ENERGY BALANCE, HEALTH AND FECUNDITY AMONG WOMEN OF , ,

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree of Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Sharon R. Williams, M.A.

* * * * *

The Ohio State University 2003

Dissertation Committee:

Dr. Douglas E. Crews, Advisor Approved by Dr. Ivy L. Pike . Dr. Paul W. Sciulli Advisor Department of Anthropology Dr. Gillian H. Ice

ABSTRACT

Health is a complex construct dependent on socio-cultural, economic and

biological factors. The concept of health is very complicated; the expression of health is

the result of the interaction of many biological, economic and cultural factors and

includes both psychological and biological well-being. Health is not static. Humans are

plastic, able to respond both behaviorally and biologically to stressors that threaten their

well-being. The range of adaptive responses is highly variable and has long been an area

of interest to Anthropologists. Human life history responds to stressors. Among women,

the reproductive span is particularly sensitive to the environment. and its biological correlate fecundity are responsive to external stressors and often reflect the health and well-being of women in their environment.

This research documents how differences in social settings, health, physical environment and interact and affect urban Bhutia women in Sikkim, India and contribute to the low fertility in this population. Individual variables such as nutrition, workloads and health have been found to significantly influence female fecundity by altering the levels of the hormones that make possible. While the contributions of these individual factors have been studied in detail, very little is known

about the interactive effects of these three variables. Moreover, these three variables are

ii significantly impacted by the changes associated with economic and social development

and urbanization.

Results of this study show that seasonal climatic variation significantly

affects urban Bhutia women, their health and well-being. Measurements of energy

balance, health, psychosocial stress and fecundity reflect changes in environment.

However, responses of these variables to seasonal stressors were not consistent. Seasonal patterns in changes in energy balance, health status and fecundity were unique. Results of this study suggest that health measures influenced by anthropometric measures were not the ones impacting measures of fecundity in this sample of Bhutia women. The complex interactions between biology, behavior and environment and their influence on health and reproduction are reinforced by the results presented here.

iii

To Dan

Thank you for of your love, support, patience and understanding.

iv AKNOWLEDGMENTS

First I need to thank Dr. Barun Mukhopadhyay, without whom this project would have not existed. Both Dr. Barun and Susmita provided invaluable logistical, methodological, ideological, and emotional support during my year in India. I would like to thank my very wonderful associates in the field, Bhupal, Tsultim, Tseten, and

Sangay who made the fieldwork enjoyable and productive. I also need to thank Dr.

B.B. Rai of the Sikkim Voluntary Health Association for his assistance. And, of course, I need to thank the wonderful women who participated in this study without whom there would be no dissertation. Back at home, I wish to thank my mentors and friends, Dr. Gillian Ice and Dr. Ivy Pike for their support, patience and the time to read and comment on the endless drafts of this dissertation. I also need to thank my advisor, Dr. Douglas E. Crews for all of the opportunities he has provided for me over the past several years. And last, but not least, my family and friends for all of their support.

v VITA

January 29, 1972 ………….……………… Born – Kenton, Ohio

1994 ……………….……………………… B.S. Molecular Genetics, The Ohio State University

1996 …………………………….……….... M.A. Anthropology, The Ohio State University

1995 – 1996 …………………………….… Graduate Research Associate, The Ohio State University

1996 – present ……………………….…… Graduate Teaching Associate, The Ohio State University

PUBLICATIONS

Williams SR. “Determinants of Low Fertility among Urban Bhutia Women in Sikkim, India.” American Journal of Human Biology. 2003;15(2):290

Vallianatos H and Williams SR. “Comparison of breast feeding practice in two urban north Indian populations.” American Journal of Human Biology. 2002; 14(1):134.

Williams SR and Pike IL. “What does love have to do with it? Perceived stress levels and health in love marriages vs. arranged marriages in a tribal population of India.” American Journal of Human Biology. 2002;14(1):136.

Williams SR, Pike IL, and Sansbury L. “Hunger, illness and hard work: Turkana women’s cortisol levels and perceived stress.” American Journal of Human Biology. 2000; 12(2):269.

vi Williams, SR, IL Pike, CL Patil, and LB Sansbury. 2000. Hunger, illness, and hard work: Turkana women's cortisol levels and self-perceived stress. American Journal of Human Biology. 2000;12(2):272.

Crews, DE and Williams SR. Molecular Aspects of Blood Pressure Regulation. Human Biology. 1999; 71(4): 475-503.

Williams SR, Fitton LJ and Crews DE. “Seasonality of Births among the Cofan of Ecuador. American Journal of Human Biology. 1999; 10(1):138.

FIELDS OF STUDY

Major Field: Anthropology

Specializations: Human Population Biology Biocultural Anthropology Reproductive

vii TABLE OF CONTENTS

Page

Abstract ………………………………………………………………………………. ii

Dedication ……………………………………………………………………………. iii

Acknowledgments ……………………………………………………………………. iv

Vita …………………………………………………………………………………... v

List of Tables ………………………………………………………………………… vii

List of Figures ……………………………………………………………………….. xiii

Chapters:

1. Introduction …………………………………………………………………….. 1 1.2 Fertility and Fecundity …………………………………………………….. 3 1.2.1 Intermediate and proximate determinants of fertility ………………. 4 1.2.2 Menarche, menopause and the female fertile period ……………….. 7 1.2.3 Sources of variation within the fertile period ………………………. 9 1.3 Reproductive Ecology …………………………………………………….. 12 1.3.1 Physiological mechanisms for reproduction and the ……………………………………………………………….... 13 1.3.2 Reproductive response to stressors …………………………………. 16 1.4 Stressors affecting fertility in urban populations … ……………………... 24 1.5 The Bhutia ………………………………………………………………… 25 1.6 Hypotheses ………………………………………………………………... 28

2. Background: Situating Bhutia Women ………………………………………… 29 2.1 Introduction ………………………………………………………………… 29 2.2 Women and fertility in India ……………………………………………….. 29 2.2.1 Population/birth control policies ……………………………………. 29 2.2.2 Fertility rates and trends in India …………………………………… 31 2.2.3 Status of women in India …………………………………………… 33 2.3 Sikkim …………………………………………………………………….... 39 2.3.1 Geographical location ………………………………………………. 39 2.3.2 Environment ……………………………………………………….... 40

viii 2.3.3 Politics, history and populations ……………………………………. 42 2.3.4 Development of the state …………………………………………… 43 2.3.5 Health status, demography and fertility of Sikkim …………………. 44 2.3.6 Health in Sikkim …………………………………………………… 45 2.3.7 Status of women in Sikkim ……………………………………….... 47 2.4 Bhutia Women …………………………………………………………… 50

3. Sample and Methods ……………………………………………………………. 52 3.1 Research design ……………………………………………………………. 52 3.2 Study population …………………………………………………………… 53 3.2.1 Sample selection and characteristics ..……………………………..... 54 3.3 Education and occupation ………………………………………………….. 55 3.3.1 Household characteristics …………………………………………... 57 3.3.2 Marriage and Family ………………………………………………... 58 3.4 Data collection techniques ………………………………………………….. 63 3.4.1 Overview ……………………………………………………………. 63 3.4.2 Procedures …………………………………………………………... 63 3.5 Statistical analysis ………………………………………………………….. 71

4. Results I: energy balance and seasonality ……………………………………… 72 4.1 Introduction ………………………………………………………………… 72 4.1.1 Body composition of the sample ……………………………………. 72 4.2 Measures of energy balance ………………………………………………... 75 4.3 Seasonal changes in measures and components of energy balance ………... 77 4.3.1 Large scale changes in energy balance ……………………………… 77 4.3.2 Changes in anthropometric measurements ………………………….. 83 4.3.3 Anthropometric indices ……………………………………………… 87 4.4 Summary …………………………………………………………………… 90

5. Results II: Energy Balance and Fecundity ……………………………………. 91 5.1 Introduction ………………………………………………………………… 91 5.2 Measures of fertility and fecundity ………………………………………… 92 5.2.1 Measures of population fertility …………………………………….. 92 5.2.2 The menstrual cycle ………………………………………………… 92 5.2.3 Lutenizing hormone (LH) and Follicle Stimulating Hormone (FSH) ………………………………………………………………… 97 5.3 Measures of health status ………………………………………………….. 98 5.3.1 Health history ………………………………………………………. 98 5.3.2 Anemia and hemoglobin levels …………………………………….. 100 5.4 Seasonal measures of health ………………………………………………. 102 5.5 Energy balance and fecundity ……………………………………………... 113 5.5.1 Predictors of average menstrual cycle length ……………………… 113 5.6 Energy balance and health status …………………………………………. 121 5.7 Health and fecundity ………………………………………………………. 125 5.8 Summary ………………………………………………………………….. 130

ix 6. Results III: social wellbeing and socioeconomic status ……………………….. 131 6.1 Introduction ……………………………………………………………….. 131 6.2 Socioeconomic status ……………………………………………………... 132 6.2.1 Measures of socioeconomic status …………………………………. 132 6.2.2 Socioeconomic index and SES rank ……………………………….. 136 6.3 Social well-being …………………………………………………………. 137 6.3.1 measures of social well-being ……………………………………… 137 6.4 Social well-being, socioeconomic status and energy deficit …………..…. 142 6.4.1 Socioeconomic status and social wellbeing ………………………… 142 6.4.2 Socioeconomic status, social support and perceived stress ………… 143 6.4.3 Socioeconomic status and energy deficit …………………………… 144 6.5 Summary …………………………………………………………………... 153

7. Discussion and Conclusions ………………………………………………...... 154 7.1 Introduction ………………………………………………………………... 154 7.2 Hypothesis 1 ……………………………………………………………… 155 7.2.1 Summary of seasonal changes in energy balance measures ……….. 155 7.2.2 Explanation for seasonal pattern of change in energy balance measures ……………………………………………………………. 157 7.2.3 Contributions and context …………………………………………... 160 7.3 Hypothesis 2 ………………………………………………………………. 161 7.3.1 Seasonality of health ………………………………………………… 161 7.3.2 Explanation for seasonal changes in measures of health status …... 163 7.3.3 Energy balance and measures of fecundity ………………………... 166 7.3.4 Energy balance and health status …………………………………... 169 7.3.5 Health status and fecundity ………………………………………… 170 7.3.6 Contributions and context…………………………………………… 170 7.4 Hypothesis 3 ……………………………………………………………… 171 7.4.1 Seasonality of stress ……………………………………………….. 171 7.4.2 Socioeconomic status, social support and energy balance ………... 172 7.5 Conclusions and future direction …………………………………………. 174

Bibliography ………………………………………………………………………… 175

Appendix A Season 1 questionnaire ……………………………………………… 187

Appendix B Perceived stress questionnaire ……………………………………… 198

Appendix C Anthropometric indices …………………………………………….. 200

x LIST OF TABLES

Table Page

2.1 Reported contraception preference rates for rural and urban married women in Sikkim districts ………………………………………………….. 44

2.2 Age specific fertility rates for urban and rural married women in Sikkim ………………………………………………………………………. 45

2.3 Cause specific mortality rates in Sikkim …………………………………… 46

2.4 Sex ratio for selected Indian States …………………………………………. 48

3.1 General sample characteristics ……………………………………………… 55

3.2 Common reasons reported by this sample for moving to Gangtok ………… 58

4.1 All India and study sample basic characteristics …………………………… 73

4.2 Sample measures of body composition for Season 1 – Winter …………….. 76

4.3 Seasonal mean values and sample sizes for weight, body fat and BMI ……. 77

4.4 Seasonal sample net change in body weight and percentage of sample that lost, gained or did not change weight …………………………………. 79

4.5 Seasonal sample net change in body fat and percentage of sample that lost, gained or did not change body fat ……………………………………… 80

4.6 Seasonal sample net change in BMI and percentage of sample that lost, gained or did not change BMI ………………………………………………. 81

4.7 Seasonal distribution of sample BMI classifications ………………………... 81

4.8 Seasonal changes in anthropometric measurements ………………………… 84

xi 4.9 Statistical results for significant seasonal variation in anthropometric measurements ………………………………………………………………… 8

4.10 Seasonal changes in anthropometric indices …………………………………. 87

4.11 Statistical results for significant seasonal variation in anthropometric indices ………………………………………………………………………… 89

5.1 Average cycle length and distribution of irregular cycles per season ……… 93

5.2 Percentage of women experiencing irregular cycles ……………………….. 97

5.3 Sample follicle stimulating hormone (FSH) and leutenizing hormone (LH) values (IU/L) ………………………………………………………….. 98

5.4 Common ailments reported by Bhutia women in this sample ……………… 99

5.5 Sample measures of health status indicators for season 1 – Winter ………... 102

5.6 Seasonal changes in health status measures ………………………………... 104

5.7 Statistical results for significant seasonal variation in health status measurements ……………………………………………………………….. 106

5.8 Seasonal sample net change in health recall and percentage of sample that lost, gained or did not change number of health recall items ………….. 102

5.9 Seasonal changes in C-reactive protein (CRP) levels ………………………. 108

5.10 Seasonal sample net change in Hb and percentage of the sample who lost, gained or did not change Hb levels ……….………………………. 110

5.11 Seasonal sample net change in systolic blood pressure and percentage of sample that lost, gained, or did not change systolic BP levels ……………… 112

5.12 Seasonal sample net change in Diastolic BP and percentage of sample that lost, gained or did not change Diastolic BP levels ………………………….. 113

5.13 Linear regression results for average cycle length and averages of anthropometric measures ………………………………………………….... 114

5.14 Linear regression results for average cycle length and seasonal changes in anthropometric measures ………………………………………………… 115

5.15 Linear regression results for follicle stimulating hormone (FSH) low and peak values and measures of energy balance averages …………………….. 117 xii 5.16 Linear regression results for leutenizing hormone (LH) and peak values and measures of energy balance ……………………………………………. 117

5.17 Linear regression results for FSH low and peak values and magnitude of change between seasons in measures of energy balance ………………… 119

5.18 Linear regression results for LH low and peak values and magnitude of change between seasons in measures of energy balance …………………… 120

5.19 Linear regression results for average measures of energy balance and average measures of self reported health, C-reactive protein (CRP) and hemoglobin (Hb) …………………………………………………………… 121

5.20 Linear regression results for average measures of energy balance and average measures of systolic and diastolic blood pressure (BP) …………… 122

5.21 Linear regression analysis results for average measures of self-reported health items, CRP, and Hb and magnitude of change between seasons in measures of energy balance ………………………………………………… 123

5.22 Linear regression analysis results for average measures of systolic and diastolic BP and magnitude of change between seasons in measures of energy balance ……………………………………………………………… 124

5.23 Linear regression results for average cycle length and average measures of health status ……………………………………………………………… 125

5.24 Linear regression results for FSH low and peak values and average measures of health status …………………………………………………... 126

5.25 Linear regression results for LH low and peak values and average measures of health status …………………………………………………… 126

5.26 Linear regression results for average cycle length and seasonal changes in health status measures ……………………………………………………… 127

5.27 Linear regression results for FSH low and peak values and seasonal changes in health status measures …………………………………………. 128

5.28 Linear regression results for LH low and peak values and seasonal changes in health status measures …………………………………………. 129

6.1 Sample characteristics in home construction, ownership and size ………… 132

6.2 Number and percentage of women owning other land, houses or buildings …………………………………………………………………… 133 xiii 6.3 Sample housing characteristics ……………………………………………. 134

6.4 Bhutia sample occupational status ………………………………………… 134

6.5 Assets owned by Bhutia women in this sample …………………………… 136

6.6 Seasonal measures for perceived stress question sum ……………………... 138

6.7 Seasonal sample net change in perceived stress sum and percentage of sample who increased, decreased or did not change stress sum …………... 140

6.8 Statistical results for significant seasonal variation in anthropometric measurements ……………………………………………………………… 140

6.9 Linear regression results for Socioeconomic Status Index (SES) values and Social support Index (SSI) ……………………………………………. 142

6.10 Linear regression results for SES values and SSI rank …………………… 143

6.11 Linear regression results for perceived stress sum and measures of SES and SSI …………………………………………………………………….. 143

6.12 Linear regression results for measures of SES and SSI and change in perceived stress sum from winter to fall ………………………………….. 144

6.13 Linear regression results for SES index and SES rank and average anthropometric measures …………………………………………………. 145

6.14 Linear regression results for SES index and SES rank and change in anthropometric measures from beginning to end of the study period (winter-fall) ………………………………………………………………... 147

6.15 Linear regression results for SSI index and SSI rank and average anthropometric measures ………………………………………………….. 149

6.16 Linear regression results for SSI index and SSI rank and change in anthropometric measures from beginning to end of the study period (winter-fall) ……………………………………………………………….. 150

6.17 Linear regression results for perceived stress sum and average anthropometric measures ………………………………………………….. 151

6.18 Linear regression results for perceived stress sum and change in anthropometric measures from beginning to end of the study period (winter-fall) ………………………………………………………… 152

xiv

LIST OF FIGURES

Figure Page

1.1 The proximate determinants of fertility…………………………………….. 6

1.2 Important events in the female menstrual cycle …………………………… 15

1.3 Human physiological response to stress in the Hypothalamic-Pituitary- Adrenal (HPA) axis …..…………………………………………………… 21

1.4 Mechanism for the influence of the human stress response (from the HPA axis) on human reproductive function …………………………………….. 22

1.5 Proposed model for the cultural, behavioral and physiological factors responsible for Bhutia low fertility rates ………………………………….. 27

2.1 Total fertility rates for selected Indian states based on Government of India (2000) estimates from data collected 1995-1997 …………………. 32

2.2 Sex ratios for selected Indian States from 2001 Census data ……………… 35

2.3 Female literacy rates for selected Indian states from 2001 census data …… 36

2.4 Female workforce participation rates for selected Indian states from 2001 census data …………………………………………………………… 38

2.5 Monthly minimum and maximum temperature (0C) in Gangtok ………….. 41

2.6 Mean monthly rainfall (mm) in Gangtok ………………………………….. 42

2.7 Age distribution of males and females in rural and urban Sikkim ………... 49

3.1 Educational levels of Bhutia women in this sample ………………………. 56

3.2 Educational distribution of the husbands of the Bhutia women in this sample ……………………………………………………………………… 60

xv 3.3 Desired number of children reported by women in this sample …………... 62

4.1 Sample BMI distribution and all-India BMI distribution based on World Health Organization standard categorical classification ..………………… 71

4.2 Seasonal changes in mean weight, body fat and BMI ……………………… 75

4.3 Seasonal changes in distribution of BMI classifications …………………… 79

4.4 Percentage of women who lost weight, body fat and BMI between each season and from the beginning to the end of the study period ……………… 80

4.5 Seasonal changes in statistically significant values for anthropometric measures …………………………………………………………………….. 82

4.6 Seasonal changes in Sum of Five, Sum of Two, and Arm Fat anthropometric indices ……………………………………………………… 85

5.1 Number of conceptions/season in 2001 for Bhutia women living in Gangtok (based on hospital records) ……………………………………….. 92

5.2 Seasonal distribution of irregular, short and long cycles and number of Bhutia women experiencing irregular cycles ……………………………….. 94

5.3 Average menstrual cycle length/season ……………………………………… 95

5.4 Percentage of menstrual cycles that were classified as irregular per season ………………………………………………………………………… 96

5.5 Distribution of hemoglobin status for all-India averages (DHS 1998), all-Sikkim averages (DHS 1998) and the study sample based on DHS classifications (1998) ………………………………………………………... 101

5.6 Proportion of seasonal answers to the questions “My health is the best in?” and “My health is the worst in?” ……………………………………….. 103

5.7 Seasonal changes in average health recall items …………………………….. 107

5.8 Seasonal changes in C-reactive protein (CRP) levels ……………………….. 108

5.9 Seasonal distribution of normal and high CRP values ………………………. 109 5.10 Seasonal distribution of hemoglobin (HB) status …………………………… 111

5.11 Seasonal changes in systolic and diastolic blood pressure ………………….. 112

xvi 6.1 Bhutia women’s responses to the question “I feel the most stress in ?” …….. 138

6.2 Seasonal variation in perceived stress sum averages ………………………... 139

xvii CHAPTER 1

INTRODUCTION

1.1 Introduction

Health is a construct interpreted on many levels, including physical, mental and social well-being. Health is not static. Humans are plastic, able to respond both behaviorally and biologically when factors threaten their well-being, creating stressors.

Such plasticity produces long term consequences. Fertility is a particularly important aspect of women’s health that is responsive to stressors. In the absence of contraception and widespread sexually transmitted diseases, fertility often reflects the health and well- being of women in their environment. This research documents how differences in social settings, health, physical environment, biology and fertility alter reproductive hormones and female fecundity. Health represents interactions among biological, economic and socio-cultural factors.

Stressors generated by development and urbanization influence multiple aspects of health (Barondess, 2001; McDade & Adair, 2001). One goal of economic and social development is increasing the quality of life for all individuals. However, processes of development spread unevenly across populations. Different areas and peoples of the developing world face different challenges and conditions. Migration to urban areas

1 involves changing activities, nutrition and social situations which alter health. While such changes affect both men and women, women often suffer more severe consequences. Women act as buffers for their families, frequently sacrificing their own nutrition or increasing their workloads to aid family survival (Tinker, 1976; Buvinic,

1997; Htun, 1999; Tolhurst & Thebald, 2001). Because of this, urban women are likely

more affected by negative consequences of economic and social development than are

other members of society.

Among women, the reproductive span is particularly sensitive to the environment.

Costs of plasticity during women’s reproductive span are best measured by fertility

(number of offspring). In the evolutionary sense, fertility reflects fitness. Factors

affecting fitness work directly, although not exclusively, through reproductive function.

Many biological, social, economic and behavioral factors contribute to fertility.

Fecundity is the biological capacity to produce offspring. Although defined separately,

fertility and fecundity are biologically linked. Factors impacting fecundity are likely to

also impact fertility. Many factors historically identified to impact fertility also alter

fecundity (e.g., Ellison, 1990; 1994; Lunn, 1994; Wood, 1994; O’Connor et al, 1998;

Smits et al, 1998; Vitzhtum et al; 2002).

This study is designed to explore how social roles, culture, personal attitudes,

environment and biology of urban Bhutia women in Sikkim, India have affected their

health and well-being in the context of socioeconomic change, development and

urbanization. Previous studies suggest that the completed fertility of Bhutia women is

low, only 3.9 children (Bhasin, 1991). This rate, lower than most other populations in

India, has not been explained. In this study the everyday lives, activities and choices of

2 Bhutia women were examined to determine factors important in determining their low fertility. This bio-cultural approach to fertility pays special attention to factors directly affecting fecundity. This research was conducted as part of a larger study on the health of the Bhutia, “Modernization and Health in the Sikkim Himalaya”, under the direction of Dr. Barun Mukhopadhyay (The Indian Statistical Institute of Calcutta).

1.2 Fertility and Fecundity

Fertility, the number of offspring produced, is a quantitative biological outcome with socio-cultural, economic, and biological contributors and may be examined at the population or individual level. Fertility and reproductive processes are keys to the survival of species. Sociologists and anthropologists have shown particular interest in human fertility. Countless factors contribute to both individual and population level fertility. Variables affect fertility on many levels, ranging from global - affecting many populations, to individual decision making, which may reflect larger social contexts as well as individual preferences.

Sociologists and demographers first organized studies of human fertility. They found that behaviors and decisions associated with fertility are embedded in social institutions, norms and cultural traditions (Henry, 1961; Bongaarts & Watkins, 1976;

Kohler, 2001). Demographic studies of fertility generally concentrate on large scale and survey-based research. The goal of these studies is to understand fertility, fertility differences, and fertility change in populations. In general, demographers assume fecundity is constant and focus their research on social variables such as age at marriage, income and education (Caldwell, 1982; Bongaarts, 1983; Kohler, 2001).

3 The anthropological approach to the study of human fertility tends to be small- scale and directed toward understanding individual contributions to overall population fertility. These include timing of individual fertility as well as the subtle individual variations in response to external stimuli (e.g. Ellison, 1986; Rosetta, 1994). Behaviors such as contraceptive use and coital frequency may directly affect fertility. Fertility is also, at least partially, dependent on fecundity. Both behavioral and biological responses to the environment can work as to prevent at times when the outcome would most likely be negative to both mother and child.

Fertility varies both within and between populations (Henry, 1957; Wood, 1994;

Dunbar, 1995). Data on the determinants of fertility are not currently available for all populations nor are current explanations for fertility changes or differentials adequate

(Siddiqui, 2001). Even less is known regarding the underlying variation in fecundity.

Neither determinants nor patterns of fertility and fecundity of Bhutia women of Sikkim are known.

1.2.1 Intermediate and proximate determinants of fertility

A major contribution of demography to fertility research has been the development of frameworks for organizing important fertility variables. Davis & Blake

(1956) proposed “intermediate fertility variables” to limit the list of fertility variables from nearly infinite to a more manageable number. Intermediate variables act as filters for social phenomena. These eleven variables are assembled into three classes: factors affecting exposure to intercourse, factors affecting exposure to conception, and factors affecting gestation and successful parturition.

4 Bongaarts (1978), built on and refined this model by proposing “proximate determinants” that mediate all other variables (e.g., social, economic, and cultural). He proposes seven variables controlling length of birth intervals. Birth intervals are the significant source of variation in actual fertility rates. Together, these seven variables

(Figure 1.1) create variation in the length of the three components of the birth intervals: post-partum infecundity, waiting time to conception, and full term pregnancy. Population variation in birth intervals creates population variation in fertility rates. What is lacking from these models is an explanation of how biological, economic, environmental, cultural and behavioral processes help structure these seven proximate determinants and three classes of exposure.

5

Exposure Deliberate Factors Fertility Control Factors

proportion contraceptive use prevalence of married induced abortion

Natural Marital Fertility Factors

lactational waiting time probability of prevalence of infecundability to conception fetal loss permanent sterility

Figure 1.1: The proximate determinants of fertility (Bongaarts, 1983)

6

1.2.2 Menarche, menopause and the female fertile period

Women’s fertile period includes the period from menarche the onset of

menstruation and ovulation) to menopause (cessation of menstruation and ovulation). The

female fertile period begins with the first ovulation, or ovum release, and the first of

many menstrual cycles a woman will experience. Cycling generally begins during late

puberty following the development of most secondary sexual characteristics (Wood,

1994; Wu et al, 2002). Like fertility, the age at menarche varies both within and between

populations and individuals. In the United States, the average age at menarche has not changed significantly in the past 50 years (Yen et al, 1999) and currently ranges from

12.3 – 12.8 years, with slight variation between ethnic groups (MacMahon, 1973; Malina

& Bouchard, 1991; Herman-Giddens et al, 1997; Wu et al, 2002). Based on clinical definitions, girls in the U.S. who reach menarche between the ages of 10 and 16 are considered clinically normal, while menarche outside this range is considered pathological (Carr & Blackwell, 1998; Yen et al, 1999). Average age at menarche in populations in other developed countries show similar ranges (Papadimitriou et al, 1999;

Marrodan et al 2000; Thomas et al, 2001).

