UNIVERSITY OF CALGARY

Possible Genetic Contribution to Growth Faltering Among Makushi Children of the Rupununi

Savannah, Guyana

by

Erin Barr

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FUFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTERS OF ARTS

DEPARTMENT OF ARCHAEOLOGY

CALGARY, ALBERTA

MAY, 2008

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The author retains copyright L'auteur conserve la propriete du droit d'auteur ownership and moral rights in et des droits moraux qui protege cette these. this thesis. Neither the thesis Ni la these ni des extraits substantiels de nor substantial extracts from it celle-ci ne doivent etre imprimes ou autrement may be printed or otherwise reproduits sans son autorisation. reproduced without the author's permission.

In compliance with the Canadian Conformement a la loi canadienne Privacy Act some supporting sur la protection de la vie privee, forms may have been removed quelques formulaires secondaires from this thesis. ont ete enleves de cette these.

While these forms may be included Bien que ces formulaires in the document page count, aient inclus dans la pagination, their removal does not represent il n'y aura aucun contenu manquant. any loss of content from the thesis. Canada ABSTRACT

The purpose of this study is to evaluate whether or not genes may be contributing to the observed variation in stature identified among Amerindian and non-Amerindian children in neighboring villages in Guyana. Well-nourished children of diverse ethnic backgrounds around the world follow similar growth curves. Consequently, the National

Center for Health Statistics and World Health Organization hold that one set of growth standards is appropriate for use among all ethnic groups. The comparison between the stature of Amerindian and non-Amerindian children in this study documents that children with Amerindian ancestry were two-to-three times more likely to be stunted than children of mixed ancestry. These data suggest a genetic difference in growth potential between these two groups and that international growth standards may not apply to Amerindians of South America's tropical lowlands.

111 ACKNOWLEDGMENTS

There are a number of people without whose help this thesis would not have been possible and I am greatly indebted to them. I would like to start off by thanking the members of my examination committee, Dr. M. Anne Katzenberg, Dr. Charles Mather, Dr. Benedikt

Hallgrimsson and Dr. Warren Wilson. Their time and consideration of the material in this thesis is greatly appreciated and has made it a stronger piece of work. An extended thanks needs to go to my supervisor Dr. Warren Wilson. Dr. Wilson is the person who first introduced me to biological anthropology in my undergraduate degree and it was his enthusiasm for the discipline that made me pursue it at a Masters level. Dr. Wilson's wealth of knowledge and willingness to always share it with me was indispensable during the writing of this thesis, as was his never- ending patience while editing my thesis numerous times; a task which I do not envy.

I would also like to thank my parents, Sharon and Audy, who have each read this thesis at least five times. This is quite an accomplishment as neither have a background in this field, yet despite this, they never once said no when I asked them to edit my thesis. Their constant support and willingness to help wherever they could, even if it was just being someone to vent off thesis frustrations with, or complain to about sleeping with cockroaches in Guyana, is something which

I will never be able to thank them enough for. I would also like to thank my sister, Tammy, and my partner Jackson, without whose love, support and patience in putting up with me throughout this whole process, I could not have finished. There are also members of my extended family, my Auntie Sandy and Uncle Daryl especially, whom I would like to thank. I most certainly could not forget to thank my officemate Murray Lobb in this section; He has listened to hours upon hours of my thesis problems and statistical rants, and has always been there with great advice or

iv a joke to make me laugh when I needed it. I could not have asked for a better, more supportive, officemate and friend.

Thank you to Graduate Studies for several Graduate Research Scholarships and to the

Department of Archaeology for both its funding and for providing me with a great place to learn and become a stronger academic. The staff at the front office, Nicole Ethier and Lilly Wong, deserve a special thanks for always answering all of my questions and helping me with all the forms that needed to be completed, not to mention lending me a key to my office the many times

I have locked myself out. Thank you also to all my friends in the department. You have all made these past years a wonderful experience which I will never forget.

