234

Reis, M., (2012). Food insecurity and the relarionship between household income

and children’s health and nutrition in Brazil. Health Economics, 21, 405-427.

Rice, A. L., Hyder, A., Caulfield, L., Stoltzfus, R., Fishman, S., Frangakis C. et

al., (2002). Burden of disease caused by childhood undernutrition. In: R. E. Black & K.

F.Michaelsen (Eds.), Public health issues in infant and child nutrition, (pp 1-17).

Philadelphia: Lippincott Williams & Wilkins.

Robinson, S. et al., (2007). Dietary patterns in infancy: the importance of maternal and family influences on feeding practice. British Journal of Nutrition, 98,

1029-1037.

Rockett, H. R., Wolf, A. M., & Colditz, G. A. (1995). Development and reproducibility of a food frequency questionnaire to assess diets of older children and adolescents. Journal of American Dietetic Association, 95, 336-340.

Rodriguez, N. R. (2005). Optimal quantity and composition of protein for growing children. Journal of the American College of Nutrition, 24(2), 150S-154S.

Rosas. L.G., Harley, K., Fernald, L. C., Guenelman, S., Mejia, F., Neufel, L. M., et al. (2009). Dietary associations of household food insecurity among children of

Mexican descent: Results of a binational study. American Dietetic Association, 109,

2001-2009.

Rose, D., Chotard, S., Oliveira, L. Mock, N. & Libombo, M. (2008). A comparative, evaluation of dietary indicators used in food consumption assessments of at- risk populations. Food & Nutrition Bulletin, 29(2), 113-122.

Ruel, M. T. (2003). Operationalizing Dietary diversity: A review of measurement issues and research priorities. Journal of Nutrition, 133, 3911S-3926S.

235

Rutishauser, I. H. E., & Black, A. E. (2002). Measuring food intake In M. J.

Gibney., H. H. Vorster, & F. J Kok, Introduction to human nutrition (pp. 225-

248).Malden: Blackwell Science.

Saarilehto, S., Lapinlemu, H., Keskinen, S., Helenius, H., Talvia, S., & Simell, O.

(2004). Growth, energy intake, and meal pattern in five-year-old children considered as

poor eaters. Journal of Pediatrics, 144, 363-367.

Sallies, J. F., Owen, N., & Fisher, E. B. (2008). Ecological models of health

behavior. In: Glanz, K., Rimer, B. K., & Viswanath, K. (Eds). Health behavior and

health education theory, research, and practice (4th ed.). San Francisco: Josssey-Bass

Wiley.

Schneider , M., & Stokols, D. (2009). Multilevel theories of behavior change: A

social ecological framework. In: S.A. Shumaker., J. K. Ockene, & K. A Riekert, (Eds.),

The handbook of health behavior change (pp 85-105). New York: Springer Publishing

Company.

Schoeman, S.E. Hendricks, M. K., Hattingh, S. P., Benade, A. J. S., Laubscher, J.

A., & Dhansay, M. A. (2006). The targeting of nutritionally at – risk children attending a primary health care facility in Western Cape Province of South Africa. Public Health

Nutrition, 9(8), 1007-1012.

Sepp, H., Lennernas, M., & Abrahamsson, L. (2006). Preschool children’s meal patterns analyzed using the Food-Based Classification of Eating Episodes model.

Scandinavian Journal of Food and Nutrition, 50(30), 131-138.

236

Shetty, P. (2002). Food and nutrition: the global challenge. In: M. J. Gibney., H.

H. Vorster., & F. J. Kok, (Eds.), Introduction to human nutrition, (pp 318-333). Oxford:

Blackwell Science.

Silva, M. R., Dias, G., Ferreira, C. L., Franceschini, S. C., & Costa, N. M. (2008).

Growth of preschool children was improved when fed an iron-fortified fermented milk beverage supplemented with lactobacillus acidophilus. Nutrition Research, 28, 226-232.

Singh, M. B., Fotedar, R., Lakshminarayana, J., & Anand, P. K. (2006). Studies

on the nutritional status of children 0–5 years in a drought- affected desert area of western Rajasthan, India. Public Health Nutrition, 9(8), 961-967.

Skinner, J. D., Carruth, B. R., Bounds, W., & Ziegler, P. J. (2002). Children’s food preferences: A longitudinal analysis. Journal of American Dietetic Association, 102,

1638-1647.

Slusser, W., Prelip, M., Kinsler, J., Erausquin, J. T., Thai, C., & Neumann, C.

(2011) Challenges to parent nutrition education: A qualitative study of parents of urban children attending low-income schools. Public Health Nutrition, 14(10), 1833-1841.

Spiel, C., & Gluck, J. (1998). Item response models for assessing change in dichotomous items. International journal of Behavioral Development, 22(3), 517-536.