Very few recent studies on the age at menarche have been conducted in developing counties. However based on available reports, girls reach menarche later in developing countries compared to developed countires. Based on data compiled by

Thomas, et al (2001), average age of menarche reported for 67 countries was highest in

Senegal (16.1 years) and lowest in Greece (12.0 years). The average for developed 7 countries from this list is 13.0 years and for developing countries the average is 13.8 years.

The exact cause of this variation in age at menarche is unknown, but appears to be due to genetic, biological and socioeconomic factors. In most cases, younger age at menarche reflects healthier populations (Yen et al, 1999). While there is a definite genetic component to age at menarche (Wood, 1994; Ellison, 1996; Thomas et al, 2001), a decrease in age at menarche through time in many populations suggests other factors can have important impacts. Historical studies in the United States and other western countries have suggested that nutrition plays a key role (Zacharias and Wurtman, 1969;

Wyshak and Frisch, 1982; Okasha et al, 2001). In addition, other studies have suggested socioeconomic status, seasonality, physical activities and altitude also may have significant impacts on age at menarche (Thomas et al, 2001).

Although the first signs of ovulation may appear at a very young age, peak fecundity and fertility are not reached immediately. Early studies on age-specific fertility suggested that rates increase with age and attain peak values in the 30s (Montague, 1957;

Bendel & Hua, 1978). While lower fertility rates during the years immediately following menarche may be related to behavior (later age at marriage, etc.), there also appears to be a significant period of sub-fecundity not related to exposure to sexual intercourse. A significantly higher number of anovulatory cycles, an irregular pattern of menstrual cycles and cycles with short luteal phases create a period of adolescent subfecundity

(Apter et al, 1978; WHO, 1986; Lipson and Ellison, 1992; Wood 1994).

8 Onset of menopause reflects cessation of ovulation and the end of female fertility.

The exact timing of the end of the fertile period has not been well studied as it is difficult to detect without intrusive methods. Menopause is referred to as an extended transitional period, also known as perimenopause, lasting up to five years (Pavelka & Fedigan, 1991).

Menstrual cycles in perimenopausal women tend to be highly irregular. Clinically menopause is as an absence of menstruation for 12 consecutive months (Yen et al, 1999).

Age at menopause also varies within and between populations. The same important contributors to age at menarche have also been found to impact age at menopause (Thomas et al, 2001). Later age at menopause is thought to reflect a healthier population than earlier age at menopause (Yen et al, 1999; Reynolds and Obermeyer,

2001). In addition, socio-demographic variables such as number of children and behaviors such as smoking (Do et al, 1998; Reynolds and Obermeyer, 2001) also impact age at menopause.

Although menopause marks the end of female reproduction, fertility tends to decline well before menopause (Henry, 1961; Bongaarts & Potter, 1983; Wood, 1989,

1994; O’Rourke et al, 1996). This trend can not be explained entirely by behavioral changes (Weinstein et al, 1990). According to Yen et al (1999), fertility begins to decline around 10 years prior, hormone levels begin to change around 8 years prior and menstrual cycle irregularities begin around 4 years prior to menopause.

1.2.3 Sources of variation within the fertile period

Fertility rates vary greatly both within and between populations, for reasons other than just length of the fertile period. The theoretical maximum fertility rate for a natural

9 fertility population (one that does not actively limit number of ) is around 15 births (Wood, 1994). However, researchers rarely see populations with average fertility levels that high. Many factors have been identified which contribute to variation in fertility between populations. These factors affect women throughout their reproductive lives.

Besides biological determinants associated with entering into the fertile period, the timing of exposure to intercourse is a major source of variation in age-specific fertility rates. Exposure to intercourse is assumed to begin at marriage, however in many populations, this is not the case. In some populations, such as U.S., exposure to intercourse and risk of pregnancy commonly begins sometime after puberty, but before marriage. Whether marriage is the first exposure to intercourse or not, the age at which girls actually begin their fertile lives varies greatly both within and between populations

(Bongaarts 1983; Wood, 1994). Early marriage/intercourse exposure can significantly extend the reproductive period, while late entry into marriage/intercourse exposure can shorten the reproductive period.

Contraceptive use and abortion also limit fertility whether used to delay beginning childbearing, increase spacing between children or stop fertility after the desired number of children has been reached. National politics and population policies are keys in determining the cost, availability, and spectrum of contraceptive and abortion procedures available in communities (Tsui, 1985). Population policies may also contribute to how abortion and contraception are viewed in a society and how the ideal family size is determined (for example, China’s one child policy and India’s “We two and our two” policies). On a household level, socioeconomic status, female autonomy/decision- 10 making ability, and local access to clinics are important in determining use of

contraception and induced abortion rates (Lightbourne, 1985).

Other demographic variables such as socioeconomic status, education, and migration have significant impacts on fertility. Demographic variables such as cost of living, cost of education and employment levels are important socioeconomic variables affecting fertility. These factors help to determine the cost of children within families in a population and affect decisions on fertility. Information on inheritance of assets and marriage practices along with cost of children helps determine flow of wealth and the relative economic benefits or costs of having children. For example, if the cost of having children and raising them is perceived to outweigh the benefits of children, family sizes will be smaller. This flow of wealth and the perceived benefits of children also affect decisions regarding fertility and fertility levels within a population (Caldwell, 1982;

Caldwell & Caldwell, 1987).

Demographic variables are important, but do not fully explain the low fertility rates of Bhutia women in Sikkim. They also may not be the most interesting factors when looking at reproduction from an anthropological perspective. If fertility is a reflection of how well an individual is adapted to her environment, we must understand the responsiveness of the reproductive system to external and internal factors.

Environmental and biological factors have direct impacts on reproductive physiology.

Health, nutrition, activity patterns, and psychosocial stress all have identified impacts on fertility in some populations (Foster, 1979; Bongaarts, 1983; Cleland 1985). These factors impact fecundity by directly altering levels of reproductive hormones responsible

11 for maintaining ovulation and the normal menstrual cycle. Understanding these interactions is the goal of reproductive ecology.

1.3 Reproductive ecology

Many population-based studies attempt to identify social and cultural variables that affect fertility, though many do not address the physiological mechanisms are ultimately responsible (Caldwell & Caldwell, 1987). Reproductive ecology examines the impacts of external factors on internal physiological processes that are responsible for fecundity. Therefore, reproductive ecology is the study of how external environmental, economic, socio-cultural and behavioral factors impact fecundity.

A main assumption of reproductive ecology is that modern human reproductive systems are the result of natural selection and reflect adaptive flexibility in to environmental variables (Ellison, 1990). Reproduction is energetically expensive

(Frisch, 1974, 1982, 2002; Ellison, 1990; Wood 1994). Given the costs, pregnancy and maintenance of offsping can negatively impact survival if attempted at inopportune times.

Fertility and fitness are highly correlated; the responsiveness of the human reproductive system is adaptive, because this responsiveness can limit reproduction when the environment is not favorable for support of pregnancy. The presence of variation in modern human reproductive physiology is a sign of the benefits of our adaptive flexibility.

Flexibility in human reproductive physiology allows for a continuum of responses to acts as filters through which external stressors and behavioral variability can impact fertility (Ellison, 1990). Significant sources of variation in ovarian function are age, 12 nutritional status, energy balance, diet and activity patterns (Ellison, 1990; Wood, 1994).

Specific information available on the adaptive flexibility of different populations is limited.

Women are the focus of this study and most other studies in reproductive ecology.

There are many reasons why the focus of this study is women’s reproduction as opposed to human reproduction. Due to the nature of the female reproductive cycle, women are the limiting factor in human reproduction (Wood, 1994). Because of pregnancy and the limits placed on ovulation by age and the ovarian cycle, the potential number of offspring is lower for women than men. Since ovulation occurs only once/month and sperm production occurs continuously, disruption of the female cycle impacts a larger proportion of the reproductive span of a woman than a man. Resumption of the cycle may occur more quickly in men than women.

In addition to biological factors, there are also socio-economic, political and cultural factors that make women, particularly in the developing world, more susceptible to the environment than men. Women also make up a majority of the world’s poor

(Buvinic 1997). For these reasons, factors, such as poor nutrition and health, that affect fecundity will usually affect women first and more acutely (Buvinic 1997; Htun, 1999;

Tolhurst & Thebald, 2001).

1.3.1 Physiological mechanisms for reproduction and the menstrual cycle

The menstrual cycle and ovulation are controlled by reproductive hormones released through the hypothalamic-pituitary-ovarian system. The primary hormones involved in this system are the multi-functional estrogen and progesterone, and the

13 gonadotropins leutenizing hormone (LH) and follicle stimulating hormone (FSH), which are primarily controlled by the pituitary hormone gonadotropin releasing hormone

(GnRH) (Wood, 1994; Yen et al, 1999). Estrogen and progesterone help prepare the body for pregnancy. Estrogen stimulates egg development and has important effects on the reproductive tract, as does progesterone. Both have feedback effects on the hypothalamus and pituitary which direct the other hormones involved in reproduction.

A normal menstrual cycle can be divided into two phases, the follicular and the luteal phase, with ovulation generally occurring between the two. The follicular phase is characterized by low but steady levels of LH and FSH released from the pituitary. The gonadotropins act only in the gonads to stimulate growth of the follicle (developing egg cell) and stimulate estrogen production by the follicle. Estrogen released by the follicle produces a negative feedback which keeps LH/FSH levels steady. A few days before ovulation (around mid-cycle), estrogen production, followed shortly after by LH and

FSH, production increases dramatically and rapidly, signaling ovulation. The levels of

LH and FSH decline rapidly to their pre-peak levels, usually in about 48 hours (Yen et al,

1999). Estrogen declines as well, but maintains a higher level after ovulation, during the luteal phase, than during the follicular phase. Progesterone, which is present in very low levels during the follicular phase, increases during the luteal phase, peaking around the midpoint of this phase (Figure 1.2).

14

(P = progesterone, E = estrogen, LH = leutenizing hormone, FSH = follicle stimulating hormone)

Figure 1.2: Important events in the female menstrual cycle (Wilson et al, 1991: 1776).

15 The reproductive hormones, specifically estrogen and progesterone, are also

responsible for the changes to the lining of the uterus associated with menstruation

(Wilson et al, 1991). If conception has not occurred by the end of the leuteal phase

menstruation takes place and the cycle repeats (Figure 1.2). The average length of the

menstrual cycle in women in the United States is 28 days (Yen et al 1999, Wood 1994)

but there is much variation within individuals and populations and between populations.

1.3.2 Reproductive response to stressors

Nutrition, Activity Patterns and Energy Balance

Differences in fertility rates between well and poorly nourished populations and social classes have long been observed (Frisch, 2002). Early studies of famine and starvation due to war connect poor nutritional status with and amenorrhea

(Keys et al, 1950; Stein & Susser, 1975; Cumming, 1992). Seasonal patterns of conception are also associated with seasonal changes in nutrition (Ogum & Okofor, 1979;

Bailey et al, 1992).

Early investigations into links between nutrition and fecundity focused on body composition and weight changes as a reflection of nutrition. However, the human body tends to be well buffered from weight loss (Malina, 1991; Bringer et al, 1997). Weight changes and body composition are the result of changes in energy balance. Energy balance is composed of energy intake (diet) and energy output (activities). Weight loss and changes in body composition may reflect changes either in diet or activity patterns.

16 Regardless, reduced caloric intake does seem to be associated with reduced fecundity

among athletes in the U.S. (Frisch, 1974; Hill, 1986; Kaiseraver et al, 1989).

Results concerning the contribution of dietary composition to fecundity are not as clear. Research by Frisch (1974) and others (Hill et al, 1984; Longcope, 1987; Pederson et al, 1991) suggests that different dietary components, such as protein, fiber, fat and

zinc, are associated with amenorrhea in runners. No research shows the direct impact of

specific nutrients on human reproductive function, but very few have been conducted.

Animal studies suggest that calories, not dietary composition, are important (D. Foster et

al, 1985; A. Foster et al, 1986). Wood (1994) suggests that nutritional change, not

nutritional status alone is associated with changes in fecundity.

Exact mechanisms linking either calories or dietary composition with fecundity

are not known. However, some peripheral connections have been suggested. The

response of the hormones insulin or leptin, associated with diet, may act on the

Hypothalamic-Pituitary-Adrenal (HPA) axis to alter reproductive function or certain

amino acids may be neurologically active and have the same effect (Bringer et al, 1997).

Another proposed mechanism suggests that dietary changes may directly impact the basal

metabolic rate (BMR), changes in the thermoregulation of BMR could impact the HPA

axis directly (Bringer et al, 1997).

In western settings, even moderate aerobic exercise is associated with shortened

luteal phases and anovulation (Warren, 1980; Prior 1982; Cumming et al, 1994; 1995).

This is so even when no nutritional changes are observed (Bullen et al, 1985; Ellison &

Lager, 1985; 1986). Changes in seasonal patterns of workloads also are associated with 17 seasonal patterns in conceptions in rural populations in developing countries (Ogum &

Okorafor, 1979; Panter-Brick & Ellison, 1994). Mechanisms connecting heavy workloads/high energy output and reproductive function have been suggested. These may impact the HPA axis by decreasing secretion of Gonadotropin Releasing Hormone

(GNRH). Catecolemines, B-endorphines and cortisol hormones released during exercise that may impact the HPA axis (Cumming et al, 1995).

The balance of workloads/physical activity and energy input from diet is specifically termed energy balance. When calories required during daily activities are greater than calories consumed, negative energy balance is achieved. Negative energy balance has been associated with reduced fecundity by many researchers (Ellison 1990;

Malina 1991; Panter-Brick & Ellison, 1994). Negative energy balance results in loss of weight and body fat, but since women tend to be well buffered from weight loss, energy balance is most effectively calculated by subtracting measured energy output from measured energy input (Rossetta, 1991). However, energy input and output are difficult to determine accurately in field situations. Quick and easy methods such as recalls and diaries are not accurate. Accurate measures such as the use of heavy water and direct calorimetry are time consuming, expensive and intrusive.

Health Status and Infectious disease

Relationships between health and fertility have been established; unhealthy populations have fewer children, (McFalls & McFalls, 1984; Pennington & Harpending,

1991; Townsend & McElroy, 1992). Impacts and consequences of some diseases on fertility, especially sexually transmitted diseases such as gonorrhea and syphilis, and the 18 reproductive tract of women are documented (Belsey, 1976; Mascie-Taylor, 1992). High

fevers and disease-related anemia also increase pregnancy loss (Lawson, 1967; Belsey

1976). We do not yet know how an individual’s general health status and disease burden

impact reproductive function.

Infection can be a significant source of stress on human physiology. Responses of the human immune system are energetically expensive. Individuals with chronically low health status may be adding significant energy demands on their overall energy balance.

The potential impact of negative energy balance on fecundity has already been discussed.

Disease burdens of many individuals in developing countries are high (McFalls &

McFalls, 1984; Townsend & McElroy, 1992). In India, parasitic infections, tuberculosis and countless other diseases are endemic (Gyatso & Bagdass, 1998).

Psychosocial Stress

Impacts of psychosocial stress on fecundity are not clear, in fact, contradictory results have been reported. Some researchers suggest that since “emotional upsets” and chronic anxiety do not contribute to long-term amenorrhea, psychosocial stress is not a significant factor in determining fertility (Schacter & Shoham, 1994; Bringer, 1997).

Other studies have found no significant impact of stress-related factors on fertility and fecundity. For example, physiological indicators of stress were not significantly different between menstruating and non-menstruating long distance runners (Loucks & Horvath,

1985) and stress levels were not related to probability of conception in a sample of working Danish women (Hjollund et al, 1999).

19 In the United States and Europe, psychosocial and emotional stress levels are

associated with amenorrhea and infertility in some women. According to Barga (2002) cortisol is elevated in women suffering from amenorrhea when compared to normal menstruating women and stress-reduction techniques and psychological intervention increase chances of conception in these women. A similar association between stress levels and likelihood of conception was reported in a large sample of Danish women

(Hjollund et al, 2000). Women with long menstrual cycles who reported higher levels of distress, based on a standardized questionnaire, were less likely to conceive than their less distressed counterparts. Several other studies have also concluded that in well- nourished western populations, psychosocial distress may be a risk factor for reduced fertility (Sanders & Bruce, 1997; Veldhuis et al, 1998)

Hormones associated with human stress responses, including corticotrophin releasing hormone (CRH), adrenocorticotropic hormone (ACTH) and cortisol affect the hypothalamic-pituitary-adrenal axis that controls reproductive function

(Calogero et al, 1998; Veldhuis et al, 1998). Once a neurological stress response has

been initiated, the production of stress hormones is stimulated in the hypothalamus,

pituitary and adrenal cortex (Figure 1.3).

20

Hypothalamuss

CRH Corticosteroid Adrenal binding Cortex plasma Pituitary Cortisol proteins ACTH

Figure 1.3: Human physiological stress response in the Hypothalamic-Pituitary-Adrenal (HPA) axis.

An increase in any of these hormones has been found to negatively impact the GnRH pulse generator located in the hypothalamus and may impact reproduction (Veldhuis et al, 1998) (Figure 1.4).

21 (-) GnRH Pulse SSttrreessss (+) Generator products of the HPA axis response (hypothalamus)

Pituitary

(-)Blood LH

(-) Ovulation

Figure 1.4: Mechanism for the influence of the human stress response (from the HPA axis) on human reproductive function.

22 Seasonal Effects

Climatic season is associated with fertility patterns. Seasonality of births is a

common phenomenon for many different animal species and in many human populations

(Wood, 1994; Nelson et al, 2002). Patterns of climatic variation are associated with both

behavior and biology but most studies of seasonal behavioral variation shows little

impact on fertility. Seasonal climatic change has the potential to both directly and

indirectly impact fecundity.

Only two seasonal variables have the potential to directly impact fecundity and in

both cases, only in extreme climates. High temperature is a significant stressor for

humans. In Southeastern Nigeria, increased temperatures are associated with decreased

conceptions (Ogum & Okorafor, 1979). High temperatures have negative effects on male fecundity by decreasing both sperm count and motility and are associated with shorter menstrual cycles in women (Lam & Miron, 1994). While exact mechanisms are unclear, the fact that heat is a stressor for humans, causing physiological responses, have long been established (Hanna & Brown, 1983; Hanna et al, 1989). In humans, high temperatures result in elevated blood flow, increased heart rate, increased blood pressure and the activation of eccrine sweat glands by the hypothalamus (Hanna & Brown, 1983).

By acting directly on or indirectly by causing physiological stress, temperature may be responsible for seasonal variation in births but heat has only been observed to impact men (Levine et al, 1990). The only other seasonal variable that has been associated directly with patterns of fertility is photoperiod, the amount of light/day.

Photoperiod has only been found to impact populations living in extreme climates near the poles (Ronnenberg et al, 1990; Condon, 1991; Rosetta, 1993). 23 Seasonal variations in temperature, rainfall and photoperiod all have important consequences the physical environment which, in turn, has important consequences on life. For example, these seasonal climatic changes result in agricultural cycles and the availability of resources (Speth, 1989). Resource availability determines nutrition, weight and affects overall health. Seasonal climatic variation also helps determine the presence, or absence, and levels of pathogens and diseases (Tomkins, 1994). Workloads, energy balance and psychosocial stress also vary by season in many populations (Huss-

Ashmore, 1989; Speth, 1990; Bailey et al 1993; Panter-Brick, 1996; van Houwelingen,

2001). Thus indirectly, seasonality may be responsible for changes in fecundity through multiple alterations in lifestyle, behavior and biology.

1.4 Stresses affecting fertility in urban populations

Urban environments are highly variable and have not been well studied from an anthropological perspective as rural areas are the traditional domain of anthropologists.

Generally, urban environments are used only for comparative purposes. Urban populations often are considered to be healthy controls by which the progress or health of rural populations can be measured. Only a few urban populations have been the focus of anthropological study. One current trend in biological anthropology is to focus on

“diseases of civilization” (obesity, heart disease, hypertension, non-insulin dependent diabetes mellitus) with the assumption that “rural problems” such as poor health and nutrition and heavy workloads do not exist in urban areas (Baker et al, 1986; Dressler et al, 1987; McElroy, 1989; Crews et al, 1991; McGarvey, 1999; . Reviews of health in

24 urban areas show mixed results (Wang’ombe, 1995; Verheij, 1996; McDade and Adair,

2001; Schell, 2002).

The World Health Organization (1993) estimates that between 1990 and 2020 the urban population of the world will double. This leaves a huge proportion of the human population under-studied by anthropologists. Urban environments and its impact on human adaptive responses have been under explored. Further, with the world’s population growing, but the usable land area remaining relatively constant, more and more people are being forced to move to cities for their subsistence.

We do not fully understand urban environments and their consequences for human biological systems, health and reproduction. We also do not understand human adaptive responses to urban environments. Studies of modernization, acculturation and migration do not thoroughly explore the urban environment. Studies that use urban populations purely for comparative purposes ignore the heterogeneity in urban experiences (McDade & Adair, 2001). Potentially important factors such as seasonality often are ignored because they are assumed to be unimportant because culture or technology buffer the population.

1. 5 The Bhutia

The total fertility rate of Bhutia in Sikkim is only 3.9, despite their limited use of contraception (Bhasin in 1991). This rate is only slightly higher than the fertility rate found other Indian states where contraception is common (Kumar, 1997) and much lower than would be expected in non-contraceptive populations (Wood, 1994). Female fertility is affected by many behavioral and biological factors. There are currently no

25 explanations as to why the Bhutia have so few children, but there are clues. Due to

economic, political and social factors, the Bhutia are currently undergoing major lifestyle

changes.

More and more Bhutia are moving into the cities as the amount of available land

decreases. The capital city, Gangtok, has grown considerably in the past 30 years

according to the Sikkim Department of Health (1995). The lower socioeconomic status

associated with urban migration reduces nutritional status by reducing the availability of foods. The reduction in nutritional status is due to lower income, no access to land and the limited availability of traditionally used foods.

The lower educational level of the Bhutia, relative to other residents of Sikkim

(Bhasin 1991), limits job opportunities available to the more labor intensive. In such cases, energy intake will be lower than energy output, resulting in a negative energy balance which, in turn, reduces reproductive hormone levels such as LH, FSH, and progesterone. Lower levels of these hormones result in reduced fecundity. Negative energy balance also decreases immune resistance and increases disease burdens and morbidity. Consequently, higher disease burdens and increased morbidity may also decrease fecundity. These factors may be useful in explaining the low overall fertility of the Bhutia and will reflect the status of Bhutia women in Sikkim society. The proximate determinants of fertility that are the focus of this study are summarized and their relationships modeled in Figure 1.5.

26 - STRESS + + + Social - - Support + + - - - HEALTH FECUNDITY + - - + - - Socioeconomic - Status + - ENERGY BALANCE

+ = increase; - = decrease

Figure 1.5: Proposed model for the cultural, behavioral and physiological factors responsible for Bhutia low fertility rates.

27

1.6 Hypotheses

1. The energy intake of Bhutia women in Gangtok, Sikkim, India is less than their

energy expenditure, creating an overall state of negative energy balance.

Corollary 1: magnitude of energy deficit will vary according to climatic season.

2. Negative energy balance reduces fecundity in urban Bhutia women.

Corollary 1: negative energy balance directly affects fecundity by altering hormonal

levels.

Corollary 2: negative energy balance negatively affects health status, thereby

indirectly reducing fecundity.

3. Lower social well-being and economic status results in a higher magnitude of energy

deficit.

28 CHAPTER 2

BACKGROUND:

SITUATING BHUTIA WOMEN

2.1 Introduction

Individual fertility is dependent not only on the biological capacity to reproduce, but also the external environment. Issues such as political policy, access to family planning facilities and individual autonomy and choice can impact individual reproduction. In order to understand individual fertility, that individual must be placed in a broader political and cultural context.

2.2 Women and Fertility in India

2.2.1 Population/birth control policies

In 1952, India became the first country to promote a national population program with the goal to “stabilize at a level consistent with the requirements of national economy” (Government of India, 2000: p. 3). However, since then, the topic of population has been a controversial one. Following the institution of the policy, population growth began an upward trend and actually increased from 1951-1971

(Panandiker & Umashankar, 1994).

29 One of the primary reasons for controversy was the actions of Prime Minister

Indira Ghandhi and her son Sanjay. In 1976 the Constitution act was passed, giving the central government more power in population policies and allowing the central government to set sterilization targets for states (Panandiker & Umashankar, 1994).

During the following year, 8.1 million people were sterilized, a number that has not been reached since (Panadiker & Umashankar, 1994). Primarily, poor, lower caste individuals and minority religious groups were targeted by local officials who where responsible for meeting the sterilization quotas. In 1977 when Indira Gandhi was replaced by a new political party, sterilization quotas were removed and emphasis was placed on the voluntary nature of the population program. However, many minority groups today still distrust the population programs and policies of the Indian government.

Current power to develop population policies lie with the states, but much of the funding comes from the national government. According to the current Indian

Population Policy the goal is to reduce population growth to meet the requirements of

“sustainable economic growth, social development, and environmental protection”

(Government of India, 2000: 4). Objectives include providing complete access to contraception and reproductive health care services, increasing education rates, promoting delayed marriages, decreasing infant and child mortality, increasing male participation in reproductive decisions, and promoting the small family norm

(Government of India, 2000; 2001). In fact, government promotion of the small family norm is obvious with the slogan “We two with our two” plastered in and English on billboards, buildings and hospitals throughout the country.

30 2.2.2 Fertility rates and trends in India

With 16% of the world’s population on 2.4% of the world’s land, India is one of the most densely populated countries in the world (Government of India, 2000: 3). As of the 2001 census (Government of India 2002) the total population of India was

1,027,000,000. However, since the first national population program was put in place in

1952, India has managed to reduce population growth. In 1998 the crude birth rate was

26.4, a dramatic decrease from the 40.8 rate in 1951(Government of India, 2000: 5). In addition, the total fertility rate (TFR) has decreased from 6.0 in 1951 to an average of 3.3 in 1997 (Government of India, 2000: 5). The use of birth control has also increased from

10.4% of couples in 1971 to 44% in 1999 (Government of India, 2000: 7).

Fertility rates did not show a steady decline in India during the past 50 years.

