Effect of Double Fortified Salt Intervention on the Dietary

Intakes of Women Tea Plantation Workers in India

Sudha Venkatramanan

School of Dietetics and Human Nutrition

McGill University, Montreal

April 2014

A thesis submitted to McGill University in partial fulfillment of the

requirements for the degree of Doctor of Philosophy

Abstract

The prevalence of micronutrient deficiency and underweight are high among rural and tribal Indian women. Iron deficiency affects dietary intakes, productivity, cognitive outcomes, and has implications on the overall health status of adult women in the reproductive age. Predominantly cereal-based diets with low intake of bioavailable iron and lack of dietary diversity, dispose women to iron deficiency. The main objectives of the thesis were (1) to identify the socioeconomic determinants of poor nutritional status in female tea plantation workers, (2) to determine the effect of salt dually fortified with iron and iodine on the energy and nutrient intakes of female tea plantation workers, and (3) to measure the degree of treatment contamination from the common practice of food sharing among the tea plantation workers.

The present dissertation is part of a larger randomized double-masked study to assess the effect of salt dually fortified with iron and iodine (DFS) on iron status and performance indicators; a secondary objective and the focus of this dissertation was to examine the effect of DFS on the dietary intakes of female tea plantation workers. The study participants were from two ethnicities, Adivasi or the tribal group and the Nepali community. The study participants (n=245) were randomized to receive iodized salt (IS, control) or DFS (treatment) and were followed for 10 mo. Data were collected at three time points: baseline, midpoint, and endline. At each time point, dietary intake was assessed by 1) a 7-d food frequency questionnaire, 2) direct weighing of lunch, and 3) a 24-hr dietary recall. The amount of food shared between the women was also measured at the baseline and endline lunch. Anthropometric measures of height (cm), weight (kg), and mid-upper arm circumference (MUAC) (cm) were also collected at three time points. Socioeconomic and demographic information of the participants was collected only at baseline. The results from the 1st objective indicate that the prevalence of underweight (body mass index, BMI <18.5 kg/m2) in Adivasi and Nepali ethnicities were 59.0% and 15.9%, respectively. Illiteracy (p=0.016) and unemployment (p <0.0001) of the spouse, lack of toilet facilities at household (p=0.016), and ethnicity (p=0.029) were all negatively associated with BMI and MUAC; ethnicity and lack of communalwater source were negatively associated with BMI.

The results for the 2nd objective indicated that DFS was effective in improving the dietary iron intake over three time points (p<0.001). No significant improvements in the total food, energy, macronutrients (protein, fat, and carbohydrates), and other micronutrients (calcium, zinc, vitamin A, and vitamin C) intake were observed.

Analysis of shared food intake at endline showed that both the treatment groups benefitted from food (lunch) sharing within individuals during working day. The proportion of treatment received by the IS and DFS groups during lunch are 40% and 54%, respectively. Significant ethnic differences were observed in shared foods received. The Nepali consumed a significantly greater amount of (+33 g; p<0.0001) shared foods than the Adivasi. Considering that food sharing occurred during 215 days of intervention we estimate that the total iron intake from DFS during lunch over the entire study period (289 days) in the control and DFS groups were 241 mg and 510 mg respectively. Sharing lunches resulted in the contamination of the control group, somewhat diminishing the expected group difference in iron intake. There was a significant overall improvement in the iron intake from DFS despite the treatment contamination during lunch on the tea plucking days.The improvement observed in the dietary iron intake with DFS intervention indicates that fortification can be an effective strategy to address nutritional deficiency in at risk populations.

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Résumé

La prévalence de la carence en micronutriments et l'insuffisance pondérale sont élevés parmi les femmes indiennes rurales et tribales. La carence en fer affecte la consommation alimentaire, la productivité, les résultats cognitifs, et a des répercussions sur l'état de santé global des femmes adultes en âge de procréer. Un régime principalement à base de céréales avec un faible apport en fer « bio-disponible » et le manque de diversité du régime alimentaire, soumet ces femmes à une carence en fer. Les principaux objectifs de cette thèse sont 1. d'identifier les facteurs socio- économiques du pauvre état nutritionnel des ouvrières de plantations de thé 2. de déterminer l'effet du sel doublement fortifié en fer et en iode sur les apports énergétiques et nutritionnels des ouvrières de plantations de thé et 3. de mesurer la proportion du traitement qui fut contaminé par le partage de la nourriture et d'étudier les implications de partage de la nourriture entre les ouvrières de plantations de thé.

Le présent mémoire fait partie d'une plus grande étude randomisée en double aveugle en vue d'évaluer les effets du sel doublement fortifié en fer et en iodine (SDF) sur le statut du fer et les indicateurs de performance; un second objectif et la focalisation de ce mémoire fut d'examiner l'effet du SDF sur les apports du régime alimentaire des ouvrières de plantations de thé. Les participantes à l'étude provenaient de deux ethnies: Adivasi ou le groupe tribal et la communauté népalaise. Les participantes à l'étude (n = 245) furent randomisées pour recevoir du sel iodé (contrôle) ou SDF (traitement) et furent suivies pendant 10 mois. L'apport alimentaire fut évalué par 1) un questionnaire d'une semaine sur la fréquence de consommation 2) une pesée de la portion des repas et 3) un rappel alimentaire de 24 heures. Les données furent recueillies à trois différentes périodes: au début, à mi-chemin et en fin d'étude. La quantité de nourriture partagée entre les femmes fut également mesurée au début et au terme du repas. Les mesures anthropométriques de la hauteur (cm), du poids (kg) et le périmètre brachial (PB) (cm) ont également été recueillies à trois points dans le temps. Les informations socioéconomiques et démographiques des participantes furent recueillies à un point dans le temps.

Les résultats du premier objectif indiquent la prévalence de l'insuffisance pondérale (indice de masse corporelle, IMC <18.5 kg/m2) chez les ethnies adivasis et népalaises qui sont de 59.0 % et 15.9 % respectivement.. L'analphabétisme (p=0.016) le chômage (p <0.0001) de l'époux, le manque d'installations sanitaires au foyer (p=0.016), et dans le milieu ethnique (p=0.029) s'avérèrent être des faux négatifs en relation avec l'IMC et le PB; l'éthnicité et le manque d'une source d'eau commune s'avérèrent être des faux négatifs en relation avec le IMC. Les résultats pour le 2ème objectif indiquèrent que le SDF a permis d'améliorer de manière significative l'apport alimentaire en fer à trois points dans le temps (p =0.008). Aucune amélioration significative de la prise alimentaire et de l'apport énergétique total furent observées.

L'analyse de la prise commune de nourriture au terme de l'étude a montré que les deux groupes de traitement ont bénéficié du partage de la nourriture. La proportion du traitement reçu par les groupes IS et SDF sont respectivement 40% et 54%. Des différences ethniques significatives furent observées dans les aliments communs reçus. La népalaise consomma significativement (p <0.0001) une plus large quantité de (33 g) plus d'aliments communs que l'Adivasi. En considérant que le partage de nourriture s’est produit durant les 215 jours qu’a durée l’intervention, nous estimons que le total des apports en fer à partir du SDF au cours des déjeuners sur toute la durée de l’étude (289 jours) dans les groupes témoin et SDF était de 241 et 510 mg respectivement. Les résultats permettent de conclure que le partage des repas aboutit à la contamination du groupe témoin, diminuant quelque peu la différence dans l'apport en fer entre groupes . Malgré cette contamination du groupe témoin, une amélioration significatifive de l’apport en fer a été notée dans le groupe SDF. L'amélioration observée dans l'apport de fer alimentaire avec l'intervention SDF indique que l'enrichissement peut être une stratégie efficace pour remédier à la carence nutritionnelle chez une population à risque.

Original contributions to knowledge

The present dissertation assessed the effect of salt dually fortified with iron and iodine on the dietary intakes of female tea plantation workers from West Bengal, India. The randomized trial was successful in improving the iron intakes from the iron fortified salt. To the best of our knowledge, this is first study which provided detailed information on the dietary intakes of adult women during double fortified salt intervention trial. A novel component of the thesis is presented in Manuscript 3 which is the first research study that analyzed food sharing amongst tea plantation workers and provides information on the implications of food sharing when conducting a randomized control trial.

The research study also contributed information on the nutritional status of the women from two ethnic groups. The study showed positive associations between socioeconomic determinants and undernutrition in the women tea plantation workers of Panighata tea garden. Illiteracy, occupation of the spouse, and lack of sanitation at home was positively associated with undernutrition status of the women. Within the same working condition in the tea estate, our population was relatively heterogeneous. The study also identified ethnicity as an important determinant of the nutritional status of the women. The study identified that within the rural population, the tribal (Adivasi) population were more vulnerable to poor nutritional status than the Nepali women even after controlling for socioeconomic factors. The findings of this study will add to the existing literature on the role of socioeconomic and cultural factors on the nutritional status of women in the reproductive age from rural India.

Contributions of authors to manuscripts

This thesis is presented in the manuscript-based format for submission to scientific journals. The doctoral candidate, Sudha Venkatramanan, with the guidance of her supervisor, Dr. Grace Marquis, developed the research questions and prepared the manuscripts. The present thesis is part of a multidisciplinary research which looked at the effect of double fortified salt on productivity of women tea plantation workers from West Bengal, under the leadership of the principal investigator, Dr. Jere Haas. The research study was funded by Micronutrient Initiative, Ottawa and The Mathile Institute for the Advancement of Human Nutrition, Ohio. With the guidance of Dr. Grace Marquis, the candidate developed the data collection tools for the research and obtained ethics approval for the study. The candidate spent 16 months in the field in Panighata village in West Bengal, India for the coordination of all the data collection activities. The candidate had weekly phone meetings with Dr. Jere Haas for coordinating all the components of the research study. The candidate trained all the field staff for the dietary data collection and Dr. Jere Haas trained the field staff for the anthropometric data collection. The candidate collected all the dietary data information along with the trained field staff. The candidate worked very closely with Ms. Rula Soueida for the development of food composition database for Nepali and Adivasi foods. The candidate obtained help from a fourth year undergraduate student and four master’s student for calculating the food intake from the weighed food intake and 24-hr dietary recall recalls. Food frequency data were entered by the candidate. The candidate conducted the quantitative data analyses and prepared the initial draft of the thesis and the three manuscripts.

Dr. Grace Marquis, the supervisor of the doctoral student, is the co-investigator for this research study. Dr. Grace Marquis provided the key mentorship and invaluable guidance in the development of the research questions, implementing the research, data collection, data analysis, interpretation and critical revision of the manuscripts.

Dr. Jere Haas, the doctoral candidate’s committee member, is the principal investigator for the overall randomized trial. Dr. Haas provided key training and expertise in international nutrition. vii

He provided invaluable guidance and mentorship in the implementation of all the components of the field research in India. He reviewed the drafts, provided critical feedback, interpretation and revision of the three manuscripts presented in this thesis.

Dr. Lynnette Neufeld, from the Global Alliance for Improved Nutrition (GAIN) (formerly the Micronutrient Initiative), is the doctoral candidate’s committee member who provided guidance for the implementation of the study and is a coauthor of Manuscript 2. She reviewed the drafts and provided critical feedback of the thesis and the manuscripts.

Dr. Michel Wenger, co- investigator for the randomized trial, was in charge of the cognitive outcomes of the study. He provided guidance and support to the candidate during data collection in Panighata. Dr. Wenger is a coauthor of Manuscript 2.

Dr. Laura Murray-Kolb, was one of the co-investigators of the randomized trial and was involved in the implementation of the study. Dr. Murray-Kolb is a coauthor of Manuscript 2.

Dr. Greg Reinhart, Vice President from Mathile Institute for the Advancement of Human Nutrition, was closely involved in the production and development of double fortified salt in Gujarat, India. Dr. Reinhart provided guidance and support to the candidate during the field study and provided funding for the food intake data calculation during the second year of the study.

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Acknowledgments

This dissertation was made possible due to the support of many incredible people who accompanied me in this exciting journey. I feel extremely fortunate to have the mentorship and guidance of Dr. Grace Marquis for my doctoral studies. I am very thankful to Dr. Marquis for taking me as a graduate student and it is because of her I was able to come across this wonderful field research opportunity in India. Thanks a lot for believing in me and my abilities. I owe a debt of gratitude to her positive outlook, expertise, enthusiasm for international work, and encouragement for the accomplishment of my research. I benefitted a lot from every single meeting I had with her. She has been instrumental in enhancing my critical thinking by providing an intellectually stimulating environment. You have played an integral role in developing my love for international research. Many thanks to you, Dr. Marquis!

I wish to take this opportunity to thank my supervisory committee members, Dr. Jere Haas, Dr. Timothy Johns, and Dr. Lynnette Neufeld for all their help, support, and constructive feedback which helped me immensely. I greatly appreciate their prompt responses to my emails and taking time to review my thesis. I am very grateful and feel extremely priviledged to have worked with Dr. Jere Haas, who is an exceptional mentor and a great researcher. Thank you so much for considering me for this field research in India. I had an incredible field experience in Panighata village! I feel very fortunate to have received hands on experience from you in conducting field work. I thoroughly enjoyed the research discussions and our weekly Thursday meetings. I used to eagerly wait for Thursdays phone calls to share “the challenges” and the field work dramas with you. Thank you so much for believing in me and supporting me throughout this journey. I will forever remember our great momo lunch, rice cooker meals in Panighata field office and the famous Dominos dinner in Siliguri!

I am truly thankful to Dr. Roger Cue for all his support in statistical analyses. He is such an incredible teacher and I am very glad to have taken Statistical Methods course with him. He made me face my fear with statistics and was always available when I needed help. I owe a debt

of gratitude to him. I am very thankful to the School of Dietetics and Human Nutrition, especially Ms. Lise Grant for always being incredibly supportive with her warm smile.

I am thankful to Ms. Pasang Bhutia and The Child In Need Institute, for all their help in approaching the community and who provided logistics support for the study. My appreciation goes to the managers, assistant managers and supervisors of Panighata Tea Estate garden for all their support in data collection. I am especially thankful to the amazing field workers, Akhil, Maya, Paras, Preeti, Rakesh, Rogena, Romola, Sona, and Vinita for understanding the demanding nature of data collection process and for working every day without complaining. Thanks to the community workers Bikash, Brigit, Germina, Laxmi, Ranju, and Srijana for their tireless efforts during the data collection. Many thanks to Sanjayda for his dedication and coordination of the salt distribution process in the village. Thanks to Jotilda, who drove us to the village every single day and prepared great tea and coffee for the team. Special thanks to the study participants and their families from the Panighata tea garden. This work was made possible due to your understanding and willingness to participate in the study. I am very grateful to the villagers for welcoming me and be part of their family in Panighata. Also special thanks to Rakhi and Subodh for taking care of my dinner during the intense endline data collection.

I am very grateful to the staff at Micronutrient Initiative from Ottawa and New Delhi office: Dr. Annie Wesley, Dr. Anand Lakshman, Hema Sharma and Mini Varghese. You were very helpful during my field work. I am very thankful to other researchers from the double fortified salt intervention trial: Dr. Greg Reinhart, Dr. Laura Murray Kolb, and Dr. Michael Wenger. I would like to thank Micronutrient Initiative and McGill University for providing me with the funding opportunity.

I would not have reached this far without the unconditional love and support of my family members. I am deeply indebted to my incredible parents, my late father, Mr. P. Venkatramanan and my mother Mrs. Santha Venkatramanan, for their unconditional love, encouragement, sacrifice, inculcating the importance of hardwork, and the value of higher education. My mother has been such an inspiration and backbone with her great wisdom, unwavering care, love, and x

support. Big thanks to my amazing sister Hema, who inspired me to do a PhD and my brother-in- law Badri, who filled the void of my father, for always being there for me in everything in life. Thanks to my niece and nephew, Rohith and Jahnavi for making things so much lighter and happier. My special appreciation to Sevag, my husband for his immense patience, incredible support, and constantly encouraging me in my dreams. Many thanks to my in-laws, the Derghazarians and all the family members for their unconditional love, understanding, and support throughout this journey.

My special thanks to the members of Dr. Marquis group: Agartha, Alison, Helga, Hoda, Husein, Karim, Katie, Matilda, Marion, Valerie, Yella, and Yvette, for all your friendship, and support. I have learnt a lot from all of you. Thanks a lot to Rula for all her help and support in building the nutrient composition database and for her wonderful friendship. Special thanks to Jiawei Wang for his friendship, for the frequent meetings to discuss our progress and motivating each other. Last but not the least, I am blessed with great friends who made my journey at McGill and Montreal smooth and I am very indebted to all their love and support: Alexandra, Ambika, Amrit, Meenu, Mithu, Praty, Rachna, Ralph, Samer, Sowmya, and Vani.

Dedication

I dedicate this thesis to the two men who have inspired me greatly in my life, my late father, P. Venkatramanan and my late uncle, Embar Vasudevan for their wisdom, encouragement, love, and mentorship.

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Table of Contents

ABSTRACT ...... II

RÉSUMÉ ...... IV

ORIGINAL CONTRIBUTIONS TO KNOWLEDGE ...... VI

CONTRIBUTIONS OF AUTHORS TO MANUSCRIPTS ...... VII

ACKNOWLEDGMENTS ...... IX

DEDICATION...... XII

LIST OF TABLES ...... XIX

LIST OF FIGURES ...... XXI

LIST OF ABBREVIATIONS ...... XXII

CHAPTER 1: INTRODUCTION ...... 1

1.1. BACKGROUND AND RATIONALE ...... 1

1.2. OVERALL OBJECTIVE ...... 3

1.3. SPECIFIC OBJECTIVES ...... 4

1.4. REFERENCES ...... 5

CHAPTER 2: LITERATURE REVIEW ...... 12

2.1. IRON NUTRITION...... 12

2.2. HEME AND NON-HEME IRON ...... 12

2.3. IRON ABSORPTION ...... 13

2.3.1. Absorption of non-heme iron ...... 13

2.3.2. Absorption of heme iron ...... 14

2.3.3. Loss of iron ...... 14 xiii

2.4. STAGES OF IDA ...... 15

2.5. MEASUREMENT OF ANEMIA ...... 17

2.5.1. Cyanmethemoglobin method ...... 17

2.5.2. Hemocue method ...... 17

2.6. MEASUREMENT OF IRON STATUS ...... 17

2.6.1. Serum ferritin ...... 18

2.6.2. Zinc protoporphyrin ...... 18

2.6.3. Serum transferrin receptor ...... 19

2.7. STRATEGIES OF ADDRESSING IDA ...... 19

2.7.1. Food-based approaches ...... 19

2.7.2. Supplementation and deworming ...... 20

2.7.3. Biofortification ...... 22

2.7.4. Fortification ...... 24

2.7.5. Selection of food vehicle ...... 25

2.8. DOUBLE FORTIFIED SALT ...... 26

2.8.1. Double-fortified salt intervention studies ...... 27

2.9. IRON INTERVENTION STUDIES WITH OTHER FOOD VEHICLES ...... 29

2.10. FACTORS AFFECTING DIETARY IRON ABSORPTION ...... 31

2.11. DIETARY ASSESSMENT METHODS IN RELATION TO IRON INTAKE ...... 31

2.12. IRON AND APPETITE ...... 33

2.13. EFFECT OF IRON STATUS ON PHYSICAL ACTIVITY AND PRODUCTIVITY ...... 34

2.14. IRON STATUS AND COGNITIVE FUNCTION ...... 35

2.15. IRON AND ANTHROPOMETRY ...... 37

2.16. UNDERWEIGHT AND SOCIOECONOMIC STATUS IN INDIA ...... 37

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2.17. DIETARY INTAKES OF RURAL INDIAN WOMEN ...... 41

2.18. TEA PLANTATION AND ETHNIC GROUPS IN INDIA ...... 42

2.19. ADIVASIS OR THE TRIBALS OF INDIA ...... 43

2.20. NEPALESE OF INDIA ...... 44

2.21. SUMMARY ...... 46

2.22. REFERENCES ...... 48

CHAPTER 3: GENERAL METHODOLOGY ...... 82

3.1. STUDY OVERVIEW ...... 82

3.2. ETHICS CLEARANCE ...... 82

3.3. DESCRIPTION OF THE STUDY SITE ...... 82

3.4. TRAINING THE FIELD STAFF ...... 83

3.5. THE STUDY PARTICIPANTS’ SELECTION CRITERIA AND RANDOMIZATION ...... 84

3.5. 1. Deworming ...... 84

3.5.2. Randomization and consort ...... 84

3.6. PRODUCTION OF DFS ...... 85

3.7. ANEMIA SCREENING- HEMOCUE ...... 86

3.8. SOCIOECONOMIC AND DEMOGRAPHIC INFORMATION ...... 86

3.9. ANTHROPOMETRY ...... 86

3.10. DIETARY DATA COLLECTION ...... 87

3.10.1. Food frequency questionnaire ...... 87

3.10.2. Weighed food intake and dietary recall ...... 87

3.10.3. Shared foods ...... 88

3.11. BLOOD MEASURES USED TO CLASSIY THE STRATIFICATION GROUPS ...... 89

3.12. SALT DISTRIBUTION AND COMPLIANCE OF SALT INTAKE ...... 89

3.13. DATA ANALYSES FOR THE EFFECT OF DOUBLE FORTIFIED SALT INTERVENTION ON THE DIETARY

INTAKES OF WOMEN TEA PLANTATION WORKERS ...... 90

3.13.1. Descriptive statistics ...... 90

3.13.2. Mixed modeling repeated measures ...... 90

3.13.3. Shared food intake data analyses ...... 91

3.13.4. Association between the socioeconomic determinants and nutritional status ...... 91

3.14. REFERENCES ...... 95

BRIDGE STATEMENT 1 ...... 98

CHAPTER 4: MANUSCRIPT 1 ...... 99

SOCIOECONOMIC FACTORS AND ETHNICITY ARE DETERMINANTS OF

UNDERNUTRITION AMONGST WOMEN WORKING IN A TEA PLANTATION IN INDIA

...... 99

4.1. ABSTRACT ...... 100

4.2. INTRODUCTION ...... 101

4.3. METHODS ...... 102

4.3.1.Study participants ...... 102

4.3.2. Nutritional status ...... 102

4.3.3.Dietary data collection ...... 103

4.3.4.Socioeconomic and demographic status ...... 103

4.3.5. WHO and Indian cut-offs for BMI and the grades of underweight ...... 104

4.4. STATISTICAL ANALYSIS ...... 104

4.5. RESULTS ...... 104

4.5.1. Anthropometry ...... 104

4.5.2. Socioeconomic and demographic information ...... 105 xvi

4.5.3. Analysis of covariance: Socioeconomic determinants of nutritional status ...... 105

4.6. DISCUSSION ...... 106

4.7. REFERENCES ...... 110

BRIDGE STATEMENT 2 ...... 126

CHAPTER 5: MANUSCRIPT 2 ...... 127

IRON INTAKE INCREASED WITH DOUBLE FORTIFIED SALT BUT OTHER DIETARY

COMPONENTS AND NUTRITIONAL STATUS WERE INFLUENCED BY ETHNICITY OF

WOMEN TEA PLANTATION WORKERS FROM INDIA ...... 127

5.1. ABSTRACT ...... 128

5.2. INTRODUCTION ...... 129

5.3. METHODS ...... 130

5.3.1. Study site and study participants ...... 130

5.3.2. Screening and randomization ...... 131

5.3.3. Anthropometry ...... 132

5.3.4. Food frequency questionnaire ...... 132

5.3.5. Weighed food intake and dietary recall ...... 132

5.4. RESULTS ...... 134

5.4.1. Subjects characteristics ...... 134

5.4.2. Anthropometry ...... 134

5.4.3. Food groups and food consumption patterns ...... 134

5.4.4. Nutrient intake ...... 136

5.5. DISCUSSION ...... 137

5.6. REFERENCES ...... 143

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BRIDGE STATEMENT 3 ...... 166

CHAPTER 6: MANUSCRIPT 3 ...... 167

SHARED LUNCH INTAKE: IMPLICATIONS OF FOOD SHARING IN A DOUBLE-

FORTIFIED SALT INTERVENTION TRIAL ...... 167

6.1. ABSTRACT ...... 168

6.2. INTRODUCTION ...... 170

6.3. METHODS ...... 171

6.3.1. Research setting ...... 171

6.3.2. Dietary data collection- Lunch intake and 24 hr recall ...... 171

6.3.3. Shared lunch intake ...... 172

6.5. RESULTS ...... 173

6.6. DISCUSSION ...... 175

6.7. REFERENCES ...... 179

CHAPTER 7: SUMMARY, CONCLUSIONS, AND POLICY IMPLICATIONS ...... 192

7.1. REFERENCES ...... 198

APPENDICES ...... 204

APPENDIX 1: BAYESIAN INFORMATION CRITERION (BIC) ...... 205

APPENDIX 2: INFORMED CONSENT FORM ...... 209

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List of Tables

Table 2.1. Daily loss of iron in the different age/sex/physiological state categories………... 15

Table 2.2. Thresholds of anemia for different categories...... 16

Table 2.3. Cut-offs to screen undernutrition in women by BMI and MUAC classification.... 38

Table 4.1. Anthropometric characteristics based on ethnicity...... 118

Table 4.2. Socioeconomic and demographic characteristics of the study participants, by ethnicity status...... 122

Table 4.3. Ethnicity, illiteracy, occcupation of the spouse, and sanitation as determinants of women’s nutritional status...... 124

Table 5.1. Baseline characteristics based on treatment groups...... 151

Table 5.2. Anthropometric measures at three time points…………………………………… 152

Table 5.3. Baseline characteristics food frequency by treatment...... 153

Table 5.4. Frequency of intake of food groups at three timepoints- Ethnicity-by-stage interaction……………………………………………………. 154

Table 5.5. Baseline, midpoint and endline nutrient intakes of IS and DFS group...... 156

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Table 5.6. Repeated measures, mixed effect models for macro- and micronutrient intakes per day at three timepoints………………………………... 157

Table 5.7. Food groups and top contributors of energy, calcium, iron and vitamin A in women from Panighata village, India...... 161

Table 6.1. Descriptive characteristics of nutrient intakes of lunch intake...... 184

Table 6.2. Multiple linear regression coefficients of the proportion of treatment effect on the iron and energy intakes from lunch...... 188

Table 6.3. Least square means for ethnicity on the energy and iron intake from shared meals received from others...... 189

Table 6.4. Least square means for ethnicity on rice intake alone from lunch...... 189

Table 6.5. Measure of DFS consumed by the treatment and control from lunch sharing during the plucking season...... 190

Table 6.6. Least square means for treatment on the nutrients intake from dinner...... 191

Table 6.7. Least square means for treatment on the nutrients intake from 24 hr intake...... 191

List of Figures

Figure 2.1. Conceptual framework for the determinants of poor nutritional status and micronutrient deficiency……………………………………………. 40

Figure 3.1. Timeline of the activities of the field study...... 92

Figure 3.3. Map of research site in the Darjeeling district...... 93

Figure 3.5. CONSORT diagram of DFS intervention...... 94

Figure 4.1. Classification of BMI according to Indian and World Health Organization cut-offs...... 119

Figure 4.2. Frequency distribution of BMI by ethnicity...... 120

Figure 4.3. Classification of chronic energy deficiency by ethnicity...... 121

Figure 5.1. Energy intake kcal per day-Repeated measures at three timepoints (P=treatment-by-stage interaction)...... 158

Figure 5.2. Vitamin A intake µg RAE per day-Repeated measures at three timepoints (P=treatment-by-stage interaction)...... 159

Figure 5.3. Calcium (mg/d) intake per day-Repeated measures at three timepoints (P=treatment-by-stage interaction)...... 160

Figure 6.1. Mean proportion of iron received from shared lunch intake...... 185

Figure 6.2. Mean proportion of iron received from shared lunch intake by ethnicity...... 186

Figure 6.3. Percentage of meals and energy contribution per day...... 187 xxi

List of abbreviations

µg: microgram AGP: alpha-1-acid glycoprotein ANCOVA: Analysis of covariance ANOVA: Analysis of variance BMI: Body mass index CCK hormone: Cholecystokinin CED: Chronic energy deficiency CINI: Child In Need Institute cm: centimeter CRP: C-reactive protein DALYs: Disability adjusted life years DFS: Double fortified salt EFF: Encapsulated Ferrous Fumarate FAO: Food and Agriculture Organization FFQ: Food frequency questionnaire GLM: Generalized linear model GLV: Green leafy vegetables Hb: Hemoglobin HH: Household hr: hour ICMR: Indian Council of Medical Research ID: Iron deficiency IDA: Iron deficiency anemia IDD: Iodine deficiency disorder IIDS: Indian Institute of Dalit Studies IMA: Indian Medical Association INP: Indian Nutrition Profile xxii

IS: Iodized salt ITDA: Integrated Tribal Developmental Agency kcal: kilocalorie kg: kilogram

KIO3- Potassium iodate LSM: Least square means MDG: Millenium Development Goal MDI- Mental Development Index MFPP: Micronized ferric pyrophosphate mg: milligram MGFePP: Micronized ground ferric pyrophosphate MI: Micronutrient initiative MIXED: Mixed model MMN: Multiple micronutrients MUAC: Mid upper arm circumference NGO: Non-Governmental Organization NNACP: National Nutritional Anemia Control Program NNMB: National Nutrition Monitoring Bureau NPNL: Non-Pregnant, Non-Lactating NSSO: National Sample Survey Organization NTD: Neural tube defects NVIF: Nutritive Value of Indian Foods OSP: Orange sweet potato PDS: Public Distribution System PROC: Procedure RCT: Randomized controlled trial RDA: Recommended Dietary Allowance SC: Schedule caste SD: Standard deviation SEM: Standard error of the mean xxiii

SES: Socioeconomic status SF: Serum ferritin ST: Schedule tribe sTfR: Soluble transferrin receptor STH: Soil transmitted helminth UN: United Nations UNSCN: United Nations Standing Committee on Nutrition WAZ: Weight-for-age z score WHO: World Health Organization WHZ: Weight-for-height z score

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Chapter 1: Introduction

1.1. Background and rationale Undernutrition or malnutrition is a major public health concern in low income countries (WHO, 2013). Globally, 842 million people suffer from chronic undernutrition and more than 90% are from low income countries (FAO, 2013). It is well established that poverty and nutrition are inter-related. Low socioeconomic status leads to poor nutrition due to a monotonous diet deficient in micronutrients thereby affecting productivity, thus perpetuating the vicious cycle of malnutrition (Varadharajan et al., 2013). Micronutrient deficiencies or “Hidden Hunger” and underweight are major threats for the women in reproductive age in rural India. More than one- third of Indian women suffer from underweight (BMI <18.5 kg/m2) and poor nutritional status is strongly associated with socioeconomic status (Griffiths and Bentley, 2003). According to World Bank estimates, 394 million Indians live with less than $1.25 (INR 77.41) a day (World Bank, 2013).

Of the micronutrient deficiencies, iron deficiency (ID) is one of the most common single nutrient deficiencies that affect women in the reproductive age, pre-menopausal women, infants, and young children (Tuchinksy, 2010). Inadequate intake of iron results in loss of appetite (Lawless et al., 1994) and decreased growth of children (Lawless et al., 1994; Kanani and Pujara, 2000), increased maternal mortality (Christian and Stewart, 2010; Van den Broek, 2003), increased risk of chronic diseases (Mason et al., 1999), impaired cognitive functions (Prado et al., 2012; Lozoff et al., 2003; Black, 2003), decreased reproductive performance (Christian and Stewart, 2010; Christian, 2003; Seshadri, 2001), reduced physical activity (Thankachan et al., 2012; Haas and Brownlie, 2001; Horton and Levin, 2001) and reduced productivity (Li et al., 1994; Kennedy and Meyers, 2005).

Worldwide there are 2 billion individuals with anemia and 50% of anemia is attributed to iron deficiency (Lopez Sierra et al., 2012; Müller and Krawinkel, 2005). Globally, ID accounts for about 840,000 deaths and 35,057,000 global disability adjusted life years (DALYs) (Black, 2003; 1

Stoltzfus, 2003). The National Family Health Survey (NFHS)-3 of India, shows anemia prevalence of 56.2% in women of 15-49 years, 57.9% in pregnant women, 79.2% among children aged 6-35 months and 24.3% in men aged 15-49 years (NFHS-3 report, 2007; Nair and Iyengar, 2009; Agarwal et al., 2006). The high prevalence of iron deficiency in India affects productivity and has economic costs associated with it (Gautam et al., 2008; Horton and Ross, 2003). India loses $2.5 billion dollars annually to micronutrient deficiencies and DALYs due to iron deficiency alone in India are 3,672,000 with an estimated loss of adult productivity of 1.25% gross domestic product (GDP) (Alderman, 2005; Gragnolati et al., 2005). In order to address the concern of ID or iron deficiency anemia (IDA), numerous intervention programs has been implemented in India with the support of non-governmental organizations (NGOs) and governmental programs (Rammohan et al., 2012). However, the prevalence of anemia in women in the reproductive age remained higher in 2007 (56.2% (NFHS-3, 2007) compared to the survey conducted in 1998- 1999 (51.8%, NFHS-2, 2000).