Last, but certainly not least, I would like to thank the Makushi Research Unit, as well as the Makushi, for opening up their hearts and homes to me while in Guyana. Without their kindness and willingness to help in anyway they could there would be no thesis at all.

v TABLE OF CONTENTS

Approval Page ii Abstract iii Acknowledgments iv Table of Contents vi List of Tables viii List of Figures x

CHAPTER ONE: INTRODUCTION 1

CHAPTER TWO: GUYANA AND THE MAKUSHI 6 Research Participants 10 History 15 Mining, Logging and Ranching 19 Amerindian Health 23 Pre-Contact 24 Post-Contact 25 20th Century 27 Recent Bio-Medical Research 31 Conclusion 42

CHAPTER THREE: GROWTH AND THE ENVIRONMENT 48 Thermoregulation 49 Climate, Body Shape, Size and Proportions 52 Genes, Climate and Body Mass 62 Birth Weight and Climate 66 Pygmies 69 Conclusion 78

CHAPTER FOUR: METHODS 80 Anthropometric and Interview Data Collection 81 Pedigrees and SES Interview 82 Data Analysis 85

CHAPTER FIVE: RESULTS 89 2000 and 2001 Data 89 2005 and 2007 Data 98 2007 Subset Data 107

CHAPTER SIX: DISCUSSION 118 Potential Confounders 124 Conclusion 127

vi REFERENCES CITED 129

APPENDIX A: SES Interview 160

APPENDIX B: Result Tables and Ethics Approval 165

vii List of Tables

TABLE 1 Percentage of Heads of Household from Each of the Above Ethnic Groups Living in Toka and Aranaputa 14 TABLE 2 Summary of the Results and Impacts of Pre- and Post European Contact with South American Amerindians as well the Results and Impacts of Acculturation 43 TABLE 3 Results of Independent t-test for Mean z-score of Children of Mixed and Amerindian Descent for 2000 and 2001 Data 90 TABLE 4 Determining if Removal of Variables in each Step was Justified for 2000 and 2001 Data 91 TABLE 5 Significance of Regression Model in each Step for 2000 and 2001 Data 92 TABLE 6 Ability of Regression Model to Predict Stunted and Not Stunted Children in each Step for 2000 and 2001 Data 92 TABLE 7 Variables in the Model and their Usefulness in each Step of the Regression for 2000 and 2001 Data 93 TABLE 8 Variables Removed in each Step of the Regression and their Significance to the Model for 2000 and 2001 Data 94 TABLE 9 Variables not in Equation 2000 and 2001 Data 165 TABLE 10 Results of Independent t-test for Number of Kids in the Families of Children of Mixed and Amerindian Descent for 2000 and 2001 Data 96 TABLE 11 Chi-Square Tests for Frequency of Stunting in each of the Seven Villages for 2000 and 2001 Data 97 TABLE 12 Results of Independent t-test for Mean z-score of Children of Mixed and Amerindian Descent for 2005 and 2007 Data 99 TABLE 13 Determining if Removal of Variables in each Step was Justified for 2005 and 2007 Data 100 TABLE 14 Significance of Regression Model in each Step for 2005 and 2007 Data 101 TABLE 15 Ability of Regression Model to Predict Stunted and Not Stunted Children in each Step for 2005 and 2007 Data ,101 TABLE 16 Variables in the Model and their Usefulness in each Step of the Regression for 2005 and 2007 Data 103 TABLE 17 Variables Removed in each Step of the Regression and their Significance to the Model for 2005 and 2007 Data 104 TABLE 18 Variables not in Equation 2005 and 2007 Data 165 TABLE 19 Chi-Square Tests for Frequency of Stunting in each of the Seven Villages for 2005 and 2007 Data 105 TABLE 20 Results of Independent t-test for Number of Kids in the Families of Children of Mixed and Amerindian Descent for 2005 and 2007 Data 107 TABLE 21 Results of Independent t-test for Mean z-score of Children of Mixed and Amerindian Descent for 2007 Subset Data 108 TABLE 22 Significance of Regression Model in each Step for 2007 Subset SES Data 110 TABLE 23 Ability of Regression Model to Predict Stunted and Not Stunted Children in each Step for 2007 Subset SES Data 110 TABLE 24 Variables in the Model and their Usefulness in each Step of the Regression for 2007 Subset SES Data 166