Following the repeal of the state sterilization act in 1977, the crude birth rate increased to

33.3 in 1978 from 33.0 (Panandiker & Umashankar, 1994). This crude birth rate remained stable until 1985 when the rate again began a downward trend. Currently, birth control pills, condoms and intra-uterine devices are provided to government hospitals and primary health clinics in all states and are sold to private pharmacies and clinics at low cost.

While the TFR in India is currently above replacement, on average, the TFR of different states are highly variable. State averages, estimated from 1995-1997 statistics range from 1.0 to 4.8 (Government of India, 2000: 12). TFRs for selected states based on the 95-97 estimates are given in the Figure 2.1.

31

6

5

4

3 TFR

2

1

0 Goa Kerala Karnataka Sikkim W. Bengal Punjab Rajastan Uttar Pradesh State

Figure 2.1: Total Fertility rates for selected Indian states based on Government of India (2000) estimates from data collected 1995-1997.

32 2.2.3 Status of women in India

The status of women in relation to men and the autonomy of women is closely

related to demographic processes (Federici , 1993). Women’s status and autonomy in

India are among the poorest in the world (Guha, 1996; Jejeebhoy, 2000; Palriwala &

Agnihotri, 1996; Singh, 1996; Sahai, 1996). According to Radcliffe-Brown (1952)

female autonomy will be the lowest in cultures with patrilineal descent, patrilocal

residence, inheritance and succession practices excluding women and male-centric

hierarchical relations. Traditional Indian society has a history of all of these traits.

Constitutionally, discrimination based on sex is illegal in India. However, India

has a long history of macro-policies that have been used to deny women land,

employment, education, health, security and legal rights (Palriwala & Agnihotri, 1996).

Conversely, women have had a long history of political activism in India. Beginning in

1917 and continuing today, there have been countless national and local women’s organizations active and vocal on Indian society and politics. Mahatma Gandhi especially understood the importance of women’s participation in the struggle for independence and the development of democracy in India. The following statement summarizes his beliefs on women: “Let us not live with one limb completely or partially paralyzed, we will be incapable of depending ourselves or healthily competing with other nations, if we allow the better half of ourselves to become paralyzed.” (Sahai, 1996: 24).

Women are currently and have been considered important participants in the democratic process. Thirty percent of places in all elected bodies of India are currently reserved for women (Palriwala & Agnihotri, 1996).

33 Women’s groups have been instrumental in the development of several laws and constitional additions for the protection and freedom of women. Shortly after independence, women in India were given the legal right to petition for divorce, inherit money and property and be adequately maintained by either husband or parent (Sahai,

1996). Trafficking of women and girls was made illegal in 1956 and the paying or receiving of dowry was made illegal in 1961 (Sahai, 1996). In the 1970’s legislation was passed to prevent female feticide and legalize abortion (Government of India, 2000). In addition, amendments to the universal criminal code in the Constitution of India were made in 1983 to give rights to rape victims and set the minimum sentence for convicted rapists at 7 years (Patel, 1996) to ensure that any unnatural death of a married woman would be investigated (Palriwala & Agnihotri, 1996).

India tends to be of two minds on the subject of women. In traditional Indian society, women are considered to be the keepers of honor and the family (Guha, 1996;

Palriwala & Agnihotri, 1996; Singh, 1996). Despite the suggestions of Gandhi, the teachings of the early Indian scholar Manu suggest a more traditional view of women in

India: “Father protects a woman during childhood, husband during youth, sons during old age, so a woman deserves not freedom for sooth.” (Guha, 1996: 89). Many current indicators on the status of women suggest that this traditional belief is still widely held.

The most recent , completed in 2001, shows the sex ratio of 933 females/1000 males (Government of India, 2002). Biologically, the ratio between males and females should be almost equal and a ratio of less than 1,000 suggests that females are at a disadvantage in India (Johansson & Nygren, 1991; Murthi et al, 1995; Mayer

1999). Higher mortality rates, at any age, will result in skewed sex ratios. As with most

34 other measures, the sex ratio is highly variable between states, ranging from 709 in

Daman and Diu to 1058 in Kerala (Governemnt of India, 2002). Sex ratios of selected

states are given in Figure 2.2.

1200 1058 964 s 934 932 1000 909 875 874 898 le 821 709

Ma 800 0 0

0 600 1 / s

le 400 a m

e 200 F 0

iu d l b n lhi la ke im m sh D a a a k ja a e & l De r k nga u s Si e P ad n Ke rnat B As a aga a . Pr m N K W r tta Da U State

Figure 2.2: Sex ratios for selected Indian States from 2001 Census data (Government of India, 2002).

35

Literacy rates for males and females also show disparities. Of the literate population of

India, males make up 60.0% with females constituting only 40% of the literate population

(Government of India, 2002). Female literacy rates are lower than male literacy rates in all states, female literacy rates of selected states are given in the Figure 2.3.

100

90

80

70

) 60 (%

e Females 50 Males racy Rat e

Lit 40

30

20

10

0 Delhi Kerala Sikkim W. Bengal Punjab Assam State

Figure 2.3: Female literacy rates for selected Indian states from 2001 census data (Government of India, 2002).

36 Women have been allowed to vote and stand for office since independence, but currently only 8.9% of seats in Parliament are held by women and a very small number of ministerial seats are held by women, 10.1% (UNDP, 2002). In addition to the political arena, women are not well represented. Only 42% of women are economically active

(UNDP, 2002) and only 60% of women have any access to money (Lama, 2001: 3). In fact, studies of workforce participation show that women’s participation in the formal workforce has actually declined since 1911. Rates of workforce participation also vary widely by state, a list of women’s participation rates for selected states are given in

Figure 2.4.

37

60

50

40 ) % n ( e

30 g Wom in k Wor

20

10

0 Orissa Kerala Sikkim W. Bengal Punjab Rajastan State

Figure 2.4: Female workforce participation rates for selected Indian states from 2001 census data (Government of India, 2002).

The devaluation of women’s work, sexual stereotyping of work, and wage disparity

(Singh, 1996; Basu, 1996) may result in the large number of women in India, 33%, living below the poverty level (Jejeebhoy, 2000:207).

Most women in India marry, and marry quite young and have very little say in the marriage. The average age of marriage in 1991 was 16.8 years (Lama, 2001:4).

Tradition in both Muslim and Hindu marriages is that they are arranged by the parents of the bride and groom based on caste, class and socio-economic status. In addition,

38 although there are laws to the contrary, the dowries are still commonly required of the

bride’s family. Generally, married women move in with the husbands’ family and have

low status within the household and little autonomy (Jejeebhoy, 2000; Guha, 1996;

Shariff, 1996). In 1991, 24% of married Indian women reported that they did not need

permission to visit friends and relatives; in addition, 21% of Indian women reported

being physically mistreated (Lama, 2001:3).

2.3 Sikkim

Sikkim is a small state in eastern India, crosscut by two major ridges of the

Himalayas. In 1975 Sikkim, formerly a principality became the 22nd state and smallest

state in India. The state of Sikkim consists of four districts covering an area of only 7,299

km2. The geography of Sikkim is mountainous ranging from less than 1,000 feet above

sea level in the Himalayan foothills of the south to its highest peak, Mount

Kanchenjunga, the third highest peak in the world, standing at 28,200 ft (8,598 m).

2.3.1 Geographical location

The state of Sikkim is located in the Northeastern region of India. To the north,

Sikkim shares a border with , giving the state strategic significance. borders the state to the East and to the West. The southern border of the state is shared with the Indian state of . Sikkim is 113 km long and 64 km wide. The capital city of Gangtok is located in the East district of the state at an official elevation of

5,000 ft. However, the city is located on the top and both sides of a mountain ridge and elevation varies by several hundred feet.

39

2.3.2 Environment

Sikkim is a highly seasonal state in both temperature and rainfall. Altitude

determines the climatic conditions with temperature varying greatly depending on

altitude. Sikkim, because of its geographical location is in the direct path of the yearly , making it one of the wettest states in India. However, areas above 5,000 m receive precipitation only in the form of . is high and constant with an average humidity level above 70% throughout the year, except at extreme altitudes. The minimum and maximum temperatures and average monthly rainfall for Gangtok in 2001, collected at the government weather station (Figure 2.5).

40

25

20

) 15 (C

e Max Temp

ur Min Temp at

er 10 p Tem

5

0

t ry h ly p rc ay ov a M Ju N nua Se a M J

Figure 2.5: Monthly minimum and maximum temperature (oC) in Gangtok.

Maximum temperatures in Gangtok are mild compared to most other areas in

India, but the minimum temperatures are considered quite cold in winter. Temperatures

are highest during the Summer (July, August, September), lowest in the Winter (January,

February, March) and moderate in the Spring (April, May, June) and Fall (October,

November, December). Rainfall is highest during the monsoons which begin in June and

taper off in August. From November to February, rainfall is almost non-existent and

moderate in the spring and fall and varies during these months depending on the exact

41 timing of the monsoons. Figure 2.6 summarizes the monthly variation in rainfall,

collected at the government weather station in Gangtok in 2001.

700

600

500 )

m 400 ll (m fa

n 300 Rai

200

100

0

h y ry ay l pt ov rc M Ju N nua Se a Ma J

Figure 2.6: Mean monthly rainfall (mm) in Gangtok.

2.3.3 Politics, history and populations

Because of its strategic location along the border with Tibet, Sikkim is considered a restricted state. Because of this, few researchers have worked among the populations and prior studies are few. Recent population information is available from the 2001

42 census of India (Government of India, 2001) and the Demographic Health Survey (1998-

1999).

According to the 2001 census (Government of India) the population of Sikkim

was 540,493. There are five major ethnic groups currently living in Gangtok: Lepcha,

Bhutia, Sherpa, Nepali, and Indian. The earliest inhabitants of the state are unknown as

the archaeological record has not been examined. Migration from Southern China and

Assam is thought to have begun as early as 1000 B.C. (Zurick & Karan, 1999). Of the

current residents of the state, the Lepcha are thought to be the earliest inhabitants,

migrating into the area from Assam around that time (Subba 1984; Bhasin 1991; Zurick

& Karan 1999). The Bhutia, of Tibetan origin, probably began their migration into the

area about 2000 years ago (Zurick & Karan 1999).

The majority of the current residents of the state are Hindu (68.4%) originally

from Nepal or other Indian states. Buddhists (tribal populations) comprise 27.2% of the

population and Muslims, Christians, Sikhs, and other religions make up the remaining

small percentage. Sikkim is the least populated state in India, but does not have the smallest population density (Government of India, 2002).

2.3.4 Development of the state

After taking over governmental operations, India began to increase tourism and industrial production in the state. The city of Gangtok, in the eastern district of the state,

was named the capital and quickly became the industrial center. There are however,

limited industries available in Gangtok.

According to the 2001 census (Government of India, 2002), of the 35 Indian

states, Sikkim ranks highly for household telephone ownership, 13th, tap as source of

43 drinking water , 7th, households with electricity, 15th, and inside bathrooms, 7th . Sikkim does not rank as well in television ownership, 19th, access to gas for cooking, 20th, and

22nd for cars.

2.3.5 Health status, demography and fertility of Sikkim

The majority of urban married women in the East district of Sikkim in 1998

reported using no method of birth control, 63.0%, and in rural areas the percentage was

greater, 68.3% (Gyatso & Bagdass, 1998: 89-90). Among rural married women, the most

popular forms of birth control were oral pills (11.4%) and tubectomy (11.2%), in urban

areas the most popular form was tubectomy (19.0%). Contraceptive preference rates for

married women (rural and urban) in each district are reported in Table 2.1.

METHOD NORTH EAST SOUTH WEST TOTAL (%) R U R U R U R U R U Condom 5.2 0.8 3.7 5.7 6.2 5.0 1.4 9.1 3.9 5.4 IUD 5.2 12.1 4.6 4.6 3.9 3.9 4.3 0.0 4.4 4.8 Oral Pills 23.8 10.5 11.2 7.0 7.5 8.5 10. 19.1 11.4 7.4 Tubectomy 6.4 2.4 17.0 21.1 6.9 13.2 7.2 31.8 11.2 19.0 Vasectomy 0.2 0.0 0.7 0.4 1.9 0.7 0.3 0.0 0.8 0.4 None 59.2 74.2 62.8 61.2 73.6 68.7 76.7 50.0 68.3 63.0

Table 2.1: Reported contraception preference rates for rural and urban married women in Sikkim districts.

Only 0.21% of married rural women reported having an abortion and 0.34% in

urban areas. However, 5.4% of rural and 2.2% of women in urban areas reported a still

birth (Gyatso & Bagdass, 1998: 98). Very high rates of still births were reported in the 44 youngest age category of rural women (14.0%, N=46). The age specific fertility rates

(Gyatso & Bagdass, 1998: 96-97) for rural and urban married women are given in the

Table 2.2.

AGE OF 0 (%) 1 (%) 2 (%) 3-5 (%) 6+ (%) WOMEN R U R U R U R U R U < 14 83.7 72.2 9.3 16.7 7.0 11.1 0.0 0.0 0.0 0.0 15-17 63.2 56.7 31.6 40.0 5.3 3.3 0.0 0.0 0.0 0.0 18-20 51.6 51.1 34.6 33.2 12.5 14.0 1.4 1.7 0.0 0.0 21-25 32.4 34.4 25.3 28.3 26.0 23.6 16.1 3.6 0.3 0.2 26-30 28.6 28.0 13.6 16.2 23.2 29.9 32.1 5.2 2.5 0.7 31-35 25.1 22.5 6.8 9.1 19.1 20.8 40.6 4.7 8.5 2.9 36-40 24.4 17.3 5.4 7.9 15.5 21.3 41.6 5.8 14.0 7.7 41-45 24.1 15.7 2.9 10.7 8.9 23.6 42.0 6.0 22.2 14.0 > 46 19.8 14.3 3.5 7.1 5.9 11.9 31.2 0.5 39.6 26.2

Table 2.2: Age specific fertility rates for rural and urban married women in Sikkim.

2.3.6 Health in Sikkim

According to the (Gyatso & Bagdass., 1998) the cause of

the majority of deaths is unknown. However, deaths from infectious diseases, such as

measles, meningitis, pneumonia, and TB, are not uncommon. Chronic diseases such as

anemia, heart disease, asthma, cancer and diabetes are also common. Cause-specific

mortality rates are given in Table 2.3 (from: Gyatso & Bagdass,1998: 111).

45

CAUSE NO. OF DEATHS % OF TOTAL DEATHS accident 40 1.8 anemia 15 0.7 asthma 80 3.7 cancer 60 2.8 cardiac 80 3.7 diabetes 20 0.9 epilepsy 15 0.7 gastrointestinal 65 3.0 headache 15 0.7 jaundice 45 2.1 measles 5 0.2 meningitis 5 0.2 pneumonia 105 4.8 suicide 10 0.5 TB 170 7.8 whooping cough 5 0.2 others 95 4.4 unknown 1306 60.2

Table 2.3: Cause specific mortality rates in Sikkim.

46 The infant mortality rate in Sikkim is 51/1000 live births with a slightly lower rate of 49/1000 births in urban areas (Gyatso & Bagdass, 1998: 113). In Sikkim, 21.2% of males and 14.7% of females report consumption of alcohol (Gyatso & Bagdass, 1998:

114) and 11.6% of males and 7.5% of females reported smoking (Gyatso & Bagdass,

1998: 117) with older age groups being more likely to both smoke and drink. Smoking and drinking were also more likely in rural populations than in urban populations. In

Addition, 13.5% of males and 7.2% of females reported chewing tobacco (Gyatso &

Bagdass, 1998: 120-121). Similarly, rural populations and higher age groups reported more use. Primary Health Centers and Hospitals were the medical facilities reportedly used more often, with private practice and independent healthcare services (traditional medicine) being used much less often (Gyatso & Bagdass , 1998: 123).

2.3.7 Status of women in Sikkim

Women in Sikkim are generally thought to enjoy higher status in society as well as higher status within the family than elsewhere in India. However, this depends on religion and area. Generally, women in Sikkim marry later than the rest of India at an average age of 19.8 (Lama, 2001: 4). In addition, the Buddhist populations do not follow the strict caste system, are less strict about arranging marriages and do not generally pay dowry. They also enjoy relatively greater autonomy and status than other Indian women with 42% of women report themselves free to visit others without permission and only

11% report physical mistreatment (Lama, 2001: 4).

The literacy rate in Sikkim is 70% with 77% of males and only 62% of females being literate (Government of India, 2001). In 1991 (Government of India) the literacy rate was 57% with 66% of males and 47% of females capable of reading and writing.

47 While the literacy rate has increased by more than 12%, the male to female literacy rates

have only increased by about 4%. In Gangtok, the literacy rate is higher, 79%, with 82%

of males and 75% of females literate (Government of India, 2001). Although smaller than the gender differences for the state, there remains an 8% difference in male/female literacy rates in Gangtok. In all districts, both rural and urban areas, the percentage of uneducated women is greater than the percentage of uneducated males by more than 10%

(Gyatso & Bagdass, 1998: 24-5).

The ratio of females to males in Sikkim is 875/1000, ranking somewhere in the middle of the Indian states (Government of India, 2001). The female/male ratio for selected states in India is given in Table 2.4.

STATE NO. OF FEMALES /1000 MALES Kerala 1058 Chatisgarh 990 West Bengal 934 India Total 933 Sikkim 875 Punjab 874 Hariana 861 Daman & Diu 709

Table 2.4: Sex ratio for selected Indian States.

These numbers disagree with the sex ratio determined in the 1997 health survey

conducted in Sikkim. According to this report (Gyatso & Bagdass, 1998: 16), the sex

48 ratio of the state is 924 females/1000 males with a lower ratio (875/1000) in the more urban areas of Sikkim.

The age distribution of males and females in rural and urban populations in

Sikkim is given in the Figure 2.7.

70

60

50

n 40 o i t a l rural u

p urban o

% P 30

20

10

0 male female male female male female male female male female male female 0-4 5--9 10--14 15-44 45-49 >60 Age

Figure 2.7: Age distribution of males and females in rural and urban Sikkim.

In urban areas of Sikkim, women constitute only 19.3% of the workforce. In both rural and urban areas, the most common occupation for women was housewife (Gyatso &

Bagdass, 1998: 28).

49 The crude birth rate in Sikkim in 1997 was 25.9/1000 with rates higher in rural

(28.9) than in urban areas (20.7) (Gyatso & Bagdass, 1998: 85). Average family size in

Sikkim is high, generally over 4 with an average family size of 5.2 in rural areas and 4.9

in urban areas. Age at marriage in Sikkim is generally later than in other states of India with the highest proportion of women in both rural and urban areas being married between 18-20 years of age (In both rural and urban areas, women in Sikkim have children later in age, after the age of 21 (Gyatso & Bagdass, 1998: 87). The average age of marriage for urban women is 19.2 years and 20.3 years in rural areas. The average age of marriage for urban men is 22.9 years and 23.5 years for rural men. The proportion of women who marry extremely young (<14 years) is very low, only 4.8% in rural areas,

8.8% in urban areas. Rural/urban patterns in marriage in Sikkim tend to be opposite the pattern observed in other Indian states with later ages of marriage in rural areas than in urban areas.

2.4 Bhutia women

Bhutia women participate in the workforce, especially in the Eastern district of

Sikkim. are Buddhists; according to their religion, Bhutia families should be as large as possible. Most Bhutia women follow this principle with over 80% of the population not practicing family planning (Bhasin, 1991). This makes the Bhutias a population.

While the number of Bhutias has been rising rapidly, the Bhutia percentage of the population has been dropping rapidly with the recent influx of migrants from

Nepal and Tibet, as well as from other states in India. The increased number of

50 individuals in the area has also increased the person to land ratio (Subba, 1984), making urban centers and urban jobs more appealing. The Bhutias, however, have almost no influence in governmental affairs and do not control any businesses. As a consequence, Bhutias tend to be marginalized, especially in urban areas. Levels of education, income, and access to health care tend to be lower for the Bhutias than for the population in general (Bhasin, 1991).

There have been no prior studies aimed at determining status and levels of autonomy among Bhutia women. But several cultural differences suggest that Bhutia women do enjoy a relatively high status and high level of autonomy. Bhutia marriage tends to be similar to other Indian marriages as the best match possible is made by family standards of socio-economic status and class. However, there seems to be a lot more flexibility in these marriages. Although they generally must be accepted by the parents, matches can be suggested by friends and the children themselves. Arrangements are made with the agreement of both sides, but no dowry is paid. On the contrary, traditionally the groom’s family is expected to bring gifts to the bride’s family during the official negotiations.

51 CHAPTER 3

SAMPLE AND METHODS

3.1 Research Design

The current study was carried out as part of a larger project examining modernization and health among the populations of Sikkim, funded by the Indian

Statistical Institutes. Directed by Dr. Barun Makhopadhyay, the larger project is designed to contribute data on the results of modernization on the general and reproductive health of Bhutia women. The Bhutia are a population in transition, such transitions are unique and short-term. How this transition is affecting health and reproduction is unknown, but the results of this study will provide information to improve the understanding of this transition and its consequences for the Bhutia.

Due to local and global conditions and the goals of development policies, migration from rural areas is causing the rapid growth of urban areas throughout the world. Politically motivated structural adjustment policies currently are being implemented throughout the world. Adjustment policies reduce availability of social and health programs in developing countries and will result in exacerbation of the negative affects of urban migration on individuals (Tinker, 1976; Goulet & Wilber, 1992; Gupta et al, 2000;

52 Ricupera, 2000; Moss 2002). Clearer understanding of the specific impacts of migration

on health and well-being may provide clearer understanding of populations needs and

how to effectively direct available resources.

To aid in understanding how environmental and social factors impact health and

reproduction, biocultural and physiological methods were combined. Cultural

information was collected to assess life style, health, psychosocial stress, menstrual

cycles and nutrition. Biological and physiological data collected included anthropometric

and blood pressure measurements, finger-stick blood to determine hemoglobin and C-

reactive protein and urine samples for reproductive hormones. The specific aims of this

study are to:

1) Describe and understand the daily life, health status and stresses of urban Bhutia

women.

2) Identify biological, physiological, and social factors that impacting their

reproductive function.

3) Identify economic and social factors influencing these biological and

physiological factors.

3.2 Study Population

Women living in Gangtok, Sikkim and the immediately surrounding areas were

recruited as participants in this study. Officially, 29,162 people live in the city of

Gangtok (Government of India, 2002). However, there are several small villages in close proximity to Gangtok, on the same mountain ridge. For inclusion in this study, all villages on the local taxi line were considered to be part of Gangtok. Most individuals 53 living in this area, work, purchase their food and most other goods in Gangtok, and receive basic utilities (e.g.: water, electricity, phone, cable) from Gangtok. The desired sample size was 200 women to complete four seasons of questionnaires and measurements. Assuming not all enlisted participants would complete the entire study,

238 women were initially enrolled in the study and 198 women remained enrolled at the end of the fourth season.

3.2.1 Sample selection and characteristics

A field team consisting of myself and three local research assistants visited a section of the study area on two days, one work day and one non-working or holiday.

The team then went door to door to identify any Bhutia women living in the area. All buildings, both homes and businesses were visited. Non-pregnant women who would be between age 25-35 during the study year, non-lactating and not currently using contraception were asked to participate in the study. Those who agreed were provided a consent form to sign. Consent forms were also read to each participant in Nepali by a native speaker. Recruiting was conducted during the first three month season of the year

(January – March, 2001). Initial interviews were also conducted at this time and appointments were made for a follow-up visit to obtain anthropometric measurements.

At the beginning of the study period, the age range of women in this study was

24-35 (mean = 28.8 years). Of the original 238 women, 131 (55.0%) women were married, 4 (1.7%) were widowed or divorced, and the remaining 103 (43.3%) were single. Years of education in this sample (mean = 10.3 years) ranges from none to college educated. Single women are more educated than married women (11.5 vs. 9.3 years). The average age of marriage was 22 years with a range of 15-33. All women

54 were Buddhist except one, who had recently converted to . Table 3.1 summarizes the general characteristics of this sample of Bhutia women.

N=238 Range Mean Median S.D. Age 24-36 28.8 28 3.8

Height 140.3-168.2 154.51 154.3 6.80 (cm) Weight 31.0-96.8 54.58 54.4 8.79 (kg) Education 0-16 10.3 12 4.1

Age at Marriage 15-33 22.11 22 3.5 # of Children 0-4 1.4 1 .8 (married women only)

Table 3.1: General sample characteristics

3.3 Education and occupation

Most participants worked (63.4%). Among single women 76.7% worked and

53.3% of the married women worked. Working participants held a variety of jobs, including government service, teaching, small shops, and nursing. Of the unemployed women, nine were students at the government college or state-sponsored cottage industry technical school.

This sample of women was well educated. Years of education averaged 10.3 and only a small percentage, 5.6%, were completely uneducated. Most women completed some form of higher education: three-year degrees in nursing, secretarial training, or vocational training, college, or, post-graduate work (Figure 3.1). 55 30

25 24.4

20.1 20 18.4

16.2

) 15 (%

9.8 10

5.6 5 4.3

1.3

0 None Class 1-4 Class 5-9 Class 10 Class 12 3-yr. Degree College Post-Grad Years of Schooling

Figure 3.1: Educational levels of Bhutia women in this sample.

56 As expected, this generation is better educated than their parents. The average number of years of education for participants’ mothers was 1.4 and 3.6 years on average for participant’s fathers. The majority of both mothers and fathers in the previous generation were completely uneducated: 81.9% of mothers and 60.5% of fathers had no schooling.

3.3.1 Household characteristics

The majority of women lives in family-owned houses (56.7%), the remaining

43.3% rent homes. Few women (9.8%) live in houses made of something other than modern concrete construction. The average number of rooms in these houses was 5 with a wide range of 1-19. Only 14 women sampled had to carry the water for their household. The majority of the sample (94.1%) had a piped water source in or near their home, only one subject did not have a toilet inside the house. The average number of people living in each house was 6 including family and servants with a range of 1-25 individuals. Fifty-seven of the households included servants (23.9%) with between 1-11 servants in these households.

Slightly more than half of the women in this study were born in Gangtok, others moved to Gangtok either as adults, for various reasons, or as children with there families.

Women who had moved to Gangtok had lived there for 11.6 years, on average, but with a wide range, 1-34 years. Common reasons given for moving to Gangtok are given in

Table 3.2.

57

REASON FOR MOVE TO N (%) GANGTOK (TOTAL = 232) Birth 117 50.4 Marriage 47 20.3 Education 33 14.2 Job 18 7.8 For or with family 12 5.2 Child’s Education 2 0.9 Husband’s Job 2 0.9 Army moved into home area 1 0.4

Table 3.2: Common reasons reported by this sample for moving to Gangtok.