Consumption of non-heme iron from plant food sources which also have high phytate and dietary fibre is an important contributing factor of ID in India (Thankachan et al., 2007). The other common causes of iron deficiency include loss of blood due to intestinal parasites (Ghosh and Bharti, 2003; Bhanusali et al., 2011; Kang et al., 1998), socio-economic status (Pasricha et al., 2011;Thankachan et al., 2007), pregnancy (Gautam et al., 2008) and menstruation (Beard, 2000). Addressing micronutrient deficiency with food fortification is an effective strategy to address Millennium Development Goals (MDG), especially in eradicating hunger and poverty and improving maternal health (WHO, 2001). The different strategies to address iron deficiency or anemia include iron supplementation (Dongre et al., 2011), food-based approaches or nutrition education (Garcia-Casal et al., 2011), intestinal parasite control (Stoltzfus et al., 1997), biofortification (Haas et al., 2005), and fortification of staple foods (Andersson et al., 2008). A detailed review on the implications of ID and different strategies to address ID is presented in the literature review section of the thesis.

Fortification is an effective public health intervention strategy to address IDA. The purpose of this strategy is to increase the intake of specific nutrient(s) that have been identified as 2

inadequate in the food (Huma et al., 2007). Fortification of salt is cost effective and salt is consumed universally. Fortification of table salt with iron and iodine has been suggested as cost effective and practical vehicle to combat two of the primary micronutrient deficiencies: IDA and iodine deficiency disorders (IDD) (Sivakumar et al., 2001; Ranganathan and Karmarkar, 2006). Universal salt iodization was introduced in India in 1984 (Mannar and Dunn, 1994) after the success in the control of endemic goitre in the Himalayan foothills from 1956 -1972 (Ramalingaswami, 1953; Sooch et al., 1973). Salt iodization is an example of a successful large scale fortification that is cost effective, consumed on daily basis everywhere and easily available to the population through the public distribution system (PDS) (Mannar and Sankar, 2004; Andersson et al., 2008). The efficacy of salt dually fortified with iron and iodine or double fortified salt (DFS) has been found to be effective in reducing the rates of IDA in children and women (Andersson et al., 2008; Asibey- Berko et al., 2007). The current thesis is part of a larger study to study the effect of DFS on work productivity of women tea plantation workers of two different ethnicities, Adivasi or indigenous and Nepali, from Panighata tea garden, West Bengal, India. It is well established that DFS reduced the prevalence of ID and IDA and studies have also confirmed that iron supplementation improves work productivity and cognitive outcomes. Iron supplementation studies conducted in school children (Lawless et al., 1994; Kanani and Poojara, 2000) and rubber plantation workers in Indonesia (Basta et al., 1979) have shown improvements in appetite and energy intake. Little is known about the effect of iron fortification on food intake in adults and only qualitative data have been reported in adults (Basta et al., 1979; Galloway et al., 2002). To the best of our knowledge, this is the first work which had addressed the effect of salt dually fortified with iron and iodine on the dietary intakes of adult tea plantation workers.

1.2. Overall objective The overall objective of the present research is to determine the effect of salt dually fortified with iron and iodine on the food, energy and nutrient intake of women tea plantation workers of Panighata tea garden, West Bengal, India.

1.3. Specific objectives

1. To determine the association between the nutritional indicators and socioeconomic status of women tea plantation workers from Panighata tea garden, India (Manuscript 1).

2. To determine if consumption of DFS in women tea plantation workers improves their energy and nutrient intakes (protein, carbohydrates, fat, calcium, zinc, iron, vitamins A and C) using weighed food intakes, 24-hour recall and food frequency questionnaire (Manuscript 2).

3. To examine if the intake of DFS changed the anthropometric measures of body mass index (BMI) and mid-upper-arm circumference (MUAC) of women tea plantation workers (Manuscript 2).

4. To determine the differences in food and nutrient intakes between the two ethnic groups, Adivasi and Nepali (Manuscript 2).

5. To assess the effect of food sharing on the DFS consumption during lunch of women tea plantation workers. Loss or gain of iron from food sharing during the intervention was also assessed (Manuscript 3).

1.4. References

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Andersson M, Thankachan P, Muthayya S, Goud RB, Kurpad AV, Hurrell RF and Zimmermann MB. Dual fortification of salt with iodine and iron: a randomized, double- blind, controlled trial of micronized ferric pyrophosphate and encapsulated ferrous fumarate in southern India. The American Journal of Clinical Nutrition. 2008; 88: 1378- 1387.

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Basta SS, Soekirman D Sc, Karyadi D and Scrimshaw NS. Iron deficiency anemia and the productivity of adult males in Indonesia. The American Journal of Clinical Nutrition. 1979; 32:916-925.

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Haas JD, Brownlie T IV. Iron deficiency and reduced work capacity: a critical review of the research to determine a causal relationship. The Journal of Nutrition. 2001; 131:S676- 688; S688-690.

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Horton S, Ross J. The economics of iron deficiency. Food Policy. 2003; 28:51-75.

Huma N, Salim-Ur-Rehman, Anjum FM, Murtaza MA and Sheikh MA. Food fortification strategy- Preventing iron deficiency anemia: a review. Critical Reviews in Food Science and Nutrition. 2007; 47: 259-265.

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Kang G, Mathew MS, Rajan DP, Daniel JD, Mathan MM, Mathan VI and Muliyil JP. Prevalence of intestinal parasites in rural Southern Indians. Tropical Medicine and International Health. 1998; 3: 70-75.

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Lawless JW, Latham MC, Stephenson LS, Kinoti SN and Pertet AM. Iron supplementation improves appetite and growth in anemic Kenyan primary school children. The Journal of Nutrition. 1994; 124: 645-654.

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López-Sierra M, Calderón S, Gómez J and Pilleux L. Clinical Study: Prevalence of Anemia and Evaluation of Transferrin Receptor (sTfR) in the Diagnosis of Iron Deficiency in the Hospitalized Elderly Patients: Anemia Clinical Studies in Chile. Anemia. 2012; 1-6.

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Chapter 2: Literature Review

2.1. Iron nutrition Iron is an important component of every living cell and has been recognized for centuries as an essential element for the maintenance of health. Iron plays a significant role in numerous biochemical reactions mainly involved in the oxygen transport and storage, adenosine triphosphate synthesis (ATP), deoxyribonucleic acid synthesis and electron transport pathways (Clark, 2008; Gillespie, 1998). The metabolism of iron is very unique. The cellular uptake and storage of iron is well regulated and the rate of internalization of iron by the cells is based on the level of transferrin receptors (TfR) expression (Casey et al., 1989). Absorption of iron is based on the body’s requirements. Extra iron is stored in the body as ferritin and is utilized when there is an increased iron demand (Hallberg, 2001). Body iron stores are mainly determined by the intestinal absorption, with increased absorption during iron deficiency and decreased absorption during increased iron stores (McCance and Widdowson, 1937). Total amount of iron in the body is between 2-5 g which varies with the body weight and hemoglobin levels (Harris and Kellermeyer, 1970). Normal routes of iron loss of ~2-5 g from the body are through gastrointestinal tract, skin and kidneys (Groff and Gropper, 1999).

2.2. Heme and non-heme iron Dietary iron exists in two forms, heme and non-heme iron. Hemoproteins are iron bound proteins, where iron in the centre is attached to four nitrogen containing pyrole ring structures forming porphyrin molecule or heme molecule and to one or two axial ligands from the protein (Crichton, 2009). The major part, around 60%, of iron is present as hemoglobin in erythrocytes in the blood and 10% of iron as myoglobin in muscle tissues (Zijp et al., 2000). Dietary heme iron is derived from hemoglobin and myoglobin from animal foods like meat, fish and poultry; these foodstuffs also contain nonheme iron (Whitney and Rolfes, 2002). Non-heme proteins contain iron without porphyrin groups. They are usually involved in oxidation-reduction reactions, an example is ferrodoxins involved in electron chain transport reactions (Lehninger, 1993). Non-heme iron is the major source of dietary iron. Non-heme iron present in plant (in the plant mitochondria) based foods like nuts, fruits, vegetables, grains and dairy are poorly 12

absorbed compared to heme iron (Groff and Gropper, 1999; Rehman et al., 2010; Zijp et al, 2000).

2.3. Iron absorption The intestinal absorption of iron depends on different dietary factors. The major factors affecting iron absorption are the amounts of heme and non-heme iron intake, dietary factors influencing iron bioavailability and the iron status of each individual (Hallberg, 1981). Heme iron absorption ranges from 15% when the iron stores are normal to 35% during iron-deficiency and non-heme iron absorption varies from 2% to 20%. Absorption of non-heme iron is usually affected by the presence of phytates in nuts, vegetables (Gillooly et al., 1983) and cereals (Gillooly et al., 1984; Tuntawiroon et al., 1991) and polyphenolic compounds present in coffee (Thankachan et al., 2007), tea (Thankachan et al., 2008), herbal teas (Hurrell et al., 1999) red wine (Cook et al., 1995) and cocoa (Gillooly et al., 1984). The phytates, tannins and polyphenols inhibit the absorption of iron bound to transferrin protein. A possible mechanism of the inhibitory activity of polyphenols is through the formation of complexes between the hydroxyl groups of the phenolic compounds and iron molecules hindering the iron uptake by the cells making the iron unavailable for absorption. Non-heme iron absorption also depends on the presence of other dietary components (enhancers) like ascorbic acid and organic acids [e.g., sprouted and fermented foods, vitamin A-β-carotene] (Cook et al., 1984; Hunt et al., 1994; Diaz et al., 2003; Chintapally, 2011). The enhancing effect is due to the ability of the enhancers to reduce the ferric iron to ferrous state thereby improving the absorption of iron (Hurrell and Egli, 2010). Ferrous form of iron is more soluble and is highly absorbable compared to the ferric state (Sharp and Srai, 2007).

2.3.1. Absorption of non-heme iron The concentration in the body of iron is regulated by cellular uptake. Absorption of iron occurs in the proximal small intestine (Zimmerman and Hurrell, 2007). Heme and non-heme iron forms have specific transporters during absorption (Nair and Iyengar, 2009). In the enterocytes the uptake of ferrous iron is mediated by ferro-reductases and a transport protein called divalent metal ion transporter 1 (DMT 1) (Nemeth and Ganz, 2006). Non-heme iron through enzymatic

reactions is liberated from the foods in the gastrointestinal tract for absorption. Once released from the food components, the iron present in the ferric form Fe 3+ in the stomach (Groff and Gropper, 1999) gets reduced to ferrous by the brush border ferric reductases or ascorbic acid (Zimmerman and Hurrell, 2007; Miret et al., 2003). Inside the enterocyte, the iron that is not directly transferred to the circulation gets stored as ferritin in the mucosal cells (Domenico et al., 2008). The remaining iron gets transported by transferrin (a transport protein) which regulates the levels of free iron in the blood. The binding of iron to transferrin is mediated through two proteins called ferroportin 1, which is hepcidin receptor and hephaestin (Andres and Schmidt, 2007; Nemeth and Ganz, 2006). Hepcidin is a regulatory protein synthesized in the liver and binds to ferroportin and regulates the iron homeostasis in the blood (Nemeth and Ganz, 2006). Hepcidin limits iron entry into the plasma from macrophages, intestinal enterocytes and other cells by binding to ferroportin, and facilitates iron removal from the plasma membrane. The levels of plasma hepcidin are regulated by plasma iron, cytokines and anemia (de Domenico et al 2007).

2.3.2. Absorption of heme iron Heme iron gets hydrolyzed by proteases from the globin part of hemoglobin or myoglobin before absorption in the stomach and small intestine. The iron uptake takes place in the duodenum and upper jejunum mucosal membrane to reach the plasma (Domenico et al., 2008). The heme iron gets transported through the enterocytes by heme carrier proteins (HCP-1). In the intestinal mucosal cells, the heme iron gets digested by the heme oxygenase and the liberated iron enters the same pathway as non-heme iron (Andres and Schmidt, 2007).

2.3.3. Loss of iron Iron is not actively excreted from the body. The main routes of excretion of iron are through the gastrointestinal tract (~0.6 mg), skin (~ 0.2 to 0.3 mg) and kidneys (~0.1 mg) (Groff and Gropper, 1999). In postmenopausal women and males, the daily loss of iron varies from ~ 0.9- 1.0 mg/day (Zijp et al., 2000). In premenopausal women, the loss of iron varies from 1.3 to 1.4 mg/day due to menstruation. During menstrual cycle, the losses are approximately 1.35 mg/day

(Groff and Gropper, 1999). Table 2.1 shows the daily loss of iron in the different age/sex/physiological state categories.

Table 2.1. Daily loss of iron in different age, sex, physiological state categories

Age, sex, physiological state Loss of iron (mg/d)

Women Menstruation 1.36 Post menopausal 0.87 Lactation 1.15 Children 0.25-1 y 0.77 1-2 y 0.49 2-6 y 0.56 6-12 y 0.94 Men 0.98 Adolescent boys 12-16 y 1.46 Adolescent girls 12-16 y 1.73 Reference: Zijp et al., 2000

2.4. Stages of IDA The main factors contributing to iron deficiency includes inadequate dietary iron intakes (Hurrell and Egli, 2010), increased demand at the different stages of life especially pregnancy, early childhood and adolescence, socioeconomic factors, parasites infestation and limited food availability (Huma et al., 2007). Iron deficiency results when the body’s need for iron is not met

by the intake of iron in the diet (Killip et al., 2007). There are three different stages in the development of anemia:

1. Iron depletion or latent iron deficiency- is a decrease of body iron stores as measured by a decrease in the serum ferritin levels. The serum ferritin level falls to < 20 ng/mL (Gillespie, 1998; Porter et al., 2011; Hershko et al., 1985; Verloop, 1970).

2. The second stage is iron deficient erythropoises when iron absorption is insufficient to counteract the amount lost from the body and the red blood cell synthesis in impaired with decreased hemoglobin (Hb) concentrations (Gillespie, 1998; Huma et al., 2007).

3. The third, most severe form of iron deficiency is IDA, resulting in hemoglobin concentrations below the established threshold (Crichton, 2009; WHO Global database on anemia, 2008). Table 2.2 provides the thresholds of anemia for different categories.

Table 2.2. Hemoglobin thresholds of anemia, by age, sex, and physiological state

Age/sex Hemoglobin threshold

(g/L)

Non pregnant women 120

≥ 15 y

Pregnant women 110

Children

0.5-4.99 y 110

5-11.99 y 115

12-14.99 y 120

Men ≥ 15 y 130

Reference: WHO Global database on anemia, 2008

2.5. Measurement of anemia The anemia prevalence in a population is determined typically by measuring the Hb concentration. However, the use of Hb concentatration as the sole laboratory method has limitations in identifying iron deficiency due to the low sensitivity and specificity (Zimmerman, 2008; Cook, 2005).

2.5.1. Cyanmethemoglobin method The photometric cyanmethemoglobin method is one of the oldest methods used for the measurement of Hb in the blood. The cyanmethemoglobin is measured with a filter photometer with an absorbance maximum of 540-545 nm (von Schenck et al., 1986). Blood is diluted with a solution of potassium ferricyanide and potassium cyanide. The ferrous ions of the Hb are oxidized by potassium ferricyanide to ferric ions. This results in the formation of methemoglobin which reacts with the cyanide molecules to form cyanmethemoglobin which is measured spectrophotometrically at 540 nm (van Kampen and Zijlstra, 1965).

2.5.2. Hemocue method Hemocue is a reliable screening method for assessing Hb especially in field settings (von Schenck et al., 1986; Conway et al 1998). The Hemocue system requires only 10 µl of whole blood and the results are displayed within 60 seconds (Neville, 1987). It consists of disposable microcuvettes with reagents in dried form. The dried reagents consists of sodium desoxychrolate to hemolyse the red cells, sodium nitrite to convert Hb to methemoglobin and sodium azide to convert methemoglobin to hemoglobinazide (Lardi et al., 1998). Whole blood is drawn up into the microcuvette by capillary action and is placed in the photometer and the absorbance of hemoglobinazide is measured at 570 nm (von Schenck et al., 1986).

2.6. Measurement of iron status Iron status can be measured through several tests in addition to those mentioned above. In anemic individuals such tests are usually used to confirm the type of anemia (WHO Global database on anemia, 2008).

2.6.1. Serum ferritin The concentration of ferritin in serum provides a quantitative measure of the amount of storage iron in normal or non iron-deficient individuals and those with iron deficiency. Ferritin is a large protein of 450 kDa consisting of a protein coat of 24 kDa subunits with iron as the core at several points on the inner surface forming iron-protein interface (Theil et al., 1987). It is usually present in the cytoplasm of the reticuloendothelial cells and liver cells (Jacobs et al., 1972). The role of ferritin includes recycling iron in macrophages, storage of iron in the liver and intracellular housekeeping including providing a reserve of iron for cytochromes, nitrogenase and ribonucleoreductases (Theil et al., 1987). Serum ferritin concentrations are normally within the range of 15-300 µg/l. In individuals with IDA, the serum ferritin concentrations fall below 12-15 µg/l (Worwood, 1997). Serum ferritin tests usually measure the size of iron stores. An important and valuable feature of the measure of serum ferritin is that the concentration of ferritin is directly proportional to body iron stores in healthy individuals (Cook, 2005). In healthy individuals, 1 µg/l of serum ferritin corresponds to 8 to 10 mg of storage iron (Finch et al., 1986). In individuals at high risk for iron deficiency, serum ferritin values <30 µg/l with anemia is an indication for iron deficiency. Ferritin is also an acute phase reactant protein similar to C- reactive protein (CRP) whose concentration gets altered by at least 25% in response to inflammation. Inflammation elevates serum ferritin independently of iron stores. Hence a CRP test can be used to exclude elevated serum ferritin due to inflammation (Gabay and Kushner, 1999; Worwood, 1997; Cook, 2005).

2.6.2. Zinc protoporphyrin The levels of zinc protoporphyrin (ZnPP) increases during iron deficiency. ZnPP is a metabolite formed in trace amounts during heme biosynthesis. The final step of the heme biosynthetic pathway is the chelation of iron with protoprphyrin. During iron insufficiency zinc replaces the iron in the formation of protoporphyrin ring (Zimmerman, 2008). In the ZnPP assay, the ratio of ZnPP/heme is directly measured using a hematofluorimeter using a drop of blood (Cook, 2005; Worwood, 1997). The normal levels of ZnPP are <80 µmol/mol of heme (Labbe et al., 1999). The specificity of ZnPP can be limited as the levels of ZnPP can be elevated in lead poisoning

(Lamola et al., 1975), chronic diseases, inflammation (Zimmerman, 2008; Lasthuizen et al., 1998) and sickle cell anemia (Hirsch et al., 1991).

2.6.3. Serum transferrin receptor Measurement of circulating serum transferrin receptor (sTfR) is a reliable tool to measure IDA or iron deficiency (Skikne et al., 1990). The sTfR is a 760- amino acid glycoprotein that transfers circulating iron into developing red blood cells (Beguin, 2003). Unlike ZnPP and ferritin, sTfR concentrations are very specific to iron deficiency or IDA and are unaffected by inflammation (Zimmerman, 2010; Skikne et al., 1990). The levels of sTfR increases during iron deficiency due to the decrease in the supply of iron to the tissues (Skikne et al., 1990). The sTfR levels are usually detected using radioimmuno assays and are a standard and sensitive test to detect IDA (Zimmerman, 2010; Kohgo et al., 1987).

2.7. Strategies of addressing IDA The five different strategies addressing IDA include food-based approaches (dietary improvement), iron supplementation, parasites control, biofortification and fortification.

2.7.1. Food-based approaches Food based approaches or dietary improvement is a preventive measure to address IDA. Improved dietary practices can include diet diversification to increase the intake of iron rich foods or to increase the enhancers of iron absorption (Nair and Iyengar, 2009), reduction of the inhibitory properties of phytates by germination of seeds or fermentation, and increasing iron in meals through cooking in iron pots (Viteri, 1997). This was shown in a 6 mo community based intervention in children (3-7 y) from Malawi, using diversification and simple traditional cooking measures such as germination and sprouting to reduce the intake of phytates. The intervention was successful in improving the diet diversity and reduced the phytate intake in the intervention group 638(466,870) compared to the control 812 (502, 1170), p=0.007 (Yeudall et al., 2005). Another example of an effective food based strategy was by nutrition education based intervention in rural Malawi. Improvement in the iron status of the children was noticed when the families used maize flour with phytase added to it. Iron status improved significantly in the children after the intervention. Serum transferrin receptors reduced from 15.5 ± 5.5 mg/L to 11.7 19

± 3.6 mg/L (p< 0.05) and zinc protoporphyrin levels decreased from 110 ± 51 to 83 ± 20 µmol/mol heme (Manary et al., 2002). Cooking in iron pots is another measure to improve iron status (Sharma, 2003). This was demonstrated in a community based randomized trial in Ethiopia with 407 children 2-5 y (Adish et al., 1999). Prevalence of anemia decreased from 57 to 13% in the iron group compared to the control. However the limited use of iron pots in traditional cooking limits their value as an intervention method to control iron-deficient anemia.

2.7.2. Supplementation and deworming Supplementation is usually used to treat existing IDA and is used as a preventive public health measure for treating IDA (WHO, 2001). Parasite infestation or soil transmitted helminth infections (STH) such as Ascaris lumbricoides or roundworms, Trichuris trichiura or whipworms, and Necator americanus or hookworm affects micronutrient absorption, increased school absenteeism and poor cognitive outcomes, impaired growth and increases iron deficiency (Suchdev et al., 2014; Bethony et al., 2006). Parasites like hookworms infect approximately 1 billion people worldwide and are one of the major contributing factors of malnutrition (Awasthi and Pande, 1997). Hookworm results in chronic intestinal blood loss resulting in IDA. Iron supplementation along with treatment for parasites can be very effective treatment to control anemia in at risk populations (Cota et al., 2010; Casey et al., 2009). This was demonstrated in a longitudinal cross-sectional study from Vietnam conducted with women (n=380) in the reproductive age (Casey et al., 2010). The women were supplemented with 200 mg ferrous sulphate providing 60 mg elemental iron with 0.4 mg folic acid and 400 mg albendazole for every four months. Albendazole is a benzimidazole developed 20 years ago (Venkatesan, 1998; Marriner et al., 1986). The benzimidazole carbamates are heterocyclic aromatic drugs for the control of helminth parasites in mammals (Gottschall et al., 1990). Albendazole binds to the β tubulin of the parasite and inhibits the further polymerization and glucose uptake (Venkatesan, 1998). After 30 months of intervention, there was an 84% increase in mean ferritin level and a 30% decrease in mean soluble transferrin receptor (TfR) levels from baseline (Casey et al., 2010). In the follow up from the same study in rural Vietnam it was demonstrated that deworming can be an effective strategy in sustainably reducing the prevalence of ID and STH (Casey et al., 2013) in women in the reproductive age range.The prevalence of ID reduced from

23% (95% CI [17%, 29%] to 8% (95% CI [4%, 12%] and the prevalence of hookworm infection fell from 76% to 11% after 54 months of intervention with weekly iron and folic acid and once every 4 to 6 months of albendazole (Casey et al., 2013). A cluster randomized placebo controlled trial of the effect of school deworming and iron supplementation was studied on anemia and cognitive outcomes of school children in Sri Lanka from tea and rubber plantations (Ebenezer et al., 2013). The children were given weekly iron supplements (200 mg ferrous sulphate providing 60 mg elemental iron) or placebo and one dose of 500 mg albendazole or placebo at the baseline. After six months intervention, the prevalence of STH except hookworm, declined significantly from 26.2% to 18.5% in the treatment group and significant differences were observed between the treatment and control groups (p<0.0001). No significant improvements were noted in the Hb levels and cognitive outcomes between the treatment and control groups (Ebenezer et al., 2013). The lack of effect of iron supplementation plus deworming on the prevalence of anemia was probably due to the insufficient dosage of albendazole. Albendazole was administered only at the baseline which was probably not sufficient to reduce the prevalence of hookworm and anemia levels. Based on Cochrane review data, single dose of antihelminth drugs showed little or no effect on Hb levels in school children (Taylor-Robinson et al., 2012).

The main limitation of supplementation as a response to IDA are inadequate supply and poor compliance due to side effects like nausea and constipation and lack of motivation to take the supplements (Stephenson, 1995). Poor adherence to the intervention was noticed in the iron supplementation study from Indonesia. Pregnant women (n=45) were given 300 mg ferrous sulphate per day for 2 months and the prevalence of anemia failed to decrease during the study period. However, significant increase by 55.86% was observed in the serum ferritin levels in the compliant group (n=12), (p<0.05) (Schultink et al., 1993). The study also did not provide information on the dietary intakes of the mothers which is an important component to determine the factors that affected the iron absorption. Supplementation can be targeted towards high risk populations. It is cost effective, however the logistics involved in distribution and compliance can be a challenge (Zimmerman and Hurrell, 2007). In India, the National Nutritional Anemia Control Program (NNACP) has been actively involved through the Integrated Child 21

Development Services (ICDS)1 and primary health care centers to improve the iron intake through supplementation strategy in children, pregnant, and lactating women (Sachdev and Gera, 2013). Nutrition education to the health care staff and the mothers or community along with supplementation can be more effective than supplementation alone in addressing IDA (Charoenlarp et al., 1988; Schultink et al., 1993).

2.7.3. Biofortification Biofortification approach is a practical and effective strategy to address the micronutrient malnutrition by delivering the nutrients through staple crops by conventional breeding and other crop breeding techniques (Kodkany et al., 2013; Meenakshi et al., 2007). The staples that are currently used for biofortification are rice and wheat for zinc, rice and beans for iron, cassava, sweet potatoes and maize for vitamin A (Meenakshi, 2009). Plant based foods such as rice, wheat and pulses are staples for many at-risk populations worldwide. Though plant based foods contain micronutrients in small quantities, these have low bioavailability and are insufficient to meet the dietary requirements. One example is that iron content is high in rice leaves but low in the grains (Bouis, 2003; Hirschi, 2009). With biofortification efforts, through crop management the levels of iron can be increased in the leaves and the seeds of crop species (Grillet et al., 2013). For successful biofortification, three essential rate limiting steps are required 1) uptake of micronutrients from the soil 2) translocation of the nutrients through the transport tissues of the vascular plant (xylem and phloem), and 3) storage of the micronutrient in the endosperm (Bhullar and Gruessem, 2013; Sperotto et al., 2012).

Animal model studies with biofortification have shown promising results. When piglet models were fed biofortied beans for 5 wk, significant improvement in the total body iron was observed from wk 0 to wk 5 in the high iron (429 ± 24 mg) group compared to the control (361 ± 33 mg) (p = 0.034) (Tako et al., 2009). The main goal of biofortification techniques is not only to increase the micronutrient content of the grains but also to increase the bioavaialable nutrient in

1 Government of India sponsored programme, launched on 2nd October 1975, to address malnutrition and health problems in children from 0-6 years, pregnant, and lactating mothers

the human body (Sperotto et al., 2012). This was demonstrated in a recent absorption study conducted with iron deficient young children from Karnataka, India (Khodkany et al., 2013). The absorption of iron and zinc from biofortified pearl millet compared to control pearl millet was studied in 44 children (22-35 mo) who received three pearl millet based test meals in the form of porridge and breads for an entire day. The absorption analysis using stable isotope technique revealed that the mean quantities of iron (0.67 ± 0.47 vs. 0.23 ± 0.15 mg/d; p <0.0001) and zinc absorbed (0.95 ± 0.47 vs. 0.67 ± 0.24 mg/d; p =0.03) from the biofortified grain were significantly greater than the control group (Khodkany et al., 2013).

To date, there has been a very limited number of efficacy studies conducted in humans with iron biofortified grains. One study conducted in Philippines tested the efficacy of iron fortified rice in religious sisters (n=192, 18-45 y) from a monastery who were randomly assigned to high iron and low iron group as control (Haas et al., 2005). There was a 17% increase in the dietary iron intake in the high iron group compared to the control. No significant treatment effect was observed on Hb levels. However, when the analysis was carried out separately for anemic and non-anemic women, the non anemic women responded well to the treatment. This was probably because at baseline, the ID rate was higher in the control (32%) than the high iron intervention group (25%), which could have contributed to this improvement in the control arm. This was the first human feeding trial that tested the efficacy of biofortified rice.

Successful intervention studies with orange sweet potato (OSP) on vitamin A status of women and children have been reported in Bangladesh and Africa (Turner et al., 2013; Hotz et al., 2012a; Hotz et al., 2012b). A cluster randomized control trial to test the effectiveness of OSP on vitamin A intake was conducted in rural Mozambique for three years in young children 6-35 months and the women in the reproductive age. The intervention comprised of three educational components: agriculture, nutrition education and market development for OSP traders. In the intervention group 1, all the education components were included and in group 2, the agricultural and nutrition education components were given only in the first year. Significant improvements was observed (p<0.01) in the intake of OSP in group 1 compared to the control group (children

(56 vs. 11 g/d) and women (144 vs. 48 g/d)) and significant (p<0.01) net increase in the vitamin A intake was also noted in children and women (254 and 492 µg retinol activity equivalents/d) in group 1 of the intervention (Hotz et al., 2012b). This large scale intervention is one example which provides evidence of successfully addressing vitamin A deficiency in a population by incorporation of biofortified sweet potato which is a staple in the community.

2.7.4. Fortification Fortification of foods is considered as a cost effective, efficacious means to prevent iron deficiencies (Sultan et al., 2014; Zimmerman et al., 2003). It refers to the “addition of nutrients to food stuffs that may or may not be present naturally in the food and so improve its overall nutritional quality” (Gillespie, 1998). The main goal of food fortification is aimed at providing a meaningful level of the nutrient intake, from 30-50% of the daily adult requirement (Mannar and Sankar, 2004). The different types of fortification programs include (Allen et al., 2006): a. Mass fortification: this involves fortification of micronutrients that are consumed by the entire population. E.g., flour fortification. b. Targeted fortification: where a particular group is targeted. E.g., infant formulas. c. Voluntary fortification: addition of nutrients to the foods is under the control of food industry. However, the government can regulate the amount and type of nutrients that can be added. E.g., breakfast cereals. d. Mandatory fortification: regulated by the government and implemented when there is an evidence of micronutrient deficiency in the population. E.g., salt fortification

According to the Copenhagen Consensus expert panel, micronutrient fortification of foods was ranked as the first solution to address malnutrition (Copenhagen Consensus, 2012). The history of food fortification in North America dates back to 1920s when Iodine-fortified salt was distributed in the United States by the Morton salt company of Chicago to address endemic goitre (Markel, 1987). Since 1949, salt iodization became mandatory in Canada and goitre was successfully eliminated (Nathoo et al., 2005) followed by vitamin D fortification in 1965 to address rickets and folic acid fortification in 1998 to eliminate neural tube defects (NTD)

(Turner, 1998). With successful food fortification programs, the consequences of vitamin and mineral deficiencies such as goitre, pellagra, and rickets which were once a threat in developed nations, have been eliminated to a great extent (Miller and Welch, 2013). Salt iodization was introduced in India in the 1960s after preliminary experiments from the Kangra valley project for the prevention of endemic goitre in the Himalyan belt (Sivakumar et al., 2001). Iodized salt was distributed in the different zones of the valley from 1957-1962 and the prevalence of goitre reduced from 38% to 19% and the results from the Kangra valley project paved way for successful salt iodization programs in the remaining parts of India with the support of Government of India and public sectors (Rah et al., 2013; Sooch and Ramalingaswami, 1965). The sale of non-iodized salt was banned in 1997 by the government and mandatory salt iodization was introduced in 1998 in India (Berry et al., 2012).

The first step involved in food fortification programs is the identification of the prevalence of a specific micronutrient deficiency in the population through epidemiological surveys. The next step is to determine the micronutrient intake by 24 hr recall and food frequency questionnaire and to obtain food consumption data for potential food vehicles and to determine the micronutrient availability in the diet (Engle-Stone, 2012; Allen et al., 2006). With the support from the government, policy makers and industrial support, the food fortification technology is implemented (Backstrand, 2002; Bishai and Nalubola, 2002). Once the appropriate technology is determined, organoleptic, storage, stability, and bioavailability are assessed before conducting the efficacy and effectiveness trials (Lotfi et al., 1996).