Stephenson, K., Amthor, R., Mallow, S., Nungo, R., Maziya-Dixon, B., Gichuki,

S., et al. (2010). Consuming cassava as a staple food places children 2-5 years old at risk for inadequate protein intake, an observational study in Kenya and Nigeria. Nutrition

Journal, 9,9 Accessed on 2/17/2012 Available from: http://www.nutritionj.com/content/9/1/9.

237

Stevens, C. A. (2010). Exploring food insecurity among young mothers (15-24 years). Journal for Specialist in Pediatric Nursing, 15(2), 163–171.

Steyn, N. P., Nel, J. H., Nantel, G., Kennedy, G., & Labadorios, D. (2005). Food variety and dietary diversity scores in children: are they good indicators of dietary adequacy?. Public Health Nutrition, 9(5), 644-650.

Stokols, D. (1992). Establishing and Maintaining Healthy Environments.

American Psychologist, 47(1), 6-22.

Stokols, D. (2000). The Social Ecological Paradigm of Wellness Promotion: pg

21-37 In Jamner, M. S., & Stokols, D. (2000). Promoting Human Wellness: New

Frontiers of Research, Practice and Policy. Berkeley: University of California Press.

Stokols, D., Grzywacz, J. G., McMahan, S., & Phillips, K. (2003). Increasing the

Health Promotive Capacity of Human.

Sudfeld, C. R., Navar, A. M., & Halsey, N. A. (2010). Effectiveness of measles vaccination and vitamin A treatment. International Journal of Epidemiology, 39, 148-

155.

Tharakan, C. T., & Suchindran, C.M. (1999). Determinants of child malnutrition and intervention model for Botswana. Nutrition Research, 19(6), 843-860.

Theron, M., Amissah, A., Kleynhans, I. C., Albertse, E., & MacIntyre, U. E.

(2006). Inadequate dietary intake is not the cause of stunting amongst young children living in an informal settlement in Gauteng and rural Limpopo Province in South Africa: the NutriGro study. Public Health Nutrition, 10(4), 379-389.

Tomkins, A. (2000). Malnutrition, morbidity and mortality in children and their mothers. Proceedings of the Nutrition Society, 59, 135-146.

238

Tomlinson, M., & Landman, M. (2007). “It’s not just about food”: mother-infant interaction and the wider context of nutrition. Maternal and Child Nutrition, 3, 292-302.

Uauy, R., Kain, J., Mericq, V., Rojas, J., & Carvalan, C. (2008). Nutrition, child growth and chronic disease prevention. Annals of Medicine, 40, 11-20.

United Nations Children’s Fund Executive Board. (1990 session: New York).

(January 01, 1990). Strategy for improving nutrition of children and women in developing countries. Report of the Executive Board, 16-27 April 1990. - E/1990/28-

E/icef/1990/13.-1990. - P. 41-42.-(escor, 1990, Suppl. No. 8).

UNICEF /United Nation Children’s Fund. (1998). The state of the world’s children 1998 (Focus on nutrition). New York: Oxford University Press.

UNICEF /United Nation Children’s Fund. (2009). Tracking progress on child and maternal nutrition: A survival and development priority. New York, Division of

Communication. UNICEF.

UNICEF & The World Bank. (2002, September). Background papers World

Bank/UNICEF nutrition assessment. Nutrition Section, Programme Division & Health,

Nutrition & Population Unit. UNICEF: New York: The World Bank: Washington, DC.

UNICEF, WHO & Ministry of Health (2009). Accelerated Child Survival and

Development (ACSD) Strategy 2009/10–2015/16. (May 2009) Gaborone: Botswana

UN Millennium Project. (2005). Investing in development: A practical plan to achieve the millennium development goals: http://www.unmillenniumproject.org/reports/index.htm

Accessed on 12/23/2010.

239

Dietary Guidelines of Americans 2010. (2010). [U Washington, D.C]: U.S. Dept. of Health & Human Services, U.S. Dept. of Agriculture.

van Heerden, S. M. & Schonfeldt, H. C. (2004). The need for food composition tables for southern Africa. Journal of Food Composition and Analysis, 17, 531-537.

van Staveren, W. A., & Ocke, M. C. (2001). Estimation of dietary intake .In: B.

Bowman, & R. M. Russell, (Eds.), Present knowledge in nutrition (8th ed.,) (pp 605–

616). Washington, DC: International Life Sciences Institute.

Vereecken, C., Hybrechts, I., Maes, L., & De Hanauw, S. (2008). Food consumption among pre-scholars: Does the school make a difference? Appetite, 51, 723-

726.

Vereecken, C. A., Keukelier, E., & Maes, L. (2004). Influence of mother’s educational level on food parenting practices and food habits of young children. Appetite,

43, 93-103.