Education and jobs, whether the participant’s or her family’s, were common reasons given for moving to Gangtok. While the government offers free schools in all districts of

Sikkim, government schools are generally thought to be inferior to private ones available in the Gangtok. In addition, higher education is only available in the state at the

Government College in Gangtok. Technical education is also offered at the government sponsored Handicraft Institute located in Gangtok. The majority of the non-agricultural jobs are also found in Gangtok. Jobs in tourism, government service, health-care, retail sales and industry are primarily located in Gangtok.

3.3.2 Marriage and family

The majority of women, 57%, were married at the time of the study. On average, women in this sample married later than women in Sikkim and India in general. Average age at marriage in this sample was 22.1, while the average for Sikkim is 18.9 and India,

58 16.8 (Lama, 2001). The earliest marriage in this sample was at 15 years and the latest at

32 years. Several unmarried women in this sample are at or over the age of 32 and do not consider marriage to be unlikely or unattainable. The average age of unmarried women was 26.8 years; the average age of married women was slightly higher at 30.3 years.

Married women tend to be less educated than unmarried women. The average number of years of education for married women was 9.4, for unmarried women, 11.5.

The average education of the 131 husbands of this sample was slightly higher than there wives, at 11.0 years. The breakdown of the education distribution of husbands is provided in Figure 3.2.

59

30

26.7

25 24.4

21.4

20 19.1

) 15 (%

10

5 3.1 3.1

1.5 0.8

0 None Class 1-4 Class 5-9 Class 10 Class 12 3-yr Degree College Post Grad Years of Schooling

Figure 3.2: Educational distribution of the husbands of the Bhutia women in this sample.

60

Fewer husbands were completely uneducated than their wives and more held college degrees than the women sampled. In general, husbands tended to be more educated than their wives, however in 18.3% of the couples, the wives were more educated than their husbands.

Contrary to the norm in Indian society, Bhutia women’s marriages were not usually arranged. In fact, only 44.1% of the married women in this sample reported that their marriage was arranged, the majority of women in this sample reported marrying for love. Based on a standard decision-making questionnaire, women whose marriages were arranged did not have less decision-making ability within their household. The majority of married participants had access to money and had almost equal decision-making ability within the household whether they married for love or not. Bhutia women also do not follow the traditional Indian patrilocal living arrangements. After marriage, the majority of married couples, 81.8%, lives independently while 11.8% live with the husband’s family and 6.4% live with the wife’s family.

Married women had relatively small family sizes, on average, less than two children and no women in this sample had more than 4 children and no unmarried women had children. On average, women in this sample came from families that were only slightly larger, with an average of 4 children in the family. However, the range of family sizes was larger in the previous generation (0-10 siblings). Aggressive advertising of the

National Population Policy appears to be working, at least for these women. A majority of women in this sample, 92.0%, reported wanting 2 or fewer children and no one reported a desired number of children over 4 (Figure 3.3).

61

60 56.6

50

40

34.2

) 30 (%

20

10 6.7

1.3 1.3 0 None One Two Three Four Desired Number of Children

Figure 3.3: Desired number of children reported by women in this sample.

Although women were accepted into the study only if they were not using birth control during the study, a small number of married women (3/131) reported that their husband’s used condoms. In addition, 10 women reported a past use of birth control pills, 3 had taken injections in the past, and 19 had tried an IUD at some point during their marriage. Abortions were not uncommon in this population either. Seventeen of the 131 married women reported at least one induced abortion. Reproductive health of this population also seemed to be good in this population with only 5 women reporting a

62 history of problems of the reproductive tract, all being minor and only 5 women reported spontaneous abortions. Further, gynecologists at the government hospital reported that problems of the reproductive tract were rarely seen among Bhutia women in Gangtok.

3.4 Data Collection Techniques

3.4.1 Overview

Initial interviews included basic demographic information, reproductive history, family history and household information (Appendix A). Questions also assessed socio- economic status, health status, education and occupation. All interviews were conducted in Nepali, the native language for all participants. Questions were asked by trained research assistants who where well educated Bhutia women. The research assistants were native Nepali speakers who were members of the community.

During each of 4 seasons, anthropometric, biological and physiological data were collected along with psychosocial stress levels, activity and nutritional information. All data were collected by a trained research assistant or the researcher. The research assistant collecting the biological and anthropometric data received a nursing degree in

New Delhi and was trained in the specific methodology by the researcher. Menstrual cycle data was collected on a monthly basis and urine samples were collected, to determine reproductive hormone levels, at the end of the study year.

3.4.2 Procedures

Anthropometry

Standard anthropometric techniques were followed to measure stature, weight, five skinfolds, and five circumferences (Lohman et al., 1988). All stature, skin fold

63 measurements and circumferences were conducted three times on the left side of the

body. Averages of these measurements were used in the final analysis. Measurements

were made during each season by the same research assistant, a nurse, who was trained in

anthropometric techniques and supervised by the researcher. On the few days this

assistant was absent, measurements were taken by the researcher. Skinfolds were

measured with a Lange® skin fold caliper. Five skinfolds measured were tricep, bicep,

supscapular, suprailliac, and mid-calf. Circumferences were measured using a standard

nylon tape measure at the mid-arm, abdomen, waist, hip and mid-calf. Reliability was

assessed using standard protocols (Mueller & Martorell, 1988). Stature was measured

using a Gnuepel® anthropometer. Body weight was measured with a portable scale and a

Tanita® combination scale/body fat monitor. Participants were measured wearing light

clothing and bare feet and weight was recorded to the nearest tenth of a kilogram. Body

fat was measured by bio-electrical impedance using the step-on Tanita® scale and a

hand-held Omron® Body Fat Monitor and was recorded to the nearest tenth of a percent.

Anthropometric Indices

Anthropometric measurements were used to calculate several anthropometric

indices for use in statistical analyses. The following indices were calculated: body mass

index (BMI), total upper arm area (TUA), upper arm muscle area (UMA), upper arm fat

area (AFA), arm fat index (AFI), sum of two skinfolds, sum of five skinfolds, and waist

hip ratio (WHR). Formulas used to calculate these indices are presented in Appendix C.

Blood Pressure

Both systolic and diastolic blood pressures were measured three times for each participant. Blood pressure measurements were made on the left arm using a standard

64 Littman® cuff, sphygmomanometer and stethoscope. Systolic and diastolic levels were

measured on each participant in a seated position after at least five minutes of rest. Blood pressure measurements, in mm Hg, were recorded to the nearest digit.

Health Status Assessment

Blood analysis

Fingerstick blood samples were obtained from most participants on a seasonal basis and were used for analysis of hemoglobin and C-reactive protein. A single cutaneous puncture was made manually by the researcher and the first drop of blood was discarded. Blood was then allowed to pool and a 20ul sample was collected with a

Pipetteman micropipette. The 20 ul sample was placed in vial of buffer solution. At the end of each day, these vials were delivered to the laboratory of Dr. Varma at Care

Diagnostics where they were analyzed by experienced personnel for hemoglobin concentrations using standard techniques. An additional 20 ul sample of blood was collected and placed on filter paper for C-reactive protein analysis. The blood samples on filter paper were allowed to dry in a clean area for at least twelve hours. After drying they were frozen at -40oC until transport to Calcutta for analysis.

C-reactive protein analysis

C-reactive protein analysis was conducted in the laboratory of Dr. Chakraborty at

Calcutta University Medical School and methods were determined by that laboratory. A

20 µl sample of blood from each subject (obtained from finger puncture) was spotted on

blotting paper in duplicate. Blotting paper strips were transported to the laboratory in an

ice box and stored at -20°C until assays were completed. Each spot was cut out accurately

and blood eluted in 100 µl phosphate-buffered saline ph 7.4 (PBS) at 4°C in 1.5 ml micro

65 centrifuge tubes for 16 hours. CRP in the eluted blood in PBS was determined by and

immuno-turbidimetric method (SPINREACT, S.A., SPAIN) following the procedure

supplied with the kit. Latex particles coated with specific human anti-CRP were

agglutinated when mixed with samples containing CRP. The absorbance change was

noted at 540 nm after 1 minute. A calibrator containing known amount of CRP was used

as a standard. Since the kit method was standardized for serum, we re-standardized the

procedure for suitable diluted fresh and eluted blood in PBS. Assay of CRP from dried

blood spot gave a 15-20% lower value compared to that in serum.

Self-reported health

In addition to health status measures obtained from blood pressure and blood

samples, a series of questions were asked about the individual’s current and past health.

During each season’s visit, a 14-day self-reported health assessment was included in the

seasonal questionnaire. This health assessment included a list of common ailments which

had been determined based on initial focus group interviews, consultation with area

nurses and personnel from the Voluntary Health Association of Sikkim. In addition to the list provided, women were asked if they had experienced any other ailments. The trained nurses who collected this information were able to clarify reports and make additional observations and enquiries. A copy of this form is presented in Appendix A.

Social Wellbeing

Socioeconomic index

A socioeconomic status (SES) index was created for this study by combining

information on occupation, housing information and household assets. Income from

occupation was not available for this sample. Women felt uncomfortable revealing their

66 or their husband’s income. However, based on job title, a rank 1-3 (1 = lowest) was assigned to each occupation. Home information collected included house ownership, construction and number of rooms and bathrooms. In addition to this home information, water source – piped water or outside well, toilet type – bathroom or outhouse was obtained. Significant assets included: servants, other land, buildings, businesses, computers, washing machines, televisions, ovens, and telephones. Questions regarding

SES were administered in the Season 1 questionnaire (see Appendix A).

In order to develop a meaningful and significant index with adequate internal validity, a test item analysis was preformed on all categories of data. Based on these results, items were added to or subtracted from the index to create a sum that received a

Cronbach’s alpha score > 0.7 (Raynaldo & Santos, 1999). The final index included the following data categories: Home ownership, number of rooms, other land owned, number of servants, number of significant assets and occupation. This SES index ranged from 3-56 with a Cronbach’s alpha of 0.721.

Socioeconomic rank

Socioeconomic ranking was determined based on the SES index. SES index values were plotted on a scatter plot. Based on observed clustering individuals were ranked 1-3. Low SES index values, ≤ 10, were assigned a 1, values >10<22 were assigned a 2 and values ≥ 22 were assigned a 3.

Social support index

Information on location and numbers of family and friends, number of visits and phone calls support networks was collected in order to develop a Social Support Index

(SSI) for this sample. Women were asked to estimate the number of friends and family

67 within 1, 10, and 50 km as well as the number of friendly visits and phone calls they made on a daily, weekly and monthly basis. Marital status, number of family living in the immediate household, and years of residence in Gangtok were determined for use in this index. In addition, information on additional sources of support and were obtained.

These questions are located in the seasonally administered questionnaires and copies are located in Appendix A.

Due to the wide variation in number of friends and family members, these categories of data were standardized using Z-scores. In an attempt to develop a meaningful index with adequate internal validity, test item analysis was preformed on all categories of data. However, the results of this test item analysis did not yield a

Cronbach’s alpha value of > 0.7. A maximum score of 0.6 was observed, suggesting less than optimal internal validity. The final social support index included the following data categories: marital status, number of family within 1, 10, and 50km, number of friends within 1, 10 and 50km and years of residence in Gangtok . SSI index ranged -4.2 to 29.4.

Social support rank

Social Support ranking was determined in the same manner as socioeconomic status ranking. Clustering of SSI values also suggested 3 categories of social support ranking (1 = lowest rank). Values ≤ 0 received a rank of 1, > 0 < 5 were assigned a rank of 2, and values ≥ 5 were ranked 3.

Perceived stress

The perceived stress questionnaire used in this research was based on one created and validated by Levenstein et al (1993) (Appendix B). This questionnaire uses a likert- type scale ranking frequency of response to questions on a ranked scale of 1-4 (never,

68 almost never, sometime, often). Thirty-one questions were translated into Nepali by native speaker and tested for clarity and validity on a small focus group. Answers to each question were given a point value based on their ranking (1 = never, 2 = almost never, 3

= sometimes, 4 = often) and the sum of these values was used in statistical analyses.

Measures of Fecundity

Menstrual cycle

Women participating in this study were asked to document the first and last days of menstrual bleeding. This information was collected for all women on a seasonal basis and more frequently (a monthly basis) for those women who did not comply with the request to keep a menstrual diary. A total of 1067 cycles were collected from 201 of the study participants. Cycle length was determined by counting the first day of menstrual bleeding and counting forward to the beginning of the next cycle. Cycles were classified as irregular cycles if they were short (< 25 days) or long (>36 days) as both long and short cycles are associated with reduced fecundity.

Leutenizing hormone

Leutenizing hormone (LH) assays were collected from urine samples collected during the final season of the study (October-December). Both LH and follicle stimulating hormone assays were also completed in the laboratory of Dr. Chakraborty in

Calcutta and based on methods developed in this laboratory. The mid-menstrual cycle point was determined by obtaining the average cycle length for women who had had at least two recent cycles of similar length. For three days before this mid-cycle point, women were asked to collect their entire first morning’s urine in sterile, disposable cups provided to them. Urine samples were collected each day and alloquatted into 1 ml units

69 which were then stored at -40 oC until transported to Calcutta. Both LH and FSH was assayed from the same urine samples.

LH in urine samples was measured by an immunoenzymatic assay using a commercial kit (Radim,ITALY). Two different anti-LH antibodies were used; one coated on the wells and the other conjugated to horseradish peroxidase (HRP). During the incubation, LH present in samples or standards was bound to both monoclonal antibodies at once by forming a sandwich. The unbound materials was aspirated and washed off.

The residual enzyme activity found in wells was proportional to LH concentration in standards and samples. The color took place after the addition of chromogen (tetra methylebenzidine/H2O2). The color changed to yellow after the addition of stop solution

(1N H2SO4). The O.D. was measured at 450 nm in an ELISA reader. Six standards were used with each batch of samples.

Follicle stimulating hormone

FSH in urine samples was determined by ELISA using a commercial kit

(Medicorp, Canada). The primary antibody was against hFSH coated on wells. The kit was based on a solid-phase sandwich ELISA Method, using a second anti-hFSH antibody conjugated to HRP. A blue color developed after the addition of the chromogen. The reaction was stopped by the addition of 0.5 M H2 SO4 when the blue color changed into yellow. The optical density (O. D.) was measured at 414 nm in an ELISA reader. A series of six standards was used with each batch of samples.

70 3.5 Statistical Analysis

Statistical data analysis was completed using SYSTAT 10. Descriptive statistics, including minimum and maximum values, mean, median and standard deviation were obtained for all parametric variables. Seasonal variation in anthropometric, health and stress measurements was tested using analysis of variance (ANOVA). Statistical significance was determined at p < 0.05. For measures that were significant for seasonal differences, Tukey post-hoc analysis for pairwise comparisons was completed.

Magnitude of change was also determined for seasonal measures by subtracting individual seasonal values from the previous value and last season from first season.

Linear regression models were used to test the predictive value of variables and magnitude of change for all seasonally collected data. Potentially confounding variables, age and parity, were tested using Pearson’s correlations and were not significantly related to any variables included in this study. The limited variation in age (25-35) and parity (0-

3) in this sample resulted in minimal statistical impact in regression models and these variables are not controlled for in statistical analyses.

71 CHAPTER 4

RESULTS I:

ENERGY BALANCE AND SEASONALITY

4.1 Introduction

Hypothesis 1: The energy intake of Bhutia women in Gangtok, Sikkim, India is generally less than their energy expenditure, creating an overall state of negative energy balance.

Corollary 1: magnitude of energy deficit will vary according to climatic season.

In order to test hypothesis 1, general descriptions of body composition measurements and comparisons of these measurements according to season are presented. Seasonal measurements are analyzed to determine if significant seasonal variation exists. Magnitude of change in energy balance is determined for each season.

Magnitude of change in energy balance is determined by subtracting a seasonal measure from the prior season’s measurement. Anthropometric indices are calculated and also used for seasonal comparisons of energy balance.

4.1.1 Body composition of the sample

Participants averaged 154.5 cm tall, weighed 54.4 kg, have 26.3% body fat, and have 22.7 BMI. Compared with recent data from the Demographic Health Survey (1998-

72 1999), these women are taller, heavier and have a higher BMI than the average

Indian woman (Table 4.1).

Mean S.D. India Sample India Sample Height (cm) 151.2 154.5 5.8 6.8 Weight (kg) 45.6 54.4 8.2 8.8 BMI (kg/m2) 19.6 22.7 3.1 3.5

Table 4.1: All India and study sample basic characteristics.

BMI measures relative weight-for-height. Low BMI is associated with being underweight and high BMI is associated with being overweight. Cut-off’s for BMI categories are based on World Health Organization (1986) standards: severely low, moderately low, mildly low, normal, high, and obese. The majority of women in this sample fall into the normal BMI range (67.7%), only 9.8% fall into the low BMI ranges and 20.4% fall in the high BMI ranges. BMI distribution for the average values of this sample is given in Figure 4.1.

73 80

70 67.7

60

53.2

50

India

% 40 Sample

30 24.3

20 17.7

10.1 10 8.5 6.8 4.6 2.7 0.4 0.9 1 0 severe moderate mild normal high obese BMI Category

Figure 4.1: Sample and all India average BMI distribution based on World Health Organization standard categorical classifications.

74

Not only is average BMI higher in this sample than the all-India average, the distribution of BMI classifications of this sample is very different from the distribution for India women, based on the 1998-99 DHS. Fewer Bhutia women fell in the low categories

(severe, moderate and mild) than in India, in general. A higher percentage of Bhutia women would be considered normal, overweight or obese (Figure 4.1).

4.2 Measures of Energy Balance

Energy balance is determined by subtracting the total energy output (measured in calories) from the total energy intake (also measured in calories). Energy balance is positive if the energy intake exceeds energy output, negative if energy intake is less than output and neutral if intake and output are equal. Negative energy balance, if substantial and prolonged will result in weight or fat loss and may negatively impact anthropometric measures. Positive energy balance will have the opposite effect, whereas a balance between intake and output should not lead to changes in weight, body fat or anthropometric measurements. Beginning weight, body fat and anthropometric measurements for this sample are given in Table 4.2.

75

MEASURE MINIMUM MAXIMUM MEAN MEDIAN STANDARD DEVIATION Weight (kg) 36.9 89.5 54.7 54.0 8.9 Body Fat (%) 11.0 45.0 27.0 27.5 6.9 BMI 15.6 39.7 22.9 22.7 3.6 Mid-Arm 18.5 32.4 23.7 23.5 2.5 Circumference (cm) Waist 59.4 117.4 74.9 73.4 9.2 Circumference (cm) Abdominal 64.7 119.7 89.3 88.1 9. Circumference (cm) Hip 78.4 119.9 94.1 93.0 7.0 Circumference (cm) Mid-Calf 23.5 53.8 32.5 32.1 3.6 Circumference (cm) Tricep Skinfold 8.0 37.0 20.2 20.0 5.5 (mm) Bicep 3.0 27.0 11.5 10.3 5.3 Skinfold (mm) Subscapular Skin- 9.0 43.3 19.6 19.0 6.2 fold (mm) Suprailiac Skin- 6.0 43.3 18.8 17.7 6.5 fold (mm) Mid-Calf 8.3 47.3 21.7 21.0 6.4 Skinfold (mm) Waist Hip Ratio 0.68 1.00 0.80 0.79 0.06

Arm Fat Index 23.2 67.4 45.9 46.0 8.8

Sum of 2 18.3 66.0 39.9 38.0 10.3 Skinfolds (mm) Sum of 5 37.3 159.0 91.9 91.3 23.2 Skinfolds (mm)

Table 4.2: Sample measures of body composition for Season 1 – Winter.

76

4.3 Seasonal Changes in Measures and Components of Energy Balance

4.3.1 Large scale changes in energy balance

Large scale changes in energy balance were determined as changes in weight, body fat and BMI across seasons (Table 4.3 and Figure 4.2). Seasonal variation in weight, body fat and BMI did not show a statistically significant difference.

Winter Spring Summer Fall N 159 210 203 177 WEIGHT (kg) 54.7 54.6 54.5 54.6 BODY FAT (%) 29.5 29.6 28.8 29.2 BMI (kg/m2) 22.9 22.6 22.6 22.6

Table 4.3: Seasonal mean values and sample sizes for weight, body fat and BMI.

77

60 50 Weight (kg) 40 30 Body Fat (%) 20 10 BMI 0

r ll er ing int r Fa mme W Sp u S Season

Figure 4.2: Seasonal changes in mean weight, body fat and BMI.

Although seasonal variation for the sample is not significant, when individual losses and gains are examined, a definite pattern emerges. Most participants did change weight between seasons. A majority lost weight from winter to spring (51.1%) and spring and summer (55.4%). Conversely, most participants (58.5%) gained weight from summer to fall. A net loss (total loss by all participants) of 26.4 kg was recorded over the course of this study with just over half of the women (51.1%) losing weight. Seasonal weight changes for this sample are presented in Table 4.4.

78 Weight Net Change % of women Average % of women Average % of women (kg) who lost weight lost who gained weight – no change weight weight gained Winter- Spring -24.2 51.1 1.3 46.1 1.1 3.0 (N=141) Spring- Summer -54.8 55.4 1.4 42.6 1.1 11.9 (N=195) Summer- Fall +78.3 37.9 1.2 58.5 1.4 7.9 (N=195) Winter- Fall -26.4 51.1 2.2 46.0 2.0 0.8 (N=139)

Table 4.4: Seasonal sample net change in body weight and percentage of sample that lost, gained or did not change weight.

Similar results were observed with body fat measurements. Between winter and spring and spring and summer a net loss of body fat was observed but a net gain of body fat was observed between summer and fall. Most women lost body fat after winter

(54.1%) and spring (52.8%) and gained body fat after the summer season (63.7%). A net loss of body fat was recorded in this sample over the course of the year (Table 4.5).

79

Body Fat Net Change % of women Average fat % of women Average fat % of women (%) who lost lost who gained gained – no change body fat body fat Winter- Spring -150.1 54.1 6.4 43.9 6.4 3.0 (N=135) Spring- Summer -114.7 52.8 1.7 34.5 1.4 11.9 (N=194) Summer- Fall +144.0 28.4 2.0 63.7 1.9 7.9 (N=190) Winter- Fall -197.9 65.9 6.6 33.3 6.6 0.8 (N=129)

Table 4.5: Seasonal sample net change in body fat and percentage of sample that lost, gained or did not change body fat.

Between winter and spring, most participants showed decreases in BMI values.

Of the 144 women who were measured in both winter and spring, 61.1% decreased BMI,

31.9% increased and 6.9% remained the same BMI in both seasons. Net loss of BMI for

winter to spring was 4.3 kg/m2. From spring to summer there was a larger net loss of

BMI in this sample, 9.3 kg/m2 (N=200). Almost half (48.0%) of women gained body

mass from the previous season, 52.0% lost body mass. Although the majority of women

increased BMI between summer and winter (52.3%) there was a net loss of 8.3 kg/m2

(N=197). Of the remaining women, 44.2% lost body mass and 2.5% did not change.

Over the course of the year, 60.6% of women lost body mass (Table 4.6).

80

BMI Net Change % of Average % of Average % of (kg/m2) women who BMI lost women who BMI gained women – lost BMI gained BMI no change Winter- Spring -4.3 61.2 0.8 31.9 0.7 6.9 (N=144) Spring- Summer -9.0 52.0 0.7 48.0 0.6 0 (N=200) Summer- Fall +8.3 44.2 0.6 53.3 0.6 2.5 (N=197) Winter- Fall -46.7 60.6 1.1 39.4 0.8 0 (N=137)

Table 4.6: Seasonal sample net change in BMI and percentage of sample that lost, gained or did not change BMI.

A proportional distribution of participants across BMI categories follows the pattern of changing BMI. Although the percentage of women falling into the normal

BMI category did not change significantly, the percentage of women in the low BMI categories increased from 7.7% to 10.6%. However, the percentage of women in the high categories decreased from 27.4% in winter to 20.9% in spring (Figure 4.3 and Table

4.7).

Winter Spring Summer Fall Severely Low 0.6 1.0 1.0 0.5 Moderately Low 0.6 1.0 0.4 1.0 Mildly Low 6.4 8.6 8.4 7.6 Normal 65.0 68.6 69.3 69.2 High 25.5 17.6 17.3 18.7 Obese 1.9 3.3 3.5 3.0

Table 4.7: Seasonal distribution of BMI classifications. 81 80

70

60

50 severe moderate

) mild 40 (% normal high obese 30

20

10

0 Winter Spring Summer Fall Season

Figure 4.3: Seasonal changes in distribution of BMI classifications.

Women in this sample tended to lose weight, body fat, and BMI from winter to spring and spring to summer. On the contrary, from summer to fall, women tended to gain weight, body fat and BMI. Women also generally weighed less, had a lower body fat, and a lower BMI at the end of the year than at the beginning of the year (Figure 4.4).

82

70

60

50

40 Weight

% Body Fat BMI 30

20

10

0 Winter-Spring Spring-Summer Summer-Fall Beginning-End Season

Figure 4.4: Percentage of women who lost weight, body fat and BMI between each season and from the beginning to the end of the study period.

4.3.2 Changes in anthropometric measurements

Seasonal variation in both skin fold measures and circumferences were statistically significant (Table 4.8). Of the skinfolds, bicep, tricep, subscapular and medial calf skinfolds showed statistically significant seasonal variation (*p < 0.05). Of the circumferences, only arm showed significant seasonal variation.

83 SKINFOLDS (MM) WINTER SPRING SUMMER FALL N 159 210 203 177 BICEP* 11.4 11.6 8.5 9.4 TRICEP* 20.2 23.4 17.4 16.5 SUBSCAPULAR* 19.6 22.7 21.4 19.8 SUPRAILLIAC 18.8 20.1 20.3 19.4 MEDIAL CALF* 21.7 21.9 15.6 15.4 CIRCUMFERENCES (CM) ARM* 23.5 24.5 23.0 22.8 ABDOMINAL 88.8 89.4 89.2 89.1 WAIST 74.5 73.6 72.4 74.5 HIP 93.5 94.1 93.1 93.1

Table 4.8: Seasonal changes in anthropometric measurements (* = p<0.05).

Seasonal patterns of variation for significant skin fold and circumference measurements were similar for all statistically significant measures. As with the large scale measurements, there was a loss recorded between spring and summer (Figure 4.5).

84 25 20 mm)

( 15 ld Bicep o 10 f

in 5 Tricep k

S 0 Subscapular r g ll e n er nt ri m Fa Calf Wi Sp m Su Season

Figure 4.5: Seasonal changes in statistically significant values for skin fold measures.