2.7.5. Selection of food vehicle Identification of a suitable vehicle or a carrier is a very important factor to consider (Spohrer et al., 2013; Report of the Working Group on Fortification of Salt with Iron, 1982). The selection of a vehicle requires several factors to be considered including technological factors such as manufacturing unit with centralized processing facilities and cost-effectiveness. Salt fortification is considered cost effective and it costs $0.05 per person per year (Horton, 2006). It is also important that the food vehicle is regularly consumed by the target population and is easily 25

affordable by the poor segments (Spohrer et al., 2013). It is effective when the basic staples are used as food vehicles, such as salt, sugar, oil, bread and rice. Other factors to consider are less potential for over intake, the stability and bioavailability of the micronutrients and stability of color, taste, and appearance of the food after fortification (Dwyer et al., 2014; Lotfi et al., 1996). Examples of food vehicles and the respective micronutrients which are commonly used for fortification include: a. Salt used as a vehicle for iodine and iron (Andersson et al., 2008) b. Flour used as a vehicle for B vitamins, vitamin A and iron (Crandall et al., 2013) c. Fats and oils for vitamin A (Laillou et al., 2013) d. Milk for vitamin A, D, and calcium (Calvo and Whiting, 2013; Calvo et al., 2005)

In India, salt iodization is one example of a successful large scale fortification. Salt is consumed on a daily basis and in India it is easily available to the population through the public distribution system (PDS)2 (Mannar and Sankar, 2004; Andersson et al., 2008). Different intervention studies with salt dually fortified with iron and iodine and other food vehicles will be discussed in the sections below.

2.8. Double fortified salt Double fortified salt (DFS) is one of the several fortified salt formulas which is a free flowing cooking salt containing 50 ppm potassium iodate as the iodine source and 1000 ppm encapsulated ferrous fumarate as the iron source (Diosady et al., 2002). The preliminary development work for the fortification of salt with iron and iodine was started in India in the early 1970`s (Rao and Vijayasarathy, 1975; Hurrell, 1997). Addition of ferrous iron to low-grade salt caused color changes due to the oxidation resulting in the formation of ferric oxides and resulted in a rusty orange brown salt product (Wegmuller et al., 2006; Diosady et al., 2002). The key to successful double fortification of salt is finding ways to prevent iron and iodine interactions either through the use of stabilizers like sodium hexametaphosphate and through encapsulation of one or both of the micronutrients in an inert, digestible material.

2 Public distribution system in India, distributes food grains to the poor segments of the society at a subsidised price 26

Microencapsulation is a process which, in the case of DFS, encloses the iron in an inert matrix. This prevents the reactions of the encapsulated iron with the environment during processing and further distribution (Oshiwono et al., 2004). The commonly used iron fortificants include: a. Ferrous sulphate: highly bioavailable but affects the color and odour of foods (Gillespie, 1998). b. Ferrous fumarate: highly bioavailable, reacts less, highly expensive (Oshiwono et al., 2004; Gillespie, 1998). c. Ferric pyrophosphate: less bioavailable Sodium iron ethylene diamine tetra acetate (EDTA): expensive in production (Gillespie, 1998).

The government of India has shown an increased policy attention to mandatory fortification of DFS. According to a news release from theGovernment of India (Ministry of Women and Child Development, 2011), the federal nutrition and health programmes such as ICDS and mid-day meal3 are required to use DFS for their feeding programmes. This news was released after a meeting that was held at the Prime Minister’s office regarding the use of DFS to battle malnutrition in the country. One of the main outcomes that were agreed upon from the meeting was to examine the possibility of supplying DFS to the poor segments of the country through the PDS (Prime Minister’s Office, 2011). The state of Tamil Nadu had already introduced DFS as part of the mid-day meal programme in April 1996 in the districts of Nilgiris, Coimbatore, Salem, and Trichy (Swaminathan et al., 2004) and has made progress in addressing micronutrient deficiencies. With the recent support of the central government, it is now possible to tackle the chaellenges associated with malnutrition on a nationwide basis.

2.8.1. Double-fortified salt intervention studies Efficacy studies have shown that salt double-fortified with iodine and iron can significantly reduce the incidence rates of IDA and iodine deficiency disorders (IDD). DFS can be prepared by mixing microencapsulated iron compounds into conventionally iodated salt (Yuan et al.,

3 Government of India sponsored school feeding programmeto tackle food insecurity and malnutrition. The government provides free lunch to primary and upper primary students from government schools 27

2008). The efficacy of DFS made with two formulas, one fortified with iron as micronized ground ferric pyrophosphate (MGFePP) and the other with iron as encapsulated ferrous fumarate (EFF) was studied among schoolchildren (5-15 y) in rural southern India (Andersson et al., 2008). Both the formulations were effective in reducing the prevalence of anemia (from 16.8% to 7.7%, p<0.05 MGFePP) and (15% to 5%, p<0.01). However, with EFF formulation, color changes in the foods were observed by 26% of the households and MGFePP formulation, affected the iodine stability due to the moisture content.

Results from another study conducted in Ghana showed the effectiveness of DFS in non- pregnant, non-lactating women (Asibey- Berko et al., 2007). In this study (8 mo), DFS (DFS plus placebo) was compared with iron supplementation (IS plus iron supplement) and control or IS. No significant differences were noted on the prevalence of anemia in the women, however the prevalence of anemia significantly increased in the control group (19.5%). A subgroup of the children of the women were also randomized to DFS or IS and found that the prevalence of anemia significantly decreased in the DFS (21.7%, p=0.025) compared to the control. Color change in the food items was noticed in the study. In another intervention, the efficacy of local iodized salt blended with ground FePP compared to IS was studied in rural, tropical Côte d’Ivoire with school children (5-15 y; n=123) (Wegmüller et al., 2006). At the end of the intervention, serum ferritin increased from 48 to 63 µg/ L in the DFS group and 58 to 61 µg/ L in the IS. However the Hb concentration and prevalence of anemia did not change in either group. Compared with the previous studies conducted in South India and Ghana, in the present study, iron utilization may have been affected by high prevalence of malaria in Côte d’Ivoire. Ghana is endemic to malaria. However, in the study conducted in Ghana, 2.5% of the study participants were tested positive for malaria which did not have a significant effect on the Hb measures (Asibey- Berko et al., 2007). Malaria arises from the infection of parasites belonging to the genus Plasmodium. The entry of the parasite into the erythrocytes results in decreased production of erythrocytes in the bone marrow and increased removal of erythrocytes from the blood resulting in anemia (Haldar and Mohandas, 2009).

2.9. Iron intervention studies with other food vehicles

Delivery of iron through other food vehicles such as fruit juice, fish sauce, maize flour, milk, and rice have also been studied in children and women (Thankachan et al., 2013; Longfils et al., 2008; Macharia-Mutie et al., 2012; Toxqui et al., 2014; Muthayya et al., 2012; Radhika et al., 2011; Nogueira et al., 2013). In a recent, school based, randomized controlled trial from India studied the effect of multiple micronutrients (MMN) fortified drink on reducing the prevalence of iron deficiency, IDA, and anemia (Thankachan et al., 2013). The children were (n=246; 6-12 y) randomized to receive either a multiple micronutrient fortified drink or an unfortified control. Serum ferritin levels in the intervention group significantly improved (20.5 µg/l) after 8 weeks of intervention compared to the control drink (12.7 µg/l) (p<0.001). The prevalence of iron deficiency reduced from 64% at the baseline to 23% at endline in the treatment group (p<0.001). The prevalence of iron deficiency in the control group slightly increased from 64% at baseline to 66% at endline. The study was also effective in reducing the prevalence of IDA, vitamin C, and

B12 deficiencies in this population. Iron fortified rice is one example, which has been demonstrated as an efficacious strategy to reduce the prevalence of anemia in women in the reproductive age (Hotz et al., 2008). Rice is a commonly consumed staple in many parts of South Asia (Bishwajit et al., 2013), and in many other parts of the world and is a promising food vehicle to deliver iron to the target population (Pinkaew et al., 2013). The effect of iron in the form of micronized ferric pyrophosphate (MFPP) added to rice kernels on the prevalence of iron deficiency was studied in school children (n=140; 5-11 y) from Andhra Pradesh, India. The children were randomized to receive the treatment or control rice dishes as part of the noon meal program for 8-mo. The prevalence of iron deficiency reduced to 14.3% in the treatment compared to 36.9% in the control (p<0.05) (Radhika et al., 2011). A similar study tested the efficacy of iron fortified rice in reducing the prevalence of anemia in women in their reproductive age from Mexico. This six months intervention provided 13 mg iron/d from the fortified rice and demonstrated an absolute reduction in the prevalence of IDA [by 8.5 percentage points control vs. 14.7 percentage points in the treatment group p <0.05] (Hotz et al., 2008). The study did not provide information on the dietary iron intakes and lack of adherence to the intervention was reported by the investigators. 29

Wheat is another major staple next to rice in India and contributes greater than 50% of energy intake in Indians (NSSO Report, 2012; Muthayya et al., 2012). The effect of iron fortified flour as (NaFeEDTA) on the iron status was demonstrated in iron deficient school children from India. Whole wheat can be a challenge to fortify with iron due to the presence of phytates, an inhibitor of iron absorption. Iron bound to EDTA is an effective carrier when used as a fortificant in wheat flour, as the EDTA moiety protects the iron from phytates and facilitates the absorption (Hurrell et al., 2000). Hence, NaFeEDTA is recommended by the WHO and other partnering agencies as the suitable iron fortificant for flours (WHO and other agencies, 2009). In the randomized control trial from Karnataka (6-13 y) and Maharashtra (7-15 y), n=401 primary school children were randomly assigned to either a control, unfortified meal or meal cooked with fortified NaFeEDTA. The prevalence of anemia decreased in the treatment from 20.5% to 14.1% vs. control 19.2% to 24.4% (p<0.05) and the prevalence of iron deficiency (62.5% to 20.5%; p<0.001) and IDA (17.7% to 8.6%; p<0.001) decreased significantly in the treatment groups with no change in the control. The meals prepared for lunch using the fortified wheat flour was also well accepted by the children and could be another strategy similar to DFS to be introduced in the mid day meal programmes in schools or subsidized distribution through the PDS. It is essential to assess the long term impact of such fortification programmes on the health of the women and children. The effect of mandatory flour fortification with iron in Brazil was evaluated in pregnant women (da Silva et al., 2012). Retrospective data on the prevalence of anemia before and after fortification in pregnant women has shown encouraging outcomes. This study from Brazil showed that mandatory flour fortification with iron was a protective factor against anemia during pregnancy and the prevalence of anemia fell from 40.3% before fortification to 28.8% after fortification (p<0.0001) (da Silva et al., 2012). A recent systematic review of eleven randomized trials of iron fortification with different food vehicles in women (from low, middle, and high income countries), all showed a positive effect of iron fortification on Hb, serum ferritin, and the prevalence of anemia (Das et al., 2013).

2.10. Factors affecting dietary iron absorption Iron rich foods with high bioavailability are often a challenge to obtain for the poor segments of the society, as most of the heme iron rich foods are not affordable (Yip, 1994). In plant-based diets, most of the iron consumed is from the non- heme sources like cereals, pulses and vegetables. National survey data from India indicate iron density of Indian foods to be around 9 mg per 1000 kcal diet and non-heme iron is the main contributor to iron in Indian diets (Nair and Iyengar, 2009). In the Indian diets, low heme iron intake, poor intake of fruits, vegetables and vitamin C rich fruits and high intake of phytates in cereals, green leafy vegetables (GLV), nuts and legumes results in poor bioavailability (Tupe et al., 2009). An intervention was conducted to study the effect of vitamin C and tea on iron absorption (Thankachan et al., 2008). The investigators observed that consumption of 1 cup of black tea decreased iron absorption in the control by 49% (p< 0.05) and 2 cups of black tea decreased iron absorption by 66% (p< 0.01) and in the IDA group, 1 or 2 cups of black tea decreased iron absorption by 59% (p< 0.001) and 67% (p<0.001), respectively. Addition of lemon juice to tea has been found to minimize the inhibitory effects (Sharma, 2003). Addition of ascorbic acid increased iron absorption by 270% or 343% (1 or 2 cups) in control and by 291% or 350% respectively in IDA subjects (p< 0.001) compared to the control (Thankachan et al., 2008). This demonstrates the enhancing effect of ascorbic acid on iron absorption. In another study conducted in New Zealand with 89 healthy women, 18-44 y, were randomized to receive iron fortified cereal with banana and iron fortified cereals with kiwi, a good source of ascorbic acid (Beck et al., 2010). Baseline and endline iron status and dietary data were measured after 16 weeks. Median serum ferritin increased significantly in the kiwifruit group (n=33) compared with the banana group (n= 36), with 10·0 (25th, 75th percentiles 3·0, 17·5) vs. 1·0 (25th, 75th percentiles 22·8, 6·5) mg/l (p< 0·001) (Beck et al., 2011).

2.11. Dietary assessment methods in relation to iron intake As discussed in the previous section, numerous dietary factors affect iron absorption, either as enhancers or inhibitors from various foods that are consumed on a daily basis. Obtaining dietary information on a single or specific food that affect iron absorption eg., phytate or vitamin C rich foods can be useful and is traditionally followed (Newby and Tucker, 2004; Hu, 2002).

However, on a daily basis, a regular meal consists of variety of foods and nutrients which can affect the iron absorption (Hu, 2002). The commonly used methods to determine dietary intake are: 1) weighed food intake, where all the foods including drinks consumed are weighed directly. 2) estimated food record where the amounts of food consumed are estimated from measuring cups, spoons, ladles, and photographs 3) 24 hr dietary recall, a recall of all the foods consumed over the previous 24 hr 4) dietary records, to record the usual food intake over a day and 5) food frequency questionnaire (FFQ), to collect the frequency of consumption of a food item over a specific time period (Gibson and Ferguson, 1999; Beck and Heath, 2013). In rural population, due to illiteracy, usually trained field workers complete the FFQ, food weighing or dietary recalls (Rao et al., 2009). Use of weighed food intake by trained field assistants is an effective practice to collect quantitative information in epidemiological studies. However, due to logistical issues and time constraints, 24-hr recall is faster and less invasive and is well suited in a population where there is less diet diversity (Gibson and Ferguson, 1999). Food frequency questionnaires are very useful to provide qualitative information on the frequency of consumption of food groups over a week or longer (Cade et al., 2002). Recent technological advancement using metabolomics approach (Penn et al., 2012), digital imaging (Dahl Lassen et al., 2008), personal digital assistants (PDAs) (Oliver et al., 2013; Fukuo et al., 2009; Fowles and Gentry, 2008) and computer programmes are also used to collect dietary information which are much more efficient and reliable and provides more accurate information on the dietary intakes (Illner et al., 2012; Hillier et al., 2012).

Dietary assessment methods are often validated against a reference method, e.g., validation of FFQ against 24 hr recall or weighed food intake and also by a biochemical measure (Vandevijvere et al., 2013; Beck and Heath, 2013). One example from a study in rural India, where a modified 24 hr recall method was validated against a reference method, weighed food intake. The estimates obtained from both the methods correlated significantly, e.g., energy intake from the reference vs. 24 hr recall were 7795 (1841) vs. 7615 (1824), r=0.75, p <0.01. No information on the micronutrients intake was provided in this study. In a study conducted in New Zealand, a FFQ related to dietary iron intake was developed and validated against a weighed food intake data. Iron related FFQ was completed by women in the reproductive ages n=116 at 32

two time points and a weighed food record of 4 days was recorded to determine the validity of the iron related FFQ. A significant correlation was observed between the two methods (FFQ and dietary records), r=0.34, p<0.001 (Beck et al., 2012). This validated FFQ was used to determine associations between the dietary iron and suboptimal iron status (serum ferritin <20µg/L) in premenopausal women n=375 (18-44 y) from New Zealand. Logistic regression analysis revealed that women who consumed a vegetable and meat dietary pattern was associated with a lower risk of sub optimal iron status (ferritin <20µg/L) by 41% [95% CI: 18% to 58; p <0.002] and dairy intake was associated with a greater risk of suboptimal iron status by 50% [95% CI: 15% to 96%; p=0.003] (Beck et al., 2013). Calcium from dairy is a known inhibitor of iron absorption (Hallberg et al., 1991) and vegetables such as cooked broccoli are good sources of ascorbic acid which enhances iron absorption (Mangels et al., 1993). Collecting dietary data from the poor and illiterate from the rural settings in low and middle income countries can be a challenge. In such scenarios, it is also essential to incorporate the local foods consumed, which vary greatly and to validate dietary estimates against another reference method or blood biomarker (Rao et al., 2009; Gibson and Ferguson, 1999).

2.12. Iron and appetite Loss of appetite, lethargy, and weight loss has been associated with iron deficiency (Yehuda and Mostofsky, 2004). One of the possible physiological mechanisms of ID on appetite could be due to an increase in pancreatic cells which triggers the action of gut hormone cholecystokinin (CCK-8) which causes a reduction in food intake (Yehuda and Mostofsky, 2004; Guilmeau et al., 2002). It is also hypothesized that low levels of ghrelin and insulin could be one of the reasons for poor appetite in iron deficiency in children (Isguven et al., 2007). Ghrelin is a 28- amino acid peptide hormone secreted in the fudus region of the stomach and stimulates the feeling of hunger (Yagi et al., 2012). Signficantly low levels of ghrelin were observed in mild-moderate IDA children compared to the normal controls (Isguven et al., 2007). In order to determine the relationship between plasma ghrelin, hepcidin, and leptin levels and IDA, 2 groups of children (6 mo-6y), the IDA group (n=30) and healthy controls (n=28) were followed in a hospital setting for three months. In this case study, significant improvements in serum hepicidin (65 ng/mL vs. 51 ng/mL; p=0.038) and ghrelin levels (10 ng/mL vs. 7 ng/mL; p<0.01) were noted in IDA group

after treatment compared to the control (Dogan et al., 2013). Hepcidin levels increase with an increase in plasma iron levels and is a potential biomarker of iron status (Lynch, 2011). Levels of serum ghrelin are associated with iron deficiency. However, no clear mechanism has been elucidated so far. Most of the studies above were conducted in a clinical setting and the use of hepcidin as a biomarker for iron deficiency in future studies could be another useful approach (Lynch, 2011).

An improvement in appetite was observed with iron supplementation in anemic children and adults (Stoltzfus et al., 2004; Lawless et al., 1994; Basta et al., 1979). Statistically significant increase in the appetite (p<0. 01) and energy (p<0. 01) was noticed in school-going children (6- 11 y) after supplementation with 150 mg of ferrous sulphate for 14 wk (Lawless et al., 1994). Mean energy intake increased by 10% in the supplemented group compared to the control. In an iron supplementation (100 mg ferrous sulphate) study from Indonesia with male rubber plantation workers (n=400), aged 16-40 y, researchers reported that the intake of greens and fruits increased significantly (p <0.01) post-intervention. This was mainly due to the ability to afford greens and fruits from the incentives they received. The intake of greens and fruits increased the dietary iron and vitamin C intake from 1.7 to 5 mg and 80 to 130 mg per day respectively (Basta et al., 1979). There was no increase in the energy intake in these participants. The lack of effect on energy may have been due to the increase in price of the main source of energy, rice, throughout the country due to poor weather. In spite of the lack of increase in the energy intake, qualitative information on better appetite was reported by the participants. To date, there is a paucity of literature in relation to ID and food intake.

2.13. Effect of iron status on physical activity and productivity Iron deficiency anemia affects work physical activity and productivity. Many studies with children and adults have shown that when individuals with low Hb received iron supplement, their physical work performance improved (Swaminathan et al., 2013; Thankachan et al., 2012; Panjikkaran and Usha, 2010; Dellavalle and Haas, 2013). Animal studies have demonstrated that iron deficient anemic rats had a lower work tolerance as measured by oxygen consumption than the adult rats with normal Hb levels (Ohira et al., 1981). In a randomized double-blinded, lab- based study, 37 women with iron depletion without anemia, aged 19- 36 y old were assigned to 34

receive iron supplement 135 mg/day or a placebo. After 4 weeks of treatment, the iron supplemented group showed a 103% increase in serum ferritin (p= 0.003) and 14% decrease in sTfR (p= 0.01) compared to the control (Zhu et al., 1998). Endurance capacity was assessed during a 15 km simulated time trial on a cycle ergometer before and after 8 weeks treatment. In the iron supplemented group this resulted in a 2.0 KJ/min lower energy expenditure and a 5.1% lower %VO2 peak compared with the control group. This suggests that the iron supplemented group was able to perform exercise like the placebo but with less physical exertion (Zhu et al., 1998). Improvement in iron status was observed in (n=31) iron depleted female rowers from North America, who were randomized to either iron supplement or placebo. Significant improvements in the energetic efficiency (p=0.03) and energy expenditure (p=0.01) in the treatment group compared to the control was observed after 6 weeks intervention (Dellavalle and Haas, 2013). A recent review by Swaminathan et al., 2013 reported a relationship between iron deficiency and other B vitamins deficiencies on work output in school children from India. Limited data are present on the effect of micronutrients deficiency and work output in adult women in a field setting. Improvement in aerobic capacity and iron status was observed in field studies with adults (Gardner et al., 1975; Gardner et al., 1977; Edgerton et al., 1979). One of the earliest studies which looked at the effect on iron supplementation on work productivity was conducted in Sri Lanka with female tea plantation workers (20-60 y) (Edgerton et al., 1979). The quantity of tea picked per day was measured before and after iron supplementation or placebo treatment. The productivity increased from 15.6 to 17.5 kg tea plucked/day (p < 0.001) (Edgerton et al., 1979). One of the possible mechanisms of reduced work output in iron deficiency could be due to a reduction in the oxygen carrying capacity of hemoglobin to the tissues thereby affecting the work output (Haas and Brownlie, 2001).

2.14. Iron status and cognitive function Many human studies have examined the possible linkages between iron deficiency and cognitive outcomes (McClung and Murray-Kolb, 2013; McCann and Ames, 2007). Addressing ID with iron supplementation can improve motor skills and cognitive outcomes (Seshadri and Gopaldas, 1989; Lozoff et al., 2003). One of the earliest studies conducted in 24 iron deficient children reported significant improvement in Mental Development Index (MDI) scores post intramuscular

iron treatment (p =0.01) in the treatment arm compared to the control (Oski and Honig, 1978). Studies conducted with children have shown positive behavioral and developmental outcomes (Lozoff et al., 2003; Chang et al., 2011; Roncagliolo et al., 1998; Friel et al., 2003). Iron is an important component in all the central nervous system activities like myelination and dopaminergic functions. Dopamine is a neurotransmitter which regulates cognition and emotion, reward and pleasure, movement, hormone release, attention and learning. Iron deficiency is shown to affect the brain function, mainly affecting the production of neurotransmitters (Lozoff, 2011). In studies conducted in rat models, IDA caused changes in myelination, resulting in hypomyelination (McGregor and Ani, 2001).

One of the earliest studies that looked that the effect of iron supplementation in adult women n=47 (20 y and above), did not find any significant effect of iron supplementation on Hb levels and a series of psychomotor outcomes including concentration, short-term memory, and attention (Elwood and Hughes, 1970). The lack of effect of iron supplements on cognition was probably the women were not iron deficient in the beginning of the intervention. The study did not collect information on other iron biomarkers such as serum ferritim or transferrin receptors. The relationship between mild to moderate ID on the neuropsychological outcomes was studied in women in the reproductive age. This was an observational study and the participants (n=42) were young college women (19-30 y) on whom the cognitive outcomes and iron biomarkers were measured. Significant inverse association between the body iron status and executive function of the brain [move 4=(r= -0.39, p<0.01) and move 5=(r= -0.47, p<0.01)] was assessed (function determined by the ability to move discs from the start to goal position using standardized tests) (Blanton et al., 2013). This study provides important findings that reduced iron status without anemia is associated with functional outcomes.

In order to determine whether IDA in mothers would alter the maternal postpartum emotions and cognitive outcomes, a randomized double-blinded intervention was conducted in 81 South African mothers (Beard et al., 2005). The study groups were divided into control, anemic mothers receiving daily placebo, and anemic mothers who received daily iron (125 mg ferrous sulphate) for 9 months postpartum. Significant improvements (p <0.05) in the maternal

psychology test scores (increased by 25%) were observed in the iron group compared to the anemic placebo mothers (Beard et al., 2005). Other studies conducted in young women and adults also showed significant improvements in cognitive outcomes after iron supplementation (Murray-Kolb and Beard, 2007). The studies on ID and cognitive functions in adults are limited but the strong association of iron status and processing cognitive tasks is evident in these studies (McClung and Murray-Kolb, 2013; Murray-Kolb and Beard, 2007; Beard et al., 2005).

2.15. Iron and anthropometry Iron intake has been shown to improve the growth in children (Aukett et al., 1986; Lawless et al., 1994; Chwang et al., 1988; Briend et al., 1990). Most of the iron-based interventions were mainly conducted amongst children and the anthropometric data in relation to iron status with undernourished adult population are very limited. A meta-analysis conducted on the effect of iron supplementation on growth has shown inconsistent results (Vucic et al., 2013). Micronutrient deficiency can increase the risk of morbidity which in turn affects the growth of children. Improvement in growth with iron supplementation was observed in school-going anemic children in Kenya (Lawless et al., 1994). After 14 wk supplementation with 150 mg iron, mean increase in height and weight in the intervention compared to control were 1.4 cm and 1.5 kg, respectively (p <0.01). Consistent results in the anthropometric outcomes were observed in studies from England, Indonesia and Bangladesh (Aukett et al., 1986; Chwang et al., 1988; Briend et al., 1990). A positive growth was also observed in anemic children who were supplemented with 30 mg iron for 2 months. Increase in the height was 1.8 times greater in the treatment (change 2.7 cm) compared to the control (change 1.5 cm), (p <0.0001) (Angeles et al 1993). There is a dearth of information on the effect of iron supplementation on the anthropometric outcomes in adult women.

2.16. Underweight and socioeconomic status in India The commonly used tools to measure the nutritional status are body mass index (BMI) and mid- upper-arm-circumference (MUAC) (Dasgupta et al., 2010). Table 2.3 shows the cut-offs to screen for undernutrition in women by BMI and MUAC classification. The prevalence of underweight (BMI <18.5 kg/m2) in Indian women in the reproductive age (14-49 y) is 36% according to the National Family Health Survey (NFHS-3) (NFHS-3, 2007). Underweight is 37

related to risk of mortality and morbidity and causes physiological impairments (Khongsdier, 2005). The presence of chronic energy deficiency (CED) is higher in the rural women (40%) than the urban parts of India where overweight is rising (24%) due to rapid economic growth and nutrition transition (NFHS-3, 2007; Balarajan and Villamor, 2009).

Table 2.3. Cut-offs to screen undernutrition in women by BMI and MUAC classification

Classification Cut-offs

BMI (kg/m2)

Normal 18.50 - 24.99

Underweight <18.50

MUAC (cm)

Normal ≥ 22

Moderate acute malnutrition ≥ 21.4 and ≤ 22.1

Severe acute malnutrition < 21.4

Reference: (WHO, 1995; UNSCN, 2011)

Within rural regions of India, the scheduled tribes are more vulnerable to undernutrition (Rao et al., 2010). A cross-sectional study that included 303 females in rural West Bengal reported that 64.7% of women were undernourished according to the MUAC (Das and Bose, 2012). The tribal population of India accounts for 8.6% (Census India, 2011) of the total population and nearly 427 tribes exists in India (Basu, 2000). A review of studies conducted in different tribal populations of India have shown varying prevalence of underweight within tribal groups with the highest prevalence of underweight in Jharkhand (76%) to the lowest in Meghalaya (14.3%) (Das and Bose, 2012). Presence of underweight is strongly associated with the poor socioeconomic determinants (Griffiths and Bentley, 2001). There are immediate, underlying and basic determinants of poor nutritional status in women in the low and middle income countries 38

(UNICEF, 1990) (Figure 1). Gender inequality (Barker et al., 2006), illiteracy (Shukla et al., 2002), poor diet diversity (Mittal and Srivastava, 2006), morbidity (Khongsdier, 2002), and lack of health care facilities in rural parts of India are contributing factors to chronic undernutrition and micronutrient deficiencies (Muller and Krawinkel, 2005). Ethnic differences that exist within India are also associated with low BMI. A cross-sectional study of nationally representative data collected socioeconomic and health information of 90,303 women from 26 states in India. This study found positive associations between socioeconomic factors and low BMI. Women employed as manual laborers were at more risk of being underweight than non- manual labor jobs [1.06 (1.00, 1.13) vs. 0.85 (0.77, 0.93), p <0.0001] and women from upper socioeconomic status were at greater risk of being overweight or obese (Subramanian and Smith, 2006). This association was observed partly because in this study higher standard of living was associated with better nutritional status (determined by healthly BMI). A recent cross-sectional study from Southern India observed that women employed as farmers were at greater risk of being chronically undernourished [2.2, 95% (1.39-3.49), p=0.001] compared with other type of employment. Consequently, low income was also significantly associated with CED in this population. This study did not observe significant associations of CED with ethnic groups and information about other socioeconomic determinants such as household assets, marital status, and sanitation were not included in the analysis (Subhasinghe et al., 2014). Higher expenditure on food items was related to better BMI and women employed as agricultural workers or manual laborers were not able to afford diverse foods and the energy intake was predominantly cereal based (Mittal and Srivastava, 2006).

Figure 2.1. Conceptual framework for the determinants of poor nutritional status and micronutrient deficiency

Micronutrient malnutrition and Manifestation underweight

Immediate Poor dietary intakes Disease determinants

Food Inadequate Poor hygiene Underlying insecurity maternal and and sanitation determinants child care

Illiteracy, unemployment, poverty, and lack of knowledge about diet and health

Human, economic and organizational Basic resources determinants

Political, economic and ideological factors

Potential resources

(UNICEF, 1990)

2.17. Dietary intakes of rural Indian women It is important for a balanced diet to include foods from variety of food groups contributing the essential micro- and macronutrients for the individual’s well-being (FAO, 2004). The food consumption patterns of rural and tribal Indian women are predominantly traditional, cereal- based diet with poor diet diversity (Schmid et al., 2006). Commonly consumed cereal varieties are rice, wheat, millet and maize. According to National Sample Survey Organization (NSSO), 2009-2010 (NSSO Report, 2012), the percentage of households who consumed cereals in a 30-d periods was high in both rural (98.4%) and urban India (93.5%). Frequently consumed pulses are moong4, urad5, tuvar,6 chana,7 and masoor8 lentils. Pulse consumption varies by regions of India (Reddy, 2004). Southern states of India prefer mostly tuvar and urad varieties and chana dal is more common in the Northern regions. Most of the low income rural families consume red lentils as they are less expensive (Agriculture and Agri-foods Canada, 2009). Diet diversity data obtained from NFHS-2 showed that women from urban setting consumed more diverse foods than rural women, particularly with fruits and dairy intakes. These were data obtained from 26 states and 90,000 women in the reproductive age (15-49 y). However, no information on the quantity of food intake was provided. National Nutrition Monitoring Bureau (NNMB) and India Nutrition Profile (INP) regularly conduct 24-hr dietary recalls across the different states of India. The data obtained from NNMB (2000-2001) showed that the intake of fats and oils did not improve (14 CUs/d) since 1975s (NNMB, 2002). Energy intake was mainly from carbohydrates and was consistent across the different states of India and better diet diversity was seen in middle income and high income families in rural and urban regions due to their ability to afford variety of foods (Ramachandran, 2007). In a cross-sectional study which compared the dietary intakes of non-pregnant non-lactating (NPNL) women from rural (n= 7,078) and tribal (n= 8,036) households (HH) in India found that the rural women were deficient in the intake of micronutrients iron, vitamin A, riboflavin and folic acid compared to their urban counterparts. In this survey, data were randomly chosen from NNMB 2005-06 and Integrated Tribal

4 Green gram 5 Black gram 6 Pigeon peas 7 Split chickpeas 8 Red lentils 41

Development Agency (ITDA) for tribal selection from 9 states of India. Intake of dietary iron was 9.9 and 11.4 mg/d in tribal and rural women respectively and more than 70% of the population failed to meet 50% of the recommended dietary allowances (RDA). The dietary reference intakes, of which RDA represents 97-98% of the population requirements as one of the estimates, were developed based on the physiological needs of the individuals by the life stage and sex. The recommendation for each nutrient was based on the metabolic function, dietary intake patterns, and upper tolerable intake levels of each nutrient (Institute of Medicine, 2006). For example for iron, the dietary reference intakes for women in the reproductive ages are basedon the iron absorbed, physiological iron requirements, body iron stores, basal iron loss, menstrual loss, and the factors affecting the iron aborption (Indian Council of Medical Research, 2008).The consumption of fats and oils was higher in the rural (13 g/d) than the tribal (7 g/d) women. The intake of dairy was also lower in rural (29 g/d) than urban (80 g/d) women (Rao et al., 2010). The existing dietary surveys revealed that due to poor diet diversity rural women are more undernourished than the urban women. Within rural India, tribals are much more at risk for micronutrient deficiency (Haddad et al., 2012).