Vereecken, C., & Maes, L. (2010). Young children’s habits and associations with the mothers’ nutritional knowledge and attitude. Appetite, 54, 44-51.

Vereecken, C., Rovner, A., & Maes, L. (2010). Associations of parenting styles, parental feeding practices and child characteristics with young children’s fruit and vegetable consumption. Appetite, 55, 589–596.

Vesel, L., Bahl, R., Martines, J., Penny, M., Bhandari, N., Kirkwood, B. R. et al.

(2010). Use of new World Health Organization child growth standards to assess how infant malnutrition relates to breastfeeding and mortality. Bulletin of World Health

Organization, 88, 39-48.

240

Victora, C. G., de Onis, M., Hallal, P. C., Blossner, M., & Shrimpton, R. (2010).

Worldwide timing of growth faltering: Revisiting implications for interventions.

Pediatrics, 125, e473-e480.

Vignerova, J., & Lhotska, L. (2006). A fresh look at growth assessment of infants and young children in Czech Republic in context of international developments. Central

European Journal of Public Health, 14(2), 97-100.

Vitola, M. R., Rauber, F., Dal Bo Campagnolo, P., Feldens, C. A., & Hoffman, D.

J. (2010). Maternal dietary counseling in the first year of life is associated with a higher

Healthy Eating Index in childhood. The Journal of Nutrition, 140, 2002-2007.

Wardlaw, G. M., Hampl, J. S., & DiSilvestro, R. A. (2004). (6th ed.). Perspective in nutrition McGraw-Hill: Boston.

Wardlaw, G. M., & Smith, A. M. (2009). Contemporary Nutrition (7th ed.).

McGraw-Hill: Boston.

Wardle, J., Carnell, S., & Cooke, L. (2005). Parental control of over feeding and children’s fruit and vegetable intake: How are they related?. Journal of the American

Dietetic Association, 105, 227-232.

Weisstaub, G., Araya., M.. Hill, A . & Uauy, R. (2008).Childhood malnutrition; prevention and control at the national level, Accessed on: 5/12/2012. Available from: http://anhi.org/learning/pdfs/bcdecker/Childhood_Malnutrition_Prevention_Control_Nati onal_Level.pdf.

Willet, W. C. (1998). Recall of remote diet In: Willett, W eds. (1998). p 148-156.

Nutritional Epidemiology (2nd ed.). New York: Oxford

241

Willet, W. C., & Buzzard, I. M. (1998). Food & nutrients In: Willett, W eds.

(1998). p 18-32. Nutritional Epidemiology (2nd ed.). New York: Oxford

Willey, B. A., Cameron, N., Norris, S.A., Pettifor, J. M., & Griffiths P. L. (2009).

Socio-economic predictors of stunting in preschool children –a population-based study

from Johannesburg and Soweto. South African Medical Journal, 99(6), 450-456.

Willows, N. D., Barbarich, B. N., Wang, L.C., Olstad, D. L., & Clandinin, M.T.

(2011). Dietary inadequacy is associated with anemia and suboptimal growth among

preschool-aged children in Yunnan Province, China. Nutrition Research, 31, 88-96.

Wilson, A. M., & Lewis, R. D. (2004). Disagreement between energy and macronutrient intakes estimated from food frequency questionnaire and a 3-day diet

record in girls aged 4-9 years of age. Journal of American Dietetic Association, 104, 373-

378.

Wilson, J. F. (2000). Lunch eating behavior of preschool children: Effects of age,

gender, and type of beverage served. Physiology & Behavior, 70, 27–33.

WHO Working Group. (1986). Use and interpretation of anthropometric

indicators of nutritional status. Bulletin of the World Health Organization, 64(6), 929-

941.

WHO (2004). The importance of caregiver-child interactions for the survival and

healthy development of young children. Department of Child and Adolescent Health and

Development.

WHO Multicentre Growth Study Reference Group. (2006 a). WHO child growth

standards based on length/height, weight and age. Acta Paediatrica, S450, 76-85.

242

WHO Multicentre Growth Study Reference Group. (2006 b). Complementary

feeding in the WHO Multicentre Growth Reference Study Acta Paediatrica, S450, 27-37.

WHO Multicentre Growth Study Reference Group. (2006 c). Assessment of linear growth among populations in the WHO Multicentre Growth Reference Study. Acta

Paediatrica, S450, 56-65.

WHO Anthro (Version 3.2.2, January 2011) and macros (2011). Available from: http://www.who.int/childgrowth/software/en/ Accessed on March 7, 2011.

World Health Organization/World Food Program/ United Nations System

Standing Committee on Nutrition/The United Nations Children’s Fund, (2007).

Community –based management of severe acute malnutrition. Geneva: WHO,

Department of Nutrition for Health & Development.

243