F-ratio, p-values from ANOVA tests and Tukey probability results for pairwise

comparisons between seasons for measures with significant seasonal variation are given in Table 4.9.

85

Winter Spring Summer Fall Bicep ANOVA F- ratio = 26.180 p = 0.000 Winter 1.000 Spring 0.974 1.000 Summer 0.000 0.000 1.000 Fall 0.000 0.000 0.119 1.000 Tricep ANOVA F-ratio = 67.585 p = 0.000 Winter 1.000 Spring 0.000 1.000 Summer 0.000 0.000 1.000 Fall 0.000 0.000 0.358 1.000 Subscapular ANOVA F-ratio = 8.591 p = 0.000 Winter 1.000 Spring 0.000 1.000 Summer 0.069 0.239 1.000 Fall 0.997 0.000 0.081 1.000 Calf ANOVA F-ratio = 89.766 p = 0.000 Winter 1.000 Spring 0.974 1.000 Summer 0.000 0.000 1.000 Fall 0.000 0.000 0.986 1.000 Arm Circumference ANOVA F-ratio = 15.242 p = 0.000 Winter 1.000 Spring 0.006 1.000 Summer 0.303 0.000 1.000 Fall 0.044 0.000 0.755 1.000

Table 4.9: Anova results and Tukey pairwise seasonal probabilities for significant seasonal variation in anthropometric measurements. (significant = p< 0.05)

86 Bicep and calf skinfolds showed a significant decrease between spring and summer and a significant increase between summer and fall. There was also a significant decrease between the beginning and end of the year. Tricep skinfolds also showed a significant decrease from spring to summer and the beginning to end of the year.

Subscapular skinfolds showed a significant increase from winter to spring. Arm circumferences showed a significant increase from winter to spring, and significant decreases from spring to summer and the beginning and end of the study.

4.3.3 Anthropometric indices

All anthropometric indices showed significant seasonal variation. The mean values for each season are listed in Table 4.10 and charted below in Figure 4.6.

ANTHROPOMETRIC WINTER SPRING SUMMER FALL INDICES N 159 210 203 177 ARM FAT INDEX* 45.9 50.1 41.1 40.2 WAIST-HIP RATIO* 0.80 0.78 0.78 0.80 SUM OF TWO* 39.9 45.8 38.8 36.3 SUM OF FIVE* 91.9 99.7 83.1 80.4

Table 4.10: Seasonal changes in anthropometric indices (* = p<0.05)

87 120 100 Sum of Five 80 60 Sum of Two 40 20 Arm Fat Index 0

r g r ll e n e t i Fa in pr W S mm Su Season

Figure 4.6: Seasonal changes in Sum of Five, Sum of Two and Arm Fat anthropometric indices.

F-ratio, p-values and Tukey probability results for pairwise comparisons between seasons for the indices are presented in Table 4.11.

88

Winter Spring Summer Fall Arm Fat Index (AFI) F- ratio = 60.008 p = 0.000 Winter 1.000 Spring 0.000 1.000 Summer 0.000 0.000 1.000 Fall 0.000 0.000 0.682 1.000 Waist-Hip Ratio (WHR) F-ratio = 6.745 p = 0.000 Winter 1.000 Spring 0.167 1.000 Summer 0.012 0.697 1.000 Fall 0.852 0.015 0.000 1.000 Sum of Two F-ratio = 26.698 p = 0.000 Winter 1.000 Spring 0.000 1.000 Summer 0.816 0.000 1.000 Fall 0.013 0.000 0.098 1.000 Sum of Five F-ratio = 29.755 p = 0.000 Winter 1.000 Spring 0.007 1.000 Summer 0.001 0.000 1.000 Fall 0.000 0.000 0.687 1.000

Table 4.11: ANOVA results and Tukey pairwise seasonal probabilities for significant seasonal variation in anthropometric indices. (significant = p<0.05)

89 The AFI and Sum of Two skinfolds showed significant increases between winter and spring, and significant decreases between spring and summer and the beginning and end of the study. Waist-Hip Ratio showed significant change only between the beginning and end of the study, also a loss. The Sum of Five skinfolds showed a significant increase between winter and spring, and significant decreases spring-summer, summer-fall and beginning-end.

4.4 Summary

On average, this sample is taller, heavier and has a higher BMI than rage Indian women, on average. These women showed significant variation in measures of energy balance across seasons. However, large scale measures of energy balance, weight, body fat and BMI, did not show significant seasonal variation. Still, most women lost weight/body fat/BMI from winter to spring, spring to summer, and over the course of the study, but gained weight/body fat/BMI from summer to fall. Similar patterns were observed in a variety of anthropometric measures and indices. Bicep, tricep, subscapular and calf skinfolds, mid-arm circumference, arm fat index, sum of two skinfolds, sum of five skinfolds and waist hip ratio all varied significantly by season. These results support hypothesis 1. Most Bhutia women did experience negative energy balance between the beginning and end of the study year. Furthermore, the magnitude of energy change did varied significantly by climatic season.

90 CHAPTER 5

RESULTS II:

ENERGY BALANCE AND FECUNDITY

5.1 Introduction

Hypothesis 2: Negative energy balance reduces fecundity in urban Bhutia women.

Corollary 1: negative energy balance directly affects fecundity by altering hormonal levels. Corollary 2: negative energy balance negatively affects health status, thereby indirectly reducing fecundity.

Associations between energy balance measures, selected aspects of health and

fecundity measures are presented in this chapter. Hypothesis 2 is tested using seasonal menstrual cycle lengths and one time measures of leutenizing homone (LH) and follicle stimulating hormone (FSH). Energy balance, changes in energy balance and health status are compared with these measures in order to identify patterns and relationships between these three categories of variables.

91 5.2 Measures of Fertility and Fecundity

5.2.1 Measures of population fertility

Bhutia women living in Gangtok exhibit a definite seasonal pattern of births and

conceptions. Information from birth registration records, hospital records, and the births

of women in this sample all show a similar pattern of seasonality. The fewest births take

place in spring (summer conceptions), the most births take place in fall (winter

conceptions). Pattern of conceptions during the study period, based on hospital birth records (N=213 births) are given in Figure 5.1. Differences in number of conceptions by season were statistically significant (x2 = 34.4; p = 0.024).

Seasonality of Conception 2001

80 f ons o

i 60 r t e p

b 40 e

m 20 onc Nu

C 0 Winter Spring Summer Fall Season

Figure 5.1: Number of conceptions/season in 2001 for Bhutia women living in Gangtok (based on hospital records).

5.2.2 The menstrual cycle

Seasonal differences in menstrual cycle patterns were observed in this sample.

Information on 1067 cycles was obtained for 202 women across four seasons. On

92 average, 5.28 cycles were recorded for each participant during the year with a total number of cycles per women ranging from 1-12. Cycle lengths were determined to be irregular if they were < 25 days (short cycles) or > 35 days (long cycles). Table 5.1 shows the number of cycles collected during each season and the corresponding patterns and these values are graphed in Figure 5.2. Wide variation in menstrual cycle length was observed.

WINTER SPRING SUMMER FALL N 228 365 310 164 Average cycle length (days) 29.7 30.1 30.1 28.5 S.D. 7.7 8.9 8.8 3.0 % of cycle(s) -- irregular 25.0 23.9 22.9 5.5 % of cycles -- short (< 25 days) 11.0 10.1 9.7 2.5 % of cycles -- long 14.0 13.7 13.2 3.0 (> 25 days) % individuals experiencing irregular cycles 39.8 53.9 53.2 7.3

Table 5.1: Average cycle length and distribution of irregular cycles per season.

93 60 50 irregular cycles 40

% 30 20 short cycles 10 0 long cycles g r ll er in int Fa pr mme W S u irregular S individuals Season

Figure 5.2: Seasonal distribution of irregular, short and long cycles and number of Bhutia women experiencing irregular cycles.

Menstrual cycle length varied significantly by season. Average cycle length was similar in winter, spring and summer, but the average menstrual cycle length was significantly shorter during fall (Figure 5.3).

94

h t 35 g

n 34

e 33 32 e L 31 ays)

ycl 30

d 29 (

e C 28 27 ag 26 er

v 25 A Winter Spring Summer Fall Season

Figure 5.3: Average menstrual cycle length/season.

A clearer pattern is observed when menstrual cycles are categorized as irregular with cycles under 22 days being classified as short and cycles over 35 days as being long.

(Figure 5.4) shows the seasonal pattern of regular vs. irregular cycles. A statistically significantly lower percentage of irregular cycles occur during the fall (p=0.00), with a significantly smaller proportion of individuals who experienced irregular menstrual cycles during this season (p=0.000) (Figure 5.4).

95 s 30 e

lc 25 y

C 20 r

la 15 u

g 10 e r 5

% Ir 0 Winter Spring Summer Fall Season

Figure 5.4: Percentage of menstrual cycles that were classified as irregular per season.

Most women sampled (61.4%) did experience at least one irregular menstrual cycle with 43.5% of the 1067 cycles being classified as irregular. Of those women experiencing irregular cycles, most women experienced only long cycles (25.7%) while

19.3% of women experienced only short cycles. In this sample, 16.3% of women experienced at least one long and one short cycle in this year. Individual cycle variation is summarized in Table 5.2. The seasonal pattern of long and short cycles for individuals

was not different from the seasonal pattern of irregular cycles previously identified in this

sample.

96

AVERAGE % % ONLY % LONG % NUMBER EXPERIENCING SHORT ONLY BOTH CYCLES/PERSON IRREGULAR CYCLES 5.28 61.39 19.31 25.74 16.34

Table 5.2: Percentage of women experiencing irregular cycles.

5.2.3 Leutenizing hormone (LH) and follicle stimulating hormone (FSH)

Because of the difficulties in determining mid-cycle for highly variable cycles, urine samples were collected from 201 women once, during the final season of the study.

Leutenizing hormone (LH) low levels ranged between 0.1 IU/L to 22 IU/L with an mean of 4.0 IU/L. Follicle stimulating hormone (FSH) low levels ranged between 0.2-13.8

IU/L with an average of 2.1 IU/L. LH peak levels ranged from 1.1-74.3 IU with an average of 24.4 IU/L. FSH peak levels ranged from 1.2-67.3 IU with an average of 10.0

IU/L. On average the magnitude of LH peak was 20.6 and FSH peak was 7.9). The averages for the low and high values of LH and FSH an magnitude of change are given in

Table 5.3.

97

N=201 RANGE MEAN MEDIAN S.D. LH Low 0.1-22 4.0 3.2 3.2 LH High 1.1-74.3 24.4 22.5 16.5 FSH Low 0.02-13.8 2.1 1.7 2.0 FSH High 1.2-67.3 10.0 9.2 7.5 LH Peak 0.3-62.2 20.6 18.9 15.9 Magnitude FSH Peak 0.3-53.5 7.9 6.5 6.8 Magnitude

Table 5.3: Sample follicle stimulating hormone (FSH) and luteinising hormone (LH) values (IU/L).

5.3 Measures of Health Status

5.3.1 Health history

Participants reported many illnesses common throughout India. Measles, mumps and chicken pox were frequently reported as childhood illnesses. Malaria, however, had only been reported by one subject who had contracted the disease while at school in New

Delhi. Many women also complained of cavities and dental problems as well as poor eyesight. Most had a history of intestinal parasites and complained of ongoing gastric problems, including upset stomach, intestinal cramping, and/or mild diarrhea. Only 7.7% of this sample reported a past diagnosis of anemia. This number is extremely low compared to earlier reports of prevalence of anemia based on data collected by the DHS for Indian and Sikkimese samples (1998). According to these surveys, 51.8% of Indian women were anemic (Hb < 12 g/dL) and 61.1% of women in Sikkim were anemic. A list of common ailments and their reported numbers are provided in Table 5.4.

98

N % anemia 18 7.7 appendicitis 7 3.0 broken bones 13 5.6 chicken pox 42 18.0 dental carries 68 29.2 gastric problems 129 55.4 intestinal parasites 141 60.5 jaundice 14 6.0 malaria 1 0.4 measles 144 61.8 mumps 57 24.5 pneumonia 14 6.0 severe fever 32 13.7 (unknown origin) tuberculosis 12 5.2 typhoid 6 2.6 urinary tract 28 12.0 infection vision problems 55 23.6

Table 5.4: Common ailments reported by Bhutia women in this sample.

99 5.3.2 Anemia and hemoglobin levels

Few participants reported ever being diagnosed with anemia. Analysis of blood obtained during this study, shows hemoglobin levels for each woman in the sample, this sample proved to be more in line with those previous reports. Average hemoglobin (Hb) levels taken throughout the study year indicate that 46.3% of the sample had normal Hb levels. This proportion is similar to the 48.2% found by the DHS (1998) for India but much greater than the 38.9 % found in Sikkimese populations. Of the women in this sample who were considered to be anemic, most were categorized as mildly anemic (<12

– 9 g/dL) while very few were considered moderate (<9 – 6 g/dL) and none were severely anemic (< 6 g/dL). Comparisons between the average Hb levels for this sample and data obtained from the DHS are summarized below (Figure 5.5).

100 60

50

40 on i t

la India u

p 30 Sikkim o

P Sample of %

20

10

0 Normal Mild Moderate Severe Hemeglobin Level

Figure 5.5: Distribution of hemoglobin status for all-India averages, all-Sikkim averages and the study sample based on DHS classifications (1998).

101 Beginning health status

The average number of items reported on the self reported 14-day health recall for all four seasons was 1.6. C-reactive protein (CRP), diastolic blood pressure and systolic blood pressure averaged in the normal range (CRP < 3.0 mg/L; Systolic BP < 140 mmHg; Diastolic < 90 mmHg). Mean Hb was low (< 12 g/dL). At the begging of the study period 2.0 was the average number of items on the self reported health recall; CRP and blood pressure values fell in the normal ranges but Hb was low. Values for health status measures from the beginning of the study period are given in the table below

(Table 5.5).

Minimum Maximum Mean Median Standard Deviation Health Recall (items) 0 9 2.0 1 2.0 CRP 0.2 46.0 2.4 1.9 4.2 (mg/L) Hemoglobin 6 14.5 11.3 11.5 2.1 (g/dL) Systolic BP (mmHg) 91 140 115.7 114 10.5 Diastolic BP 59 100 77.8 79 8.0 (mmHg)

Table 5.5: Sample measures of health status indicators for season 1 – Winter.

5.4 Seasonal Measures of Health

This sample of Bhutia women had very clear ideas about the impact of season on their health, but the majority of self reports were almost equally split between winter and summer (Figure 5.6).

102

60 50 Winter 40

) Spring 30 (% Summer 20 Fall 10 0 Best Health Worst Health

Figure 5.6: Proportion of seasonal answers to the questions “My health is the best in?” and “My health is the worst in?”

A slight majority reported their health best in winter (52.6%) and worst in summer

(51.1%). However, a large proportion reported the opposite, health worst in winter

(44.2%) and best in summer (42.1%). Few women reported better or worse health in spring or fall.

Patterns of seasonal variation in health status measures did not follow the opinions reported above. Seasonal changes in health status are detailed in the table below

(Table 5.6). Health recall responses, Hb levels and systolic blood pressure varied significantly with season (Table 5.7).

103 Winter Spring Summer Fall N 159 210 203 177 Self-reported health* 2.0 1.6 1.3 1.5 CRP 2.5 2.0 1.7 2.1 Hemoglobin* 11.3 11.3 12.2 11.7 Systolic BP* 115.5 111.3 110.9 110.4 Diastolic BP 77.6 77.3 78.3 78.0

Table 5.6: Seasonal changes in health status measures (* = p<0.05).

F-ratio, p-values, and Turkey probability results for pairwise comparisons between seasons for significant results are given in Table 5.7.

104

Winter Spring Summer Fall Self-Reported Health F- ratio = 7.104 p = 0.000 Winter 1.000 Spring 0.034 1.000 Summer 0.000 0.193 1.000 Fall 0.011 0.973 0.429 1.000 Hemoglobin F-ratio = 8.667 p = 0.000 Winter 1.000 Spring 0.981 1.000 Summer 0.000 0.000 1.000 Fall 0.215 0.231 0.088 1.000 Systolic BP F-ratio = 9.209 p = 0.000 Winter 1.000 Spring 0.000 1.000 Summer 0.000 0.979 1.000 Fall 0.000 0.798 0.956 1.000

Table 5.7: ANOVA results and Tukey pairwise probabilities for significant seasonal variation in health status measurements. (significant = p<0.05)

105

Health recall

Item totals from the 14-day health recall varied significantly with season. Winter

values were significantly higher than in any other season. Individual changes also

followed a seasonal pattern (Table 5.8). There was an overall decrease in reported items

winter-spring, spring-summer and beginning to end. Between spring and fall there was

an increase in number of items reported. Seasonal change in reported health problems is

diagrammed in Figure 5.7.

Health Net Change % of Average % of Average % of Recall women item women item women - no (items) reported decrease reporting increase change fewer items more items Winter- Spring -63 43.4 2.0 30.9 2.4 25.7 (N=144) Spring- Summer -67 36.0 2.2 30.5 1.5 33.5 (N=200) Summer- Fall +44 25.9 1.4 42.3 1.5 31.8 (N=197) Winter-Fall (N=137) -18 40.9 2.4 36.6 1.6 22.5

Table 5.8: Seasonal sample net change in health recall and percentage of sample that lost, gained or did not change number of health recall items.

106

Health Recall

2.5 2 s 1.5 m

e 1 It 0.5 0 Winter Spring Summer Fall Season

Figure 5.7: Seasonal changes in average health recall items.

C-reactive protein

Although the contribution of season to variation in CRP was not significant, levels followed a seasonal pattern similar to those seen in the health recall. Between winter and spring, spring and summer and over the study year there were net losses in

CRP levels. From summer to fall net increase in CRP levels were observed. Magnitude of change and % of sample experiencing change are given in the table below (Table 5.9).

A diagram of the seasonal changes in average CRP levels is given below (Figure 5.8).

107

CRP Net Change % of Average % of Average % of (mg/L) women CRP women CRP women - no decreased decrease increased increase change CRP CRP Winter- Spring -19.8 53.3 2.0 45.8 1.9 0.9 (N=144) Spring- Summer -54.4 55.9 1.8 44.1 1.4 0 (N=200) Summer- Fall +29.5 44.0 1.5 56.0 1.7 0 (N=197) Winter-Fall (N=137) -13.6 50.0 1.9 50.0 1.5 0

Table 5.9: Seasonal sample net change in C-reactive protein (CRP) and percentage of sample that lost, gained or did not change CRP levels.

2.5 2 L)

g/ 1.5 m ( 1 P R

C 0.5 0 Winter Spring Summer Fall Summer

Figure 5.8: Seasonal changes in C-reactive protein (CRP) levels.

108

The number of individuals with normal vs. high CRP (> 3 mg/L) also changed on a seasonal basis. Winter had the most number of high readings (25.2%) and summer had the fewest (12%). In the spring 20% of the sample was categorized as high and 23% was categorized as high in the fall. High vs. Normal reading for each season are given in

Figure 5.9.

100 80

) 60 Normal

(% 40 High 20 0 Winter Spring Summer Fall Season

Figure 5.9: Seasonal distribution of normal and high CRP values.

Hemoglobin

Variation in Hemoglobin (Hb) levels was significant between spring and summer.

Hb was highest in summer, the only season where average HB levels fell in the normal range (12.2 g/dL). Hemoglobin decreased in the majority of individuals in winter-spring and summer-fall but increased for most individuals from spring-summer and during the study year. These changes are summarized in Table 5.10 and seasonal changes in Hb status are diagrammed in Figure 5.10.

109

Hemoglobi Net Change % of Average Hb % of Average Hb % of n (g/dL) women decrease women increase women - no decreased increased change Hb Hb Winter- Spring -4.4 51.4 2.1 43.2 2.6 5.4 (N=144) Spring- Summer +130.4 26.5 1.8 68.0 1.9 5.6 (N=200) Summer- Fall -90.2 57.1 2.1 38.8 1.6 4.1 (N=197) Winter-Fall (N=137) +16.3 42.1 2.2 50.9 2.4 7.0

Table 5.10: Seasonal sample net change in Hb and percentage of sample who lost, gained or did not change Hb levels.

110 70

60

50

40 Normal

) Mild

(% Moderate Severe 30

20

10

0 Winter Spring Summer Fall

Figure 5.10: Seasonal distribution of Hemoglobin (Hb) status.

Systolic blood pressure varied significantly only between winter and all other seasons but always remained in the normal range. Diastolic blood pressure levels were also normal and there was no significant change in diastolic blood pressure. The pattern for individual changes is summarized in Table 5.11 for systolic blood pressure and Table

5.12 for diastolic blood pressure. Seasonal changes for both systolic and diastolic blood pressure are diagrammed in Figure 5.11.

111 Systolic BP Net Change % of Average BP % of Average BP % of (mmHg) women decrease women increase women - no decreased increased change BP BP Winter- Spring -608.5 65.5 9.9 33.1 7.6 1.4 (N=145) Spring- Summer -161.0 47.3 9.0 48.3 7.1 4.5 (N=200) Summer- Fall -92.0 47.0 7.7 44.4 7.1 8.6 (N=198) Winter-Fall (N=139) -750.0 65.5 11.1 25.2 7.4 9.4

Table 5.11: Seasonal sample net change in systolic blood pressure and percentage of sample that lost, gained or did not change systolic BP levels.

Overall, most individuals’ systolic blood pressure decreased winter-spring, summer-fall and during the study year. An increase in the majority of individual’s systolic blood pressure was observed between spring and summer.

150

g 100 Systolic BP 50 Diastolic BP mmH 0

g r ll er n e a nt ri m F m Wi Sp u S Season

Figure 5.11: Seasonal changes in systolic and diastolic blood pressure.

112

Diastolic blood pressure showed little change between seasons. Most individuals decreased slightly winter-spring and summer-fall and increased spring-summer and over the course of the study year.

Diastolic Net Change % of Average BP % of Average BP % of BP women decrease women increase women - no (mmHg) decreased increased change BP BP Winter- Spring -58.0 47.6 7.7 45.5 7.0 6.9 (N=145) Spring- Summer +118.0 42.3 7.2 48.8 7.6 9.0 (N=200) Summer- Fall -43.0 49.5 6.5 44.9 6.5 5.6 (N=198) Winter-Fall (N=139) +28.5 46.8 6.9 46.0 7.8 7.2

Table 5.12: Seasonal sample net change in Diastolic BP and percentage of sample that lost, gained or did not change Diastolic BP levels.

5.5 Energy Balance and Fecundity

5.5.1 Predictors of average menstrual cycle length

Average menstrual cycle length was not correlated with age these 25-35 year old

Bhutia women. Weight, body fat, BMI and anthropometric indices averages were not generally significant determinants of average menstrual cycle length. Linear regression

113 results were not significant and r2 values are low. Results of linear regression analysis, r2,

F-ratio and p-values, are presented in Table 5.13.

Dependent Variable: Average Cycle Length

Independent Variable r2 F-ratio p-value Sum of Two 0.002 0.362 0.548 Sum of Five 0.001 0.099 0.754 Arm Fat Index 0.001 0.291 0.590 Waist Hip Ratio 0.019 3.820 0.052 Weight 0.000 0.052 0.820 Body Fat 0.000 0.045 0.831 BMI 0.000 0.019 0.890

Table 5.13: Linear Regression results for Average Cycle Length and averages of anthropometric measures.

To determine how change in energy balance was associated with average cycle length, change in anthropometric indices, weight, fat and BMI were calculated by season.

These were used as independent variables in linear regression models. The summary of the linear regressions, r2, F-ratio and p-values, with average cycle length as the dependent variable are given in Table 5.14.

114

Average Cycle Length

Independent Variable r2 F-ratio p-value Sum of Two Winter-Spring* 0.033 4.622 0.033 Spring-Summer 0.000 0.026 0.872 Summer-Fall 0.009 1.740 0.189 Winter-Fall 0.010 1.381 0.242 Sum of Five Winter-Spring* 0.040 5.635 0.019 Spring-Summer 0.000 0.008 0.930 Summer-Fall* 0.021 3.986 0.047 Winter-Fall 0.007 0.897 0.345 Arm Fat Index Winter-Spring* 0.042 5.822 0.017 Spring-Summer 0.000 0.010 0.922 Summer-Fall 0.000 0.003 0.957 Winter-Fall* 0.033 4.060 0.046 Waist Hip Ratio Winter-Spring 0.000 0.013 0.911 Spring-Summer 0.000 0.070 0.791 Summer-Fall 0.016 2.662 0.105 Winter-Fall 0.010 1.162 0.283 Weight Winter-Spring 0.004 0.520 0.472 Spring-Summer 0.000 0.041 0.839 Summer-Fall 0.011 2.068 0.152 Winter-Fall 0.001 0.088 0.768 Body Fat 0.001 0.066 0.798 Winter-Spring 0.006 1.125 0.290 Spring-Summer 0.002 0.330 0.556 Summer-Fall 0.002 0.306 0.581 Winter-Fall 0.002 0.138 0.711 BMI Winter-Spring 0.001 0.197 0.658 Spring-Summer 0.004 0.772 0.381 Summer-Fall* 0.035 6.818 0.010 Winter-Fall 0.013 1.662 0.200

Table 5.14: Linear Regression results for average cycle length and seasonal changes in anthropometric measures. 115

No statistically significant relationships were observed between change in weight, body fat and WHR during any season and average cycle length. Change from winter to spring in sum of two (p=0.033), sum of five (p=0.019), and AFI (p=0.017) were associated with average cycle length, explaining 3.3, 4.0, and 4.2 percent of the total variation, respectively. Change in AFI (p=0.046) from beginning to end of the study were significantly associated with average cycle length and explained 3.3% of the total variation. Both sum of five (p=0.047) and BMI (p=0.10), summer-fall changes were also significant, explaining 2.1 and 3.5 percent of variation.

Similar results were observed with LH and FSH peak and low values. Results of the linear regressions for FSH are given in Table 5.15 and for LH in Table 5.16.

Averages for anthropometric measurements were not significant predictors of FSH and

LH low or peak values.

116 FSH Low FSH Peak Independent Variable r2 F-ratio p-value r2 F-ratio p-value Sum of Two 0.002 0.268 0.605 0.002 0.279 0.598 Sum of Five 0.013 1.876 0.173 0.000 0.014 0.907 Arm Fat Index 0.000 0.068 0.795 0.002 0.229 0.633 Waist Hip Ratio 0.000 0.067 0.797 0.003 0.387 0.535 Weight 0.010 1.355 0.246 0.000 0.000 0.996 Body Fat 0.015 2.073 0.152 0.001 0.084 0.772 BMI 0.011 1.519 0.220 0.000 0.003 0.959

Table 5.15: Linear regression results for follicle stimulating hormone (FSH) low and peak values and measures of energy balance averages.