2.18. Tea plantation and ethnic groups in India The first tea plantation in India was established in Assam in 1839 by the British East India Company and subsequently plantations were started in Darjeeling in West Bengal and Nilgiris and Waynad in the Southern parts of India. West Bengal contributes to 20% of the tea produced in the country from the regions of Dooars9, Terai10, and Darjeeling hills (Bhowmik, 2011). Women form more than 50% of work force in tea plantations because women are considered more efficient in plucking tea leaves than men who are employed in maintenance of factory and plantation as supervisors (Indian Labor Journal, 2013; Sarkar and Bhowmik, 2006). Factory workers are involved in the processing of the tea leaves to black tea (Thapa, 2013). The entire family in the tea estate region is usually employed in the plantation, they follow a multigenerational tradition of working in the tea garden with poor living conditions and lack of education (Bhowmik, 2011). A socio-cultural study conducted with four different tea estates with

9 Gateway to Bhutan from India, located in Jalpaiguri District 10 Lowland belt of India 42

100 women in Assam found a prevalence of illiteracy rate to be 72% and 90% of the respondents reported that the monthly HH income was 1000-2000 INR/mo (Timung et al., 2013). The tea plantation workers are paid according to the amount of tea leaves they pluck. The base standard quota fixed by the plantation for per day to be plucked is 24 kg, called “tikha” and an incentive of 1.50 INR is usually given for every extra kg of leaves they pluck. The daily base wage (INR) for tea plucking was around 47.00 INR (0.78 cents) according to the data from 2010 (Bhowmik, 2011). In West Bengal, the peak tea plucking times are July to August when the women earn double the amount they usually pluck (Bhowmik, 2011) because of tikha. The labor force in Dooars, Terai and Darjeeling hills of West Bengal has workers from Adivasis11 (Mittal and Srivastava, 2006) and Nepali speaking population (Besky, 2008) and they live in their respective housing lines or living quarters in the tea estate village (Thapa, 2013).

2.19. Adivasis or the tribals of India Adivasis or Vanvasis12constitute 8.6% of Indian’s population (Census India, 2011) and they are referred to as Scheduled Tribes (ST) according to the Article 342 of the Indian Constitution in 1950 (Constitution of India, 1950). For administrative and bureaucratic purposes by the Indian Constitution they are called ST (Nilsen, 2012). However, in the present thesis, the terms Adivasis and tribals are used interchangeably. More than 370 million indigenous people inhabit the whole world (Economic and Social Affairs, 2009) and more than 100 million according to the 2011 Census India live in India. They are heterogeneous people spread across India in the 26 states and 7 union territories with 700 communities speaking 105 different languages (Ackerson et al., 2008; Basu, 2000). The highest concentrations of Adivasis are in the North Eastern region (65% and more), 13-32 % are spread out in Bihar, Madhya Pradesh, Orissa, Andhra Pradesh, and West Bengal and the least are in the southern parts of India (Subramanian et al., 2006). The Adivasi communities of Santals, Oraons, Hos, Kharias and Mundas originated from the Jharkhand region of India (Bhengra et al., 1999). Oraon is one of the major tribal groups of India and along with Mundas, Kharias and Santhals are usually employed in tea plantations in West Bengal (Bhowmik, 2011). They are also employed as artisans, cattle herders, folk artists, wage laborers,

11 Indigenous people of India. In Sanskrit, they are referred as original inhabitants 12 Forest dwellers 43

hunters and food gatherers (Basu, 2000). Along with Dalits13 they are repressed and considered inferior in the Indian society. Though tribals, unlike Dalits, were never part of the Ancient Indian caste system they are still treated as untouchables by the society (Sedwal and Kamat, 2008).

Due to low socioeconomic status, lack of proper health care facilities along with high prevalence of underweight, poor diet diversity, the tribals are often at risk for malnutrition (Bala and Thiruselvakumar, 2009; Mohindra and Labonte, 2010). The food consumption patterns of tribals vary across the country; however, their staple foods are rice, wheat and lentils (Basu, 2000). A study on the dietary habits of tribal women in Orissa showed that consumption of wild indigenous flowers and greens is common (Pradhan et al., 2011). Use of tobacco and homemade alcohol (rice beer) consumption is very common in the Adivasi tribes (Mittal and Srivastava, 2006; Bhuyan and Baishya, 2013). Consumption of wine from fruits such as dates, tamarind and mango are also common in Adivasis from Orissa (Pradhan et al., 2011). Alcohol consumption is considered traditional and important for their social and religious practices (Mittal and Srivastava, 2006; Mishra and Pradhan, 2011). A cross-sectional study with the dataset from NFHS-2 studied the health inequalities between the indigenous and non-indigenous groups and found that the prevalence of alcohol consumption (26% vs. 18%) tobacco chewing (36% vs. 19%) and cigarette smoking (25% vs. 9%) were greater in the indigenous group compared to the non-indigenous groups, respectively (Subramaian et al., 2006).

2.20. Nepalese of India India and Nepal share an open border and migration of Nepalis to India has been common since the 1950 India-Nepal Peace and Friendship Treaty (Thapliyal, 2012). The treaty allows the members of both the countries to freely travel, have trade relations, reside, and find employment (Hall, 1996). The first Nepalese migrants (as Gorkhas) arrived in India during 1814-1816 to be part of the British India army. Though educated Nepalese migrate to Gulf and other countries such as North America, and Europe, the migration of illiterate and rural Nepalese to India continues today (Bhattrai, 2007; Behera, 2011). Also, the socio-cultural background of India and Nepal are similar, this makes it easier for the Nepalese to adapt to the Indian culture. A survey of

13 Untouchables or repressed community 44

Nepali migrants conducted between 2010-2011 in Delhi and Mumbai showed that 31% of Nepali migrants in India never attended school (Samuels et al., 2011). Around 7 million Nepalese live in India, residing in the North-Eastern regions. They are usually employed as manual laborers in the tea plantations of Sikkim, Darjeeling and Jalpaiguri districts of West Bengal, Assam, and Meghalaya (Behera, 2011). They are also referred to as Gorkhas14 and Nepali men are commonly employed as security guards in India (Bhattrai, 2007).

The Nepali has an inbuilt caste system with different Janajati15 and religious groups. Untouchability is still a practice in Nepali communities (Indian Institute of Dalit Studies (IIDS), 2008). The caste system includes the upper castes (e.g., Bahuns, Chhetris, Upadhyaya Brahmins), Matawalis or alcohol drinking castes groups (e.g., Gurungs, Sunwars), impure but touchable class (e.g., Kasai, Kusle, Kulu), lower castes (Newars), untouchable groups or dalits (e.g., Kamis, Sarkis, Janajatis), and many unidentified groups. Within each caste, many sub- castes are also present (Subedi, 2010). Nepali is the main language, however many sub languages exist. Physical features of Nepalese vary from Mongloid to Indian features with short stature. The Sherpas, Gurungs and Tamangs have Tibetan-Mongloid features, Newars have Mongloid features, Chettris have Indo-Aryan or North Indian features, and people from Terai region also have Indian Bihari features (Dhungel, 1999). The diet consists of cereals, pulses, tubers, meat and dairy. Consumption of fermented foods such as Kinema16, Selroti17, Sinki18, Gundruk19, and Chhurpi 20(Dahal et al., 2010) is very traditional in Nepali cuisine. There is a high prevalence of micronutrient deficiency in the Terai region of Nepal bordering India due to poor dietary intakes and low socioeconomic status and 37% of people in the Terai region fail to meet the minimum energy requirement (Makhoul et al., 2012; FAO/WFP, 2007). There is a paucity of data on the nutritional status of Nepali migrants in India. According to the Nepal’s food security bulletin (WFP, 2009), food insecurity is a major cause of concern in the Nepali

14 Name derived from “Gorkhali” army of the 18th century in Nepal 15 Nepali indigenous ethnic groups 16 Fermented soybeans 17 Fermented rice, fried cereal paste 18 Fermented radish pickle 19 Fermented dried radish greens and other greens 20 Fermented Yak milk 45

migrants in India. A dietary survey with two indigenous Nepali tribes from the Terai area bordering India found that the main diet was rice with a little lentils and a vegetable dish and intake of iron and vitamin A was less than 50% of the RDA. Rice contributed to more than 80% of the total dietary intake per day and intake of vegetables was insignificant, which could probably be one of the reasons for micronutrient deficiency in this population (Parajuli et al., 2012).

2.21. Summary Iron deficiency is a global public health concern and in spite of the existing programs to address ID, the prevalence is high in low and middle income countries. The preceding literature review provided some evidence that addressing ID improved work output and cognitive outcomes in women in the reproductive age and children. Poor dietary intakes along with the socioeconomic barriers are major contributors to the underlying health concerns of the rural Indian women. The present literature review discussed the different strategies that are currently in practice for addressing ID in the target population. Of the different strategies, dual fortification of salt with iron and iodine is cost effective and there is evidence for reducing the prevalence of iron deficiency in school children in the southern regions of India and other countries. However, successful DFS interventions in adult Indian women are limited. The food intake patterns and nutritional needs of women in India vary considerably due to diverse cultural and socioeconomic factors. In spite of rapid economic growth in India, the diet of rural and tribal women has remained unchanged with a high intake of cereals and poor diet diversity. Though the literature on iron deficiency and appetite is limited and has mixed outcomes, studies conducted in anemic children clearly shows potential in improving body weight and energy intakes. The past studies conducted on iron and dietary intakes provided iron in the form of supplements for a shorter duration. There is limited data on the effect of DFS intervention on the diet and nutrient intakes of adult women.

Undernutrition and micronutrient malnutrition in the tea plantation workers has negative implications on the work output and overall well-being of the women. Addressing the challenges associated with underweight in women is another major concern in India, especially in the rural

segments. The cross-sectional data across India also indicate that the tribal women are more at risk for undernutrition than their rural counterparts. The existing literature also demonstrates that socioeconomic factors play a key role in determining the nutritional status of the women, this will be true also for tribal and rural women within a tea plantation setting.

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Chapter 3: General Methodology

3.1. Study overview The double fortified salt intervention (DFS) intervention was conducted in Panighata tea estate garden in the Darjeeling district, West Bengal, India. The duration of the study was for 16 months and the intervention lasted for 10 months. The study participants were randomized from October 2009-September 2010 to either an experimental group that consumed DFS containing iron and iodine or a control group that consumed only iodized salt (IS). The assignment of the subjects to consume either DFS or IS was through randomization of households within the tea estate area. Baseline data collection of anthropometry, food frequency questionnaire (FFQ), weighed lunch intake, and 24-hr dietary recall was conducted in June-September 2009. Midpoint data collection of anthropometry, FFQ, weighed food intake and recall was measured from January-March 2010. All the baseline measures were repeated at the endline from May 2010- July 2010 to assess the effect of the intervention on the dietary intakes. A timeline of all the activities of the field study are presented in Figure 3.1. The logistics support, administration, and the field staff for the study were provided by the Child In Need Institute (CINI), Siliguri, West Bengal, India.

3.2. Ethics clearance Ethical clearance for the study was obtained from the McGill University’s Institutional Review board and also by the ethics review board of the CINI, Kolkata, India. The study was also registered with clinicaltrials.gov- Registration Number: NCT01032005. The nature of the study was clearly explained to all the study participants and the family members. Oral and written consent was obtained from all the participants and their respective spouses.

3.3. Description of the study site The research study was conducted in the tea estate garden within the borders of Panighata, West Bengal, India. Panighata village is a forest range situated across the banks of river Balason in the Darjeeling district, the Northern part of West Bengal province. Darjeeling district has a 82

population of 1,842,034 (Government of West Bengal, Economic Review, 2011). Darjeeling is one of the main districts of North Bengal region and the headquarters for the three main towns of Siliguri, Kalimpong and Kurseong. Darjeeling is bordered by Sikkim to the north, the province of Bihar on the south, Jalpaiguri district to the east and Nepal on the west (Chhetri and Tamang, 2013). Darjeeling is composed of many hills and plains. The foothill of the district is called the Terai region, one of the important locations for tea plantations in the Himalyas, along with Dooars and Darjeeling hills (Tea Industry, 2010). Panighata village is located at the plains of Darjeeling and is approximately 26 km from Siliguri town with an altitude of 398 feet. The population of the village is approximately 5316 people from two ethnicities, Adivasi, the tribal people and Nepali, migrants from Nepal. The total population of permanent employees (including the family members and management staff) in the tea estate of Panighata is 1952 members (Adivasi=1145; Nepali=807). The tea estate employees reside in clusters of houses on the estate regions called as “Lines”. The lines in the Panighata estate are NF Colony, Bazar line, 10 Number line, Dara line, Bich line, Naya line, 5 Number line, and Girmit line. Adivasi and Nepali reside in separate lines. Adivasi houses are in 5 Number and Girmit lines and Nepalis live in NF colony, Bazar, 10 Number, Dara, Bich, and Naya lines.

3.4. Training the field staff The research team consisted of 16 field workers in total, 10 field workers and 2 community workers for Adivasi ethnicity and 4 community workers for Nepali ethnic group. The field staff was recruited through CINI, Siliguri. They were university graduates and were fluent in English, Hindi, Nepali and Bengali. The Adivasi community workers were fluent in Adivasi languages Oraon and Sadri, Hindi, Nepali and Bengali. The Nepali community workers were fluent in Nepali and Hindi. The field staff was trained for 1 week in all the data collection tools which included dietary data collection, anthropometry, socioeconomic and demographic information and Hemocue. Training involved lecture, observation and practice measurements on other members of the research team. The data collection tools were pretested with the volunteers who were not involved in the study. The field staff was also trained on other measurements which are not included in the dissertation. Other data that were collected as part of the intervention include: 1) hemoglobin (Hb) measured by Hemocue 2) venous blood sampling to measure Hb, complete 83

blood count (CBC), serum ferritin, transferrin receptors, C-reactive protein (CRP), alpha 1-acid glycoprotein B-12, folate and urinary Iodine 3) productivity 4) physical activity measured using an accelerometer 5) heart rate monitors to calculate production efficiency, and 6) computerized cognitive tests.

3.5. The study participants’ selection criteria and randomization The study participants were women tea pickers from the Panighata village. The women were from two ethnicities, Adivasi (aboriginal population of India) and Nepali, who were imported from Nepal by the British around 1815 for job opportunities in the tea estate gardens of India (Bhattrai, 2007). Many Nepalese in Panighata have a multi-generational history of working on the tea garden. The main occupation of the men and women is in tea plantation, as tea pluckers or manual labours in the tea estate factory. Women are employed as tea pluckers and men work in the factory. The study participants’ age was 18 y and above, not pregnant, not breastfeeding, experienced, permanent, full-time tea pickers. Information on the list of full-time tea pickers were obtained from the Panighata tea estate factory registry. With the support of local community leaders and Panighata tea estate hospital staff, the study participants were approached door-to-door to collect the names of permanent employees and they were invited to participate in the research study.

3.5. 1. Deworming With the help of community volunteers and Panighata hospital staff, we approached every single household to deworm all the members in the family. Deworming was carried out with Albendazole (400 mg, GlaxoSmithkline) and was repeated once every 4 months during the study duration.

3.5.2. Randomization and consort Eight hundred women were dewormed using Albendazole once the non- pregnant status was confirmed using the home pregnancy test kit to detect the presence of Human Chorionic Gonadotrophon (hCG) in urine. Anemia screening was offered to all the women on the tea estate and n=498 attended screening for hemoglobin (Hb) using Hemocue. From the preliminary

screening, n=217 were dropped from the pool of potential study subjects for venous blood sampling. They were excluded because of poor health conditions, pregnancy, severe anemia, age, non-plucker, non-resident of Panighata, or due to lack of interest to participate. Baseline measures of anthropometry, productivity and venous blood sampling were collected on 281 participants. From the 281 participants, 36 women were not included in the study due to insufficient baseline data, poor health, severely anaemic, injured, and refusals. Two hundred and forty five women were stratified based on the iron status into iron deficient anemic (IDA), iron deficient not anemic (IDNA), anemic without iron deficiency (ANID), non anemic and not iron deficient (NOR), and elevated ferritin group (HFR). Within each strata, the subject identification numbers were randomized to one of the four color groups of salt bags (each bag weighing 500g), red, blue, green, and black. With a drop-out of 33 women, 212 women completed the follow-up study; 211 women had weighed lunch intake and 24-hr dietary recall data and 209 had FFQ data. A flow of the sample and data collection is shown in the CONSORT figure (Figure 3.5).

3.6. Production of DFS Double fortified salt technology used in the present study was developed by the researchers from the Department of Chemical Engineering at the University of Toronto with the support of the Micronutrient Initiative, Ontario, Canada (Diosady et al., 2002). Per gram DFS salt consists of 0.05 mg of potassium iodate and 1.1 mg microencapsulated ferrous fumarate (0.363 mg elemental iron) (Diosady et al., 2002; Haas et al., 2014). Salt iodization and the processing were carried out at Ankur Chemfoods, Gujarat, India. Refined salt of particle size < 1 mm iodized with potassium iodate (KIO3) at a concentration of 30 µg I/g salt at the Ankur salt factory was purchased and blended with encapsulated ferrous fumarate premix. The DFS premix (encapsulated ferrous fumarate) was manufactured at Pam Glatt Pharma Technologies (Mumbai, India). Premix to salt ratio of 1 to 150 was used to arrive at 1000 ppm elemental iron in the final product. For the production of 900 Kg of DFS, 6.4 Kg premix or ferrous fumarate and 43.6 Kg iodized salt (NaCl) was properly mixed in a blender for 10 minutes to produce 50 Kg salt premix. The premix was divided into 3 parts of 16.66 Kg. The salt premix was transferred in big blender through screw conveyor belts and blended with iodized salt for 20 minutes. Iron content was measured using UV-visible spectrophotometer in samples from 6 defined positions in the 85

barrel, to ensure a homogenous mixing of the iron and the salt (Protocol from Ankur salts, 2010; Romita et al., 2011; Diosady et al., 2002).

3.7. Anemia screening- Hemocue All the subjects were screened for Hb levels using blood from a finger puncture that was analyzed using Hemocue (June- July 2009). Severely anaemic women with Hb < 80 g/L were immediately treated with therapeutic iron supplements. Those women with high Hb > 140 g/L were not included in the study. The remaining 245 participants with Hb between 80 and 140 g/L were selected for randomization to the two intervention groups. Hemocue was repeated at midpoint (January 2010) to screen for anyone was severe anemia (Hb <80 g/L); two participants with severe anemia were treated with therapeutic iron supplements.

3.8. Socioeconomic and demographic information Socioeconomic information was collected by questionnaire and direct observation at the baseline on family composition, ability to read and write (subject and her spouse), educational level (subject and her spouse), occupation of the spouse, household income, total number of household members, appliances, type of housing, household quality, water facilities, sanitation, electricity and fuel used for cooking (NFHS-3, 2007).

3.9. Anthropometry Anthropometric measures, height, weight, and mid-upper arm circumference (MUAC) were measured at the baseline (July-September 2009), midpoint (January-March 2010), and endline (May 2010- July 2010) of the intervention (WHO, 1995). Two field staff in charge of anthropometry measures were trained and standardized to an expert before they started working in the field. Height (nearest cm), weight (nearest 0.5 kg), MUAC (nearest cm) were measured at the field office hospital with a stadiometer, digital scale, and a flexible tape, respectively.

3.10. Dietary data collection

3.10.1. Food frequency questionnaire Food frequency questionnaire (FFQ) to determine the frequency of consumption of food groups over one week was administered at the baseline, midpoint, and the endline of the study. Information was collected on the frequency and quantity of consumption of individual foods from the Indian food groups consisting of: cereals, pulses and legumes, nuts and oilseeds, green leafy vegetables, other vegetables, roots and tubers, citrus fruits, other fruits, dairy products, meat and poultry, sea foods, sugar, fats/oils, beverages, snacks, other traditional foods. The FFQ also consisted of all the locally consumed vegetables, greens, fruits, snacks, and other food items. Most of the FFQ data were collected during the weighed food intake and 24 hr dietary recall at the work site in tea estate garden or at the study participant’s residence. Other FFQ data were collected along with socioeconomic status (SES) data collection at the field office hospital in Panighata tea estate. A diet diversity score (DDS) was generated for each food group consumed during the week (0-14) by summing up the number of food groups consumed by the women. A score of 1 was given if the woman consumed a particular food group and 0 was given if the woman did not consume the food group over 7 days period. The scores were summed up to provide a DDS out of 14 as previously done by Clausen et al 2005 and Oldewage-Theron and Kruger, 2011.

3.10.2. Weighed food intake and dietary recall Before the baseline data collection, the households in Adivasi and Nepali communities were visited to observe the food preparations, type of foods consumed, quantity of food items used for a family per meal, the types of pots, pans, and ladles used for cooking, and the general practices that are usually followed during food preparations. The field staff was familiarized with the types of ladles, spoons and cups commonly used in the village. The field staff was trained to weigh different foods items using Contech portable balance of precision 0.1 g.

The lunch intakes of the subjects were directly weighed at three times points. The lunch intake at the baseline and endline was measured in the tea estate garden during the subjects two hour

mid-day lunch break. The reasons only lunch intake was weighed were because a) lunch was a main meal which contributed the most energy during the day b) weighing of total daily intake was logistically challenging, and c) food sharing between the subjects occured only during lunch time (the third objective of the study). The data collected includes the form of food, ingredients, weight of the plate (g), food served (g), amount consumed (g), shared food received from others and food given to others in the lunch group (g), and the recipes. A dietary recall of breakfast, snacks and dinner to complete the 24-hr dietary recall intake was also collected. During the midpoint of the study, January to March 2010 (winter) when no tea plucking occurs, the data were collected at the subjects’ residence. All the raw weights of the ingredients were measured directly and the serving portions, all the units of measures, ladles, cups, spoons and glasses were also displayed and recorded.

The grams of intake of each food consumed were calculated. A food composition database of 331 food items along with food codes of Adivasi and Nepali foods and recipes was developed. Each food item that was consumed by the mothers was entered in the database and nutrient composition for the food items were obtained from the Nutritive Value of Indian Foods (NVIF) (Gopalan et al., 2004). When food composition values were missing from the NVIF, data were obtained and imputed from Nepali foods database (HMG, Ministry of Agriculture, Agriculture Development Department, 1994) and USDA nutrient database (USDA, 2010). To account for the variations in the moisture content of the cooked vegetables, traditional foods, meat dishes and other food items, the raw ingredients were weighed first and then cooked foods were weighed after cooking, both locally in Panighata and in Montreal to calculate the moisture lost or retained. Computation of the final energy and nutrient (protein, fat, carbohydrate, vitamin A, vitamin C, calcium, iron, and zinc) intakes from the grams of raw foods was calculated using SAS version 9.2.

3.10.3. Shared foods Food sharing during lunch was a common practice among the tea plantation workers which imparted a cultural and social significance to the women. The act of sharing created a sense of family bond between the women and all women received a share of food from each other. When 88

sharing lunch at noon, Adivasi and Nepali women did not mingle with each other. Lunch was shared only in baseline and endline. During midpoint data collection, women ate lunch at home, as there was no tea picking activity due to winter season. The lunch comprised rice or roti (chapatti)21, sometimes lentils, and a vegetable dish. Only the vegetable dish was shared between the women. All the food items were weighed before and after consumption. Weight of food to be shared was weighed before sharing and weighed after sharing. The number of tablespoons of food given to every other member in the group was recorded to estimate the proportion each received. The amount of shared foods given by other members was weighed (g). To account for the contamination factor of the DFS and IS, the treatment codes of each women in the group was noted.

3.11. Blood measures used to classiy the stratification groups Venous blood samples were collected at baseline to measure Hemoglobin, serum ferritin, and C- reactive protein (CRP) levels used to stratify the subjects. Hemoglobin was analyzed with a Coulter Counter (Beckman) by the Super Religare Laboratories (SRL), Kolkata branch, India. Serum ferritin and CRP were analyzed at the Molecular Diagnostics Laboratory, Lucknow, India (Haas et al., 2014).

3.12. Salt distribution and compliance of salt intake Randomization and salt distribution started in October 2009. Baseline data were collected before DFS was distributed. The exposure to the DFS was between 7 and 9 months, with subjects who completed endline measures first in June 2010 having been exposed to the intervention for less time than those who were measured at the end of the study in August 2010. DFS or IS in four color coded bags (red, blue, green, and black) was distributed to the entire household and the salt was used for all meals prepared in the home except boiled rice. Salt is not added to rice when cooking in India. For the study participants, salt was distributed from the field office at the estate hospital. For all other employees of the tea estate, iodized salt was distributed in the tea estate factory through the public distribution system (PDS). Before the salt distribution, all the local shopkeepers in the tea estate region were approached for a yearly

21 Round flat bread made with unleavened flour 89

compensation of the profit lost by not selling iodized salt in the estate area. All the shops were regularly monitored twice every month to confirm that they were not selling salt. Total household salt consumption was monitored throughout the DFS intervention. The household salt container was weighed on three consecutive days at the beginning and the end of the study. The amount of salt consumed per day during the 3 d was calculated. This amount was divided by the number of people in the household including children (Andersson et al., 2008). Salt intake was also measured through the weighed food intake and 24-hr dietary recall data at three time points. Only the salt intake data obtained from the dietary data collection (by weighed food intake and 24 hr recall methods) was used for the analysis purpose, as the data were collected through direct measure and were more accurate than reported salt use.

3.13. Data analyses for the effect of double fortified salt intervention on the dietary intakes of women tea plantation workers

3.13.1. Descriptive statistics Descriptive analysis was carried out for the dependent variables of interest: total food intake per day, energy, protein, fat, carbohydrates, calcium, iron, zinc, vitamins A and C. One-way analysis of variance (ANOVA) was performed for the normally distributed data and Wilcoxon- Mann Whitney test for data which were not normally distributed. Statistical significance was set at P<0.05. Data were presented as means ± SD. All data analyses were conducted using SAS version 9.2 (SAS institute, Cary, NC, 2010).

3.13.2. Mixed modeling repeated measures Mixed modeling (PROC MIXED) for repeated measures (three time points) was used to determine the effects of treatment on the anthropometric measures and dietary intakes. The main effects of treatment, time, treatment-by-time interaction were analyzed separately. Ethnicity (Adivasi and Nepali) and mutually exclusive stratification (iron deficient anemic, iron deficient not anemic, anemic without iron deficiency, normal, high ferritin group), were also tested as covariates for each outcome variable. Model fit was based on the Bayesian information criteria (BIC). In the anthropometric measures, the model predicting BMI (kg/m2), compound symmetry 90

covariance structure was chosen based on the lowest BIC values. For the model predicting mid- upper arm circumference (MUAC) (cm), body weight (kg), food frequency intakes, total food intake, and nutrient intakes, unstructured covariance matrix provided the best fit. Multiple comparisons were corrected for using Bonferroni’s method (Littell et al., 2006).

3.13.3. Shared food intake data analyses The proportion of the shared food that had DFS that was received by each woman was calculated and ranged from 0 (no DFS in shared foods) to 1 (only received meals made with DFS). The total proportion of treatment obtained from sharing meals by each woman in the group was calculated by arithmetic mean. PROC GLM was used for multiple linear regressions. The proportion of treatment received from sharing was the independent variable. The covariates, ethnicity, and strata were accounted for in the analyses. Least square means were generated for the categorical variables and regression coefficients for the continuous variable.

3.13.4. Association between the socioeconomic determinants and nutritional status Analysis of covariance (ANCOVA) was used to determine the association between the anthropometric indicators of nutritional status and the socioeconomic variables. Covariates (age, energy intake, and diet diversity) were tested in the model. Results are presented as least square means (LSM). Dependent variables were anthropometric indices and the independent variables were ethnicity, marital status, literacy of the woman/spouse, occupation of the spouse, household income, family type, number of members in the household, housing materials, toilet facilities, water source, electricity, fuel for cooking, and appliances.

Figure 3.1. Timeline of the activities of the field study

Figure 3.3. Map of research site in the Darjeeling district

Figure 3.5. CONSORT diagram of DFS intervention

Screened for anemia with Hemocue (n=498)

n=217 not qualified Non-resident, age, severe anemia, poor health, pregnant, not interested

Baseline data collection (n=281)

n=36 not qualified Severe anemia, poor health, injured, pregnant, insufficient baseline data, Qualified for randomization refused (n=245)

Stratified and randomized into 4 colors of salt bags 500 g each

DFS IS DFS IS n=66 n=67 n=56 n=56

n=33 dropped out

DFS IS n=104 n=108

3.14. References

Bhattarai R. Open borders, closed citizenships: Nepali labor migrants in Delhi. Institute of Social Studies. The Netherlands. 2007

Chhetri B and Tamang L. Population growth and associated problems: A case study of Darjeeling town. International Journal of Humanities and Social Science Invention. 2013; 2: 63-67.

Clausen T, Charlton KE, Gobotswang KSM, Homboe-Ottsen G. Predictors of food variety and dietary diversity among older persons in Botswana. Nutrition.2005; 21: 86- 95.

Diosady LL, Alberti JO, Ramcharan K, and Mannar MGV. Iodine stability in salt double fortified with iron and iodine. Food and Nutrition Bulletin. 2002; 23: 196-207.

Government of West Bengal. Economic Review 2011-2012. (Accessed in December 2013). http://www.wbfin.nic.in/writereaddata/EconomicReview11_Part2.pdf

Gopalan C, Rama Sastri BV and Balasubramanian SC. Nutritive value of Indian foods. National Institute of Nutrition. Indian Council of Medical Research. Hyderabad, India, 2009.

Haas JD, Rahn M, Venkatramanan S, Marquis GS, Wenger MJ, Murray-Kolb LE, Wesley AS, Reinhart GA. Efficacy of double fortified salt in improving indicators of iron deficiency in female Indian tea pluckers. The Journal of Nutrition. 2014; 144:957-964.

Littell RC, Milliken GA, Stroup WW, Wolfinger RD, Schabenberger O. SAS for mixed models. Second Edition. SAS Institute Inc., Cary, North Carolina, USA. 2006.

National Family Health Survey NFHS-3. 2007. India 2005-2006. International Institute of Population Sciences, Mumbai, India and ORC Macro, Calverton, Maryland, USA.

HMG, Ministry of Agriculture, Nutrient Contents in Nepalese Foods. Agriculture Development Department, Nutrition Programme Section. Babarmahal, Kathmandu. (Hindi and English). 1994.

Oldewage-Theron W and Kruger R. Dietary diversity and adequacy of women caregivers in a peri-urban informal settlement in South Africa. Nutrition. 2011; 27: 420-427.

Protocol from Ankur salts, Ankur Chemfoods Ltd., Gandhidham, Gujarat, India. 2010.

Romita D, Cheng Y and Diosady LL. Microencapsulation of ferrous fumarate for the production of salt double fortified with iron and iodine. International Journal of Food Engineering. 2011; 7: 1-14.

SAS Institute Inc. Release 9.2. SAS Institute, Cary, North Carolina, USA. 2010.

Tea Industry Annual Report 2009-2010. West Bengal Industrial Development Corporation. (Accessed in December 2013). http://www.wbidc.com/overview/annual_report_09-10.htm

United States Department of Agriculture (USDA) Agricultural Research Service. 2010. National Nutrient Database for Standard Reference. (Accessed in May 2013). http://ndb.nal.usda.gov/ndb/search/list

World Health Organization (WHO). Physical status: The use and interpretation of anthropometry. 1995. (Accessed in June 2013). http://whqlibdoc.who.int/trs/WHO_TRS_854.pdf

Bridge statement 1

The prevalence of underweight is high among women in rural India. The challenges associated with poor nutritional status are multidimensional. The literature review of studies from rural India showed that prevalence of underweight varies depending on ethnicity and socioeconomic status. Tribal women in the rural parts are much more disadvantaged for nutritional outcomes than other ethnicities. Poverty and related socioeconomic factors influence dietary intakes and affect the nutritional status of the women. It is important to identify and address the components of socioeconomic factors which can influence the nutritional outcomes of the poor. In the first manuscript, we examined the differences in the socioeconomic variables that influenced the nutritional status of the tea plantation workers from West Bengal. We also examined the differences in nutritional status that were attributed to ethnicity between the tribal Adivasi and Nepali women.

Chapter 4: Manuscript 1

Socioeconomic factors and ethnicity are determinants of undernutrition amongst women working in a tea plantation in India

Sudha Venkatramanan, Grace S Marquis and Jere D Haas

4.1. Abstract

Background: In spite of a rapidly growing economy, undernutrition continues to be a major challenge amongst rural Indian women. The objectives of the present study were to analyse the socioeconomic and demographic determinants of nutritional status of tea plantation workers from West Bengal, India. Methods: Cross-sectional data from baseline were collected on the nutritional and socioeconomic status of 246 female tea plantation workers from Panighata tea garden, West Bengal. The participants were full-time, experienced, permanent, non-pregnant female workers of Adivasi and Nepali ethnicities. Analysis of covariance was used to assess the relationship between anthropometric indicators (body mass index [BMI] and mid-upper arm circumference [MUAC]) and the independent socioeconomic and demographic variables. Results: The results of the analysis of covariance showed that illiteracy (p=0.016), unemployment of spouse (p<0.0001), lack of toilet facilities (p=0.016), community water source (p=0.038), and ethnicity (p=0.029) were all negatively associated with BMI. Unemployment of spouse (p<0.0001) and lack toilet facilities (p<0.0001) were also negatively associated with MUAC. The level of thinness (BMI<18.5 kg/m2) in Adivasi and Nepali women was 59% and 16% (p<0.0001), respectively. Conclusion: Poor socioeconomic status challenges the health of rural Indian women. Ethnicity was an additional barrier affecting the nutritional status of the tea plantation workers with the tribal population at the greatest risk for underweight. To improve women’s nutritional situation in rural India, it is important to implement comprehensive nutritional, educational, and health programmes considering the ethnic heterogeneity along with the existing rural development policies and programmes.

Keywords: Adivasi, Nepali, socioeconomic determinants, illiteracy, sanitation, nutritional status, BMI, MUAC

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4.2. Introduction Chronic undernutrition is an important risk factor of poor nutritional status compromising the health outcomes and productivity of adult women (FAO, 2013; Murray and Lopez, 1997). Worldwide, 842 million individuals are undernourished and the rate of prevalence of undernourishment from Asia is 13.5% (FAO, 2013). The total number of undernourished individuals in India alone is 217 million. The prevalence of underweight is greater in rural (33%) than urban (17.5%) women (Das and Bose, 2012). The overall prevalence of underweight amongst Indian women declined by three percent from the second National Family Health Survey in 2000 (36%) to 33 % from the 2006 national survey (NHFS-2, 2000; NFHS-3, 2007). Inspite of the rapid economic development in India, poor nutritional status of rural women still persists (Barker et al., 2006).

The measure of body mass index (BMI) is considered to be one of the indicators of poor nutritional status (Chockalingam et al., 2011; WHO, 1995). Adults with a BMI of <18.5 kg/m2 are categorized as undernourished (NFHS-3, 2007). Studies have shown that a decrease below the BMI cut-off (<18.5 kg/m2) is associated with an increased risk of mortality (Sauvaget et al., 2008) and morbidity (Chakraborty et al., 2009; Bose et al., 2007; Khongsdier, 2002) in adults. Another tool that is simple, inexpensive, and commonly used in surveys to estimate undernutrition is the measurement of mid-upper-arm circumference (MUAC) (Jeyakumar et al., 2013; Luzzi and James, 1996; Collins, 1996). A cut-off of <21.4 cm is considered severely malnourished and a cut-off between 21.4 and 22.1 cm is moderate acute malnutrition (UNSCN, 2011). A cross-sectional study from Purulia district of West Bengal showed a high prevalence 64.7% of adult women undernourished based on MUAC values (Das and Bose, 2012).

Tea plantations in India started in 1839 and the laborers of the plantation are migrants from Nepal and a mixture of different tribal people from the regions of Jharkhand, Chattisgarh, and Orissa, usually referred to as Adivasi (Bhowmick, 2011). The state of West Bengal in India is the second largest tea producer in India and tea plantation workers are often challenged with poor living conditions, poor sanitation and low education levels (Kundu et al., 2013). The causes of malnutrition are multidimensional and include economic, social and political factors (Black et 101

al., 2008). The causes of micronutrient malnutrition and undernutrition depend on poverty and illiteracy, which in turn affects the diet diversity and overall health of the woman (Muller and Krawinkel, 2005). Data from NFHS-3 in India show that poor socioeconomic status is related to low nutritional status in the rural population (Kulkarni et al., 2013; Ackerson et al., 2008; Subramaniam and Smith, 2006). A community-based cross-sectional study conducted on a tea plantation in Assam, India reported that 73% of women were underweight (<18.5 kg/m2). The level of underweight (52.8%) was significantly (p <0.001) lower amongst women who had education beyond primary school (Medhi et al., 2006). The main objectives of the present study are to determine the socioeconomic determinants of the anthropometric indicators of nutritional status in full-time female workers of the tea plantation and also to examine the differences in the socioeconomic and demographic variables associated with the nutritional status of the tea plantation workers.

4.3. Methods

4.3.1.Study participants The present study was conducted in Panighata tea garden in West Bengal. Cross-sectional information on nutritional status and socioeconomic status was collected from 246 full-time, experienced, permanent, non-pregnant female tea plantation workers who were at least 18 y of age. The participants were from the two ethnicities in the village, Adivasi (n=128) and Nepali (n=118), and they followed a traditional multi-generational family system, where all the members of the family worked in the tea plantation and the employment was passed on for generations. The ethics approval for the study was obtained from McGill University’s Institutional Review Board and ethics clearance was obtained from Child in Need Institute (CINI), a national Non- Governmental Organization (NGO) which provided the logistics support for the study. The nature of the study was explained to the participants and the family members; written and oral consent was obtained from all the members of the study.

4.3.2. Nutritional status Height was measured to the nearest (cm) with a stadiometer. Each participant’s weight was measured using a standard weighing digital scale (nearest 0.5 kg). BMI was calculated by dividing weight by square

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of height (kg/m2). Mid-upper arm circumference was measured mid- way from the tip of the shoulder and the elbow of the left arm using a flexible measuring tape (nearest cm) (WHO, 1995). The following cut-offs are used to screen undernutrition based on the BMI (kg/m2) and MUAC (cm) values: normal BMI: 18.5-24.9; underweight: <18.5; normal MUAC: > 22; moderate acute malnutrition: ≥ 21.4 and ≤ 22.1; severe acute malnutrition ≤ 21.4 (WHO, 1995; UNSCN, 2011).

4.3.3.Dietary data collection Lunch intake was measured directly and a recall of breakfast, snacks and dinner was collected to complete the 24 hr intake of food. The grams of intake of each food item were calculated and the energy intake was calculated from the food composition database (Nutritive Value of Indian Foods, 2004). Frequency of intake of food groups over one week was collected. Diet diversity score (DDS) was calculated for one week as reported in previous studies (Clausen et al 2005; Oldewage-Theron and Kruger, 2011)

4.3.4.Socioeconomic and demographic status Socioeconomic information was collected using the NFHS-3, 2007 as a reference (NFHS-3, 2007). Information was collected on marital staus (married vs single [separated/divorced/never married]), ability of the participant to read and write (yes vs no), education level of the woman (attended primary school vs. never attended school), education level of the spouse (attended primary school vs. never attended school), occupation of the spouse (single, not working, manual labor, family business, office job), and total income of the household (above vs. below 1000 Indian rupees (INR) per month, ). Household information on the number of family members, number of children less than and greater than 18 y, type of family (joint vs. nuclear), type of diet (vegetarian vs. non-vegetarian), type of housing (quarters [housing given by tea estate management] vs. traditional housing), water source (communal pipes vs. piped into yard/well/rainwater), toilet facilities (present vs. not present), fuel for cooking (gas vs. charcoal), electricity (present vs. not present), and appliances (presence of any of the appliances: radio, television, automobile, sewing machine, and bicycle).

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4.3.5. WHO and Indian cut-offs for BMI and the grades of underweight The World Health Organization (WHO) cut-offs for BMI (kg/m2) were used to define underweight, normal, overweight and obese: underweight: < 18.5; normal: 18.5 - 24.99; overweight: ≥ 25.0; obese: ≥ 30.0 (WHO, 1995). The health ministry of India uses a slightly modified cut-off (Union Health Ministry of India, 2008) which is as follows: underweight: <18.5; normal: 18.50 - 22.99; overweight: 23.0 - 24.9; obese ≥ 25.0 (Thakkar et al., 2010). The different grades of underweight or chronic energy deficiency (CED) are as follows: grade 1: 17.8-18.4 kg/m2; grade 2: 16-16.9 kg/m2; grade 3: <16.0 kg/m2 (Ferro-Luzzi et al., 1992).

4.4. Statistical analysis Data are presented as means ± SD for continuous variables and categorical data were analyzed using chi-square tests. PROC GLM was used for analysis of covariance (ANCOVA) to determine the association between the dependent and independent variables of interest. Data are presented as regression estimates for continuous variables and least square means (LSM) for categorical variables. Dependent variables were anthropometric indices and the independent variables were ethnicity, socioeconomic and demographic variables. Age, energy intake, and diet diversity score (DDS) were also tested as covariates in the model. Data were analyzed using SAS version 9.2 (SAS institute, Cary, NC, 2010).

4.5. Results

4.5.1. Anthropometry Significant differences in BMI (kg/m2), MUAC (cm) and weight (kg), but not height, were observed between Adivasi and Nepali ethnicities (Table 4.1). The prevalence of underweight in Adivasi and Nepali were 59% and 16%, (p<0.0001), respectively. A comparison of Indian and WHO BMI classifications are presented in Figure 4.1. Being overweight was more common in Nepali (16.8%) than Adivasi community (4.7%), (p<0.0001); obesity was seen in the Nepali community (10.9%) only when the data were categorized using the Indian cutoffs. Analysis of

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the level of thinness (<18.5 kg/m2) showed that the severity of thinness grades (1, 2, 3) was significantly greater in Adivasi than Nepali (p<0.0001) (Figure 4.3). The BMI range in both the ethnicity varied from 13.7 to 29.0 kg/m2 (Figure 4.2).

4.5.2. Socioeconomic and demographic information The study participants were 40 ± 8 y (range: 19-60 y). The main source of income was picking tea leaves and the women were paid for the amount of tea leaves they plucked. The proportion of literacy in Nepali women was nearly twice that of Adivasi women (44% vs. 24%, respectively; p=0.014). Similar outcome patterns were also seen for the spouse literacy (p=0.033) and spouse’s education level (p< 0.0001); Nepali were more literate than Adivasi. When the occupation of the spouses was compared between the two ethnicities, it was found that Nepali men were better employed and four times more Nepali households had toilet facilities compared to Adivasi (p <0.0001). The diet was predominantly non-vegetarian in both the ethnic groups. No significant ethnic differences were observed for household income, electricity, and appliances (Table 4.2).

4.5.3. Analysis of covariance: Socioeconomic determinants of nutritional status The results of the ANCOVA showed that socioeconomic variables and ethnicity were important determinants of BMI and MUAC (Tables 4.3 and 4.4). Illiteracy of spouse was a significant predictor of BMI (p=0.016) and MUAC (p=0.0003). ANCOVA analysis of the relationship between occupation of the spouse and nutritional status showed that unemployment was negatively associated with BMI and MUAC in women compared to women whose spouses were employed in office jobs (p<0.0001). Lack of toilet facilities (p=0.016) and absence of communal water source (p=0.038) were also negatively associated with BMI and MUAC respectively in the women. Ethnicity was a significant predictor of BMI. Adivasi were of lower average weight (<18.5 kg/m2) than Nepali women (p=0.029). No significant association between ethnicity and MUAC was observed (p= 0.44) and no significant interactions of socioeconomic variables and ethnicity were detected.

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4.6. Discussion In the present study, we found significant associations between the socioeconomic variables and indicators of poor nutritional status amongst the women tea plantation workers in Panighata tea garden. Studies from other tea planations have reported also a high prevalence of underweight (Kundu et al., 2013). A cross-sectional study from Dooars tea plantation in West Bengal reported 44% prevalence of underweight in women tea plantation workers (n=100) (Kundu et al., 2013). Dooars tea plantation in West Bengal has tea plantation workers from Adivasi, Nepali and Bengali groups (Bhowmik, 2011). The ethnic group differences were not included in the analysis. However, poor standard of living index was related to low BMI in this region (Kundu et al., 2013). In low income countries, especially in rural populations, illiteracy limits job opportunities and men are usually employed in manual labour with poor wages (Shukla et al., 2002). In our study, lack of occupation of the spouse was negatively associated with BMI and MUAC in the women. Similar association between the nutritional status of the women and spousal occupation have been reported in other studies (Subasinghe et al., 2014; Bharti et al., 2007). The relationship between women's nutritional status and spouse employment was examined among 81,712 women in the reproductive age from 26 states of India. The prevalences of underweight in rural Indian women married to unemployed men were 34.5% compared to men employed in professional jobs (26.8%) (Bharati et al., 2007). A different outcome from our study was observed in a recent study from India which showed that farming was associated with underweight in men and women (Subasinghe et al., 2014). In this study the investigators also observed that lack of occupation in men was related to underweight in men, which was slightly different from our outcome. The prevalence of being underweight in men was twice than the other categories of employment (Subhasinge et al., 2014).

Another socioeconomic and environmental determinant of the nutritional status of participants was the presence or absence of toilet facilities in the house. Presence of toilet facilities at house was associated with better outcomes of the nutritional indicators BMI and MUAC. This was also observed in the national survey data from India. NFHS-3 data were used to study the role of social determinants on the nutritional status of women in the reproductive age. The study revealed that 72% of rural women lacked toilet facilities and women with toilet facilities were 106

50% more nourished or of normal BMI than women who lacked these facilities (Jose and Navaneetham, 2010). Similar to this study, we found significant associations between the lack of toilet facitlities and poor nutritional status, with low BMI and MUAC. This suggests that lack of proper sanitation acts as a proxy indicator for poor nutritional status (Kulkarni et al., 2013). Most of the tribal sections of the tea plantation used the open fields and bushes for defecation, a practice which constitutes a major indicator of poor socioeconomic status. According to a recent World Bank report, 53% of Indian households defecate in the open and this is a main cause for infections and diarrhoea (Spears and Lamba, 2013). A cross-sectional study from different regions of Ethiopia on maternal health determinants found that women who dwelled in households with toilet facilties were 15% less likely to be underweight than those without toilet facilities women (OR: 0.85, 95% CI [0.759, 0.955]) (UNFPA, 2012). This, lack of proper sanitation can have a negative role on the nutritional outcomes of the women due to numerous infections (Ramachandran, 2007; Jose, 2008).

The cross-sectional data from our study showed a greater percentage of Adivasi women as underweight. A cross-sectional study with Adivasis from Orissa, India reported a high prevalence of underweight in women based on the BMI cut-offs (64.5%) (Bose and Chakraborty, 2005). Other cross-sectional studies conducted in different groups of Adivasi populations from other regions of India confirm these findings of high prevalence of underweight in the tribal population (Chakrabarty et al., 2008; Bose et al., 2006; Kapoor et al., 2010; Ghosh and Bala, 2006; Mittal and Srivastava, 2006; Reddy, 1998). Although the working conditions (pay per amount of tea plucked) of both the Adivasi and Nepali ethnicities were the same on the tea plantation, there was significant variation in the living conditions that may have contributed to the differences in nutritional status observed between the two ethnic groups. In our study we also found a range of thinness below 18.5 kg/m2, especially in the Adivasi women and our results are consistent with results from the NFHS-3, West Bengal, and Rajasthan (NFHS-3, 2007; Bhattacharjee et al., 2010; Bhasin and Jain, 2007).

The Adivasi populations of India are historically discriminated against in the society. Although they do not fall under the ancient caste system (Sundaram and Tendulkar, 2003), Adivasis are 107

considered equivalent to Dalits or Scheduled Castes, who are considered as doing unskilled labor jobs. As a result they have maintained the lowest position in economic development resulting in illiteracy, poverty, lack of hygiene, and poor living conditions (Agarwal and Agarwal, 2010; Subramanian et al., 2006). Comparison of socioeconomic variables by ethnicity showed that the Adivasis are more disadvantaged than the Nepali. The Adivasi of Panighata, comprised of tribal groups such as Oraon, Santhals, Mundas and Lohars, all had similar living conditions. The Nepali community consisted of a hierarchical range of castes including upper castes (such as Chhetris, Bahun, Newars), alcohol-drinking castes (such as Tamang, Sherpa, Sunwars) and untouchables or repressed castes (such as Sarki and Kamis); all were of Nepali background. The predominant Nepali castes in Panighata are Chhetri, Tamang, Gurung, Rai, Sherpa, Sunwar, and the Kami. Although caste discrimination exists within the Nepali community and follows a hierarchy from the upper towards the lower classes, especially the repressed class, the Adivasis are discriminated against even by the repressed caste of the Nepali community, putting them at the bottom of the scale in the society which affects their quality of living. In this socioeconomically disadvantaged segment, we found remarkable variation in the underweight status in the poorest of the poor population. In our analysis, after accounting for the socioeconomic variables, ethnicity is still a main determinant of BMI similar to the findings from a study in Kerala, India which compared the health status of tribals and non-tribals (Haddad et al., 2012). Data from different parts of India uniformly show that Adivasis suffer from thinness or chronic energy deficiency and one of the reasons could be due to biological factors such as increased risk of morbidity due to poor standards of living (Khongsdier, 2002; Chakrabarty et al., 2008).

Genetic makeup makes Indians more prone to abdominal obesity which is linked to metabolic syndrome as compared to the western population (Bhatt et al., 2012). Increased presence of overweight and obesity was observed in Nepali women with a better standard of living. Similar to what we observed in Nepali women, presence of overweight and obesity was also observed in other rural parts of India (Kaur et al., 2011; Agarwal et al 2006). One of possible reasons could be due to the use of fats and oils for deep frying in traditional Nepali cuisine (Tamang and Sarkar 1988). The results of the present study are in agreement with previous findings on the effect of 108

socioeconomic variables on the nutritional status of rural Indian women. In a tea garden community where the work demands are high on women with high prevalence of underweight, lack of sanitation and hygiene can contribute to the vicious cycle of malnutrition. The findings of the present study that link low socioeconomic status with poor nutritional outcomes can contribute useful information for future health interventions. This can help improve targeting in already existing health policies for rural Indian women. In addition, an interesting and unique outcome of the study was the ethnic differences within the same tea garden village. The additional challenges associated with disadvantaged Adivashi exposes them to a higher rate of poor nutritional outcomes. The Government of India has several policies and programs in place for improving the living conditions of the Adivasi population. With the support of Total Sanitation Campaign (TSC), the Government of India started construction of open pit latrines in 1999 for rural households in the different rural districts across India (1 latrine for 10 people) from 2001 to 2011. The success of the TSC scheme reduced infant mortality rate (IMF) by four deaths per 1000 live births (Spears, 2012). As part of the ninth five year plan of the Government of India, poverty alleviation programmes provided basic housing and sanitary latrines were provided to the tribal population (Tenth five year plan, 2007). In spite of the existing programmes, underdevelopment especially in regards to the health situation is still a cause of concern in Adivasis mainly because of the ongoing marginalization. To be able to effectively address the health needs of Adivasis, it is important to develop comprehensive health care interventions tailored specifically to the tribal population (Mohindra and Labonte, 2010; Prabhakar and Manoharan, 2005). This will take into account factors specific to this population within the regular healthcare framework such as empowerment of illiterate women. Women can be encouraged to take leadership roles in the health care system and small scale industries can be encouraged in the rural industries. These programs can be developed through consultations and interventions with the Adivasi population and must account for the regional differences that exist between the different Adivasi groups across India.

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Table 4.1. Anthropometric characteristics based on ethnicity1

Variable Adivasi Nepali p-value1

Mean SD Mean SD

Anthropometry (n=127) (n=119)

BMI (kg/m2) 18.3 2.4 21.1 2.9 <0.0001

MUAC (cm) 22.9 2.3 25.1 2.7 <0.0001

Weight (kg) 41.7 6.6 47.4 7.5 <0.0001

Height (cm) 150.6 5.5 149.8 5.1 0.24 1Data analyzed using one way ANOVA

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Figure 4.1. Classification of BMI according to Indian cut-offs (n=246)22

Classification of BMI according to World Health Organization cut-offs (n=246)23

UW-Underweight; NOR- Normal; OW- Overweight; OB- Obese

22 Underweight: < 18.5; normal: 18.5 - 24.99; overweight: ≥ 25.0; obese: ≥ 30.0 (WHO, 1995) 23 Underweight: <18.5; normal: 18.50 - 22.99; overweight: 23.0 - 24.9; obese: ≥ 25.0 (Thakkar et al., 2010)

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Figure 4.2. Frequency distribution of BMI by ethnicity (Adivasi=127; Nepali=119)

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Figure 4.3. Classification of chronic energy deficiency by ethnicity24

CED Grade 3: <16.0 kg/m2; CED Grade 2: 16-16.9 kg/m2; CED Grade 1: 17.8-18.4 kg/m2

24 NOR- Normal: 18.5- 22.99 kg/m2; OW- Overweight: 23.0-24.9 kg/m2; OB- Obese: ≥ 25.0 kg/m2 (Thakkar et al., 2010; Ferro-Luzzi et al., 1992)

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Table 4.2. Socioeconomic and demographic characteristics of the study participants, by ethnicity status1

Adivasi Nepali p-value2 (n=128) (n=118)

Household members (#) 5.3 ± 1.69 5.4 ± 2.02 0.74

Marital Status Married 74.0 (94) 81.5 (97) 0.15 Single, separated or widowed 25.9 (33) 18.5 (22)

Participant literate Yes 24.4 (31) 43.7 (52) 0.001 No 75.6 (96) 56.3 (67)

Spouse literate Yes 70.8 (90) 82.4 (98) 0.033 No 29.1 (37) 17.7 (21)

Participant’s education level Received primary education 33.9 (38) 46.6 (49) 0.06 Never received primary education 66.0 (74) 53.3 (56)

Spouse’s education level Received primary education 38.5 (47) 66.9 (79) 0.0001 Never received primary education 61.5 (75) 33.0 (39)

Spouse, type of work No spouse 24.2 (32) 17.6 (21) 0.013 Not working 9.5 (12) 14.3 (17) Manual labor 48.0 (61) 37.8 (45) Family business 7.1 (9) 5.0 (6) Office work 10.2 (13) 25.2 (30)

Household income per month <1000 INR3 3.1 (4) 5.9 (7) 0.30 >1000 INR3 96.9 (123) 94.1 (112)

Diet Non-vegetarian 90.6 (115) 87.4 (104) 0.42 Vegetarian 9.5 (12) 12.6 (15)

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Water source 6.3 (8) 21.9 (26) 0.0004 Piped into yard, rainwater, well 93.7 (119) 78.1 (93) Communal pipes

Electricity Yes 98.4 (125) 99.1 (118) 0.60 No 1.6 (2) 0.8 (1)

Toilet facility Yes 22.8 (23) 88.2 (75) <0.0001 No 77.2 (78) 11.7 (10)

Fuel for cooking Gas 0.0 (0.00) 2.3 (2) 0.09 Charcoal or wood 100 (123) 97.7 (86)

Appliances (radio, television, bicycle, automobile, sewing machine) Yes 92.9 (118) 94.1 (112) 0.70 No 7.0 (9) 5.9 (7)

Family type Joint family 36.2 (46) 47.9 (57) 0.06 Nuclear family 63.8 (81) 52.1 (62)

Housing Traditional housing 29.92 (38) 16.8 (20) 0.015 Permanent housing 70.0 (89) 83.2 (99) 1 Data are presented as means ± SD or % (n); 2Data analyzed using Student’s t-test or Chi-square test 3 INR- Indian Rupees; 1 USD=45 INR in 2010

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Table 4.3. ANCOVA results: Ethnicity, illiteracy, occcupation of the spouse, and sanitation as determinants of women’s nutritional status

BMI p-value MUAC p-value1

Ethnicity Adivasi 18.2 ± 0.4 0.029 22.9 ± 0.4 0.44

Nepali 19.4 ± 0.5 23.4 ± 0.4

Literacy of spouse Yes 19.3 ± 0.3 0.016 23.8 ± 0.3 0.0003

No 18.3 ± 0.5 22.4 ± 0.4 Occupation of spouse2 No spouse 17.9a ± 0.5 <0.0001 22.3a ± 0.4 <0.0001

Husband unemployed 17.6a ± 0.6 21.9a ± 0.5 Manual labor 19.0a ± 0.4 23.4a ± 0.3 Family business 18.5a ± 0.9 22.7a ± 0.8 Office work 20.9b ± 0.6 25.0b ± 0.5

1Model: n= 246 [Variables in the final model: ethnicity; diet diversity score; energy intake; spouse’s ability to read and write, occupation, water source and toilet facilities] Values are least square means (LSM) ± standard error of the mean (SEM) 2Bonferroni correction

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Table 4.3. Continued.

BMI p-value MUAC p-value2

Toilet facilities

Yes 19.3 ± 0.3 0.016 24.1 ± 0.4 <0.0001

No 18.3 ± 0.5 22.1 ± 0.4

Water facilities

Water facilities in the 18.1 ± 0.6 0.04 22.6 ± 0.5 0.07 yard Communal pipes 19.4 ± 0.3 23.6 ± 0.2

1Model: n= 246 [Variables in the final model: ethnicity; diet diversity score; energy intake; spouse’s ability to read and write, occupation, water source and toilet facilities]; Values are least square means (LSM) ± standard error of the mean (SEM); 2 p <0.0001

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Bridge statement 2

In Manuscript 1, we examined the association between socioeconomic variables and nutritional status indicators of women tea plantation workers and found a high prevalence of underweight in the population. Along with underweight, micronutrient deficiency is another important health concern that is faced by the women of reproductive age. Iron deficiency is common among Indian women, especially rural women consuming a monotonous diet. The iron intake from Indian foods has poor bioavailability due to the high intake of phytate-rich greens and teas. Poor appetite resulting in decreased food intake is one of the consequences of iron deficiency along with poor work output, fatigue, and poor cognitive outcomes. In the second manuscript, we examined the effect of salt dually fortified with iron and iodine on the dietary intakes of women tea plantation workers.

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Chapter 5: Manuscript 2

Iron intake increased with double fortified salt but other dietary components and nutritional status were influenced by ethnicity of women tea plantation workers from India

Sudha Venkatramanan, Grace S Marquis, Lynnette M Neufeld, Michel Wenger, Laura E Murray-Kolb, Greg Reinhart and Jere D Haas

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5.1. Abstract

Background: Iron deficiency is highly prevalent in India and has adverse health complications for women of reproductive age. We hypothesized that improving the iron status would improve food intake resulting in higher energy and nutrient intakes of women tea plantation workers consuming double fortified salt. Methods: A randomized double-masked study was completed to test the efficacy of salt dually fortified with iron and iodine (DFS) to improve the iron status of female tea plantation employees in West Bengal, India. Adivasi and Nepali participants (n=245) were randomized to iodized salt (control) or DFS (treatment) and followed for 10 months. To test the effect of DFS on intake, diets were assessed at baseline, midpoint, and endline. At each time point, dietary intake was assessed by a 7-d food frequency questionnaire, which provided information on the frequency of intake of food groups. In addition, a weighed food intake of the lunch meal, and a 24-hr dietary recall were used to estimate the nutrient intakes. Results: A significant increase in the iron intake from DFS was observed in the treatment group in comparison to the control (p =0.0008). No other dietary differences could be attributed to treatement. No significant treatment effect was observed on the anthropometric indicators. Ethnicity was a significant predictor of total grams of food (p <0.0001), energy (p <0.0001), protein (p <0.0001), carbohydrates (p <0.0001), and zinc (p =0.004) as well as BMI (p <0.0001) and MUAC (p <0.0001) across the time points. Despite lower mean BMI, the Adivasi consumed greater quantity of food, especially cereals and pulses; however, the Nepali consumed a more diverse diet than that of the Adivasi. Conclusion: The present study provides detailed longitudinal information of food and nutrient intakes in adult women who received DFS. Iron fortified salt improved the iron intake but did not affect the intake of energy or other nutrients. In a population with high prevalence of iron deficiency, dual fortified salt is likely to have a significant effect on iron deficiency reduction.

Keywords: Double fortified salt, iodized salt, iron deficiency, anemia, food frequency, micronutrient intakes, ethnicity, India

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5.2. Introduction Worldwide, 458 million women have anemia, with the majority of them being iron deficient (Black, 2003; Muller and Krawinkel, 2005). In India, the National Family Health Survey (NFHS-3) (National Family Health Survey NFHS-3, 2007) reported a high prevalence of anemia, affecting 55% of women aged 15-49 years. Iron deficiency (ID) and anemia causes reduced food and energy intakes (Lawless et al., 1994), results in fatigue (Verdon et al., 2003), diminishes work output and physical activity (Haas and Brownlie, 2001; Edgerton et al., 1979), impairs cognitive functions (Blanton et al., 2012; Sen and Kanani, 2009; Murray-Kolb and Beard, 2007), and results in poor reproductive outcomes (Makhoul et al., 2012; Allen, 2000; Scholl and Reilly, 2000). Intake of iron-rich foods with high bioavailability is a challenge in populations that are socioeconomically deprived (Thankachan et al., 2007; Torheim et al., 2010; Yip, 1994). In the Indian diets, minimal heme iron intake, low intake of fruits and vegetables that enhance iron absorption, and high intake of phytates from cereals and green leafy vegetables result in poor bioavailability of iron (Tupe et al., 2009). Fortification of foods is a promising strategy to address iron deficiency (Sattarzadeh and Zlotkin, 1999; Wegmüller et al., 2006).

Salt iodization in India has been very successful (Kapil, 2010; Sooch et al., 1973) and is an inexpensive and practical vehicle to deliver the iodine to the target population (Andersson et al., 2008; Ranganathan and Karmarkar, 2006). Double fortified salt (DFS) is a free flowing cooking salt containing 50 ppm potassium iodate as the iodine source and 1000 ppm encapsulated ferrous fumarate as the iron source (Diosady et al., 2002). Microencapsulation technology prevents iodine instability due to the oxidation that can result in the formation of ferric oxides affecting the organoleptic properties of the salt (Wegmüller et al., 2006; Diosady et al., 2002; Li et al., 2011). Efficacy studies in children have demonstrated that DFS can significantly reduce the incidence of ID and iron deficiency anemia (IDA) (Haas et al., 2014; Wegmüller et al., 2006; Andersson et al., 2008).

Iron deficiency can cause anorexia and reduced food intake (Topaloglu et al., 2001; Galloway et al., 2002). Poor appetite is an indicator of anemia and affects energy and nutrient intakes. Improvement in the appetite and food intake has been observed in studies among anemic 129

children, adolescents, and adults after iron supplementation (Lawless et al., 1994; Kennedy et al., 2004; Al Moussa et al., 2003; Basta et al., 1979). In an iron supplementation study conducted among male rubber plantation workers in Indonesia, significant improvements were observed in the intake of vitamins C and A in the iron supplemented group and the participants reported a better appetite (Basta et al., 1979). Similar changes in food intake over time with iron fortification have not been previously reported. In addition, the physiological relationship between the improved iron status and increased appetite is not clearly understood (Lawless et al., 1994). One of the proposed mechanisms of the relationship between ID and reduced appetite can be explained by an increase in the pancreatic cells along with cholescystokinin (CCK-8) pathway which reduces the appetite (Yehuda and Mostofsky, 2004). Cholescystokinin is a gut hormone that regulates satiety (Nishi et al., 2003) and CCK is necessary for pancreatic enzyme secretion (Louie et al 1986). In vitro studies with ID diet demonstrated that ID triggered CCK-8 pathway by increasing the leptin secretion thereby affecting the food intake (Yehuda and Mostofsky, 2004). The main objective of this study was to measure change in the dietary intakes in relation to the DFS treatment. We hypothesized that improving the iron intakes of female tea plantation workers would increase food consumption and thereby intakes of energy and macronutrients (proteins, fats, carbohydrates) and micronutrients (iron, calcium, zinc, vitamin C, vitamin A). The present study is the first of its kind to report changes in dietary intakes of adult women who consumed DFS.