LH Low LH peak

r2 F-ratio p-value r2 F-ratio p-value Sum of Two 0.002 0.289 0.592 0.002 0.240 0.625 Sum of Five 0.000 0.034 0.855 0.002 0.251 0.617 Arm Fat Index 0.009 1.211 0.273 0.002 0.267 0.606 Waist Hip Ratio 0.018 2.441 0.121 0.000 0.020 0.889 Weight 0.000 0.021 0.886 0.004 0.514 0.473 Body Fat 0.000 0.004 0.950 0.001 0.128 0.721 BMI 0.000 0.030 0.863 0.001 0.165 0.685

Table 5.16: Linear regression results for leutenizing hormone (LH) low and peak values and measures of energy balance.

Sum of two skinfolds (r2 = 5.2), AFI (r2 = 3.6) and WHR (r2 = 4.1) changes from summer to fall were significantly associated with FSH peak. No other available anthropometric measure was significantly associated with FSH peak. WHR was the sole variable statistically associated with LH peak. Both winter-spring (r2 = 4.2) and summer-

117 fall (r2 = 4.1) were significant. Most anthropometric measures were not closely associated with FSH or LH low values. Change in body fat from winter-fall was significant in predicting FSH low values (r2 = 5.5) as was change in sum of two skinfolds

summer - fall (r2 = 3.3). Only sum of two changes from summer-fall predicted LH low

values. These results for FSH are given in Table 5.17 and for LH in Table 5.18.

118

FSH Low FSH Peak Independent Variable r2 F-ratio p-value r2 F-ratio p-value Sum of Two Winter-Spring 0.013 1.276 0.262 0.001 0.073 0.788 Spring-Summer 0.020 2.778 0.098 0.014 1.775 0.185 Summer-Fall* 0.033 4.609 0.034 0.052 6.869 0.010 Winter-Fall 0.027 2.675 0.105 0.008 0.715 0.400 Sum of Five Winter-Spring 0.028 2.769 0.099 0.024 2.204 0.141 Spring-Summer 0.006 0.793 0.375 0.010 1.215 0.272 Summer-Fall 0.011 1.473 0.227 0.019 2.391 0.125 Winter-Fall 0.027 2.652 0.107 0.010 0.876 0.352 Arm Fat Index Winter-Spring 0.011 1.050 0.308 0.002 0.172 0.679 Spring-Summer 0.002 0.219 0.640 0.009 1.205 0.274 Summer-Fall* 0.013 1.636 0.203 0.036 4.266 0.041 Winter-Fall 0.032 2.857 0.095 0.862 0.742 0.391 Waist Hip Ratio Winter-Spring 0.000 0.007 0.935 0.000 0.022 0.883 Spring-Summer 0.002 0.204 0.652 0.011 1.461 0.229 Summer-Fall* 0.004 0.454 0.502 0.041 4.799 0.031 Winter-Fall 0.003 0.220 0.640 0.016 1.289 0.260 Weight Winter-Spring 0.000 0.001 0.970 0.003 0.280 0.598 Spring-Summer 0.000 0.049 0.826 0.004 0.560 0.456 Summer-Fall 0.003 0.450 0.504 0.012 1.517 0.220 Winter-Fall 0.001 0.145 0.704 0.001 0.101 0.751 Body Fat Winter-Spring 0.039 3.665 0.059 0.002 0.138 0.771 Spring-Summer 0.002 0.223 0.637 0.009 1.079 0.301 Summer-Fall 0.001 0.117 0.732 0.009 1.050 0.307 Winter-Fall* 0.055 5.214 0.025 0.000 0.010 0.922 BMI Winter-Spring 0.001 0.064 0.801 0.001 0.127 0.723 Spring-Summer 0.000 0.004 0.948 0.002 0.219 0.641 Summer-Fall 0.000 0.046 0.830 0.002 0.240 0.625 Winter-Fall 0.000 0.032 0.859 0.003 0.296 0.588

Table 5.17: Linear regression results for FSH low and peak values and magnitude of change between seasons in measures of energy balance (* = p < 0.05).

119

LH Low LH Peak Independent Variable r2 F-ratio p-value r2 F-ratio p-value Sum of Two Winter-Spring 0.002 0.157 0.693 0.008 0.721 0.398 Spring-Summer 0.010 1.356 0.246 0.001 0.163 0.687 Summer-Fall* 0.031 4.304 0.040 0.009 1.262 0.263 Winter-Fall 0.011 1.080 0.301 0.021 2.037 0.157 Sum of Five Winter-Spring 0.000 0.010 0.919 0.006 0.600 0.441 Spring-Summer 0.007 0.889 0.348 0.000 0.024 0.877 Summer-Fall 0.009 0.837 0.363 0.000 0.041 0.840 Winter-Fall 0.013 1.757 0.187 0.007 0.644 0.424

Arm Fat Index Winter-Spring 0.000 0.016 0.901 0.002 0.142 0.707 Spring-Summer 0.002 0.234 0.629 0.001 0.089 0.766 Summer-Fall 0.000 0.004 0.947 0.005 0.586 0.446 Winter-Fall 0.003 0.217 0.643 0.004 0.317 0.575 Waist Hip Ratio Winter-Spring* 0.017 1.574 0.213 0.042 4.162 0.044 Spring-Summer 0.001 0.188 0.665 0.015 1.951 0.165 Summer-Fall* 0.002 0.218 0.614 0.041 5.102 0.026 Winter-Fall 0.000 0.023 0.880 0.000 0.063 0.229 Weight Winter-Spring 0.007 0.616 0.434 0.009 0.852 0.358 Spring-Summer 0.001 0.106 0.745 0.000 0.002 0.968 Summer-Fall 0.009 1.232 0.269 0.000 0.015 0.904 Winter-Fall 0.000 0.001 0.975 0.002 0.221 0.640 Body Fat Winter-Spring 0.000 0.019 0.890 0.007 0.630 0.430 Spring-Summer 0.000 0.012 0.912 0.009 1.155 0.285 Summer-Fall 0.002 0.308 0.580 0.011 1.366 0.245 Winter-Fall 0.002 0.164 0.686 0.018 1.583 0.212 BMI Winter-Spring 0.006 0.563 0.455 0.005 0.438 0.510 Spring-Summer 0.000 0.000 0.988 0.003 0.374 0.542 Summer-Fall* 0.007 0.993 0.321 0.000 0.006 0.940 Winter-Fall 0.003 0.712 0.400 0.001 0.135 0.714

Table 5.18: Linear regression results for LH low and peak values and magnitude of change between seasons in measures of energy balance (* = p < 0.05). 120 5.6 Energy Balance and Health Status

Linear regression was used to examine associations between average energy balance measures and average health status measures (Table 5.19 and Table 5.20). Only

average BMI was associated with average self reported health items (r2 = 2.1). No average measure of energy balance tested was useful in predicting CRP or Hemoglobin.

All average measures of energy balance were associated with average measures of both systolic and diastolic blood pressure measures.

Self Reported Health CRP HB Independent Variable r2 F-ratio p-value r2 F-ratio p-value r2 F-ratio p-value Sum of Two 0.002 0.395 0.530 0.004 0.866 0.353 0.006 1.317 0.252 Sum of Five 0.007 1.503 0.222 0.005 1.083 0.299 0.001 0.230 0.632 Arm Fat 0.000 0.009 0.922 0.006 1.397 0.239 0.004 0.861 0.355 Index Waist Hip 0.013 2.836 0.094 0.000 0.098 0.754 0.002 0.437 0.493 Ratio Weight 0.000 0.051 0.822 0.001 0.265 0.640 0.000 0.003 0.955 Body Fat 0.000 0.041 0.839 0.002 0.455 0.500 0.004 0.796 0.373 BMI* 0.021 4.802 0.029 0.001 0.219 0.640 0.000 0.049 0.826

Table 5.19: Linear regression results for average measures of energy balance and average measures of self reported health, C-reactive protein (CRP) and hemoglobin (Hb) (* = p < 0.05).

121

Systolic BP Diastolic BP Independent Variable r2 F-ratio p-value r2 F-ratio p-value Sum of Two* 0.073 17.445 0.000 0.067 15.990 0.000 Sum of Five* 0.093 24.075 0.000 0.093 22.609 0.000 Arm Fat 0.025 5.687 0.018 0.024 5.454 0.020 Index* Waist Hip 0.045 10.474 0.001 0.025 5.561 0.019 Ratio* Weight* 0.101 24.567 0.000 0.111 27.119 0.000 Body Fat* 0.104 25.291 0.000 0.136 34.369 0.000 BMI* 0.135 34.566 0.000 0.104 25.708 0.000

Table 5.20: Linear regression results for average measures of energy balance and average measures of systolic and diastolic blood pressure (BP). (* = p < 0.05)

Changes in energy balance measures were not associated with hemoglobin

measures. Only BMI change was statistically significant in any of the measures. Winter-

Spring change (r2 = 3.4) was useful in predicting average number of self-reported health

items and Summer-Fall in CRP values (r2 = 3.4). Linear regression results are given in

Table 5.21. Sum of five and sum of two were useful in predicting average systolic blood

pressure levels. Winter-fall sum of two skinfolds (r2 = 3.1) and both winter-fall (r2 = 2.8) and spring-summer (r2 = 3.9) sum of five skinfolds were significant. The same measures

were also significant in determining diastolic blood pressure. Spring-summer (r2 = 3.2)

sum of two skinfolds and both winter-spring (r2 = 2.9) and spring-summer (r2 = 2.4) were

statistically significant. Linear regression results for average blood pressure

measurements are given in Table 5.22.

122 Self-reported health CRP HB

Independent Variable r2 F-ratio p-value r2 F-ratio p-value r2 F-ratio p-value Sum of Two Winter-Spring 0.000 0.004 0.947 0.016 2.378 0.125 0.001 0.108 0.743 Spring-Summer 0.000 0.003 0.959 0.001 0.119 0.731 0.004 0.727 0.395 Summer-Fall 0.001 0.291 0.590 0.002 0.366 0.546 0.003 0.651 0.421 Winter-Fall 0.004 0.619 0.433 0.003 0.462 0.498 0.000 0.004 0.950 Sum of Five Winter-Spring 0.000 0.008 0.929 0.012 1.754 0.187 0.002 0.333 0.565 Spring-Summer 0.001 0.154 0.696 0.002 0.300 0.585 0.002 0.335 0.563 Summer-Fall 0.000 0.057 0.812 0.000 0.069 0.793 0.003 0.627 0.429 Winter-Fall 0.000 0.030 0.863 0.007 0.987 0.322 0.000 0.004 0.951 Arm Fat Index Winter-Spring 0.006 0.894 0.346 0.005 0.730 0.394 0.013 1.855 0.175 Spring-Summer 0.000 0.026 0.873 0.004 0.785 0.377 0.006 1.217 0.271 Summer-Fall 0.004 0.640 0.425 0.001 0.103 0.749 0.013 2.340 0.128 Winter-Fall 0.008 0.964 0.328 0.000 0.009 0.924 0.012 1.549 0.216 Waist Hip Ratio Winter-Spring 0.000 0.015 0.904 0.004 0.529 0.468 0.010 1.478 0.226 Spring-Summer 0.002 0.364 0.547 0.000 0.086 0.770 0.000 0.012 0.914 Summer-Fall 0.014 2.466 0.118 0.003 0.562 0.455 0.001 0.134 0.715 Winter-Fall 0.022 2.780 0.098 0.016 1.996 0.160 0.000 0.001 0.974 Weight Winter-Spring 0.007 0.920 0.339 0.001 0.147 0.702 0.021 3.027 0.084 Spring-Summer 0.002 0.470 0.494 0.001 0.248 0.619 0.011 2.209 0.139 Summer-Fall 0.001 0.104 0.747 0.014 2.803 0.096 0.002 0.354 0.553 Winter-Fall 0.008 1.111 0.294 0.011 1.625 0.204 0.003 0.384 0.536 Body Fat Winter-Spring 0.012 1.659 0.200 0.000 0.037 0.848 0.001 0.138 0.711 Spring-Summer 0.003 0.674 0.413 0.013 2.507 0.115 0.001 0.180 0.672 Summer-Fall 0.012 2.270 0.134 0.000 0.027 0.869 0.001 0.120 0.729 Winter-Fall 0.010 1.233 0.269 0.000 0.008 0.927 0.006 0.792 0.375 BMI Winter-Spring* 0.034 5.028 0.026 0.002 0.277 0.599 0.019 2.714 0.102 Spring-Summer 0.016 3.260 0.073 0.001 0.241 0.624 0.004 0.779 0.378 Summer-Fall* 0.003 0.651 0.421 0.034 6.778 0.010 0.000 0.044 0.835 Winter-Fall 0.002 0.332 0.565 0.010 1.294 0.257 0.005 0.656 0.419

Table 5.21: Linear regression analysis results for average measures of self reported health items, C-reactive protein (CRP), and hemoglobin (Hb) and magnitude of change between seasons in measures of energy balance (* = p < 0.05).

123

Systolic BP Diastolic BP

Independent Variable r2 F-ratio p-value r2 F-ratio p-value Sum of Two Winter-Spring 0.008 1.138 0.288 0.025 3.689 0.057 Spring-Summer* 0.017 3.475 0.064 0.032 6.674 0.010 Summer-Fall 0.015 2.945 0.088 0.009 1.319 0.189 Winter-Fall* 0.031 10.869 0.001 0.006 0.887 0.348 Sum of Five Winter-Spring* 0.009 1.305 0.255 0.029 4.235 0.041 Spring-Summer* 0.028 5.578 0.019 0.024 4.760 0.030 Summer-Fall 0.011 2.188 0.141 0.008 1.559 0.213 Winter-Fall* 0.039 5.631 0.019 0.010 1.391 0.240 Arm Fat Index Winter-Spring 0.000 0.000 0.999 0.002 0.218 0.641 Spring-Summer 0.005 1.049 0.307 0.005 1.015 0.315 Summer-Fall* 0.013 2.202 0.140 0.005 0.888 0.347 Winter-Fall 0.003 0.354 0.553 0.000 0.020 0.887 Waist Hip Ratio Winter-Spring 0.000 0.053 0.819 0.000 0.049 0.826 Spring-Summer 0.001 0.200 0.655 0.000 0.002 0.965 Summer-Fall 0.005 0.855 0.357 0.014 2.487 0.117 Winter-Fall 0.017 2.166 0.144 0.028 3.532 0.063 Weight Winter-Spring 0.004 2.886 0.091 0.000 0.059 0.808 Spring-Summer 0.000 0.016 0.898 0.000 0.009 0.926 Summer-Fall 0.000 0.036 0.849 0.002 0.352 0.554 Winter-Fall 0.000 0.014 0.906 0.008 1.076 0.301 Body Fat Winter-Spring* 0.020 2.737 0.100 0.074 10.681 0.001 Spring-Summer 0.000 0.002 0.966 0.001 0.126 0.723 Summer-Fall 0.003 0.594 0.442 0.001 0.240 0.625 Winter-Fall* 0.022 2.895 0.091 0.086 11.887 0.001 BMI Winter-Spring 0.027 3.888 0.051 0.003 0.465 0.497 Spring-Summer 0.002 0.335 0.564 0.003 0.516 0.473 Summer-Fall 0.005 1.030 0.311 0.008 1.602 0.207 Winter-Fall 0.005 0.704 0.403 0.000 0.056 0.813

Table 5.22: Linear regression analysis results for average measures of systolic and diastolic blood pressure and magnitude of change between seasons in measures of energy balance (* = p < 0.05).

124

5.7 Health and Fecundity

Measures of health status were not significant associated with variation in average

cycle length based on the results of the linear regression. Only average self reported

health was useful in predicting FSH peak (r2 = 8.6) and average hemoglobin for LH peak

(r2 = 6.9). None of the measures of health status were significant with FSH or LH low values. Results of these linear regressions are given in Tables 5.23 – 5.25.

Average Cycle Length

Independent Variable r2 F-ratio p-value Self-Reported Health 0.000 0.020 0.886 CRP 0.003 0.649 0.421 HB 0.000 0.058 0.810 Systolic BP 0.000 0.022 0.881 Diastolic BP 0.002 0.360 0.549

Table 5.23: Linear Regression results for average cycle length and average measures of health status.

125

FSH Low FSH Peak

Independent Variable r2 F-ratio p-value r2 F-ratio p-value Self-Reported Health* 0.007 0.957 0.330 0.086 12.309 0.001 CRP 0.011 1.500 0.223 0.004 0.470 0.494 HB 0.002 0.319 0.573 0.020 2.577 0.111 Systolic BP 0.000 0.014 0.905 0.004 0.523 0.467 Diastolic BP 0.001 0.080 0.778 0.011 1.479 0.226

Table 5.24: Linear Regression results for FSH low and peak values and average measures of health status.

LH Low LH Peak

Independent Variable r2 F-ratio p-value r2 F-ratio p-value Self-Reported Health 0.000 0.005 0.946 0.009 1.199 0.275 CRP 0.007 0.922 0.339 0.004 0.552 0.459 HB* 0.001 0.194 0.660 0.069 9.911 0.002 Systolic BP 0.001 0.111 0.739 0.001 0.126 0.723 Diastolic BP 0.000 0.000 0.998 0.003 0.451 0.503

Table 5.25: Linear Regression results for LH low and peak values and average measures of health status.

Only C-reactive protein change from beginning to end of the study period was

significant (r2 = 18.7). Both winter-spring (r2 = 4.0) and spring-summer (r2 = 4.0) changes in self reported health items were useful in predicting FSH low values and spring-summer (r2 = 9.6) for FSH peak values. Systolic (r2 = 5.7) and Diastolic (r2 = 3.5)

126 BP change between summer and fall were also significant for FSH low values. No health

status measures explained variation in LH low values and only the summer to fall Hb (r2

= 5.3) changes were significant predictors of LH peak levels. Summary results for changes in health status and fecundity measures are given in Tables 5.26-28.

Average Cycle Length

Independent Variable r2 F-ratio p-value Self-Reported Health Winter-Spring 0.015 2.136 0.146 Spring-Summer 0.002 0.462 0.497 Summer-Fall 0.002 0.292 0.589 Winter-Fall 0.018 2.517 0.115 CRP Winter-Spring 0.001 0.121 0.729 Spring-Summer 0.010 1.352 0.247 Summer-Fall 0.000 0.015 0.904 Winter-Fall* 0.187 15.180 0.000 HB Winter-Spring 0.042 3.082 0.083 Spring-Summer 0.025 3.907 0.050 Summer-Fall 0.005 0.641 0.425 Winter-Fall 0.008 0.445 0.507 Systolic BP Winter-Spring 0.006 0.859 0.356 Spring-Summer 0.002 0.335 0.564 Summer-Fall 0.000 0.014 0.906 Winter-Fall 0.003 0.424 0.516 Diastolic BP Winter-Spring 0.004 0.530 0.468 Spring-Summer 0.008 1.522 0.219 Summer-Fall 0.003 0.642 0.424 Winter-Fall 0.002 0.294 0.588

Table 5.26: Linear regression results for average cycle length and seasonal changes in health status measures (* = p < 0.05).

127

FSH Low FSH Peak

Independent Variable r2 F-ratio p-value r2 F-ratio p-value Self-Reported Health Winter-Spring* 0.040 4.130 0.045 0.018 1.789 0.184 Spring-Summer* 0.040 5.483 0.024 0.096 13.147 0.000 Summer-Fall 0.005 0.631 0.428 0.003 0.368 0.545 Winter-Fall 0.000 0.000 0.992 0.021 2.008 0.160 CRP Winter-Spring 0.007 0.565 0.455 0.000 0.009 0.926 Spring-Summer 0.006 0.550 0.460 0.011 0.952 0.332 Summer-Fall 0.014 0.937 0.337 0.022 1.415 0.239 Winter-Fall 0.033 1.601 0.212 0.057 2.856 0.098 HB Winter-Spring 0.008 0.388 0.536 0.006 0.249 0.621 Spring-Summer 0.008 0.868 0.354 0.000 0.007 0.932 Summer-Fall 0.029 3.078 0.082 0.023 2.280 0.134 Winter-Fall 0.014 0.515 0.478 0.005 0.160 0.692 Systolic BP Winter-Spring 0.014 1.333 0.251 0.007 0.673 0.414 Spring-Summer 0.019 2.613 0.108 0.003 0.350 0.555 Summer-Fall* 0.057 8.196 0.005 0.015 1.971 0.163 Winter-Fall 0.003 0.307 0.581 0.001 0.080 0.778 Diastolic BP Winter-Spring 0.003 0.288 0.593 0.008 0.761 0.385 Spring-Summer 0.014 1.914 0.169 0.000 0.040 0.843 Summer-Fall* 0.035 4.929 0.028 0.007 0.891 0.347 Winter-Fall 0.000 0.001 0.978 0.000 0.006 0.939

Table 5.27: Linear regression results for FSH low and peak values and seasonal changes in seasonal changes in health status measures (* = p < 0.05).

128

LH Low LH Peak

Independent Variable r2 F-ratio p-value r2 F-ratio p-value Self-Reported Health Winter-Spring 0.005 0.533 0.467 0.000 0.037 0.848 Spring-Summer 0.006 0.740 0.391 0.001 0.190 0.664 Summer-Fall 0.006 0.817 0.368 0.008 1.049 0.308 Winter-Fall 0.007 0.664 0.417 0.000 0.031 0.861 CRP Winter-Spring 0.009 0.666 0.417 0.000 0.012 0.911 Spring-Summer 0.000 0.000 0.994 0.011 1.091 0.299 Summer-Fall 0.015 0.975 0.327 0.003 0.195 0.660 Winter-Fall 0.005 0.224 0.638 0.002 0.083 0.775 HB Winter-Spring 0.000 0.005 0.946 0.000 0.001 0.980 Spring-Summer 0.001 0.119 0.730 0.003 0.271 0.604 Summer-Fall* 0.016 1.599 0.209 0.053 5.637 0.019 Winter-Fall 0.003 0.097 0.757 0.071 2.821 0.101 Systolic BP Winter-Spring 0.000 0.034 0.854 0.014 1.396 0.240 Spring-Summer 0.000 0.005 0.946 0.010 1.363 0.245 Summer-Fall* 0.000 0.018 0.892 0.010 1.308 0.255 Winter-Fall 0.000 0.035 0.851 0.014 1.351 0.248 Diastolic BP Winter-Spring 0.002 0.162 0.688 0.002 0.166 0.685 Spring-Summer 0.002 0.240 0.625 0.000 0.028 0.867 Summer-Fall 0.002 0.203 0.653 0.002 0.229 0.633 Winter-Fall 0.011 1.016 0.316 0.001 0.066 0.798

Table 5.28: Linear regression results for LH low and peak values and seasonal changes in seasonal changes in health status measures (* = p < 0.05).

129 5.8 Summary

This sample of Bhutia women shows seasonality in patternsof births and conceptions. Most conceptions occur in winter, the fewest conceptions occur in summer.

Average menstrual cycles also varied by season but did not follow the same pattern. The fewest irregular cycles were in the fall. Self-reported health recall items, hemoglobin and systolic blood pressure all vary significantly by season. Both systolic blood pressure and numbers of self-reported health recall were highest in winter and declined over the course of the year. Hemoglobin levels were highest in the summer. Average measures of energy balance did not predict average cycle length, but magnitude of change in BMI, sum of two, sum of five, and arm fat index did predict average cycle length. Average measures of energy balance also did not impact reproductive hormone levels but magnitude of change in sum of two, arm fat index, and waist hip ratio did impact reproductive hormones. Average measures of health status did not predict average cycle length, but change in CRP did predict average cycle length. Only self-reported health items were associated with FSH peak and Hemoglobin with LH peak. Changes in self- reported health, systolic and diastolic blood pressure explained some variation in FSH levels, but only Hb was associated with LH. In general, these results provide partial support hypothesis 2, that negative energy balance reduces fecundity.

130 CHAPTER 6

RESULTS III:

SOCIAL WELL-BEING AND SOCIOECONOMIC STATUS

6.1 Introduction

Hypothesis 3: Low social wellbeing and economic status increases activity levels and decreases nutrition. Therefore, lower social wellbeing and economic status will result in a higher magnitude of energy deficit.

Examinations of the relationships between socioeconomic status and measures of energy balance and changes in these measures are presented in this chapter. Household characteristics and other components of socioeconomic status for this sample are also presented. An index of social support was created and its relationship to energy balance is examined along with perceived stress as measures of social wellbeing. All data for socioeconomic status (SES) index, social support (SSI) index, and perceived stress sum were collected using questionnaires located in Appendices A and B.

131 6.2 Socioeconomic Status

6.2.1 Measures of Socioeconomic Status

Home

Most homes in Gangtok reflect the common urban Indian home style: multistory concrete. There are, however, a few wood-constructed houses remaining in Gangtok.

Most Bhutia prefer the more modern households, but will only replace the traditional house if money is available. Wooden houses are very small and limited in their construction. Concrete houses are as big and as tall as money allows and often new floors are added when money is available. A summary of sample home size information is given in Table 6.1.

Mean number of rooms/house 4.5 Range in number of rooms/house 1-19 S.D. in number of rooms/house 3.2 Mean number rooms/wooden house 3.9 Mean number rooms/concrete house 4.6 Mean number rooms/owned house 5.8 Mean number rooms/rented house 2.9 Average number rooms/person 0.8 Range in number rooms/person 0.2-3 S.D. number rooms/person 0.4

Table 6.1: Sample characteristics in home construction, ownership and size.

The average number of rooms in each house is 4.5, with a range of 1-19 (not including bathrooms). Concrete houses generally had more rooms with an average of 4.6 in concrete houses and 3.9 in wooden houses. Owned homes were generally bigger than 132 rented homes with 5.8 rooms, on average, in owned homes and 2.9 in rented homes. The average number of rooms per person was 0.8 with a range of 0.2-3 rooms/person.

Land for these houses is generally passed on through the family. Very little land is available for new buildings. However, many Bhutia who do own land in Gangtok also own land and/or other houses in other parts of Sikkim. During negotiations with the government of the Choygal and India for transfer of control of the state, Bhutia and

Lepcha were given priority land rights, particularly in the North District. The majority of land in this district can only be legally owned by Bhutia or Lepcha individuals. The number and percentage of individual owning other land, buildings or houses is presented in Table 6.2.