5.3. Methods

5.3.1. Study site and study participants The research study was conducted in a tea plantation, in Panighata village, Darjeeling district, West Bengal, India. The total population of the village was 4623. The main source of income of the village was from working in the tea plantation as tea pluckers or manual labor in the tea estate factory. The presence of anemia amongst women in West Bengal state was 63% (NFHS-3, 2007; Unisa et al., 2010) and the prevalence of anemia was higher amongst the rural women (65%) (NFHS-3, 2007) compared to the urban women (59.4%) (NFHS-3, 2007). According to the recent data, the prevalence of anemia in women from Panighata was found to be 53% (Haas et al., 2014). 130

The participants were women tea pickers from two ethnicities, Adivasi (aboriginal population of Indians) and Nepali (first or more generation immigrants). They were recruited for a randomized, controlled, double masked study to test the efficacy of DFS to improve iron status.The participant inclusion criteria were age 18 y and above, not pregnant/lactating, experienced tea pickers, and permanent and full-time workers. The nature of the study was explained to the women and they were invited to participate in the intervention. Informed written consent was obtained from the study participants.

The study was approved by the McGill University’s institutional review board and the ethics board of Child in Need Institute (CINI), the national non-governmental organization which provided the logistics support for the study.

5.3.2. Screening and randomization The study participants and all members including men and children of the tea estate area were dewormed using Albendazole (400 mg, GlaxoSmithkline) one month before baseline blood collection and antihelminthic treatment was repeated every four months. Screening for severe anemia (hemoglobin < 8.0 g/dL) was carried out for 498 women using HemoCue AB (Quest diagnostics, Gurgaon, India). From this preliminary screening, 281 women met the above mentioned criteria. Venous blood was then sampled and productivity, physical activity, and cognitive measures were collected at baseline. From the 281 participants, 36 women were excluded at the randomization time due to incomplete baseline data, poor health, severe anemia as determined from the venous sample, injured, or refusal. A total of 245 women were stratified and randomized into either IS (iodized salt or control) or DFS (double fortified salt) into four color coded salt bags. Stratification was based on the hemoglobin and serum ferritin levels. The women were stratified into iron deficient anemic group (IDA), iron deficient not anemic (IDNA), anemic not iron deficient (ANID), normal, non anemic and non iron deficient (NOR), and high serum ferretin group (HFR).

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5.3.3. Anthropometry Anthropometric measures of height (cm), weight (kg) and mid-upper arm circumference (MUAC) (cm) were taken at the three time points: baseline, midpoint and endline of the intervention (WHO, 1995). Height (nearest cm), weight (nearest 0.5 kg), MUAC (nearest cm) were measured at the field office hospital with a stadiometer, digital scale and a flexible tape, respectively. The field staff was standardized in measurements before initiating field work.

5.3.4. Food frequency questionnaire Food frequency questionnaire (FFQ) data at baseline, midpoint and endline were collected to identify the frequency of consumed food items over the previous seven days. The frequency over the last seven days of consumption of cereals, pulses and legumes, nuts and oilseeds, green leafy vegetables (GLV), other vegetables, roots and tubers, citrus fruits, other fruits, dairy products, meat and poultry, sea foods, sugar, fats/oils, and beverages were collected. A diet diversity score (DDS) was generated for each food group consumed during the week (0-14) by summing up the number of food groups consumed by the women (Clausen et al., 2005; Oldewage-Theron, 2011).

5.3.5. Weighed food intake and dietary recall The lunch intake data from women were directly weighed at three times points: baseline midpoint and endline. The data collected included the type of food, recipes with ingredients, food served (g), amount consumed (g), and food shared among the women (g). Sharing lunch was a common practice in the tea garden culture. Amount of food shared between the tea workers was also measured in grams. All foods were weight with the Contech portable balance (Navi, Mumbai, Maharashtra) with a precision 0.1 g. A dietary recall of breakfast, snacks, and dinner was also collected to complete the 24 hr intake. The raw ingredients were measured directly at the household at midpoint and endline data collection. Due to logistics reasons, we were unable to measure the raw ingredients at baseline. Salt intake was calculated from weighed food intake and 24-hr recall methods (intake calculated from the recall and recipes).

A database comprising of 331 food items consumed by Adivasi and Nepali communities was developed. The nutrient composition for the food items was obtained from the Nutritive Value of Indian Foods (NVIF) (Gopalan et al., 2009), except for the missing nutritive values, which 132

were imputed from Nepali foods database (Nutrient Contents in Nepalese Foods, 1994) and USDA nutrient database (USDA, 2010). Computation of the final energy and nutrient (protein, fat, carbohydrate, vitamin A, vitamin C, calcium, iron and zinc) intakes from the grams of raw foods was calculated using SAS version 9.2 (SAS institute, Cary, NC, 2010). Nutrient intake estimates were compared to the Indian RDA (Indian Council of Medical Research, 2008).

5.3.6. Statistical analyses Data were analyzed using SAS version 9.2 (SAS institute, Cary, NC, 2010). Descriptive statistics were carried out for the outcome variables. Residuals of each of the outcome variable of interest were tested for normality. Bivariate tests were conducted at each time point between the treatment and nutrients. One-way analysis of variance (ANOVA) was performed for the normally distributed data and Wilcoxon- Mann Whitney test for data that were not normally distributed. Chi-square test was performed for categorical outcomes. Statistical significance was set at p<0.05.

Mixed modeling repeated measures approach (PROC MIXED) was used to determine the effects of treatment on anthropometric measures and dietary intakes at the three time points. The anthropometric and dietary outcomes were measured in separate statistical models. The main effects of treatment, time, and treatment-by-time interaction were analyzed separately. Ethnicity (Adivasi and Nepali) and stratification [iron deficient anemic (Hb <12 g/dL; Ferritin <20 µg/L), iron deficient not anemic (Hb ≥12g/dL; Ferritin <20µg/L), anemic not iron deficient (Hb <12g/dL; Ferritin ≥20 and <80µg/L), normal (Hb >12g/dL; Ferritin ≥20 and <80µg/L), and not anemic and high ferritin group(Hb >12g/dL; Ferritin ≥80µg/L)] were also tested as covariates for each outcome variable. Model fit was based on the Bayesian information criteria (BIC); the lower the BIC value, the better the model fit. In repeated measures modeling, measures on the same individual over time are likely to be correlated with each other. One way to address the effect of correlation is by modeling the covariance structure. Compound symmetry structure assumes same covariance and variance between measurements. Autoregressive covariance specifies homogenous variance and unstructured matrix is the general covariance structure with

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no constraints. The variance and covariance are heterogenous and offers the best fit (Littell et al., 2006). In the anthropometric measures, the model predicting BMI (kg/m2), compound symmetry covariance structure was chosen based on the lowest BIC values (Appendix 1). For the model predicting mid-upper arm circumference (MUAC) (cm), body weight (kg), food frequency intakes, total food intake (g), and nutrient intakes, unstructured covariance matrix provided the best fit (Appendix 1). Multiple comparisons were corrected for using Bonferroni’s method (Littell et al., 2006).

5.4. Results

5.4.1. Subjects characteristics The study participants were 40 ± 8 y (range: 19-60 y). The main source of income was picking tea leaves and the women were paid on the amount of tea leaves they plucked. The predominant diet in the village was non-vegetarian (89%); 11% of women followed a vegetarian diet.

5.4.2. Anthropometry Anthropometric baseline measures were similar between the two treatment groups (Table 5.1). No significant treatment effect was observed on the anthropometric measures. In the predictive model for MUAC (cm), the mixed model analyses showed a significant association of ethnicity- by-time interaction (p<0.014) (Table 5.2). The mixed model analyses for BMI (kg/m2) and body weight (kg) indicated a significant main effect of ethnicity (p<0.0001) and time (p<0.0001), but no significant interaction term.

5.4.3. Food groups and food consumption patterns At baseline, no significant differences were observed in the frequency of consumption of food groups between the treatment and control (Table 5.3). Significant differences in the frequency of intake of food groups were observed between the ethnicity at baseline. Weekly consumption of pulses, vegetables, citrus, fruits, dairy, meat, and fats were greater in the Nepali community than the Adivasi. Alcohol and tobacco consumption was also greater in Nepali group at baseline. The frequency of intake of beverages (black tea, milk tea, coffee, soda, and malted drinks) was greater in the Adivasi than Nepali. Significant differences in the diet diversity were also seen at

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baseline between Adivasi and Nepali groups. Nepali consumed more varieties of vegetables, citrus fruits, other fruits, dairy, and meat.

Ethnicity-by-time interaction had a significant effect for the frequency of intake of vegetables, roots and tubers, citrus fruits, meat, seafood, sugars, fats, and alcohol per week (at least p<0.01 for all) (Table 5.4). With time, the frequency of intake of food groups increased for both the ethnicities. There was a significant increase in the intake of vegetables, and roots and tubers at midpoint (during winter) in both ethnicities and the frequency of intake of vegetables was greater in Nepali than Adivasi women. Mixed model analyses of frequency of intake of food groups showed that there was a significant effect of treatment-by-strata interaction on the frequency of consumption of cereals (p=0.05) per week. However, Bonnferoni’s corrections did not provide any significant meaningful comparisons.

The food patterns of the tea plantation workers were monotonous and a typical diet consisted of breakfast, lunch, snacks, and dinner. The breakfast usually consisted of vegetables or dal (lentils) along with chapati or parboiled rice in Adivasi and white rice consumed by Nepali. Consumption of black tea with salt and sugar or black tea with dairy whitener and sugar was a common practice in the tea garden. Lunch for the day was usually prepared early morning and a part of the lunch was normally consumed as breakfast. Snacks usually consisted of black tea or milk tea (with dairy whitener) with biscuits or puffed rice. Dinner was comprised of rice or chapatti with vegetables, dal and once or twice a week, meat dishes. Most commonly consumed meat by Adivasi was beef, chicken and seafood. Nepali opted for chicken, pork or mutton and seafood. Goat meat or mutton (1 kg= INR 250) was more expensive compared to chicken (1 kg = INR150), pork (1 kg= INR 120) and Beef (1 kg=INR 100). The frequency of intake of vegetables was significantly greater in Nepali compared to Adivasi over the study period. This could be attributable to the better standard of living of Nepali compared to the Adivasi population. No significant differences were noted on the frequency of intake of food groups between the two treatment groups.

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5.4.4. Nutrient intake By treatment group differences: At baseline, the intake of fat, calcium, and vitamin A was significantly greater in the DFS group compared to the control (Table 5.1). The mean iron intake at baseline in both the treatment and control groups was below the recommended dietary allowance (RDA) of 30 mg/d for Indian women. Significant effect of treatment was observed in the total dietary iron intake (mg/d) at midpoint and endline compared to the control in the bivariate analyses (Table 5.5). The mixed effect models for macro- and micronutrient intakes are presented in Table 5.6. The mixed model analyses for energy intake showed a significant interaction of treatment-by-time (p=0.042) (Figure 5.1) but the Bonferroni multiple comparisons did not yield any significant comparisons. Among the micronutrients, significant treatment effect (p=0.0008) was observed in iron intake with an increase of 7 mg/d from the DFS salt compared to the control. The mean salt intake was 7.6 ± 4.8 g/d (range: 0-30 g/d). No significant differences in the salt intake were noted between the DFS and control groups. The mixed models for calcium and vitamin A intake also showed a significant effect of treatment-by- time interaction (p=0.016 for calcium and p=0.017 for vitamin A) without any significant multiple comparisons (Figures 5. 2 and 5.3). The main sources of calcium intake in the women were from GLV, other vegetables, pear squash or chayote, seafood, dairy whitener, cereals, and pulses. With time, the calcium intake declined by 93.2 mg in the DFS group and increased by 156.8 mg in the control group. Of the main sources of calcium the intake of milk and dairy whitener also reduced in the midpoint during winter and increased in the endline. The predictive model for vitamin C intake indicated a significant effect of time (p=0.007) on vitamin C intake.

Analysis by ethnic group differences: Given the significant association seen above with ethnicity, a second analysis focused on the role of ethnicity on dietary and nutrient intakes. The predictive model for total food intake (p<0.0001), energy (p<0.0001), protein (p=0.004), fat (p=0.002) and carbohydrates (p<0.0001) per day showed a significant main effect of ethnicity with a greater food intake in Adivasi of 500 g/d when compared to the Nepali community and fat intake per day was higher in Nepali than Adivasi group. Repeated measures analysis for zinc showed that zinc intake was significantly greater in Adivasi than Nepali (p=0.014). No significant interactions were observed for vitamin C and zinc. The main contributor of energy in both the 136

communities was parboiled and white rice (Table 5.7). The most common sources of vitamin C included green leafy vegetables, potatoes, raw chili peppers and shared meals. Potatoes were consumed on a daily basis with vegetables and greens.

5.5. Discussion The DFS intervention significantly improved the dietary iron intake from DFS of female tea plantation women over the 10 months of the study. However, no significant treatment effect was observed on their intakes of food groups, energy, and nutrients. The significant treatment-by- stage interactions that were observed had no meaningful effects on the dietary intakes. Though there was an overall significance of the interactions, the Bonnferoni adjustment did not reveal significant meaningful effects. At baseline, both the intervention groups (control and DFS) were on average below the recommended energy intake for heavy work based on the Indian nutrition guidelines by 14.5% and 10.6%, respectively (Gopalan et al., 2009). The energy intake significantly decreased in the midpoint and improved in the endline. This fluctuation likely reflected the increased intake of foods during the work season in summer, baseline and endline, where there is a higher demand of work output and as a result an increase in the energy intake was observed. The midpoint was during winter and the women were employed in pruning the tea bushes early in the morning and the work output was less. As the women were not plucking tea leaves, their incomes were lower during the midpoint which could explain the decline in energy intakes in the midpoint. Overall, no meaningful treatment effect was noted on the dietary intake variables in relation to DFS treatment. This is supported by an iron intervention study conducted from Indonesia, where a lack of treatment effect on energy intake was observed when the children were supplemented with iron and vitamin C (Angeles et al., 1993). Howewer, the study was conducted for 2 months and the population was different than our study. Results similar to our study were seen in the dietary intakes from a biofortification trial with Filipino religious sisters (Haas et al., 2005). The study showed no significant change in the energy intake between the treatment and control groups. The contribution of iron from the high iron group from the biofortification trial was less (1.79 mg iron/d) compared to the DFS intervention which provided on average 7 mg iron/d. Though the participants were women in the reproductive age in both the studies, the women from the Panighata study are socioeconomically disadvantaged compared to

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the religious sisters. In a worker productivity study conducted in Indonesia with adult male rubber tappers 16-40 y, an increase in the appetite and intake of foods rich in vitamin A and C was observed with iron supplementation. The increase in the edible greens increased the intake of dietary iron from 1.7 to 5 mg and vitamin C intakes from 80 to 130 mg per day. The participants were given 15 Indonesian Rupiah as incentive when the tablet was consumed. The increase in the greens and fruits was due to their ability to purchase green leafy vegetables with the incentive money. However, an increase in energy intake was not observed (Basta et al., 1979). The main source of energy in this study was from rice and cassava. The workers’ inability to increase their intake of rice was due to government rationing and the increased price of rice in Indonesia due to adverse climatic conditions. The participants reported that they were hungrier because of the tablets they consumed. Similar to the Indonesia study, the main source of energy for our participants was also from cereals, mainly rice, which was the staple diet. However, in our study, we did not detect an improvement in the food intake possibly due to reduced food security, as it was reported that the overall tea production was less in the endline compared to the baseline (data not shown). This is important because the plantation workers were getting paid based on the amount of tea leaves plucked; lower income during endline would have reflected lower financial resources to spend on foods.

Interestingly, Adivasi women had a lower socioeconomic status, yet the energy intake was greater than the Nepali ethnic group at all the three time points. This increased group difference in the energy intake was due to the increased intake of total foods, predominantly a rice-based diet in the Adivasi women. In a standard Indian diet, grains contribute 70-80% of the daily energy intake (Gopalan et al., 2009). Tea plucking demands intense physical labor. Along with physical labor in the tea estate, the women were also actively involved in the household chores from early morning until late night demanding higher energy intake. In a productivity study conducted in paddy plantation with Adivasi men and women in West Bengal, men who were highly productive consumed greater amount of food per day (2719 ± 1184 kcal/d) compared to those with low productivity (2177 ± 532 kcal/d) group. However, the dietary data collected was semi-quantitative in the West Bengal study and the energy intake was calculated only from the major food sources of cereals, potatoes, and rice beer and no information on protein and fat 138

intake was provided (Roy, 2002). In our study, similar to energy intake, the protein intake in Adivasi was also greater than Nepali. The Indian recommended dietary allowances (RDA) for a woman doing heavy work is 50 mg protein/d and the mean values for both groups were greater than the RDA. Cereals, pulses and seafood were the main sources of protein in Adivasi and cereals, pulses and chicken were the main contributors of protein in Nepali. The predominant protein in Indian diet is derived from cereals similar to what we observed in the present study. According to India’s national sample survey organization (NSSO), 66% of protein in rural population is from cereals, followed by pulses (Swaminathan et al 2012; NSSO Survey, 2007).Though the NSSO survey from rural India also had non-vegetarians similar to our study, meat intake was less (4%) and not the main source of protein (NSSO Survey, 2007).

The average consumption of fat was greater than the RDA for Indian women. The intake of fat was greater in Nepali than Adivasi. This was also confirmed by food frequency data where the frequency of consumption of fat was significantly greater in the Nepali group compared to the Adivasi. According to the district nutrition profile from the government of India, the consumption of fat in rural India is 31.3 g/d compared to 39.5 g/d in urban setting. Within the rural population, the fat intake ranges from 23-53.4 g/d (Shetty, 2002) according to socioeconomic levels. The mean fat intake in Adivasi from our study was 27.25 g/d compared to the Nepali consuming 33.45 g/d which is also consistent with the previous data based on the socioeconomic groups. The fat in Adivasi and Nepali diet came from visible fats, such as refined oil, mustard oil, vegetables, and leafy greens received from the members of the social group (shared meals). The higher fat content in Nepali diet may have come from the consumption of dairy, puris25, and snacks such as momos26 and selroti27. There was a variety of dairy intake in Nepali including fresh cheese, ghee28, and yoghurt (Tamang and Sarkar, 1988).

25 Deep fried bread 26 Tibetan dumplings 27 Fermented rice, deep fried with sugar or jaggery 28 Clarified butter 139

The main sources of dietary iron were green leafy vegetables, cereals, pulses, and shared meals. No significant differences were noted by the type of diet (vegetarian and non-vegetarian). Intake of red meat was limited to once or twice a week especially on pay days. The iron intake from Indian diet is predominantly from non-heme sources with a contribution of 90-95% of the total daily iron (Silva et al., 2009). Dietary iron was obtained from heme (meat) and non-heme (plant based) foods. Heme and non heme rich foods contributed 0.45 ± 1.59 and 22.04 ± 11.55 mg/100 g foods, respectively in our population (Haas et al., 2014). The significant main effect of time observed in iron intakes indicates the seasonal effect due to the presence of more varieties of green leafy vegetables in summer compared to winter when the midpoint data were collected.

The DFS treatment thus contributed an additional 25% above the RDA of iron for the diet of women in the DFS group and the regular diet without DFS contributed 90% of the RDA (27 mg of iron; RDA is 30 mg/d)). The study participants from both the groups had a significant increase in iron intake from midpoint to endline. Sharing lunch at the tea garden was a common social phenomenon, which could have contributed to the increase in iron intake in the control group as an outcome of contamination. Zinc intake was significantly greater in Adivasi community due to their higher amount of rice consumed though it is very poor source of zinc. Wheat breads, chapatti, gila, roti, and biscuits were better sources of zinc. Mean vitamin C intake was above the RDA in both the treatment and ethnicity groups. At baseline the intake was less compared to other time points. The frequency of intake of vitamin C rich greens like mustard greens was less in the baseline compared to midpoint and the varieties of other wild greens were more at endline compared to baseline. Mustard greens are seasonal and are commonly consumed in winter months January to early March.

At baseline, the mean vitamin A intake in the control (IS) group was below the Indian RDA. The vitamin A intake depended on the season with a decrease in the intake at midpoint during winter and an increase in the summer during endline data collection. There was consumption of vitamin A rich mustard and spinach which were grown in winter, but there were more varieties of GLV in the summer. The main sources of vitamin A in the form of β-carotene are GLV, cooked tomatoes, dairy and shared foods at lunch. This is because during sharing, vegetables and GLV 140

were the main foods that were shared. Both the treatment groups were below the RDA for vitamin A. In both calcium and vitamin A intakes, there was a significant decrease in the intakes at midpoint and improved significantly at endline, possibly due to seasonal effects.

The present study had some limitations. In this study, only 23% of the participants were clinically iron deficient (Ferritin <12µg/L) and 45% who were iron depleted (Ferritin <20 µg/L)

(Haas et al., 2014) and would be expected to respond to DFS. The prevalence of B12 and folate deficiencies in this population was 38% and 85%, respectively (Haas et al., 2014). Supplementation of folic acid is also involved in improvement of appetite in children (Hatamizadeh et al., 2007; Kanani and Poojara, 2000). Improvement in the food intake was also seen in patients with tropical sprue when they were provided with folic acid and B12 supplements (Westergaard, 2004; Klipstein and Corcino, 1977). The lack of effect of DFS on food and energy intakes may be due to the continued existence of B12 and folate deficiencies in these women. High intake of iron and vitamin C was also observed in the control group. This could be attributed to the increased intake of GLV, citrus, and mangoes intake in the summer season. In spite of high intakes of dietary iron, vitamin C, and regular deworming, the anemia rates were high in our study population. This could due to the increased intake of non-heme iron sources, accounting for more than 90% of iron with poor bioavailability and increased intake of polyphenols, tannins from frequent black tea consumption. An underpowered sample could have contributed to the lack of treatment effect on energy intake. Post-hoc power calculation for energy intake with an effect size of d=0.14, α=0.05, and a sample size of 212 participants, showed that the study was underpowered to detect significant differences between the treatment and control groups (1-β = 0.18). An additional of 39 subjects would have been required to detect significant differences (approximately 310 Kcal difference) on energy intake between the treatment and control groups (total n=264 with 132 in each study arm) (Faul et al., 2007).

Another limitation of the study was related to the lack of sensitive data collection tools. Twenty- four hour dietary recall can result in intra-individual variability due to dietary under- and over- reporting. However, in a rural setting where the availability of market foods is limited and there is poor diet diversity, there may be little chance to increase their consumption (Devadass et al., 141

1996). The third limitation of the study was related to appetite measurement which can be difficult to quantify (Lawless et al., 1994). Measuring food intake is one approach and in this study we were able to measure the lunch intake directly at three time points. An assessment of the appetite with a scoring system or questionnaire would have provided a subjective measure of appetite by the tea plantation workers. Anecdotally, our participants reported they were hungrier because of the study salt, especially the Adivasi women. The study was also limited by the high drop out rate in the Nepali population (15%) and a resistance to cooperate during the dietary data collection.

In summary, the intervention was successful in increasing the dietary iron but not energy and nutrient intakes in women who consumed DFS. The intervention did not improve the anthropometric indicators of the tea plantation workers. An interesting secondary outcome of the study was the consumption differences that were observed between the ethnicities within the same region. This study is the first to report longitudinal observation of food and nutrient intakes in adult women who received DFS. These findings prove that dual fortification is an effective strategy to improve the iron intakes of the rural population but do not change the dietary intake of nutrients from home diet.

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Yip R. Iron deficiency: contemporary scientific issues and international programmatic approaches. The Journal of Nutrition. 1994; 124: S1479-S1490.

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Table 5.1. Baseline characteristics based on treatment groups1

Variable IS2 DFS2 p-value

Mean SD Mean SD

Anthropometry (n=125) (n=123)

MUAC (cm) 24.0 2.7 24.0 2.7 0.88

BMI (kg/m2) 19.6 2.9 19.8 3.1 0.79

Weight (kg) 44.4 7.5 44.9 7.9 0.56 Height (cm) 149.9 5.1 150.6 5.5 0.34

Nutrient intake (per day) (n=112) (n=113)

Total intake (g) 1843.8 533.0 1883.8 606.3 0.59 Energy (kcal) 2502 770.1 2615 837.2 0.29 Protein (g) 59.1 25.3 59.7 19.9 0.83 Fat (g)2 30.3 23.7 33.1 17.8 0.30

Carbohydrates (g) 499.4 156.3 529.3 175.1 0.34

Calcium(mg)2 517.7 476.0 576.5 323.9 0.003

Iron (mg) 24.7 8.0 27.4 13.04 0.06

Zinc (mg) 10.8 3.9 11.1 3.6 0.61

Vitamin A (µg RAE)2 575.4 355.8 723.9 572.3 0.015

Vitamin C (mg)2 90.9 49.3 101.7 58.0 0.17 1Data analyzed using one way ANOVA and Wilcoxon-Mann-Whitney test when appropriate 2IS- Iodized salt or control; 2DFS- Double fortified salt Recommended dietary allowances for Indian women (heavy work) Energy= 2925 kcal/d; Protein=50 g/d; Fat=20g/d; CA=400 mg/d; FE=30mg/d; VITA=600µg Retinol/d; VITC= 40 mg/d (Indian Council of Medical Research, 2008; Gopalan et al., 2009).

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Table 5.2. Anthropometric measures at three time points (Stages) 1

2 Time points Adivasi Nepali p-interaction

(n=128) (n=120) MUAC (cm) Baseline 22.9a ± 0.3 25.0bc ± 0.3 0.014

ab bc Midpoint 23.3 ± 0.3 25.6 ± 0.3 Endline 24.4bc ± 0.3 26.1ac ± 0.3

2 a ba BMI (kg/m ) Baseline 18.2 ± 0.3 21.0 ± 0.3 0.18

b c Midpoint 18.6 ± 0.3 21.5 ± 0.3 Endline 18.7b ± 0.3 21.7c ± 0.3

a b Weight (kg) Baseline 41.8 ± 0.9 47.0 ± 0.8 0.07

ab c Midpoint 42.4 ± 0.9 48.4 ± 0.8

ab c Endline 42.9 ± 0.9 49.0 ± 0.8

1Data are presented as estimate ± SEM; 2p interaction= stage-by-ethnicity Values in a row with different superscript letters are significantly different for groups, p <0.05

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Table 5.3. Baseline food frequency characteristics, by treatment1

Variable IS2 DFS2 p-value3

Food groups (number of (n=118) (n=110) times per week)

Cereals 23.7 4.4 24.5 6.6 0.26 Pulses 13.5 7.0 13.5 10.7 0.17 GLV 3.0 (2.0, 6.0) 3.0 (2.0, 5.0) 0.99 Vegetables 18.7 11.5 17.4 10.7 0.38 Roots and tubers 44.0 20.4 43.9 20.0 0.93

Citrus 1.0 (0.0, 2.0) 1.0 (0.0, 2.0) 0.71

Fruits 1.0 (0.0, 3.0) 1.0 (0.0, 3.0) 0.49

Dairy 2.0 (0.0, 8.0) 2.0 (0.0, 14.0) 0.52

Meat 2.0 (1.0, 3.0) 2.0 (1.0, 4.0) 0.96

Seafood 0.0 (0.0, 1.0) 0.0 (0.0, 1.0) 0.88

Sugars 11.3 5.6 12.0 5.9 0.25

Fats 17.6 4.5 16.8 4.3 0.24 Beverages4 10.9 6.3 11.0 5.5 0.73

Alcohol 0.0 (0.0, 1.0) 0.0 (0.0, 1.0) 0.23

Tobacco 0.0 (0.0, 14.0) 0.0 (0.0, 1.0) 0.68

DDS 11.2 1.7 11.4 1.6 0.50

1 Data expressed as mean, SD and median (lower quartile; upper quartile) 2 IS- Iodized salt or control; DFS- Double fortified salt 3 Data analyzed using one-way ANOVA and Wilcoxon-Mann-Whitney test when appropriate 4 Beverages (mainly black tea, milk tea, coffee, soda)

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Table 5.4. Frequency of intake of food groups at three time points- Ethnicity*Stage interaction1

Timepoint Adivasi Nepali p-interaction2 Vegetables Baseline 14.2a ± 0.9 22.7bc ± 1.0 0.002 Midpoint 22.5b ± 0.7 25.4bc ± 0.8

Endline 16.2ad ± 1.0 20.4bd ± 1.1 Roots and Tubers Baseline 42.5a ± 1.8 45.3a ± 1.9 0.002 b c Midpoint 53.6 ± 1.7 66.9 ± 1.9 Endline 51.3b ± 1.7 55.8b ± 2.1 Meat Baseline 1.9a ± 0.2 3.4c ± 0.2 <0.0001 Midpoint 2.6b ± 0.2 2.7abc ± 0.2 Endline 2.4b ± 0.2 2.2b ± 0.2 Seafood Baseline 0.7 a ± 0.1 0.6a ± 0.1 0.004 Midpoint 0.7 a ± 0.1 1.4b ± 0.2

Endline 0.9 a ± 0.1 0.7a ± 0.1

Fats Baseline 15.9a ± 0.4 18.9 b ± 0.4 0.022 Midpoint 18.6 b ± 0.3 19.7 b ± 0.3 Endline 17.0 a ± 0.4 18.3 b ± 0.5

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Alcohol Baseline 0.4 a ± 0.1 1.4 ab ± 0.1 Midpoint 0.8 a ± 0.1 1.0 a ± 0.1 0.001

a a Endline 1.0 ± 0.2 0.9 ± 0.2

1Data presented as LSM ± SEM; 2 Ethnicity-by-stage interaction; 3Values in a row with different superscript letters are significantly different for groups, p <0.05

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Table 5.5. Baseline, midpoint and endline nutrient intakes of IS and DFS group1 Total Energy Protein Carbohydrates Calcium Iron Zinc Vitamin A Vitamin C Fat (g) intake (g) (kcal) (g) (g) (mg) (mg) (mg) (µg RAE) (mg) IS3 N=112 1843.8 2502 59.0 30.3 576.7 517.7 24.7 10.8 575.4 90.9 ± ± ± ± ± ± ± ± ± ± 585.4 770.1 25.3 23.7 174.0 476.0 8.0 3.9 355.8 49.3

DFS3 N=113 1883.8 2615 59.7 33.1 520.3 576.6 27.4 11.1 723.9 101.7 ± ± ± ± ± ± ± ± ± ±

Baseline 606.3 837.2 19.9 17.8 175.1 323.9 13.0 3.6 572.3 58.0

p-value2 0.59 0.29 0.84 0.30 0.35 0.28 0.06 0.61 0.02 0.13 IS3 N=114 1861.7 2552 59.3 31.1 509.3 502.5 18.1 10.9 424.6 125.8 ± ± ± ± ± ± ± ± ± ± 612.6 758.2 18.6 17.5 175.0 509.6 14.6 3.4 445.0 105.8

DFS N=98 17.96.1 2416 55.9 28.4 484.8 413.6 25.4 10.6 351.2 115.5 ± ± ± ± ± ± ± ± ± ± 487.3 653.1 16.6 15.8 143.5 281.3 14.3 2.9 349.2 66.4

Midpoint p-value2 0.39 0.16 0.16 0.21 0.27 0.24 0.0004 0.46 0.32 0.87 IS3 N=112 1902.3 2617 61.9 30.6 679.9 521.8 27.6 11.5 759.8 134.3 ± ± ± ± ± ± ± ± ± ± 586.4 786.3 29.1 16.3 1320.3 163.9 20.9 6.7 1039.1 209.7

3 DFS N=99 1874.8 2514 56.5 27.7 509.4 481.6 34.7 10.6 716.2 109.7 ± ± ± ± ± ± ± ± ± ± 449.5 594.5 16.5 11.4 131.4 243.6 16.9 2.8 497.7 59.8 Endline p-value2 0.70 0.29 0.09 0.13 0.54 0.14 0.007 0.22 0.29 0.49 1Data are presented as means (unadjusted) ± SD; 2Data for this table were tested for significance using one-way ANOVA and Wilcoxon-Mann- Whitney non-parametric test; 3IS = Iodized salt or control and DFS = Double fortified salt

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Table 5.6. Adjusted least square means for nutrient intakes per day at three time points, using repeated measures, mixed effect models 1 Vitamin Total Energy Protein Carbohydrates Calcium Iron Zinc Vitamin Fat (g) A (µg intake (g) (kcal) (g) (g) (mg) (mg) (mg) C (mg) RAE)

IS2 N=112 1699.2a 2335a 55.0 a 30.2 a 462.6 a 476.2 a 24.6 a 10.2 a 547.9ac 89.2 a ± ± ± ± ± ± ± ± ± ± 57.0 84.2 2.5 2.2 16.9 48.8 1.3 0.4 53.6 6.8

DFS2 N=113 1881.1 a 2592a 59.6 a 33.6 a 515.0 a 581.0 a 27.2 a 11.0 a 726.3 ac 100.8 a Baseline ± ± ± ± ± ± ± ± ± ± 57.8 85.6 2.5 2.2 17.1 49.3 1.3 0.4 54.2 6.8

IS2 N=114 1748.7 a 2423 a 56.6 a 30.9 a 480.9 a 471.3 ab 18.3 b 10.5 a 408.2 ad 125.4 b ± ± ± ± ± ± ± ± ± ± 57.6 77.6 2.0 1.9 16.7 49.6 1.5 0.4 47.3 9.6

DFS2 N=98 1798.7 a 2405 a 56.5 a 29.2 a 480.6 a 433.2 b 24.9 ab 10.6 a 369.0 bd 114.8 a

Midpoint ± ± ± ± ± ± ± ± ± ± 61.4 83.1 2.2 2.0 17.8 52.6 1.6 0.4 51.1 10.2

IS2 N=112 1786.9 a 2487 a 58.7 a 30.1 a 494.7 a 633.2 ab 27.3abc 10.9 a 728.3 c 130.8 ab ± ± ± ± ± ± ± ± ± ±

54.5 75.7 2.6 1.7 15.6 100.4 1.9 0.5 85.2 16.1

DFS2 N=99 1863.6 a 2488 a 56.0 a 28.1 a 502.3 a 487.9 ab 34.4 c 10.5 a 710.3 ac 107.8 a Endline ± ± ± ± ± ± ± ± ± ± 58.1 81.9 2.8 1.9 16.7 106.4 2.1 0.6 90.8 17.0

1Data presented as LSM ± SEM; 2IS = Iodized salt and DFS = Double fortified salt. Values in a row with different superscript letters are significantly different for groups, p <0.05; Variables in the model: Treatment, time, treatment-by-time, ethinicity, ethnicity-by-time, strata 157

Figure 5.1. Energy intake per day (kcal), using repeated measures analysis at three time points (P=treatment-by-stage interaction)

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Figure 5.2. Vitamin A intake per day (µg RAE), using repeated measures at three time points (P=treatment-by-stage interaction)

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Figure 5.3. Calcium intake per day (mg) using, repeated measures at three time points (P=treatment-by-stage interaction)

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Table 5.7. Top five contributors of energy, calcium, iron and vitamin A in women from Panighata village, India

Average intake per day1

Energy (Kcal) Calcium (mg) Iron (mg) Vitamin A ( µg RAE)

Cereals Parboiled rice- 797.2 Whole wheat flour-92.0 Whole wheat flour- 9.5 Samosa- 60.0 2Khouri -739.7 White rice- 79.7 Roasted corn- 5.8 Rusk-50.4 White rice-667.2 3Momos -49.7 2Khouri - 4.8 Semolina- 34.2 Roasted corn-659.3 Roasted corn-47.4 5Chewra-4.8 White bread- 9.5 Whole wheat flour-654.7 4 Chapati-27.2 Chowmein- 4.4 6 Puri- 4.6

Pulses Roasted chickpeas-158.5 Field beans green, Simi - 61.19 Chickpeas, roasted- 4.0 9 Kinema-8.4 Spicy dal mix-129.8 Kinema-30.5 Spicy dal mix- 2.8 Field beans green, Simi-3.8 7Katchori-101.8 Chickpeas, boiled- 28.6 9Kinema- 1.1 Green beans, Bodi-2.9 8 Pakoras-88.1 Chickpeas, roasted- 24.9 10 Nutrela- 0.8 Spicy dal mix- 2.7 Boiled chickpeas-56.7 10 Nutrela- 21.2 7 Katchori-0.8 Chickpeas, boiled-2.1

Green leafy vegetable 11 Gundruk achar- 149.5 Sweetpotato greens-570.0 12Gooma greens- 44.5 Drumstick greens-882.4 Sweetpotato greens- 99.7 11 Gundruk achar- 515.3 15Bhaji greens- 28.9 15Bhaji greens- 803.3 12Gooma greens- 59.9 12Gooma greens- 391.9 11 Gundruk achar- 16.2 14Junglee greens- 770.4 16Tea flowers- 55.5 13Gurbo greens- 371.3 Sweetpotato greens- 15.8 13Gurbo greens-669.6 17Borja flowers-54.3 17Borja flowers- 336.2 13Gurbo greens- 13.8 Drumstick greens, dried- 630.4

Vegetables Bitter gourd, dried- 133.6 Pear squash, Chayote, sautéed- Bitter gourd, dried- 6.8 Pumpkin sautéed- 104.2 Cauliflower, sautéed- 16.6 70.0 Spiny bitter gourd, Chatela- Tomato, sautéed- 85.9 Pumpkin, sautéed- 15.7 Field beans, sautéed- 50.9 0.8 Tomato, raw- 25.4 Bamboo shoot, boiled and Tomato, sautéed- 30.5 Cauliflower, sautéed- 0.7 Cabbage, sautéed-3.9 sautéed- 15.6 Cabbage, sautéed- 20.3 Gourd dish, Potol- 0.6 Field beans, sautéed- 3.1 Cabbage, sautéed- 14.0 Cauliflower, sautéed- 18.2 Wild mushroom, sautéed- 0.5

Roots and Tubers 18 Simbal tarul- 212.7 18 Simbal tarul- 67.8 Onion stalks- 2.4 Carrots, sautéed- 362.2 Potatoes, sautéed- 139.9 Radish, dried- 66.0 18 Simbal tarul- 1.2 Carrots, raw- 197.1 19 Ghar tarul- 99.7 Carrots, sautéed- 53.5 19 Ghar tarul- 0.9 19 Ghar tharul- 21.7 20 Kochu- 91.0 20Small kochu-37.5 Radish, dried- 0.7 Onion stalks - 14.0 Potatoes boiled- 58.7 19 Ghar tarul-24.8 Potatoes, sautéed – 0.7 Potatoes sautéed- 2.8

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Fats Roasted peanuts- 470.3 Roasted peanuts- 63.5 Roasted peanuts- 2.5 21 Ghee- 33.6 Cashewnuts, raw- 62.6 Sesame seeds sautéed- 58.4 22 Filengey- 1.3 23 Dalda- 17.3 21 Ghee- 50.4 Sesame seed paste, raw- 49.7 Cashewnuts- 0.6 22 Filengey- 0.9 Refined oil- 37.5 22 Filengey- 6.6 Sesame seeds sautéed- 0.4 Cashewnuts- 0.5 Mustard oil- 35.0 Cashewnuts, raw- 5.3 Sesame seed paste, raw- 0.3 Pistashio nuts- 0.3

Dairy Yoghurt- 118.9 Yoghurt- 295.5 Yoghurt- 0.4 Milk powder, whole- 67.5 Milk powder, whole- 79.8 Milk powder, whole- 152.9 24 Paneer- 0.3 Yoghurt- 61.5 24 Paneer- 53.8 24 Paneer- 122.0 Cow’s milk, whole- 0.2 Cow’s milk, whole- 35.9 Cow’s milk, whole- 46.9 Cow’s milk, whole- 110.8 Milk powder, whole- 0.08 24Paneer- 12.7 25 Churpi- 14.6 Milk cream- 32.5 25 Churpi- 0.04 25 Churpi-6.2

Meat Chicken maggi- 667.6 Chicken maggi- 467.6 Chicken magi- 2.5 Eggs, hen, omelette- 158.8 Beef curry- 127.7 Mutton curry- 87.3 Dried Buffalo sautéed- 1.7 Duck eggs, boiled- 143.0 Mutton curry- 112.8 Duck eggs, boiled- 24.7 Mutton curry- 1.5 Hen eggs, boiled- 125.2 Dried Buffalo sautéed- 90.4 Eggs, hen, omelette- 22.7 Beef intestine- 1.3 Beef intestine- 14.5 Beef intestine- 89.6 Pork curry- 18.3 Pork curry- 1.3 Beef curry- 127.7

Seafoods 26Rohu fish, fried- 61.9 26Rohu fish, fried- 414.4 26Rohu fish, fried- 0.6 26Rohu fish, dried- 14.6 27 Budna fish- 55.0 27 Budna fish- 368.8 27 Budna fish- 0.6 27 Budna fish- 12.9 26Rohu fish, sautéed – 50.6 26Rohu fish, sautéed- 338.8 26Rohu fish, sautéed- 0.5 26Rohu fish, sautéed- 11.9 Dried fish, Sardine- 29.3 29Cidra, dried- 128.5 30Sukti maachi, dried- 0.5 Dried fish, Sardine- 3.9 28 Katla- 26.6 28 Katla- 127.2 29Cidra, dried- 0.3 28 Katla- 1.8

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Alcoholic Rum- 329.6 31 Kodho jaar- 212.8 31 Kodho jaar- 18.2 beverages 31 Kodho jaar- 295.0 Millet/rice wine- 8.5 32 Bhaati jaar- 3.1 --- Millet/rice wine - 230.0 Rum- 5.6 Local wine- 1.9 32 Bhaati jaar- 159.1 32 Bhaati jaar- 4.9 Millet/rice wine- 0.2 Local wine- 99.6 Local wine- 3.10 Rum- 0.2

Traditional sweets 33 Kheer- 302.0 33 Kheer- 133.2 34 Selroti- 0.6 36Gulab jamoon- 71.4 34 Selroti- 282.0 36 Gulab jamoon- 121.3 38Boondi laddu- 0.5 33 Kheer- 56.3 35 Rosgolla- 223.8 35 Rosgolla- 76.8 Cake- 0.4 35 Rosgolla- 34.2 36 Gulab jamoon- 173.5 37Malpoa- 50.3 Cream biscuit- 0.4 Cream roll- 33.7 Honey- 168.5 Cream biscuit- 35.7 Biscuit, sweet and salt- 0.3 37 Malpoa- 21.9

Fruits Apples- 116.8 Papaya, green- 47.5 Jackfruit, unripe- 2.0 Mango, ripe- 272.2 Mango ripe- 89.5 Jackfruit, unripe- 36.2 Mango, ripe- 1.6 Mango, pickled, spicy- 40.6 Coconut- 80.0 Apples- 19.8 Papaya, green- 1.6 Mango, pickled, sweet- 21.6 Pear-76.9 39 Masumbi-18.9 Apples- 1.3 Guava- 8.3 Banana- 67.2 40 Kharbuja- 17.6 Pear- 0.8 39Kharbuja- 7.7

Shared 113.7 163.5 13.9 400.0 foods**

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1 Food intake measured by weighed lunch intake plus 24-hr recall; the numbers shown here are the average intakes at three time points of the study: E.g., Parboiled rice- average intake of parboiled rice at three time points contributed 797 kcal per day 2 Pasta dish made with white flour; 3 Vegetarian Tibetan dumplings; 4 Flat wheat bread; 5 Rice flakes; 6 Deep fried bread; 7 Deep fried snacks with cereals and lentils; 8 Deep friend chickpea flour snacks; 9 Fermented soy; 10 Soychunks 11 Dried radish leaves, pickled; 12-15 Wild greens; 16 Flowers from tea bush; 17 Wild flowers (For greens and flowers not present in the Indian, Nepalese, and USDA database: The Latin names were identified and the values were imputed from the closest member of the Family/Genus of the greens and flowers) 18 Sweet Yam; 19 White yam; 20 Colacasia 21 Nigella seeds 23 Hydrogenated vegetable fat 24 Fresh cheese; 25 Dried Yak cheese 26 Indian carp; 27Mullet; 28 Bengal carp; 29Olive barb; 30Bombay duck 31 Millet beer 32 Rice beer 33 Rice pudding ; 34Deep fried sweet rice sweet, traditional Nepali sweet; 35-37Milk based sweets; 38Sweet made with sugar syrup and chickpea flour 39 Sweet lime; 40 Musk melon ** Foods shared with other women in groups during lunch. Usually the side dish of the main meal, consists of a blend of vegetables and greens

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Bridge statement 3

Manuscript 1 addressed the role of socioeconomic determinants on the BMI and MUAC in adult women from Panighata village. The prevalence of underweight and chronic energy deficiency was high amongst the women in this population. Manuscript 2, through a randomized double- blinded clinical trial, examined the role of double fortified salt in improving the dietary intakes of Adivasi and Nepali women compared to the iodized salt control. Nutrient intakes were calculated by measuring the dietary intakes. Lunch intake was measured directly in the tea garden during which women shared their lunch with each other, a common social practice in this community. The analysis of Manuscript 3 examined the amount of iron that was redistributed between treatment and control groups due to treatment contamination from lunch sharing. In addition, we examined the effect of food sharing on iron intakes at lunch.

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Chapter 6: Manuscript 3

Shared lunch intake: implications of food sharing in a double-fortified salt intervention trial

Sudha Venkatramanan, Grace S Marquis, and Jere D Haas

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6.1. Abstract

Background: Treatment contamination can be a challenge in randomized control trials. Sharing foods during lunch is a common social practice in the tea garden culture and it is has significant role in creating a communal bond. When conducting a randomized control feeding trial in such a community where food sharing is a common cultural practice, treatment contamination is often unavoidable. The objective of this analysis was to assess the amount of treatment that was redistributed between treatment and control groups due to contamination in a double fortified salt trial. In addition, we examined the effect of food sharing on energy and iron intakes at lunch. Methods: A study on double fortified salt (DFS) containing iron and iodine to improve iron status of female tea plantation workers in West Bengal, India collected information on dietary intakes at three time points. The double masked study randomized 245 women to either DFS or iodized salt (IS-control) for 10 mo. Information on the amount of food shared between the women in two different ethnic groups and total amount consumed was measured by weighing the lunch intake of individual subjects. The women were 18-55 year old, Adivasi or Nepali, not pregnant or lactating and full time experienced tea pickers. The endline lunch intake data from 206 women was used to estimate the amount of treatment received from sharing and the effect of treatment on the energy and nutrients intake at lunch. Results: Food sharing resulted in both the control and treatment groups benefitting from DFS. Relative to expectation, the proportion of DFS treatment received by the IS and DFS groups are 40% and 54%, respectively. Average shared foods received from and given away to others were 91.5 g and 93.6 g, respectively. Lunch intake contributed to 37.5% of the total food intake per day. Food sharing during plucking days contributed 1.1 and 1.5 mg iron/meal in the IS and DFS groups, respectively. The amount of treatment received from sharing did not have a significant effect on the total food and energy intake at lunch. Considering that food sharing occurred during 215 days of intervention days we estimate that the total iron intake from DFS during the midday meal over the entire study period (289 days) was 241 mg in the IS control group and 510 mg in the DFS group. This resulted in an additional 0.93 mg per day of iron consumed by the DFS over the IS group during lunch.

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Conclusion: Food sharing is a very common practice amongst the workers in the tea plantation. The results showed that sharing lunches during plucking season led to both the groups benefitting from the DFS treatment, reducing the difference in iron intake between the two groups.

Keywords: Food sharing, Spill-over, Treatment contamination, RCT, India, Adivasi and Nepali

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6.2. Introduction

Iron deficiency (ID) and iron deficiency anemia (IDA) are significant public health concerns in India and among women in their reproductive age they are linked to reduced appetite, low energy levels, and reduced productivity (Thankachan et al., 2007; Galloway et al., 2002; Edgerton et al., 1979). The prevalence of IDA amongst Indian women 15-50 y of age is estimated to be 51% (WHO, 2008), and in West Bengal 62% of women in the reproductive age suffer from anemia (National Family Health Survey NFHS-3, 2007). Double fortification of salt (DFS) with iron and iodine is an effective strategy to address iron deficiency in the affected rural population (Haas et al., 2014; Yuan et al., 2008; Andersson et al., 2008).

The randomized controlled trial (RCT) is considered to be the gold standard study design to test for efficacy and eliminates selection bias due to random allocation of the participants to the treatments (Hahn et al., 2005). However, treatment contamination or spill-over can occur when study participants from the intervention are exposed to the control group (Hahn et al 2005; Sussman and Hayward, 2009). Treatment contamination in RCT has been reported to reduce the difference in exposure to treatment and control and resulting impact of the intervention between two study arms (Sussman and Hayward, 2009; Kouyate et al., 2008). One such intervention looked at the effectiveness of a community-based nutrition education program to reduce maternal and neonatal mortality was conducted in Balochistan, Pakistan. The women in the intervention arm shared the information about nutrition education with the control arm participants which resulted in treatment contamination. However, in spite of the contamination effect, the overall intervention was still successful in reducing the perinatal mortality significantly (the contamination had no effect on the outcome). In this study, the proportion of contamination was not quantified (Midhet and Becker, 2010). In another home based exercise intervention for colorectal cancer survivors, members of the control arm were also motivated to follow an exercise regime similar to the intervention and as a result there was treatment contamination of 52% in the control group. The intention-to-treat analysis did not reveal any significant changes in the home based exercise regime in the cancer survivors probably due to the contamination factor (Courneya et al., 2002). In a randomized controlled feeding trial, sharing foods between the two study groups can also alter the effectiveness of the intervention on the treatment arm (Cuzick et

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al., 1997). The above studies recognized the contamination factor, however, information on the quantification of the contaminant is limited. In this context the present study looked at systematically quantifing the contaminant in the DFS intervention trial. Sharing foods with friends or neighbors has a social and a cultural significance in many societies (Quandt et al., 1998) and is an important practice to maintain a community bond (Collings et al., 1998; Koster, 2011; Blurton Jones, 1984). Much of the literature on shared foods has observed changes in the dietary intake when foods were shared from the same bowl or plate in an intra-household setting (Shankar et al. 1998; Beckerleg, 1995; Wheeler, 1991; Abdullah and Wheeler, 1985). Food sharing can nutritionally benefit the receivers and most of the research conducted on food sharing has been based on ethnographic case studies (Quandt et al., 1998). The main objectives of the present analyses was to measure the proportion of iron that was shared between the treatment and control arms of a DFS trial and to study the effect of food sharing on iron and energy intake at lunch of female tea plantation workers in the Panighata tea garden, West Bengal, India.

6.3. Methods

6.3.1. Research setting The DFS intervention study took place in Panighata tea estate in the Darjeeling district, West Bengal, India. The intervention lasted 10 months. The study participants were from Adivasi and Nepali ethnic groups, 18-55 y, non-pregnant, non-lactating, and full time tea pickers. Institutional review boards of McGill University and Child in Need Institute (CINI) approved the study. According to the hemoglobin (Hb) values and serum ferritin levels at baseline, the 245 study participants were stratified into five groups [iron deficient anemic (IDA), iron deficient non-anemic (IDNA), anemic without iron deficiency (ANID), normal (NOR) and high ferritin (HFR)] and then randomized into one of the four color coded salt bags, either iodized control salt (IS) or DFS groups.

6.3.2. Dietary data collection- Lunch intake and 24 hr recall Weighed food intake and dietary recall were conducted at three time points, baseline (August- September 2009), midpoint (January-March 2010) and endline (June-August 2010) of the study period; only endline data are being used for this analysis, as we were able to identify the amount

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of food shared between the women and record their respective color codes. Information on the amount, type of food, and the complete recipe was recorded. Food for lunch was weighed directly. The amount of food shared and not consumed by individual subjects was measured in grams during lunch break at the garden. A dietary recall of breakfast, snacks and dinner was recorded to complete the 24 hour intake of foods (reported elsewhere). The households were visited in the evening after the women returned home to measure the raw ingredients and to check the serving sizes of ladles, cups, and spoons. The nutrient intakes of the food items were calculated using Nutritive Value of Indian foods (NVIF) (Gopalan et al., 2009), Nepali foods database (Nutrient Contents in Nepalese Foods, 1994) and USDA nutrient database (USDA, 2010).

6.3.3. Shared lunch intake Sharing lunch was a common phenomenon during the tea plucking season (May-November); during the non-plucking or low work season, lunch was consumed at their households and food was not shared among the different households. Women shared foods only within their ethnic groups [Adivasi- indigenous women from India and Nepali- several generation migrants from Nepal]. The lunch consisted of a dish of rice, sometimes lentils (usually not shared), and a dish of a vegetable stew. The weight of the vegetable dish that was brought by the women was weighed before and after sharing. During sharing, the number of tablespoons of food given to others was recorded. The total amount (g) of shared foods received from other members was also weighed. Since each social group had women from the treatment and control groups, the chance of sharing foods prepared with DFS were high. To address this, during endline data collection, each member of the group and their respective salt codes were recorded. If a group consisted of women not part of the research study, the salt intake information was also recorded. These women consumed regular iodized salt similar to the control group.

6.3.4. Statistical analyses The number of women in the DFS or IS within a social group, as well as non-participants were recorded. The proportion of the shared food that had DFS that was received by each woman was calculated and ranged from 0 (no DFS in shared foods) to 1(only received meals made with DFS). The total proportion of treatment obtained from sharing meals by each woman in the group was calculated by arithmetic mean. SAS version 9.2 (SAS Inc, 2010, Cary, NC) was used

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for the data analysis. Descriptive statistics were carried out for the outcome variables of interest, iron and energy intake from the endline data at lunch. The residuals of each outcome variable of interest were tested for normality. The treatment effect received from sharing was the independent variable. The covariates, ethnicity (Adivasi and Nepali) and strata effects (IDA, IDNA, ANID, NOR, HFR) were accounted for in the analyses. PROC GLM was used for multiple linear regression to determine association between the dependent and independent variables. Least square means were generated for the categorical variables and regression coefficients for the continuous variable. Significance was set at a p-value < 0.05.

6.5. Results Descriptive characteristics of nutrient intakes at lunch are presented in Table 6.1. No significant differences in the nutrient intakes from lunch were observed between the two treatment groups. The social lunch groups were common for both Adivasi and Nepali ethnic groups. Each ethnicity ate separately and did not mingle with each other. Food sharing between the women within the social groups resulted in both the groups benefitting from the treatment. The proportion of treatment received by the IS and DFS groups were 40% and 53 %, respectively (Figure 6.1). The proportion of treatment received within Adivasi group was 41% and 48% and within Nepali group was 39% and 59% (Figure 6.2).

The rice dish was not shared among the women. Only vegetable dishes and sometimes fruits were shared. The amount of foods given and received from others was almost of the same quantity. Average shared foods received from other members and given away to others were 91.5 g and 93.6 g, respectively. Women ate together in their social groups comprising of 5-13 women in a group. Approximately a teaspoon of vegetable was shared with every other member of the group.

Lunch total: Lunch alone contributed to 37.5% of total food intake (g) and was the main meal of the day (Figure 6.3) and contributed 1072 kcal to the total daily energy intake. Multiple regression results (Table 6.2) showed no significant effect of the proportion of treatment on iron intake. The parameter estimates revealed that for every unit increase in the iron intake, 1.8

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proportion increase in the treatment was predicted. No significant treatment-by-ethnicity effect was observed for iron intake (Table 6.2).

Shared lunch received from others: Analysis of covariance for lunch received from others showed a significant association of ethnicity on total food intake, energy and iron intakes (Table 6.3). The mean Nepali consumption was 33 g more of shared foods than Adivasi (p <0.0001). Similar results were observed for energy and iron intake. Least square means for total lunch intake [rice, vegetables and shared foods] showed that mean Adivasi consumption was significantly greater than Nepali (p <0.0001) [data not shown]. The analysis of cereal (rice, semolina and bread) intake alone showed that Adivasi consumed significantly greater amount of rice than Nepali which was the major contributor of energy (p <0.001) (Table 6.4).

Iron from DFS received from sharing: The mean DFS intake of salt at endline was 7.5 g/day (contributing 7.5 mg iron/day). Salt was added only to the vegetables and stew dishes, not in the rice. Lunch intake contributed to 37.5% of the meal. No significant difference was noted between the treatment groups. The total available iron at lunch without treatment contamination was 2.82 mg. Due to food sharing, the DFS group received 1.40 mg iron/meal (53% x 2.82 mg) and control received 1.12 mg iron/meal (40% x 2.82 mg).

Total iron shared- plucking minus non-plucking seasons: Food sharing happened during the plucking seasons. Randomization started in October and the women shared lunch during the plucking seasons which were October to December 2009 and March to August 2010. Non- plucking seasons were January to Mid March 2010. Total DFS consumed from sharing lunch by the IS and DFS accounting for the plucking and non-plucking seasons are 240.8 mg and 509.7 mg respectively (Table 6.5).

Dinner alone: Following lunch, dinner was the second main meal of the day (27.9% of total energy) and provided 854.6 kcal per meal. The iron intake from DFS during dinner was 2.09 mg (27.9% x 7.5 mg).There was a significant treatment effect on dietary iron intake at dinner (p = 0.001) and least square means (mg/d) generated for the DFS and control were 8.33 ± 1.11 and

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4.98 ± 1.05, respectively (Table 6.6). Also, 24 hour intake analysis showed a significant treatment effect on dietary iron intake (p=0.006) (Table 6.7).

Breakfast and snacks alone: Breakfast and snacks contributed to 24.1% and 10.4% of the total daily intake, respectively, and no significant treatment effect was noted on food intake, iron, and energy intakes (data not shown). The contribution of iron from DFS for breakfast (24.1% x 7.5 mg) and snacks (10.4% x 7.5 mg) are 1.8 mg and 0.78 mg respectively.

6.6. Discussion The present study is unique and provides important information on DFS treatment contamination from sharing lunch in tea plantation workers. The act of sharing foods within social groups imparts a sense of belongingness and is an important social practice in the tea garden culture. However food sharing can become a threat in RCTs. The DFS treatment effect was diluted due to sharing at lunch. Despite a significant overall difference in iron intake per day between the treatment and control groups, this difference was not evident at the noon meal due to food sharing.

In an ideal situation, i.e., in the absence of sharing food at lunch, the women in the control group would have consumed only IS, with zero treatment effect and the participants in the DFS group would have received 100% of the treatment effect. However we did not observe this pattern, a range of proportion of treatment effect was observed in the control and members in the treatment group did not consume 100% of DFS. Similar to our study, treatment contamination was also observed in other community based interventions (Brair et al., 2012; Calver et al., 2005). In our study we were able to measure the extent of treatment contamination during the study period. In the community intervention study conducted in Pakistan, women in the intervention arm shared education information about safe motherhood and neonatal health with the control group. However, in spite of sharing the educational information, overall there was a significant reduction in the neonatal mortality in the intervention arms compared to the control (24.3 vs. 39.1; p <0.05) (Midhet and Becker, 2010). Measuring treatment spillover effects in individually randomized control trials can be difficult to assess as the benefits in the control group can mask the overall treatment effects (Bundy et al., 2009). Most of the studies that looked at treatment contamination in public health or nutrition studies have been with community-based nutrition

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education interventions (Bair et al., 2012; Calver et al., 2005; Macours et al., 2008; Miguel and Kremer, 2004). Treatment contamination has also been addressed in a deworming study conducted in seventy-five rural schools in western Kenya. Students from the treatment group improved in school participation reducing the total absenteeism by 25%. However, the control groups also benefitted from the albendazole treatment after transferring to the treatment schools (Miguel and Kremer, 2004). In such population based studies, some spill over is inevitable. In the current study, we were able to actually quantify the contamination.

In our study, dinner was the second main meal of the day followed by lunch. When nutrient intake data were analyzed separately for each meal, significant treatment effect (p <0.009) was observed for iron intake at dinner in the absence of sharing. The mean salt intake per day from DFS was 7.5 g, which could have provided approximately 7.5 mg iron/d in the treatment group. Due to treatment contamination during lunch, only 54% treatment associated with lunch was received by the women in the DFS group. At dinner, breakfast and snacks, 100% of iron from the DFS was consumed by the participants in the treatment group. Lunch was shared everyday during the plucking season and the loss of treatment in the DFS group was unavoidable during the plucking days. However, when the total iron shared during lunch in plucking and non plucking seasons was calculated, the overall net treatment effect received by the DFS group (510 mg) was greater than the control (241 mg) over the study period of 289 days.

Sharing meals can be a hindrance in conducting a RCT due to the contamination factor, however, food sharing can also mitigate nutritional concerns. An anecdotal example of the benefit of sharing was noted with Participant A (an Adivasi) who, due to poor living conditions, was unable to afford vegetables for lunch. The members in her social group through food sharing contributed to improving the quality of her diet. Sharing could be less beneficial for some. Participant B (an Adivasi), gave 61 g of her food but received only 5 g as shared foods from her social circle members. These scenarios were observed in a small number of cases (n=2) within the Adivasi population. However, this did not occur in the majority the study participants, these were limited case examples which do not represent the entire population. In most of the participants, similar amounts of food were given and received as an evidence of reciprocal altruism (Hames and McCabe, 2005), food sharing with mutual benefit.

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Shared foods received from the members of the group were analyzed separately to see if there were any changes in the nutrient intakes. The food intake, iron and total energy of shared foods received by Nepali women were greater than Adivasi. However, total lunch intake including rice, vegetables and shared meals were significantly greater in Adivasi due to high consumption of cereals such as rice with poor diet diversity in Adivasi women (Waite, 2007; Rao, 2003; Rao et al., 2001). Lunch consisted of cereals, usually rice and rarely semolina or chapatti29 along with a vegetable dish. Consumption of white rice was common in Nepali and Adivasi consumed parboiled rice. The vegetable dishes that were commonly consumed included greens, bitter gourd, Iskoos30, squash varieties, and seasonal vegetables. All the vegetable dishes were usually prepared with potatoes, which was a staple in the village. During summer, eating mango or lime in a group was a common practice in both the ethnic groups. Observation of cooking and eating practices of both the ethnic groups, revealed that all the meals prepared and consumed by Nepali were richer in fats than the Adivasi women. Most of the traditional Nepali foods are fermented and deep fried (Tamang and Sarkar, 1988). The social practice of food sharing can be a very beneficial practice for Adivasi women who are at a higher risk of undernutrition than Nepali women.

The present study is the first to measure treatment contamination from shared foods in a feeding trial. For successfully interpreting the results of the intervention, it is very important to address this component as it can result in misleading outcomes. Determination of the shared foods and the identification of the salt color code for every member were logistically possible for all the study participants. This enabled us to calculate the proportion of DFS treatment that was consumed by each member of the group. Because of sharing in lunch meal, women in the treatment group did not receive the complete treatment effect which reduced the differences in iron intakes between the control and treatment arms, causing treatment dilution. The presence of food sharing during lunchtime resulted in contamination between the treatment and control groups causing the control to receive a substantial amount of iron and the treatment group to receive less than was intended. This caused the effect of the treatment during lunch to be

29 Unleavened flat bread made with whole wheat flour 30 This is called Chayote or pear squash 177

diminished during the plucking season. Despite this reduction, the overall treatment effect was was evident. Our shared food analysis allowed us to correct for treatment contamination and have a more accurate measure. During dinner which was a smaller meal compared to lunch, no contamination was observed and the DFS treatment resulted in a significant difference in iron intakes between the treatment and control groups. Since the treatment was spread over all four meals of the day, the fact that contamination occurred only during lunchtime whereas not during breakfast, snacks or dinner, still allowed a significant effect to be observed for the entire day (Haas et al., 2014). This contamination effect from sharing foods could have been avoided by randomizing by social lunch groups, as clusters rather than individuals. However this would require a larger sample size compared to the individual randomization (Giraudeau and Ravaud, 2009). This study is the first systematic attempt to address treatment contamination in a feeding trial. Though sharing lunch has a strong cultural bond in the community, DFS contamination between the two study arms during plucking days was unavoidable. We sought to measure the level of iron that was shared. Despite the spill-over effect, the treatment effect was still evident for per day and dinner intakes.

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Table 6.1. Descriptive characteristics of nutrient intakes of lunch intake after sharing1

Variable IS2 DFS3 p-value

Mean SD Mean SD

Nutrient intake (per meal) (n=110) (n=96)

Total intake (g) 732.7 201.7 720.2 170.0 0.34 Energy (kcal) 1084 278.8 1059 252.5 0.25

Total iron (mg) 16.9 9.9 19.0 9.4 0.13

1Data analyzed using one-way ANOVA 2 IS- Iodized salt 3 DFS- Double fortified salt

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Figure 6.1. Mean proportion of iron received from shared lunch intake

Proportion of DFS effect received by the control group (n=111) = 0.40 ± 0.02 (means ± SE)

Proportion of DFS effect received by the DFS group (n=96) = 0.53 ± 0.02 (mean ± SE)

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Figure 6.2. Mean proportion of iron received from shared lunch intake by ethnicity

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Figure 6.3.