N % Owns other land 155 65.1 Owns other house 25 10.5

Table 6.2: Number and percentage of women owning other land, houses or buildings.

Most women (65.1%), or their families, owned land in other areas with 10.5% of the sample owning other houses.

New homes are generally attached to the city water system with piped water.

Traditional houses or older apartments may not have access to piped water but all have wells located within ¼ mile of the household. Buildings without piped water also do not have indoor, attached toilets. In these situations, either outhouses or small outbuildings

133 with attached piped water were built. The housing characteristics of this sample are

summarized in Table 6.3.

Home ownership Home construction Water source owned rented concrete wood piped carried N 133 105 200 38 223 15 % 55.9 44.1 84.0 26.0 93.7 6.3

Table 6.3: Sample housing characteristics.

The majority of the women in this sample lived in family owned homes (55.9%) that

were constructed of concrete (84.0%), and had piped water (93.7%).

Occupation

Both married and single Bhutia women commonly work. The majority of the women in this sample were working (63.9%), of these, only 1.3% worked part-time. The remainder of the sample was unemployed, housewives or students. A summary of the occupational status of the sample is documented in Table 6.4.

Employed: Employed: Unemployed Unemployed: Unemployed: full-time Part-time Student Housewife N 149 3 23 11 52 % 62.6 1.3 9.7 4.6 21.8

Table 6.4: Bhutia sample occupational status.

134 Most of the working women in this sample (70.5%) were employed by the

Government of Sikkim in government offices, schools or the government hospital. Those

who did not work for the government worked in small private businesses, the local

private hospital, banks or private schools. Women who worked in government office

were generally employed as clerks, typists, assistants or stenographers. Only one woman

working for the government had a high-level post.

Almost all (96.2%) of the living husbands were also employed, only five

husbands were unemployed. Of the employed husbands, the majority (75.6%) of these

were employed by the government in offices, the police force, or as government

employed drivers. Husbands employed by the government were also generally employed

in low ranking positions such as clerk, driver, or peon. Although, there were several

husbands employed as engineers and auditors and one assistant deputy. Those men

employed in the private sector generally worked in business, tourism, or contracting.

Assets

In this sample, it was very common for the household to keep young servants.

Almost ¼ of the sample kept at least one servant (23.9%). In general, the servants were young girls from poor Nepali or Bhutanese families living in rural areas. Sikkim is located between Nepal and Bhutan, two of the poorest countries in the world. Many families have migrated across the borders in search of better opportunities. Fertility in these areas tends to be extremely high (Government of Sikkim, 1998) and many of these families are willing to send their daughters to the city to work for wages. Most households only kept one or two servants, but larger households kept up to 11 servants.

135 Assets associated with socioeconomic status include: telephone, computer, television, refrigerator, oven, and washing machines. Buildings other than houses, businesses and rental properties, were also considered assets. Owners of small businesses such as clothing shops, restaurants and corner stores that rented space were considered assets. A summary of the assets possessed by this sample is offered in Table 6.5.

ASSET N % Servant(s) 57 23.9 Building 36 15.2 Business 91 38.3 Computer 35 14.8 Washing Machine 19 8.0 Refrigerator 210 88.6 Television 108 45.6 Oven 17 7.2 Telephone 161 67.9 None of the Above 17 7.2

Table 6.5: Assets owned by Bhutia women in this sample.

The most common of these assets was the refrigerator (88.6%), in most cases, if only one assets was owned, it was a refrigerator. A majority of women also owned a telephone

(67.9%). Very few women owned washing machines (8.0%) and ovens (7.2%). Only

7.2% of women did not own any of these assets.

6.2.2 Socioeconomic Index and SES Rank

A socioeconomic index was created using categories of home characteristics occupation and assets. Test case analysis was used to assist in the development of the

136 SES index. Based on this analysis variables were selected until a Cronbach’s alpha value

> 0.7 was obtained. The following categories were included in the SES index: home

ownership, number of rooms, assets, number of servants, other land ownership and

occupation. Cronbach’s alpha for this scale was 0.721 suggesting high internal

consistency.

The average SES index value was 14.9 with a range of 3.0-56.0. Based on

clustering of these values on a scatterplot, an SES rank of 1-3 was assigned to each

individual. A SES index value < 10 was given a rank of 1, 10 > 22 was given a 2, and >

22 was given a 3. The highest category, 3, contained 16.9% of the sample, 48.8% ranked

as 2, and 34.3% of the sample received the lowest rank – 1.

6.3 Social Wellbeing

6.3.1 Measures of social wellbeing

Perceived Stress

Bhutia women in this sample have very clear ideas about which season is the most stressful. Most women, 74.3%, replied that summer was the most stressful season. Very few women reported that spring (2.1%) and fall (1%) were the most stressful seasons and only 22.6% suggested that winter was the most stressful season. Figure 6.1 shows the range of responses the question of stressful seasons.

137 80

60 ) 40 (% 20

0 Winter Spring Summer Fall Season

Figure 6.1: Bhutia women’s responses to the question “I feel the most stress in?”

Results of the seasonally administered perceived stress questionnaire do not follow the same pattern. Mean values for the sum score of the perceived stress questionnaire show statistically significant (p = 0.000) seasonal differences using

ANOVA. Average stress sums were lower in the winter than in any other season and similar in spring, summer and fall. Perceived stress sum seasonal averages are given in

Table 6.6 and graphed in Figure 6.2.

Winter Spring Summer Fall N 161 210 207 195 Mean Stress Sum 30.5 52.2 51.4 56.7 S.D. 7.1 7.6 9.6 7.8

Table 6.6: Seasonal measures for perceived stress question sum.

138 This perceived stress questionnaire is a Leikert type scale with 31 questions with answers ranking from 1-4, the possible range of stress sum is 31-124. Lower answers suggest lower stess levels, and higher answers suggest higher stress.

m 60 u

S 50 s s

e 40 r 30 St d

e 20 v i

e 10 c r 0

Pe Winter Spring Summer Fall Season

Figure 6.2: Seasonal variation in perceived stress sum averages.

Net change for all individuals from the lowest values in winter to spring was 3289 with an average increase of 22.5. Spring to summer stress sum for the sample decreased by 148 with the majority of women’s sums (54.7%) decreasing by an average of 6.8. The majority of stress sums increased between summer and fall with a net increase of 1009 and an average increase of 10.9. All women’s stress sums increased from the beginning of the study to the end of the study. Individual changes in perceived stress sum are summarized in Table 6.7.

139 Perceived Net Change % of Average % of Average % of Stress women with stress women with stress women – Sum lower stress decrease higher stress increase no change Winter- Spring +3289 0.7 1 98.6 22.5 0.7 (N=144) Spring- Summer -148 54.7 6.8 40.9 7.3 4.4 (N=200) Summer- Fall +1009 30.4 6.6 65.5 10.9 4.1 (N=197) Winter- Fall +3438 0 0 100.0 27.4 0 (N=137)

Table 6.7: Seasonal sample net change in perceived stress sum and percentage of sample that increased, decreased or did not change stress sum.

Statistically significant variation (p < 0.05) was observed between summer and fall, and winter and all other seasons. ANOVA results with Tukey matrix of pairwise comparisons are given in Table 6.8.

Winter Spring Summer Fall Stress Sum F- ratio = 348.1 p = 0.000 Winter 1.000 Spring 0.000 1.000 Summer 0.000 0.735 1.000 Fall 0.000 0.000 0.000 1.000

Table 6.8: Statistical results for significant seasonal variation in perceived stress sum measurements.

140 Social Support Index

The majority of women in this sample were married (60%). Regardless of marital status, Bhutia women tend to live in larger households. The average number of individuals in the household in this sample was 4.62, ranging from 1-19. Bhutia women tend to maintain close family ties and generally prefer to live in close proximity to their families. Women in this sample reported an average of 10 family members and 5 friends living within 1 km. These women also reported an average of 13.4 friendly visits to friends or family members outside of the household every month. In addition, an average of 5.7 phone calls was made to family and friends in a given week.

The women in this study had lived in Gangtok for an average of 20 years and had many years to develop systems of social support. Slightly more than half of the women in the sample (51.1%) of the women were born in Gangtok. Women who were not born in Gangtok moved there between 1 and 34 years prior to the study. The average amount of time living in Gangtok, for those not born there, was 11.6 years.

A social support index was created based on the amount of social connections perceived by the subjects. The following categories were included in the social support index (SSI): marital status, years in Gangtok, z-scores of number of family within 1,5 and 10 km, and z-scores of number of friends within 1,5, and 10 km. The Cronbach’s alpha for this scale is 6.0 suggesting moderate internal validity. Minimum SSI for this sample is -4.2, maximum value is 29.4 and the mean value is 0.606.

Based on clustering of these values on a scatterplot, a rank (1-3) was created with lowest SSI values ( < 0) given a rank of 1, moderate values (>0<5) given a 2 and high values (> 5) given a rank of 3. The lowest rank included 56.4% of the sample, 32.1% of

141 the sample were given 2s, and 11.5% of the sample were given the highest social support ranking.

6.4 Social Wellbeing, Socioeconomic Status and Energy Deficit.

6.4.1 Socioeconomic status and social wellbeing

Bhutia women in this sample with a higher socioeconomic index (SES) were more likely to have a higher Social wellbeing index. Linear regression suggests that SES is useful in predicting SSI (p = 0.003). Linear regression results for SES and SSI are given in Table 6.9.

Independend Variable: R2 F-ratio p-value SES Dependent Variable: 0.042 8.807 0.003 SSI*

Table 6.9: Linear regression results for Socioeconomic Status Index (SES) values and Social Support Index (SSI). (* = p < 0.05)

Similarly, women with a higher SES rank also tended to have a higher SSI rank based on linear regression analysis. Results of this analysis are given in Table 6.10.

142 SES Rank R2 F-ratio p-value Dependent Variable: SSI Rank* 0.027 5.518 0.020

Table 6.10: Linear regression results for Socioeconomic Status Rank (SES) values and Social Support Index Rank (SSI). (* = p < 0.05)

6.4.2 Socioeconomic status, social support and perceived stress

Average perceived stress sum was not related to measures of socioeconomic status (SES) or Social Support (SSI). SES index, SES rank, SSI index and SSI rank were not significant predictors of average perceived stress sum based on linear regression results. These results are provided in Table 6.11.

Perceived Stress Sum Independent Variable R2 F-ratio p-value SES Index 0.014 2.813 0.095 SES Rank 0.002 0.371 0.543 SSI Index 0.003 0.603 0.438 SSI Rank 0.006 1.356 0.246

Table 6.11: Linear regression results for perceived stress sum and measures of socioeconomic status (SES) and social support (SSI).

In addition, measures of SES and SSI were not significant predictors of change in perceived stress change between winter and fall. Table 6.12 contains the linear regression results for SES index, SES rank, SSI index and SSI rank.

143

Perceived Stress Change Independent Variable r2 F-ratio p-value SES Index 0.001 0.114 0.736 SES Rank 0.003 0.358 0.551 SSI Index 0.001 0.163 0.687 SSI Rank 0.000 0.018 0.892

Table 6.12: Linear regression results for measures of SES and SSI and change in perceived stress sum from winter to fall.

6.4.3 Socioeconomic status and energy deficit

Socioeconomic Index did not significantly impact average values for measures of energy balance. Based on linear regression analysis, SES index was not statistically significant in predicting any average values of anthropometric measures. Results are posted in Table 6.13. Socioeconomic rank was much more informative. Women with a higher SES rank tended to have a higher average Sum of 5 skinfold (R2 = 7.4), % body fat (R2 = 6.0), and Arm Fat Index (AFI) (R2 = 6.0). Linear regression results for SES rank and average values of anthropometric measures are also given in Table 6.13.

144 Dependent Variable SES Index SES Rank Average Sum of Two R2 0.013 0.015 F-ratio 2.531 3.029 p-value 0.113 0.083 Average Sum of Five* R2 0.016 0.022 F-ratio 3.218 4.550 p-value 0.074 0.034 Average Body Fat R2 0.018 0.033 F-ratio 3.572 6.805 p-value* 0.060 0.010 Average Weight R2 0.006 0.014 F-ratio 1.269 2.734 p-value 0.261 0.100 Average BMI R2 0.001 0.003 F-ratio 0.102 0.630 p-value 0.750 0.428 Average WHR R2 0.004 0.000 F-ratio 0.779 0.007 p-value 0.378 0.932 Average AFI* R2 0.018 0.020 F-ratio 3.589 3.962 p-value 0.060 0.048

Table 6.13: Linear regression results for SES index and SES rank and average anthropometric measures. (* = p < 0.05)

Measures of socioeconomic status were not very useful in predicting changes in energy balance. SES Index was not statistically significant for any measures of change in energy balance from season 1 – season 4 (winter-fall), based on linear regression models.

SES rank was only a significant predictor of waist hip ratio (WHR) change (R2 = 6.1).

145 Results of the linear regression analysis for SES index and rank and change in energy balance are given in Table 6.14.

146

Dependent Variable SES Index SES Rank Sum of Two Change R2 0.001 0.002 F-ratio 0.084 0.299 p-value 0.772 0.585 Sum of Five Change R2 0.027 0.006 F-ratio 3.830 0.793 p-value 0.052 0.375 Body Fat Change R2 0.007 0.012 F-ratio 0.821 1.516 p-value 0.367 0.221 Weight Change R2 0.002 0.002 F-ratio 0.243 0.254 p-value 0.623 0.615 BMI Change R2 0.000 0.003 F-ratio 0.051 0.317 p-value 0.822 0.574 WHR Change* R2 0.030 0.043 F-ratio 3.594 5.199 p-value 0.061 0.024 AFI Change R2 0.000 0.004 F-ratio 0.005 0.442 p-value 0.944 0.508

Table 6.14: Linear regression results for SES index and SES rank and change in anthropometric measures from beginning of the study period to end of the study period (Winter-Fall). (* = p < 0.05)

147

Average values of energy balance were generally not dependent on either social status index (SSI) or rank. Both SSI index and rank were only useful in predicting average waist hip ratio (WHR). Women with higher SSI index and SSI rank tended to have a higher WHR (SSI index, p=0.005, R2 = 0.034; SSI rank, p=0.015, R2 = 0.027).

Results of the linear regression analysis for SSI index and rank and average anthropometric measures are given in Table 6.15.

148

Dependent Variable SSI Index SSI Rank Average Sum of Two R2 0.001 0.001 F-ratio 0.125 0.292 p-value 0.724 0.589 Average Sum of Five R2 0.000 0.001 F-ratio 0.000 0.145 p-value 0.985 0.704 Average Body Fat R2 0.005 0.004 F-ratio 1.118 0.779 p-value 0.292 0.378 Average Weight R2 0.005 0.003 F-ratio 1.010 0.736 p-value 0.316 0.392 Average BMI R2 0.008 0.003 F-ratio 1.892 0.677 p-value 0.170 0.412 Average WHR* R2 0.034 0.027 F-ratio 7.877 6.027 p-value 0.005 0.015 Average AFI R2 0.005 0.005 F-ratio 1.093 1.211 p-value 0.297 0.272

Table 6.15: Linear regression results for SSI index and SSI rank and average anthropometric measures. (* = p < 0.05)

149 Measures of social support were not at all useful in predicting change in energy balance. None of the measures of change in energy balance was statistically significant

(p< 0.05). Linear regression results are given in Table 6.16.

Independent Variable SSI Index SSI Rank Sum of Two Change R2 0.007 0.004 F-ratio 1.037 0.540 p-value 0.310 0.464 Sum of Five Change R2 0.003 0.007 F-ratio 0.452 1.174 p-value 0.502 0.280 Body Fat Change R2 0.001 0.006 F-ratio 0.099 0.769 p-value 0.753 0.382 Weight Change R2 0.000 0.002 F-ratio 0.000 0.297 p-value 0.983 0.586 BMI Change R2 0.000 0.001 F-ratio 0.008 0.167 p-value 0.928 0.683 WHR Change R2 0.001 0.000 F-ratio 0.109 0.061 p-value 0.742 0.805 AFI Change R2 0.014 0.009 F-ratio 1.751 1.106 p-value 0.188 0.295

Table 6.16: Linear regression results for SSI index and SSI rank and change in anthropometric measures from beginning of the study period to end of the study period (Winter-Fall). (* = p < 0.05)

150 Average values of perceived stress sum did not predict average values in measures of energy balance. Linear regression results for average perceives stress sum are given in Table 6.17

Dependent Variable Perceived Stress Sum Average Sum of Two R2 0.009 F-ratio 1.961 p-value 0.163 Average Sum of Five R2 0.006 F-ratio 1.306 p-value 0.254 Average Body Fat R2 0.001 F-ratio 0.112 p-value 0.738 Average Weight R2 0.002 F-ratio 0.454 p-value 0.501 Average BMI R2 0.004 F-ratio 0.846 p-value 0.359 Average WHR R2 0.000 F-ratio 0.073 p-value 0.787 Average AFI R2 0.012 F-ratio 2.771 p-value 0.097

Table 6.17: Linear regression results for perceived stress sum and average anthropometric measures. (* = p < 0.05)

151 Perceived stress sum was useful in predicting change in both sum of five (p=0.00, R2 =

0.212) and waist-hip ratio (WHR) (p=0.006, R2 = 0.57). Linear regression results are summarized in Table 6.18.

Dependent Variable Perceived Stress Sum Average Sum of Two R2 0.000 F-ratio 0.023 p-value 0.881 Average Sum of Five* R2 0.212 F-ratio 42.310 p-value 0.000 Average Body Fat R2 0.000 F-ratio 0.006 p-value 0.937 Average Weight R2 0.003 F-ratio 0.392 p-value 0.532 Average BMI R2 0.000 F-ratio 0.009 p-value 0.925 Average WHR* R2 0.057 F-ratio 7.662 p-value 0.006 Average AFI R2 0.000 F-ratio 0.001 p-value 0.980

Table 6.18: Linear regression results for perceived stress sum and change in anthropometric measures from beginning of the study period to end of the study period (Winter-Fall). (* = p < 0.05)

152 6.5 Summary

Perceived stress sum showed significant seasonal variation with values lower in the winter than in any other season. SES index was not associated with average measures of energy balance, but SES rank associated with sum of five, AFI and body fat averages.

SES rank was only associated with WHR magnitude of change. SSI index and rank were only useful in predicting WHR average but not with any seasonal changes. These results partially support hypothesis 3.

153 CHAPTER 7

DISCUSSION AND CONCLUSIONS

7.1 Introduction

Human reproduction is extremely complex. Data presented and examined here confirms that there are no simple, all-inclusive explanations for human fertility and fecundity. In this study, many factors hypothesized to impact energy balance or fecundity showed statistically significant associations. Observed patterns of variation in measures of energy balance, health and reproduction suggest several possible influences.

In addition to the combined effects of multiple factors on fecundity complicate and compound associations of single factor influences. Patterns of association due to multiple factors may not be obvious in this sample of 200 reproductive age women. One primary goal was to determine if seasonality significantly affected energy balance and fecundity. Results presented suggest that seasonal variation alters both. Another goal was to identify factors that might explain variation in fecundity. Although this goal was partially achieved, study limitations and the complexity of interactions between measures did not allow complete understanding of these interactions.

154 7.2 Hypothesis 1

Energy intake of Bhutia women in Gangtok Sikkim, India is less than their energy expenditure, creating an overall state of negative energy balance. Corollary 1: Magnitude of energy deficit will vary according to climatic season.

On a seasonal basis, these Bhutia women generally presented with negative energy balance. Their pattern of change in anthropometric measurements suggests negative energy balance between winter and spring, spring and summer and the beginning and end of the study year. Only between summer and fall was there a general increase in measures of energy balance.

7.2.1 Summary of seasonal changes in energy balance measures

Anthropometric measures that would reflect large-scale changes in energy balance, weight, fat and BMI, did not show statistically significant seasonal changes.

Many measures that would reflect smaller-scale changes (skinfolds and circumferences) did show statistically significant seasonal variation. Most measures did vary significantly by season: Sum of Two, Sum of Five, Arm fat index (AFI), Waist hip ratio (WHR), bicep skinfold, tricep skinfold, subscapular skinfold, calf skinfold, and upper arm circumference. Even weight, fat and BMI showed the same pattern of seasonal change when analyzed on an individual basis.

Winter-Spring

In winter, the sampled Bhutia women averaged 54.7 kg, 29.5% body fat, and a

BMI of 22.9 kg/m2. Sixty-five percent of the sample was classified as normal according by BMI, 27.4% exhibited high BMI’s, and 7.6% low. From winter to spring, most women lost weight and fat and decreased BMI. The proportion of women with normal

BMIs increased slightly, but the number of women in the low BMI categories increased

155 and the number of women in the high BMI categories decreased. In addition, tricep

skinfolds, subscapular skinfolds, arm circumference, AFI, sum of two skinfolds, and sum

of five skinfolds all decreased significantly.

Spring-Summer

From spring to summer, the net change in weight, fat, and BMI was again

negative, with a higher net loss than in winter-spring. A majority of participants lost

weight, fat and BMI. The number of women in each BMI category remained nearly the

same as in the previous season, but bicep and tricep skinfolds decreased significantly

Subscapular skinfolds, calf skinfolds, upper arm circumference, AFI, sum of two

skinfolds and sum of five skinfolds also decreased over this period.

Summer-Fall

Measures of energy balance generally increased summer to fall. Weight, fat and

BMI showed net increases and the majority increased these measures. Only waist hip

ratio (WHR) increased significantly, although all other measures showed net increases.

Number of women with normal BMI did not change. However, number of women in

high categories increased and in low categories decreased, both by less than one percent.

Winter – Fall

Over the course of the year, most women lost weight, fat and BMI and all net changes were negative. Significant decreases occurred in triceps, subscapular, calf skinfolds, upper arm circumference, AFI, sum of two skinfolds and sum of five skinfolds.

The number of women in the normal BMI category increased by 4.2%, but the number of women in the high categories decreased by 5.7% and the number of women in the low categories increased by 1.5%.

156 7.2.2 Explanation for seasonal pattern of change in energy balance measures

Climatic changes in Sikkim impact many areas of Bhutia women’s lives. Winter is the season of low rainfall and low temperatures. Temperatures at night can hover around freezing and are chilly during the day. Bhutia homes are not equipped with central heating. In fact, most homes do not have any heat sources, those that do, have small electric or kerosene heaters. Homes also generally are built with large areas open to the outside and no insulation. Offices and shops also are not heated. The only escape from the cold is in the sunshine during the day or under heavy blankets.

The general strategy for warmth during the day is to stay in bed as long as possible then move outside into the sun and return to bed as early as possible. Work attendance is lower in the winter than in any other season. Many offices and all government schools go on break during the coldest part of the winter. Also due to low temperatures and rainfall, food is not grown in Sikkim during the winter except in the very low valleys. As a result, most food is imported from outside Sikkim. Food is plentiful in other parts of India and is readily shipped to Sikkim but is slightly more expensive than locally grown food. There is also not as much variety during this time of year.

A major event, independent of climate, for the Bhutia during winter is the New

Year festivals. The new year is celebrated for approximately one week with frequent visiting and feasting. Events around this time are similar to those that occur around

Christmas and the New Year in the United States. Special traditional pastries and dishes are made in vast quantities, only during this time of year. These festival foods are usually

157 stored and eaten throughout the season. Increase in food consumption and decrease in activity results in weight and fat gain.

Spring and fall are uneventful seasons in Gangtok. There are no major festivals and no major climatic challenges; both temperature and rainfall are moderate. Locally grown foods are more readily available and food from other states is easily accessible. In spring, weather improves and outdoor activities also increase. Without frequent parties and feasting, consumption decreases resulting in weight loss from the previous season.

Many challenges accompany the monsoons. Constant rain results in frequent landslides. Gangtok is particularly impacted by landslides because of its location on a mountain ridge and the single winding mountain road that connects it to the rest of India.

Locally grown foods are available during this season, but quantities are not sufficient to make the state self supporting. Food prices are high during the summer because importing food is dangerous and sometimes impossible because of the conditions of the roads. Gangtok becomes isolated during the summer months. Walking can also be treacherous on the steep roads and steps in town. Housework becomes more difficult because of the muddy conditions and women also report that office work is highest.

Fall brings relief from the rain, returning to moderate conditions. Food is cheap and readily available. Fall becomes a recovery period with decreased workloads and increased amounts of food available at cheaper prices. Most women gained weight and increased anthropometric values in this season.

Although this pattern is predictable based on seasonal information, the magnitude of change was lower than expected. Significant changes in skinfolds and circumferences do not always translate into large scale changes in weight, fat and BMI. Skinfolds

158 measure subcutaneous fat pattern in millimeters and circumferences measure both subcutaneous fat and visceral fat deposits in centimeters (Bouchard, 1988). Relative to skinfold and circumference measurements, weight, fat and BMI changes are much less sensitive to seasonal changes.

Accurate measures of body fat are difficult to obtain under field conditions. The recent addition of small, portable instruments using bioelectrical impedance to measure body fat are useful but less accurate than laboratory methods. Many factors, such as dirt and oil on the skin, and bladder contents can impact body fat readings slightly with this method but are very difficult to control for under field conditions. Although bioelectrical impedance is the best method available in the field, current technology is probably not sensitive enough to reflect the same level of change as seen in skinfold and circumference measures.

In response to negative energy balance, unequal changes across the body are common (Shimamoto et al, 2002; Heymsfield, 1988). Visceral fat in the trunk and subcutaneous fat are metabolically different and contribute independently to morbidity risks such as stroke, diabetes and heart disease (Lohman, 1992; Heymsfield; 1988; Van

Itallie, 1988). Limbs are generally the first to lose fat in times of negative energy balance. Trunk areas are buffered from loss relative to the limbs. Arm fat index measures only changes in adiposity of the arms based on both skin folds and arm circumference. This measure was the most sensitive to change in this study and was significantly different in all seasons. WHR, abdominal compared to hip girth, showed a significant increase only between summer and fall. Skin folds are highly sensitive to changes in energy balance because one may increase subcutaneous fat deposits relatively

159 rapidly. Although both sum of two skin fold, measuring upper body, and sum of five

skin folds did show significant seasonal variation, the magnitude of change was not great

enough to translate into significant weight, fat or BMI changes.

More change in body size was also expected based on suggestions by previous

studies that tribal populations in Sikkim are marginalized (Bhasin, 1991). While Bhutia women may have limited political power and opportunities for education and variety of jobs, based on body composition alone, Bhutia women do not appear marginalized. On the contrary, weight, height and BMI standards indicate that Bhutia women are much better off than their Indian counterparts, at least in Gangtok. Urban amenities such as low cost shared taxis, readily available cooking gas, cheap labor and the availability of cheap domestic servants helps keep activity levels low. Although prices, quality, variety and availability of food may vary by season, the main caloric staple, rice, is always available.