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Table 6.2. Multiple linear regression coefficients of the proportion of treatment effect on the total grams of intake, energy and iron intakes from lunch1

Variable Estimate2 SEM2 p-value

Total intake (g) 46.2 47.7 0.33

Energy (kcal) 84.9 67.7 0.21

Iron (mg) 1.8 2.5 0.48

1Model: n= 206. Variables included in the model: proportion of treatment, treatment groups, ethnicity, strata, treatment, treatment-by-ethnicity 2 Values are parameter estimate ± standard error of the mean (SEM)

Table 6.3. Least square means for ethnicity on the total food, energy, and iron intake from shared meals received from others1

Dependant variables Adivasi2 Nepali2 p-value3

(n=114) (n=76) Total intake (g) 78.5 ± 31.3 111.9 ± 43.6 <0.0001

Energy (kcal) 90 ± 36 128 ± 50 <0.0001

Iron (mg) 10.9 ± 4.4 15.6 ± 6.0 <0.0001 1Analyses performed after adjusting for proportion of treatment effect from sharing and strata; 2Values are least square means (LSM) ± standard error of the mean (SEM); 3 p <0.0001

Table 6.4. Least square means for ethnicity on rice intake alone from lunch1

Dependant variables Adivasi2 Nepali2 p-value3

(n=120) (n=87) Cereals- Rice*, Semolina*, 621.3 ± 11.9 449.7 ± 13.9 <0.0001 Chapati (g)

Energy (kcal) 955 ± 17.9 715 ± 20.9 <0.0001

Iron(mg)‡ 2.6 ± 0.05 1.6 ± 0.06 <0.0001

1Analyses performed after adjusting for proportion of treatment effect from sharing and strata; 2Values are least square means (LSM) ± standard error of the mean (SEM); 3 p <0.0001*Rice n=205, Semolina n=1; Chapati n=1; ‡iron from rice which was not shared

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Table 6.5. Measure of DFS consumed by the treatment and control from lunch sharing during the plucking season‡

IS (mg) DFS (mg)

Plucking Season 241 301 12th October 2009- December 2010 (215 days x 1.12 mg) (215 days x 1.4 mg) 15th March 2010- August 2010

Non-plucking days 0 209 January- February 2010 (74 days x 0) (74 days x 2.82 mg)

Total 241 510

‡Number of plucking days after salt distribution (minus Sundays) and the iron intake from DFS during lunch in the two groups The number of plucking days shown here is an estimate, as there were irregular days due to strikes and weather.

Total iron from DFS for the entire study (289 days) minus total iron from IS for the entire study (289 days) =2.48 mg/d

The average daily value of extra iron received by the DFS group was (2.48 x 37.5%) =0.93mg during lunch

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Table 6.6. Least square means for treatment on the nutrients intake from dinner1

Dependant variables IS2 DFS2 p-value3

Total intake (g) 518.6 ± 29.0 485.9 ± 30.5 0.35

Energy (kcal) 827 ± 45.2 788 ± 47.6 0.48

Total iron (mg) 4.9 ± 1.0 8.3 ± 1.1 0.009 1 Model: n= 206. Analyses performed after adjusting for covariates, ethnicity and strata 2 Values are least square means (LSM) ± standard error of the mean (SEM); 3 p <0.0001

Table 6.7. Least square means for treatment on the nutrients intake from 24 hr intake1

Dependant variables IS2 DFS2 p-value3

Total intake (g) 1827.7 ± 49.2 1801.80 ± 51.9 0.66

Energy (kcal) 2518 ± 71.2 2431 ± 75.0 0.31

Total iron (mg) 26.9 ± 2.1 34.16 ± 2.3 0.006 1 Model: n= 206. Analyses performed after adjusting for covariates, ethnicity and strata 2 Values are least square means (LSM) ± standard error of the mean (SEM); 3 p <0.0001

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Chapter 7: Summary, conclusions, and policy implications

Undernutrition is the underlying cause for mortality and morbidity in adult women from low and middle income countries (FAO, 2013). Undernutrition consists of a spectrum of maladies including underweight and the less visible micronutrient malnutrition, also known as “hidden hunger” (Ackerson et al., 2008). Iron deficiency is one of the most prevalent micronutrient deficiencies and contributes to 120,000 maternal deaths globally (Bhutta et al., 2012). Iron deficiency is a major public health concern for Indian women and children. According to the WHO, 52 % of Indian women in the reproductive age are anemic (WHO, 2008). In India the disease burden from micronutrient malnutrition is high. Four million DALYS are lost due to iron deficiency alone and 9.3 million are lost due to iron deficiency along with other micronutrient deficiencies (Stein and Qaim, 2007).

Nutritional status of an individual depends on the 1) fundamental factors such as political, economic and environmental determinants 2) underlying socioeconomic factors such as literacy, wealth, food security issues, access to health care, and 3) the immediate dietary factors such as the dietary intake of iron and bioavailability, resulting in the manifestation of anemia (UNICEF, 2013). Food fortification is a promising and successful strategy to address iron deficiency in the impoverished populations (Gera et al., 2012). In this project, we sought to improve the dietary intakes of iron through DFS intervention in women in the reproductive age from Panighata tea estate. The present thesis is part of a larger research project to improve productivity, physical activity, and cognition through the DFS intervention.

DFS intervention and energy and nutrient intakes The first specific objective (Manuscript 2) of the study was to address the immediate factors that are responsible for poor nutritional outcomes. The objective was to determine if consumption of double fortified salt would improve appetite and dietary intakes of women tea plantation workers. We found that the intervention successfully improved the iron intakes of these women. However, we did not see any improvement in

the energy and other nutrient intakes of the tea plantation workers. We also did not see any significant improvement in the anthropometric indicators with the intervention (second objective). Earlier studies conducted with iron intake, appetite, and nutritional outcomes were predominantly in children (Stoltzfus et al., 2004; Vucic et al., 2013). The strategy for addressing iron deficiency in these studies was through supplementation and was of shorter study duration (Lawless et al., 1994). Another possible reason we did not see an effect on the dietary and nutritional outcomes could be related to the overall productivity in the tea plantation. The level of tea production was less at the endline compared to the baseline season. The other reason we did not see an improvement in the dietary intakes with respect to iron treatment was possibly because the population also had a high prevalence of folate (85%) and B12 (38%) deficiencies (Haas et al., 2014) which are also related to appetite (Westergaard, 2004).

An interesting outcome of the study was the ethnic differences observed in the total food intake (third objective). Consumption of total food and energy intake was greater in the Adivasi than the Nepali. However, Adivasis consumed a monotonous diet with more cereals, especially rice with poor intake of vegetables and fats. This poor diet intake in Adivasi was reflected in their poor nutritional outcomes. Similar patterns were observed in a cross-sectional study (120 villages) across India with adolescents, the tribal diet had poor diet diversity compared to their rural counterparts (Rao et al., 2010). Nepali consumed more diverse foods than Adivasi especially with fats and dairy. Greater consumption of fats in the Nepali was probably due to the increased intake of deep fried traditional foods (Tamang, 2010).

The intervention was successful in improving the iron intake from DFS and the efficacy study confirmed that the prevalence of iron deficiency reduced significantly over time (p<0.05) from 45% to 22% in the treatment compared to the control (44% to 35%) (Haas et al., 2014). This emphasizes the need to extend the use of dually fortified salt in a broader population. The Government of India has numerous policies in place for the control of anemia through their various initiatives for children and women such as mid-

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day meals, Matri Suraksha Abhiyan31, and Indian Medical Association (IMA)’s Anemia free India32 (Bisoi et al., 2011). Weekly supplementation of iron plus folic acid to women in the reproductive age has been strongly encouraged by the government (WHO, 2011). Use of DFS as part of the mid-day meal program has already been very successful in the South of India (Swaminathan et al., 2004). According to the Prime Minister’s office, the use of DFS is required as part of the mid-day meal programs and ICDS in all parts of India (Prime Minister’s Office, 2011). Similarly, it would be prudent to leverage the existing public distribution system in India to distribute iron fortified salt to the rural population at a subsidized price.

Effect of food sharing on the DFS consumption during lunch The fourth main objective (Manuscript 3) of the dissertation was a separate cross- sectional analysis with food sharing. Lunch sharing is a common practice in the tea garden community and food sharing during lunch resulted in both the treatment and control benefitting from the iron from DFS. Food sharing can affect a randomized control trial by reducing the group difference between the two study arms. Our finding is consistent with other studies (Midhet and Becker, 2010; Bair et al., 2012; Miguel and Kremer, 2004), however, this was the first study to systematically quantify the amount of contamination in a feeding trial. We were able to determine the proportion of treatment that was shared between the two groups. Though the control group received a good proportion of treatment due to sharing during lunch, the overall treatment effect on iron intake per day was significant. Another important component of the analysis was when the number of plucking days and non-plucking days were calculated, the overall total effect of sharing on iron intake throughout the study was still greater in the DFS group. Also, when the per day intake was analyzed separately by meals, dinner which is the second main meal of the day, significant treatment effect on iron intake was observed.

31 Save motherhood programme, launched by the Prime Minister of India in 1998 32 Campaign to launch anemia free India, initiated by the Indian Medical Association in 2005 194

Socioeconomic determinants of the nutritional status The fifth objective (Manuscript 1) of the dissertation we aimed to identify the underlying determinants of the nutritional status of the rural and Adivasi Indian women. According to the national survey, the prevalence of underweight in rural and tribal populations is high in India (NFHS-3, 2007). In this study, we found a high prevalence of underweight amongst the rural tea plantation workers, and within the same tea estate region, the prevalence of underweight was greater in Adivasis than the Nepali women. Cross- sectional data from other rural regions of India (Subhasinghe et al., 2014; Das and Bose, 2012; Mittal and Srivastava, 2006) have reported findings similar to our study. In our study, unemployment and illiteracy of the spouse was negatively associated with BMI and MUAC in the women. Poor socioeconomic factors can limit the options to have access to quality and nutritious food. Illiteracy and lack of employment of the spouse can affect the total amount of income for the family, thereby affecting the ability to afford healthy foods (Sarlio-Lahteenkorva and Eero Lahelma, 1999). An analysis of the socioeconomic and nutritional status from the national survey, demonstrated that as the level of husband’s education increases, the level of poor nutritional status of the woman decreases (Ramesh, 2012). Another important outcome of this objective was the association that was observed between the lack of toilet facilities and underweight in women from Panighata. Our study is in accordance to other studies which explored the relationship between latrine facilities and BMI in women and children (Hasan et al., 2013, UNFPA, 2012; Ahmed et al., 2012). Possible mechanism that is suggested could be due to an increase risk for diarrheal diseases and infection (Khan et al., 2013; Ngure et al., 2014). Lack of basic and essential hygiene combined with poor socioeconomic conditions in several pockets of India can lead to poor nutritional outcomes. According to WHO and UNICEF, in India, 626 million people still defecate in the open field (WHO and UNICEF, 2012) which is strongly related to infections and stunting in children (Chambers and Medeazza, 2013). To address this, the Government of India introduced the Total Sanitation Campaign (TSC) in April 1999 to improve the water, sanitation, and health of the rural population by constructing latrine units for all poorest rural households (Government of India, 2007; Hueso and Bell, 2013). The effectiveness and success of TSC on sanitation and child health outcomes has been demonstrated in Maharashtra,

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Madhya Pradesh, Sikkim, and other parts of India (Water and Sanitation Program, 2011; Spears, 2012; Patil et al., 2013). Such public health policies geared towards creating awareness around importance basic hygiene and sanitation are essential components for the alleviation of undernutrition in women.

This study had a number of challenges. The first hurdle was associated with the complaints of color change in foods after cooking with the study salt. Salt distribution began in October 2009 and there were complaints of color change in January 2010 mainly with two specific dishes, split chick peas and chayote, which turned brownish after cooking. The rumors associated with color change affected the study in the midpoint and was one of the main reasons for a 15% drop out in Nepali community was observed. Another barrier was the misconception that was present in the village that the purpose of the study salt was purely for family planning reasons and some families refused to consume the salt as they thought it would make them infertile.

The mean salt intake per day in this population was 7.6 g which was higher than the World Health Organization (WHO) recommendation of 5 g/d. Salt was usually consumed with black tea instead of sugar in this population. Adivasi women but not Nepali reported that they consumed the study salt with water. Due to logistics reasons and lack of health care facilities, we were unable to measure their blood pressure (BP) regularly. Another challenge was associated with the political situation in the Panighata village. During the frequent political protests and road blocks, the tea plantation was closed and the workers were forbidden to pluck tea leaves. This situation had direct implications on the study and it was unsafe for the field staff to reach Panighata village for data collection. The present study has several key strengths. The study collected detailed dietary data of Adivasi and Nepali migrants in India. This study was the first to examine the effect of DFS on the dietary intakes of adult women. Another unique contribution of the thesis was the analysis that was conducted with shared foods and we were able to measure the amount of iron that was shared between the women from the intervention. The findings related to the socioecomic determinants on the poor nutritional outcomes and the ethnic

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differences in terms of underweight will contribute to the existing literature on undernutrition in adult women from low and middle income countries.

In addition to iron deficiency, the rural women also suffer from other micronutrient deficiencies, which are equally essential for the well-being of the adult women. When considering future interventions it is equally important to consider other micronutrients such as B12 and folate deficiencies along with iron. Many micronutrient deficienes in developed countries which were once a cause of concern are eliminated to a great extent now due to successful food fortification strategy (Miller and Welch, 2013). For many people in low and middle income countries, diet diversity can often be a challenge due to the existing poverty issues. In such cases, food fortification and supplementation are alternatives to address the existing deficiency. Inexpensive approaches to improve diet diversity along with nutrition education have also been practiced such as home gardens (Galhena et al., 2013). In India, poverty and nutritional status are intricately related and due to the existing marginalization between tribals, dalits, and other communities in the society. Due to this, the health status of tribals and rural women is greatly compromised. The consequence extends beyond a woman’s health to intrauterine growth restriction, child malnutrition and follows a vicious cycle (Victora et al., 2008). Future research interventions addressing women’s nutritional issues along with empowerment of illiterate women would be a great approach to address micronutrient deficiencies and chronic energy deficiency in low and middle income countries. It is also important to incorporate appropriate health programmes and interventions for the tribals through the already existing government policies.

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7.1. References

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Bair S, Bohren A, McIntosh C and Ozler B. Designing experiments to measure spillover and threshold effects. Working Paper. 2012. (Accessed in September 2013). http://cu-csds.org/wp-content/uploads/2012/01/2012-EID-McIntosh.pdf

Bhutta ZA, Imdad A, Ramakrishnan U, Martorell. Is it time to replace iron folate supplements in pregnancy with multiple micronutrients? Paediatric and Perinatal Epidemiology. 2012; 26: 27-35.

Bisoi S, Haldar D, Majumdar TK, Bhattacharya N, Sarkar GN, Ray SK. Correlates of anemia among pregnant women in a rural area of West Bengal. The Journal of Family Welfare. 2011; 57: 72-78.

Chambers R and Medeazza GV. Sanitation and stunting in India, Undernutrition’s blind spot. Economic and Political Weekly. 2013; XLVIII: 15-18.

Das S, Bose K. Nutritional assessment by mid-upper arm circumference of santal adults of Purulia, West Bengal, India. Collegium Anthropologicum. 2012; 36: 581-584.

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Galhena DH, Freed R, Maredia KM. Home gardens: a promising approach to enhance household food security and wellbeing. Agriculture and Food Security. 2013; 2: 1-13.

Gera T, Sachdev HS, Boy E. Effect of iron-fortified foods on hematologic and biological outcomes: systematic review of randomized controlled trials. The American Journal of Clinical Nutrition. 2012; 96:309-324.

Government of India report. Guidelines-central rural sanitation programme. Total Sanitation Campaign. Department of Drinking Water Supply, Ministry of Rural Development, New Delhi, India. 2007. (Accessed in March 2014). http://www.ielrc.org/content/e0716.pdf

Hasan MM, Ahmed S, Chowdhury MAH. Food security and child undernutrition: evidence from BDHS 2011. Journal of Food Security. 2013; 1: 52-57.

Hueso A and Bell B. An untold story of policy future: the Total Sanitation Campaigh in India. Water Policy. 2013; 15: 1001-1017.

Khan MMH, Gruebner O, Kramer A. Is area affected by flood or stagnant water independently associated with poorer health outcomes in urban slums of Dhaka and adjacent rural areas. Natural Hazards. 2014; 70:549-565.

Lawless JW, Latham MC, Stephenson LS, Kinoti SN and Pertet AM. Iron supplementation improves appetite and growth in anemic Kenyan primary school children. The Journal of Nutrition.1994; 124: 645-654.

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Mittal PC, Srivastava S. Diet, nutritional status and food related traditions of Oraon tribes of New Mal (West Bengal), India. Rural Remote Health. 2006; 6: 1-11.

National Family Health Survey NFHS-3. India 2005-2006. International Institute of Population Sciences, Mumbai, India and ORC Macro, Calverton, Maryland, USA. 2007.

Ngure FM, Reid BM, Humphrey JH, Mbuya MN, Pelto G, Stoltzfus RJ. Water, sanitation, and hygiene (WASH), environmental enteropathy, nutrition, and early child development: making the links. Annals of the New York Academy of Sciences. 2014;1308:118-128.

Patil SR, Arnold BF, Salvatore A, Briceno B, Colford JM Jr, Gertler PJ. A randomized, controlled study of a rural sanitation behaviour change program in Madhya Pradesh, India. Policy Research Working Paper 6702. The World Bank, Sustainable Development Network, Water and Sanitation Program, 2013: 1-43.

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Ramesh PS. Study on trend of nutritional status and health outcomes of women. Indian Journal of Maternal and Child Health. 2012; 14: 1-12.

Rao KM, Balakrishna N, Arlappa N, Laxmaiah A and Brahmam, GNV. Diet and nutritional status of women in India. Journal of Human Ecology. 2010; 29: 165-170.

Sarlio-Lahteenkorva and Lahelma E. The association of body mass index with social and economic disadvantage in women and men. International Journal of Epidemiology. 1999; 28: 445-449.

Spears D. Effects of rural sanitation on infant mortality and human capital: Evidence from India's Total Sanitation Campaign. Princeton University, USA. 2012. (Accessed in March 2014). https://www.dartmouth.edu/~neudc2012/docs/paper_86.pdf

Stein AJ and Qaim M. The human and economic cost of hidden hunger. Food and Nutrition Bulletin. 2007; 28: 125-134.

Subhasinghe AK, Walker K, Evans RG, Srikanth V, Arabshahi S, Kartik K, Kalyanram K, Thrift AG. Association between farming and chronic energy defiency in rural South India. 2014; 9: 1-10.

Swaminathan P, Jeyaranjan J, Sreenivasan R, Jayashree K. Tamil Nadu’s midday meal scheme. Where assumed benefits score over hard data .Economic and Political Weekly. 2004; 4811-4821.

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Tamang JK. Himalayan fermented foods, microbiology, nutrition and ethnic values. CRC Press. Taylor and Francis group. New York. 2010.

United Nations Children’s Fund (UNICEF) Report on improving child nutrition. (Accessed in December 2013). http://www.unicef.org/media/files/nutrition_report_2013.pdf

Victora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, Sachdev HS; Maternal and Child Undernutrition Study Group. Maternal and child undernutrition: consequences for adult health and human capital. Lancet. 2008; 371:340-357.

Westergaard H. Tropical Sprue. Current Treatment Options in Gastroenterology. 2004; 7:7–11.

Water and Sanitation Program. A decade of the Total Sanitation Campaign, Rapid assessment of processes and outcomes, Volume 1 and 2. The World Bank, New Delhi, India.2011.

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World Health Organization (WHO) Guideline: Intermittent iron and folic acid supplementation in menstruating women. World Health Organization, Geneva, Switzerland. 2011.

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Appendices

Appendix 1: Bayesian information criterion (BIC): Covariance structure and statistical fit comparison

a. Covariance structure and statistical fit comparison- Anthropometric indicators b. Covariance structure and statistical fit comparison- Nutrient intakes

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Appendix 1: Bayesian information criterion (BIC)

Bayesian information criterion (BIC): Covariance structure and statistical fit comparison- Anthropometric indicators

Covariance structure Dependent variable

MUAC (cm) BMI (kg/m2) Weight (kg)

CS 2335.3 2159.8* 3513.3

AR (1) 2474.0 2224.3 3546.8

UN 2320.0* 2178.4 3456.4*

CS= Compound symmetry; AR (1) = Autoregressive 1; UN= Unstructured; *Smaller the BIC, better the model

Nutrient intakes Covariance structure Dependent variable

Total Energy Protein (g) Fat (g) CHO (g) CA (mg) FE (mg) ZN (mg) VIT A VIT C (mg) intake (g) (kcal) (µg RAE)

CS 9599.5 10035.4 5696.5 5405.7 8036.7 10032.5 5293.2 3629.9 9928.4 7781.3

AR (1) 9630.0 10065.3 5708.6 5411.7 8076.1 10038.1 5292.9 3637.7 9929.5 7782.7

UN 9585.4* 10021.8* 5662.7* 5359.3* 8021.0* 9768.7* 5215.8* 3551.8* 9791.7* 7534.8*

CS= Compound symmetry; AR (1) = Autoregressive 1; UN= Unstructured Note: *Low values (within a column) are better. For example, for MUAC, Unstructured provides the best fit

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Covariance structure parameter estimates

Anthropometric indicators

Covariance parameter estimates Dependent variable

MUAC (cm) BMI (kg/m2) Weight (kg)

Estimate Residual Estimate Residual Estimate Residual 2 2 2 (σ e) (σ e) (σ e)

CS - - 7.5363 0.3318 - -

AR (1) ------

UN (1, 1) 6.4531 - - - 55.1681 -

UN (2, 1) 6.6480 - - - 52.4565 -

UN (2, 2) 8.4008 - - - 57.9658 -

UN (3, 1) 6.6645 - - - 52.7729 -

UN (3, 2) 7.1049 - - - 55.1120 -

UN (3, 3) 7.1857 - - - 56.0074 - Note: Example MUAC, Correlation =6.6480/√(6.4531*8.4008)=0.903

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Nutrient intakes: Macronutrients and energy

Covariance parameter estimates Dependent variable

Total intake (g) Energy (kcal) Protein (g) Fat (g) CHO (g)

Estimate Residual Estimate Residual Estimate Residual Estimate Residual Estimate Residual 2 2 2 2 2 (σ e) (σ e) (σ e) (σ e) (σ e)

CS ------

AR (1) ------

UN (1, 1) 252683 - 575276 - 507.13 - 425.77 - 22358 -

UN (2, 1) 71239 - 130264 - 93.8037 - 82.8805 - 5512.34 -

UN (2, 2) 220702 - 429204 - 574.88 - 206.88 - 18260 -

UN (3, 1) 98855 - 197927 - 107.21 - 89.3291 - 9775.91 -

UN (3, 2) 59688 - 135647 - 75.8127 - 80.9025 - 6132.42 -

UN (3, 3) 263830 - 466734 - 315.67 - 270.23 - 22333 -

Note: Example Total intake, Correlation = (71239/ √252683*220702) = 0.3016-Repeated measures are less correlated for total intake

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Micronutrients

Covariance parameter estimates Dependent variable

CA (mg) FE (mg) ZN (mg) VIT A VIT C (mg) (µg RAE) Estimate Residual Estimate Residual Estimate Residual Estimate Residual Estimate Residual 2 2 2 2 2 (σ e) (σ e) (σ e) (σ e) (σ e)

CS ------

AR (1) ------

UN (1, 1) 170447 - 123.39 - 14.5173 - 238697 - 3102.20 -

UN (2, 1) 50961 - 5.5300 - 0.9142 - 26634 - 898.59 -

UN (2, 2) 994606 - 379.01 - 27.2045 - 717732 - 26067 -

UN (3, 1) 64608 - -5.8505 - 3.3332 - 16278 - 883.99 -

UN (3, 2) 31277 - 12.8132 - 2.1483 - 10931 - -122.44 -

UN (3, 3) 181056 - 213.57 - 9.8702 - 166815 - 8320.38 -

CS= Compound symmetry; AR (1) = Autoregressive 1; UN= Unstructured

Appendix 2: Informed consent form

Double Fortified Salt intervention and work performance (productivity) of women plantation workers in West Bengal, India

Investigators: Dr. Grace S. Marquis, PhD, McGill University, Montreal, Canada Dr. Jere D. Haas, PhD, Cornell University, Ithaca, USA Dr. Annie S. Wesley, PhD, Micronutrient Initiative, Ottawa, Canada Dr. Anand Lakshman, MBBS, MHA, Micronutrient Initiative, Delhi, India

We would like to ask for your participation in this study that is being carried out by the Micronutrient Initiative (Canada and India) and McGill University (Canada). This consent form explains about the study. Please take it, read, think about it and discuss your participation with your family, as you wish. Please feel free to ask any questions and take your time in deciding if you would like to participate.

NATURE OF THE STUDY

The aim of the study is to examine the impact of double fortified salt containing iron and iodine on work performance, physical activity and attention, memory and learning of women.

WHO CAN PARTICIPATE IN THE STUDY?

Women between 18-45 years at high risk for iron deficiency, who are not pregnant and not breastfeeding, and who are experienced, permanent, full-time tea pickers who plan to work over the next 2 picking seasons.

WHAT I WILL BE ASKED FOR IF I PARTICIPATE IN THE STUDY? Initial visit If you agree to participate in the study, we will provide you first with a medicine to rid your body of common intestinal parasites that make women anemic (poor iron status) Only women who are not pregnant and are not lactating should take the medicine. If there is a possibility that you are pregnant, we will provide you first with a pregnancy test to know your status.

Next screening step Next, we will invite you for a follow-up visit to measure your iron status. In this visit, we will collect a drop or two of blood from a finger prick. You will receive an immediate report of your iron status. If you are severely anemic, we will refer you to a local health facility, you will receive the treatment of anemia from the project at no cost to you, and

your participation with this study will be finished. If you are at risk of anemia (but not severely anemic), you will be invited to continue to participate in the study as described below. This finger prick exam will be repeated half way through the project to assure that you do not become anemic.

Project activities During the study, which will last one year, we will give you salt for your household and you should use this salt for all meals prepared in the home. Half of the households will receive salt with iodine added to it. This is the exact same salt that you buy in the store. Half of the households will receive salt that has both iodine and iron added to it. Salt that has both iodine and iron is used in many feeding programs in India because it is a convenient way to supplement diets to improve women and children’s iron status. The choice of which type of salt you will receive, will be made by a lottery system. We will periodically ask you about your salt consumption and will provide more salt whenever you have used up your salt. The two salts look identical therefore neither you nor the project staff will know which salt you and your family are consuming.

Information that will be collected twice: at the beginning and the end of the study

You will be invited to come to the project office for a visit for a more complete examination of your nutritional and health status. First, 10 ml of blood (less than 1 tablespoon) will be drawn from your arm vein to measure nutrient and health factors in your blood. In addition, you will be asked to give a urine sample (30 mL, about 2 tablespoons) to measure your iodine levels.

One of our project staff will conduct an interview where you will be asked about your household (for example, the age of your family members, where they work, their educational level, and the services you have available in your home). This interview will take at most 45 minutes.

We will also measure your attention, learning and memory using a simple test on a computer. This test should take you about 1 hour to complete. Paper and pencil based questions will also be given which should take 10-15 minutes to complete. A project staff member will show you how to complete these simple tests.

During one week at the start of the study and again for one week at the end of the study, you will wear a device to measure your heart rate during the time you are picking tea. This device is worn on a belt positioned under your clothing, around your lower chest. You will wear a small watch that you will be pinned in a non-visible location on your clothing. During the same time that we measure your heart rate we will also measure your physical activity for 24 hours during 7 consecutive days (one week). This will be measured using a simple device that is worn on a belt around your waist.

Information that will be collected three times: beginning, middle, and end of the study

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One of our project staff will conduct an interview where you will be asked about your diet (such as, the type of food you consume and how often you eat it). We will also measure your size (including height, weight and mid-upper arm circumference) and your blood pressure. For some women, chosen by lottery, we will arrange with you for 2 days when it would be convenient for one of our team to stay with you during the eating hours (over a span of about 16 hr) to measure how much you consume in breakfast, lunch, dinner, and snacks. To do this we will weigh all the food and liquids that you eat and any that is left over.

Information that will be collected throughout the study

We will obtain the daily amount of tea you pick throughout the 2 seasons of picking. from the information that is recorded by the tea estate for the purpose of determining your weekly wages. This information will be collected solely for the purposes of this project and will not be shared with anyone.

ARE THERE ANY RISKS INVOLVED IF I PARTICIPATE IN THIS STUDY? There are no risks of participating in this study. The blood samples may cause minor discomfort on your finger or arm.

IS THERE ANY BENEFIT FOR PARTICIPATING? You will receive free salt for your household for the entire year that you participate in the study. The project will also provide you with free iron pills if you are determined to be severely anemic at any time during the study and you will be given medicine to rid your body of intestinal parasites at the beginning and every 4 months during the study. The information gained in this study will benefit the society by providing valuable messages for health professionals about women’s health so they develop effective nutrition recommendations. At the end of the study we will provide you with information about how to best meet your dietary needs. In addition, we will provide you with the results of your iron status, and an evaluation of weight, height and physical activity.

CAN I WITHDRAW AT ANY MOMENT? Your participation in this study is completely voluntary and you may refuse to participate or leave the study at any time. If you decide to not participate in the study or leave the study early, it will not result in any penalty or loss of benefits that you would get otherwise, including services in the local health facilities or from your job at the tea estate.

CONFIDENTIALITY OF MY DATA Records identifying participants will be kept confidential. This means that your name, address, or other personal information that could identify you will not be shown to anyone without your permission.

To ensure confidentiality you will be assigned a study code will be used on forms instead of their name. Only Project Investigators and the program supervisor will have access to

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records showing both your name and code. The records will be kept in a locked cabinet and in password-protected computer files for analysis. Only files linking your name with a study code will be destroyed at the end of the project. Your biological samples will be destroyed after analysis. If the results are published, your identity will remain confidential.

WHO SHOULD I CALL IF I HAVE QUESTIONS ABOUT THE PROJECT? If you have any question about the study, you are welcome to ask it any time. For further information, you can contact at any time: Ms. Sudha Venkatramanan (011-91-1141009801) [email protected] (INDIA) Dr. Anand Lakshman (011-91-1141009801 extn: 114) [email protected] (INDIA) Dr Grace Marquis (1-514-398-7839) [email protected] (CANADA) Dr. Annie Wesley (1-613-782-6822) [email protected] (CANADA) Dr. Jere Haas (1-607- 255-2665) [email protected] (USA).

VOLUNTARY STATEMENT OF INFORMED CONSENT FORM Your signature or thumbprint indicates that 1) you voluntarily agree to participate in this study, 2) the study has been explained to you, 3) you have been given the time to read the document, and 4) your questions have been satisfactorily answered. You will receive a copy of the signed and dated written informed consent before you begin the study.

“The study has been explained to me and my questions have been answered to my satisfaction.”

Participant’s Name (printed):

(Participant’s Signature/thumbprint) (Date)

INVESTIGATOR STATEMENT

I certify that the participant has been given adequate time to read and learn about the study and all of their questions have been answered. It is my opinion that the participant understands the purpose, benefits and the procedures that will be followed in this study and has voluntarily agreed to participate.

(Signature of Person Obtaining Informed Consent) (Date) Consent for household salt use among the children

Because the salt will be used in the household, we ask that you provide consent for your children (< 18 y old) to receive the salt. No information will be collected on the children

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directly. If there are children in the household who are not under your guardianship, please have the child’s guardian give consent on a separate form.

Your signature below indicates that you voluntarily agree to allow your children also to participate in this study.

Child’s Name (printed) ______

Child’s Name (printed)______

Child’s Name (printed) ______

Child’s Name (printed)______

Child’s Name (printed) ______

Child’s Name (printed)______

______

(Caregiver’s Signature/thumbprint) (Date)

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Consent for household salt use among the adults

All adults in the household should also provide consent that the household receive the randomly assigned salt.

Study explanation: An adult woman in your household is interested in participating in a study to examine the impact of double fortified salt containing iron and iodine on work performance, physical activity and attention, memory and learning of women. During the study, which will last one year, we will give your household salt with iodine added to it (the exact same salt that you buy in the store) or salt that has both iodine and iron added to it (the exact same salt used in many child feeding programs in India). The choice of which type of salt you will receive, will be made by a lottery system. No information will be collected from you directly.

Your signature or thumbprint indicates that 1) you have had the study explained to you and your questions answered, 2) you understand that either salt with iodine or salt with both iodine and iron will be provided at no cost to your household for one year, and 3) you agree to have your household use that salt.

“The study has been explained to me and my questions have been answered to my satisfaction.”

Household member’s Name (printed):

(Household members Signature/thumbprint) (Date)

Household member’s Name (printed):

(Household members Signature/thumbprint) (Date)

Household member’s Name (printed):

(Household members Signature/thumbprint) (Date)

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