Because of these factors, this sample represents a fairly homogenous group of women with limited variability in many SES, social, demographic and reproductive variables.

7.2.3 Contributions and context

One of the most important contributions of this section of the study is the data on seasonal changes in body composition and anthropometric measurements in this urban population of women. The presence of seasonal biological variation in response to changes in diet and activity pattern has been well established (e.g. Adair & Pollit, 1983;

Speth, 1990; Ferro-Luzzi & Branca, 1994; Panter-Brick, 1995). However, diversity in the variation in responses to seasonal climatic change is often underestimated (Leonard &

Thomas, 1989) and most studies focus primarily on rural populations (e.g., Leonard &

160 Thomas, 1989; Ferro-Luzzi et al, 1990; Panter-Brick 1995; Shell-Duncan, 1995; Jenike,

1996; Pike, 1999).

The few biological anthropology studies from urban environments do not report seasonal variation in body composition or energy balance (Gualdi-Russo, 1998; Smith,

1998; Dufour et al, 1997; Piperata et al, 2002). However, these urban studies tended to focus on large, highly populated urban centers rather than smaller centers like Gangtok, making comparisons with results reported here impossible. This study documents seasonal climatic change and its impact on Bhutia women for a smaller, but rapidly growing city. Initial information obtained for this study can be used as a starting point for future investigations of the impact of urbanization on the populations in this area.

7.3 Hypothesis 2

Negative energy balance reduces fecundity in urban Bhutia women. Corollary 1: negative energy balance directly affects fecundity by altering hormonal levels. Corollary 2: negative energy balance negatively affects health status, thereby indirectly reducing fecundity.

7.3.1 Seasonality of health

If energy balance impacts health, then we would expect the pattern of change in health to reflect change in energy balance. This was not observed in this study. Unlike measures of energy balance, different measures of health showed independent patterns of seasonal change, usually independent of changes in energy balance. C-reactive protein

(CRP), systolic and diastolic blood pressure was in the normal/healthy range, on average, in all seasons. Neither CRP nor diastolic blood pressure showed significant seasonal change. 161 Winter-Spring

The highest systolic blood pressure and the most self-reported health items were

observed in winter. Number of self-reported health items, CRP levels and number of

individuals with high CRP levels, systolic and diastolic blood pressure decreased between

winter and spring. This suggests better participant health in spring than in winter.

Differences between systolic blood pressure and number of self-reported health items

were statistically significant between these seasons. On the contrary, the majority of

women’s HB levels dropped between winter and spring, with a net loss of 4.4 g/dL. Both

winter and spring averages were below healthy levels.

Spring-Summer

As in previous seasons, CRP values and number of individuals with high CRP

values, self-reported health items, and systolic blood pressure decreased, suggesting

better health. However, none of these average differences was statistically significant.

Hemoglobin levels increased significantly such that only during summer were average

considered healthy. The majority of women, 68%, showed increased HB levels, gaining

1.9 mg hemoglobin/dL, on average. For the majority of women, diastolic blood pressure also increased, but remained in the healthy range. Average diastolic blood pressure levels between the two seasons did not increase significantly.

Summer-Fall.

From summer to fall CRP levels, number of women with high CRP levels, and

number of self-reported health items increased. However, average values did not

statistically significant seasonal variation. Hemoglobin, systolic and diastolic blood

pressure all decreased between these seasons, but also did not show statistically

162 significant seasonal variation. Average hemoglobin level was again below the healthy

range.

Winter-Fall

Most women reported fewer self-reported health items at the end of the year, than

at the beginning of the year, showing statistically significant seasonal variation. CRP and

systolic blood pressure also decreased from the beginning to the end of the study year.

The percentage of women in the sample with high CRP levels was only slightly lower

than in winter. Hemoglobin levels improved in most women from the beginning of the

study period to the end. Diastolic blood pressure increased, but not significantly.

7.3.2 Explanation for seasonal changes in measures of health status.

Although 52.6% of participants reported their health as best in winter, the most

self-reported health items were reported in winter. In addition, the highest CRP levels and the highest percentage of women with high CRP levels were found during winter.

The majority of self-reported health complaints fell into two categories: those associated with upper respiratory infections and those associated with intestinal problems.

Commonly reported problems associated with upper respiratory infections were cough, sore throat, runny nose, mild fevers. Winter appears to be cold and flu season in

Gangtok. This may explain both the high CRP levels and reports of respiratory complaints while lower activity levels may explain the healthier feeling.

Other common problems reported during winter were abdominal pain, nausea, diarrhea, and constipation. The reason for these problems could be in the water. The water supply to most participants is piped from a large collection tank in Gangtok. The primary water source in Gangtok is rainwater stored in a large ground tank that is

163 minimally treated. Since winter is the season with the lowest rainfall, winter is also the

season with the least amount of fresh water. According to interviews with doctors at the

local hospital and the Voluntary Health Association, many intestinal parasites are

endemic in Sikkim. Giardia is the most common protozoan intestinal parasite throughout

the world (Lane & Lloyd, 2002), and helminthes are the most common worms (Sackey et

al, 2003) both types of parasites can be transmitted through food and water. However,

since none of the sample was tested for intestinal parasites or diagnosed with any specific

ailments, exact cause of these symptoms remains unknown.

Winter is also the season with the highest levels of CRP and the highest

percentage of women with high CRP levels. C-Reactive protein levels increase in

response to infection or inflammation (Mattusch et al, 2000; McDade & Stallings, 2001) therefore they are used as a general marker of health, not a diagnostic tool. However, if

CRP levels reflect symptoms reported by the subjects, they are associated with the immune response to upper respiratory and intestinal issues.

Average hemoglobin levels were at their lowest point in winter and spring and are classified as mildly anemic according to World Health Organization (1968) and

Demographic Health Survey (1998) criteria. Iron deficient anemia, the most common and most probable in this sample, may result from low iron intake or high loss due to disease (Jellife, 1985). Common infectious diseases associated with iron deficient anemia are malaria and intestinal parasites (Sackey et al, 2003; Smith, 1970). Since beef is generally a daily part of the Bhutia diet, low iron intake is most likely not the cause of anemia in this sample. Also, since this population is at a very low risk of malaria due to

164 altitude, the most likely cause of the high rates of anemia in this population is intestinal parasites.

Diastolic blood pressure remained relatively consistent throughout the study period and was normal for the vast majority of individuals sampled. Systolic blood pressure was significantly higher in winter than in any other season, but remained in the normal range for most women. This pattern of seasonal change did not match patterns found in other health status measures. Systolic blood pressure increase during this season is most likely an adaptive response to cold. Higher blood pressure has been identified in winter months in U.S. samples (James et al, 1990). In rats, increase in systolic BP during cold acclimation is associated with increased blood volume (Roukoyatkina, 1999).

Decrease in self-reported health items was statistically significant from winter to spring. Similar to observations in measures of energy balance, spring and fall seem to be uneventful seasons relative to winter and summer. Average hemoglobin levels did not change and there was no significant decrease in number of women with high CRP levels between winter and spring.

Contrasting with energy balance data, health data does not suggest that summer is the most challenging season. Hemoglobin levels increased significantly and the percentage of women with high CRP levels decreased to 12%, their lowest levels.

Headaches and backaches replace runny noses, coughs and intestinal problems as the most common complaints. Contrary to what was expected, the monsoons are associated with better health in this sample. Price and availability of the primary source of dietary iron, beef, does not change during this season as it is locally produced and not available from other states in India. However, increased variety and freshness of locally grown

165 foods might improve micronutrient quality of diets during this season contributing to

better health independent of caloric intake. Health benefits appear to be limited to this

season as Hb and high CRP rates return to spring-like levels in fall.

7.3.3 Energy balance and measures of fecundity

Conceptions based on Bhutia women who gave birth during the study period,

seasonality of births of children born to participants, and seasonality of births based on 10

years of birth registrations, all roughly fit the pattern of changes in energy balance

observed in these data. Patterns of menstrual cycle irregularities also fit this pattern.

Seasons with the highest number of irregular cycles and the highest percentages of women experiencing irregular cycles were seasons when most women lost weight and energy balance decreased from the prior season. These were also the seasons with the fewest conceptions. Most conceptions and fewest irregular cycles occurred in fall. Since the women who conceived were not the women as were measured, no causal connections can be made.

Decreases in measures of energy balance, sum of two skinfolds, sum of five skinfolds and AFI from winter to spring were significant predictors of average cycle length. Both sum of five skinfolds and BMI changes between spring and fall were significantly associated with average cycle length. Between winter and fall, only AFI change was associated with average cycle length.

Seasonal changes in skinfold measures were significant predictors of cycle length when energy balance was negative. This suggests that even small-scale decreases in body composition impact menstrual cycle. Only measures of whole body change, sum of five skinfolds, and BMI, were associated average cycle length when energy balance was

166 positive. This suggests that small scale increases are not enough to impact cycle length.

Although irregular cycles are more likely to be anovulatory, cycle length is not a direct measure of ovulation. A better way to examine the relationship between magnitude of change in energy balance and fecundity would be to look at seasonal LH and FSH measures, which were not available in this study. Further, since r2 values are small, even

for significant measures, in these women, only a small amount of variation in cycle

length is explained by energy balance. Factors determining the majority of variation in

cycle length were not identified in this study.

Lutenizing hormone (LH) and follicle stimulating hormone (FSH) were measured

in urine. However, in most western studies, LH and FSH are measured from serum

samples as clinical conditions are more conducive to blood sample collection and only

one sample is usually taken. Although FSH and LH values from urine samples correlate

highly (Jurjen et al, 1998), comparisons of LH and FSH values from different sources are

difficult. Further, reliable LH and FSH samples from non-western populations are not

available and comparisons between western populations and non-western samples may be misleading. For clinical purposes in studies of fertility in western populations, normal

LH low values are 5-22 (IU/L), peak values are 30-250 (IU/L), FSH low values are 5-20

(IU/L), peak are 30-50 (IU/L) (Yen et al, 1999).

In general in this sample, FSH and LH levels did not reach western clinical standards in low or peak values. However, this does not necessarily mean that this sample is abnormal or pathological since appropriate comparisons can not be made. The timing of LH and FSH sample collection is this study may also add complications to the interpretation of the data. Timing of reproductive hormone collection is an important

167 issue in any study of this nature. The LH and FSH surge occurs 24-48 hours before mid- cycle (Wood, 1994). Determining the mid-cycle point for women in this sample was difficult. Because of the high percentage of irregular cycles, mid-cycle point could not be estimated with confidence until the final season of study. Based on reported menstrual cycle dates after the urine collection period mid-cycle points were confirmed. Although some doubts as to validity of the recall method remain, distribution of the data suggests that at least some part of the LH/FSH surge was captured in most women. Women whose levels did not increase appreciably either did not experience an LH/FSH surge during that cycle or the peak was not captured. Because of these limitations, inferences about ovulatory vs. anovulatory cycles cannot be made and data were pooled and used for statistical analysis of associations only.

Average values for measures of energy balance did not predict FSH and LH peak or low levels. However, changes in some anthropometric measures were useful for predicting LH and FSH. In this relatively healthy population, average body fat, weight and anthropometric measures had no impact on LH or FSH levels, nor was lower body fat associated with lower LH or FSH values. Populations suffering from chronic nutritional stress might show different results.

Energy balance changes statistically associated with LH or FSH levels were those between summer and fall. There are two possible explanations for these results. The timing of energy balance change is important in determining impacts on LH and FSH levels. Based on the results presented in Chapter 5, only change from the previous season was associated with these reproductive hormone levels. Earlier seasonal changes and changes from beginning to end of the year had no impact on LH and FSH, with one

168 exception, body fat change from beginning to end. Another potential explanation is that direction of change is very important. Magnitude of change was positive only between spring and fall. LH and FSH levels may be buffered against small negative changes but, increases in energy balance may allow for increased levels in LH and FSH. But, since information on hormone levels is not available for earlier seasons, definite conclusions can not be made.

7.3.4 Energy balance and health status

Average anthropometric measures, including weight and body fat, were not statistically associated with average levels of CRP or HB. Only average BMI was significantly associated with average number of self-reported health items. Increasing

BMI was associated with a decreased number of self-reported health items, but BMI only explained a small amount of variation in this measure and was not the most important factor explaining self-reported health items. Magnitude of BMI change was also the only anthropometric change associate with physiological health measures. However, magnitude of BMI change explained only a small percentage of variation in CRP and self-reported health items. Again, in a sample of women who are generally a healthy weight, body fat, and BMI, measures of body composition do not appear to play an important role in determining health status. Average anthropometric measures were all associated with systolic and diastolic blood pressure, explaining the majority of the variation in these measures. In western populations, fat distribution is a common correlate of blood pressure (Crews and Williams, 1999).

169 7.3.5 Health status and fecundity

Few health status measures were statistically associated with fecundity. No

average measures were associated with average cycle length or FSH or LH low values.

Average self-reported health items were significantly associated with average FSH peak

value; average hemoglobin was associated with LH peak. Only summer-fall change in

HB was associated with average LH peak. Winter-spring and spring-summer change in

self-reported health items were associated with FSH low values and spring summer

change was associated with FSH high values. Both systolic and diastolic blood pressure

change were associated with LH low values for spring-summer changes. This result is

unexpected and unexplained since blood pressure has not been reported to be associated

with reproductive hormone levels in any previous study.

Changes that were significant predictors were reproductive hormones were

spring-summer for most of the significant anthropometric measures (sum of two

skinfolds and sum of five skinfolds) and also for FSH low and peak values and health

measures. However, timing of change was different for LH peak values, between

summer and fall. Generally FSH and LH are considered together as a set, but results of this study suggest that different factors and timing impact these reproductive hormones.

7.3.6 Contributions and context

Lack of reproductive hormone data makes contextualizing the results of this section of the study extremely difficult. The contribution of reproductive hormone data is important in order to understand variability in response of the human reproductive system to external stressors such as negative energy balance and poor health. Very little is known about the variation in base-line reproductive hormones or the responsiveness of

170 these hormones because of the limited pool of data (Wood, 1994; Veldhuis, 1998;

Vitzthum et al, 2002).

Comparisons cannot be made until a sufficient pool of similar data has been obtained. However, the development of new field methods data collection such as those presented here and elsewhere (Worthman & Stahlings 1997; Jurjen et al, 1998; Forde

2001) is making this type of data collection feasible. Further, the closest approximation of the measure of fecundity is through these reproductive hormones. Hypothesized connections between factors such as energy balance and health and fecundity can not be definitely identified or their impacts quantified without these physiological measures.

7.4 Hypothesis 3

Hypothesis 3: Low social wellbeing and economic status increases activity levels and decreases nutrition. Therefore, lower social well-being and economic status will result in a higher magnitude of energy deficit.

7.4.1 Seasonality of perceived stress

Perceived stress levels showed significant seasonal variation, but in a pattern that did not match anthropometric or health measures. Average stress sum was significantly lower in winter than in any other season. This is probably due to a combination of lower expectations from the office and the benefits of the festival season. Almost all women

(99%) reported seeing friends and family most in winter. The highest proportion of this sample (48%) also reported feeling the most energy in winter. Perceived stress sum did not significantly impact average values in anthropometric measures, but did significantly impact the magnitude of change in waist hip ratio and sum of five skinfolds. This

171 suggests that women with higher perceived stress levels did impact energy balance.

Women with higher stress levels showed a larger deficit in energy balance.

7.4.2 Socioeconomic status, social support and energy balance

Many studies suggest that increase in socioeconomic status (SES) is associated with increase in measures of body composition (Reddy, 1998; Dressler et al, 1996;

Bindon, 1995). However, in this sample, SES index was not associated with average anthropometric values. Ranking for SES was significantly associated with average sum of five, body fat percentage and arm fat index. Benefits of an urban environment might influence these results. Since rice is the main caloric component of the Bhutia diet and is widely available for little money, it is expected that only extremely low SES would result in lower intake and lower anthropometric measures. The main activities, walking and cleaning can be minimized with a 5-10 Rupee (approximately US$ 0.10) taxi ride and readily available cheap labor. SES rank was useful in predicting change in waist hip ratio

(WHR), lower SES rank was associated with higher magnitude of change in WHR between the beginning and end of the study.

Social support index (SSI) index and SSI rank show similar results. Although associations between both physical and psychological health and social relationships have been identified (Cohen et al, 2000; Reifman, 1995) SSI and SSI rank were not associated with health status variables. SSI and SSI rank were significantly associated only with average WHR not change in WHR. WHR appears to be the measure most sensitive to change in social factors. Difficulties in creating accurate methods of measuring social support are well documented (i.e, Wills and Shinar, 2000). Since no information was

172 previously available on social networks and availability and satisfaction of support, this index was very difficult to develop. The low internal validity of the index (cronbach’s alpha = 0.60) likely contributes to these results.

7.5 Conclusions

Results of this study suggest that season is an important factor determining body composition, health status and psychosocial stress. One major goal of this research was to justify the importance of considering climatic season in studies of biological anthropology. Biological anthropologists deal with age, socioeconomic status and other demographic factors but tend to ignore seasonal climatic factors. Ignoring seasonal impact can have significant results. For example, among sampled Bhutia women, health assessment during summer would show normal Hb levels, on average and only 12% of women with high CRP levels. Health analysis in winter would reveal a significantly different situation.

Although season was an important factor and Hypothesis 1 was partially supported, the impact of season on magnitude of change in measures of energy balance was lower than expected. The healthy body composition and SES status of this sample along with urban amenities such as food availability, cheap labor and transportation, likely buffer these women from large scale changes in energy balance.

Measures of energy balance and some average anthropometric measures did impact measures of fecundity, partially supporting hypothesis 2. A few anthropometric measures impacted health and some health status measures impacted measures of fecundity. However, health measures influenced by anthropometric measures were not

173 the ones impacting measures of fecundity. Again, the relatively healthy status of these women and urban amenities probable acted to buffer these women. Future studies of rural women without urban benefits would be helpful in uncovering biological connections. For comparative analyses, more information on seasonal changes in FSH and LH need to be completed in order to determine which seasonal changes are the most important in determining fecundity.

That measures of SES and SSI impacted energy balance suggests their potential to impact fecundity. Unfortunately, not enough information on social support and the impact of perceived stress is available. Future studies will assess the importance of social support and social well-being to more fully understand mechanisms connecting these factors to fecundity. In addition, the impact of social variables on behavior associated with fertility outcomes must also be assessed in future studies. However, this study does reinforce knowledge that fecundity is the result of complex interactions between biology, behavior and environment.

174

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APPENDIX A

SEASON 1 QUESTIONNAIRE

I.D. number .

Address:

Date of Birth . Age: .

Place of Birth: . village(R.B.)/town

Marital Status: S/M/D/Other: Age at marriage: .

Date of move to current residence: . Date of move to Gangtok: . Reason for move to Gangtok:

Status of Residence: Temporary/ Permanent

Occupation: .

Family History and Household Information

187

Family type: nuclear/extended/joint number in house: Family Religion: Type of Dwelling: owned/rented concrete/wood/other no. of rooms: Food Garden: Yes/No Animals: Yes/No includes:

Other land holdings:

Significant assets: business/buildings/car/telephone/computer/T.V./refrigerator/oven/washing machine

Water: piped/carried/other: Sewage: piped/septic/none/other: Toilet: pit/Indian/commode attached/unattached no.:

Waste: local facility/removed/other:

Husband: name: . age: . occupation: . years of residence in Gangtok: .

Children: (indicate if not living in household) name age sex

Mother’s Name: . Mother’s Age (note if deceased): . Mother’s Place of Residence: R/U . Mother’s Place of Birth: R/U. 188

Father’s Name: . Father’s Age (note if deceased): . Father’s Place of Residence: R/U. Father’s Place of Birth: R/U .

Reproductive History Is this your first marriage? Yes/No if no, give details (widowed, divorced, etc.):

Are you now using any method of birth control (including rhythm/ withdrawal methods)?

Age at Menarche: .

Have you ever used any method of birth control? if yes: what: when (started and stopped): reason for stopping:

How many pregnancies have you had?

Do you have any deceased children?

Have you had any abortions (spontaneous or otherwise)?

Have you ever been diagnosed with a disease of the reproductive system?

How many children you want? Why?

How many children does your husband want? Why?

As far as you know, are you able to have children? If no, why not?

189

Education Literate? Yes / No

Level of Education: Government/Private Languages spoken:

Husband’s Education:

Government/Private Children’s Education: Government/Private Name Level of Education

Mother’s Education: Father’s Education:

190

Health History Have you ever been diagnosed with a chronic disease?

Have you ever stayed in a hospital?

Have you had immunizations:

Immunization Y/N/don’t know Date T.T. D.P.T. Polio B.C.G. Measles D.T. Other

Do you or have you ever had:

Age/Date Disease Y/N chicken pox measles mumps polio tetanus urinary tract infection sexually transmitted diseases

tape worm round worm pneumonia malaria

191

broken bones diptheria whooping cough meningitis or viral encephalitis severe fever eye disease/cataract ear disease/hearing loss heart disease night blindness skin disease chelosis glossitis angular stomatitis beri beri rickets/osteomalacia TB leprosy jaundice cancer tumors anemia goiter or thyroid problem diabetes mental disorder epilepsy gum/dental disease liver disease kidney problems gastric/ duodenal ulcers other

Does the subject appear to be healthy?

192

15 Day Recall If positive-how was it treated? Allopathic/Homeopathic/Tibetan/Ayurvedic/Indigenous(Specify) 1 2 3 4

1. sore throat or runny nose 2. sore throat or runny nose with fever 3. cough for more than a day 4. cough for more than one week (note length) 5. cough with phlegm (note color) 6. cough with blood 7. repeated indigestion and stomach upset 8. Abdominal pain lasting more than one day 9. nausea more than occasionally 10. diarrhoea for more than one day (note length) 11. blood mixed in stool 12. passed worm 13. chest pain 14. shortness of breath after light work 15. dizziness 16. sudden attack of weakness and fainting 17. feeling tired frequently 18. frequent back ache 19. frequent headache 20. headache accompanied by nausea 21. headache accompanied by light sensitivity 22. stiff joints 193

23. fever with shiver for more than one day (note length) 24. pain in the ear for more than one day 25. discharge from the ear 26. painful urination 27. vaginal itch 28. vaginal discomfort 29. unusual discharge 30. painful menstruation 31. any other (specify)

Perceived Health Do you consider yourself to be healthy?

Do you get sick often?

Do you worry about your health?

194

Social Support Number of family member within: Family = mother and father, aunts and uncles, siblings, cousins = mother-in-law and father-in-law, aunts and uncles, siblings-in- law, cousins

Distance Number of family members 1 km 5 km + 10 km + Gangtok + East District + Sikkim +

How many of these family members are also friend/confidant?

Number of friend/confidants (non-family) within:

Distance Number of friends 1 km 5 km + 10 km + Gangtok + East District + Sikkim +

How often do you visit these friends/family members? rarely/frequently

How many friendly visits do you make or receive: per day?

per week?

per month?

Do you use email regularly? Yes/No 195

If so, approximately how many times per day/week? How many friends/relatives do you phone per week?

Who can you ask for help if you (give number): need a loan?

nervous or upset?

are sick?

in debt?

need advice?

have trouble at work?

have trouble at home?

Do you have religious support? Yes/No If so, where and from whom?

Urban Life Do you consider Gangtok to be an urban area? Have you ever lived outside of a city/urban area (like Gangtok)? Yes/No If so, when and where:

Do you like living in your local community? Yes/No Do you like living in an urban area? Yes/No Do you like (or think you would like) living in a rural area? Yes/No Did you like (or think you would like) living in an urban area better than living in a rural area? Yes/No Why or why not?

What is different in the city? Are foods different? Yes/No

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better/worse

Are daily activities different? Yes/No easier/harder

Is your living situation different? Yes/No better/worse

Is your health different? Yes/No better/worse

Are you happy to live in your current accommodations? Yes/No Why or why not?

Do you have means to leave the urban areas?

How often do you leave the urban area?

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APPENDIX B

PERCEIVED STRESS QUESTIONNAIRE

Stress Questionnaire. Please circle the number that best reflects your feelings about the following questions. Please be honest.

1 = almost never 2 = sometimes 3 = often 4 = never

1. You feel rested. 1 2 3 4 2. You feel that too many demands are being made on you. 1 2 3 4 3. You are irritable or grouchy. 1 2 3 4 4. You have too many things to do. 1 2 3 4 5. You feel lonely or isolated. 1 2 3 4 6. You find yourself in situations of conflict. 1 2 3 4 7. You feel you are doing things you really like. 1 2 3 4 8. You feel tired. 1 2 3 4 9. You fear you may not reach your goals. 1 2 3 4 10. You feel calm. 1 2 3 4 11. You have too many decisions to make. 1 2 3 4 12. You feel frustrated. 1 2 3 4 13. You are full of energy. 1 2 3 4 14. You feel tense. 1 2 3 4 15. Your problems seem to be piling up. 1 2 3 4 16. You feel you are in a hurry. 1 2 3 4 17. You feel safe and protected. 1 2 3 4 18. You have many worries. 1 2 3 4 19. You are under pressure from other people at work. 1 2 3 4 20. You are under pressure from other people at home. 1 2 3 4 21. You feel discouraged. 1 2 3 4 22. You enjoy yourself. 1 2 3 4 23. You are afraid of the future. 1 2 3 4

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24. You feel you are doing things because you have to, not because you want to. 1 2 3 4 25. You feel criticized or judged. 1 2 3 4 26. You are lighthearted. 1 2 3 4 27. You feel mentally exhausted. 1 2 3 4 28. You have trouble relaxing. 1 2 3 4 29. You feel loaded down with responsibility. 1 2 3 4 30. You have enough time for yourself. 1 2 3 4 31. You feel under pressure from deadlines. 1 2 3 4

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APPENDIX C

ANTHROPOMETRIC INDICES

1. Body Mass Index (BMI) = weight (kg)/ height2 (m)

2. Total Upper Arm Area (TUA) = arm circumference2/ (4π)

3. Upper Arm Muscle Area (UMA) = (arm circumference – (tricep skinfold * π))2/(4π)

4. Upper Arm Fat Area (UFA) = TUA –UMA

5. Arm Fat Index (AFI) = (UFA/TUA) * 100

6. Sum of Two Skinfolds = tricep skinfold + subscapular skinfold

7. Sum of Five Skinfolds = tricep + bicep + subscapular + suprailliac + calf skinfolds

8. Waist Hip Ratio (WHR) = waist circumference/ hip circumference

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