Vlll TABLE 25 Variables Removed in each Step of the Regression and their Significance to the Model for 2007 Subset SES Data 171 TABLE 26 Variables not in the Equation 2007 Subset SES Data 172 TABLE 27 Determining if Removal of Variables in each Step was Justified for 2007 Subset Data Ill TABLE 28 Significance of Regression Model in each Step for 2007 Subset Data Ill TABLE 29 Ability of Regression Model to Predict Stunted and Not Stunted Children in each Step for 2007 Subset Data Ill TABLE 30 Variables in the Model and their Usefulness in each Step of the Regression for 2007 Subset Data 112 TABLE 31 Variables Removed in each Step of the Regression and their Significance to the Model for 2007 Subset Data 112 TABLE 32 Results of Independent t-test for Number of Kids in the Families of Children of Mixed and Amerindian Descent for 2007 Subset Data 114 TABLE 33 Results of Independent t-test for the Birth Order of Children of Mixed and Amerindian Descent for 2007 Subset Data 116 TABLE 34 Chi-Square Tests for Frequency of Stunting in each of the Seven Villages for 2007 Subset Data 117

ix LIST OF FIGURES

FIGURE 1. Percentage of Children who are Stunted and Not Stunted of Both Mixed and Amerindian Descent 2000 and 2001 Data 89 FIGURE 2. Mean z-score in Children of Mixed and Amerindian Descent for 2000 and 2001 Data 90 FIGURE 3. Mean Number of Kids in the Family of Stunted and Not Stunted Children of Mixed and Amerindian Descent 2000 and 2001 Data 95 FIGURE 4. Percentage of Stunted and Not Stunted Children in each of the Seven Villages 2000 and 2001 Data 97 FIGURE 5. Percentage of Children who are Stunted and Not Stunted Of Mixed and Amerindian Descent 2005 and 2007 Data 98 FIGURE 6. Mean z-score of Children of Mixed and Amerindian Descent for 2005 and 2007 Data 99 FIGURE 7. Percentage of Stunted and Not Stunted Children in each of the Seven Villages 2005 and 2007 Data 105 FIGURE 8. Mean Number of Kids in the Families of Stunted and Not Stunted Children of Mixed and Amerindian Descent 2005 and 2007 Data 106 FIGURE 9. Percentage of Children who are Stunted and Not Stunted Of Mixed and Amerindian Descent 2007 Subset Data 108 FIGURE 10. Mean z-score in Children of Mixed and Amerindian Descent for 2007 Subset Data 109 FIGURE 11. Mean Number of Kids in the Families of Stunted and Not Stunted Children of Mixed and Amerindian Descent 2007 Subset Data 113 FIGURE 12. Mean Birth Order in the Families of Stunted and Not Stunted Children Of Mixed and Amerindian Descent 2007 Subset Data 115 FIGURE 13. Percentage of Stunted and Not Stunted Children in each of the Seven Villages 2007 Subset Data 116

x Chapter One

Introduction

In April of 2006 the World Health Organization (WHO) released its new Child

Growth Standards. These standards are the result of the Multicenter Growth Reference

Study which looked at more than eight 8000 children from Brazil, Ghana, India, Norway,

Oman, and the United States of America (WHO, 2006). Like the preceding WHO (1995) growth standards, the 2006 study shows that growth in height and weight of well fed, healthy children, regardless of ethnic background and location, is remarkably similar up until the age of five. The main difference between these new growth standards and the old growth standards is that children in the new standards were selected based upon an optimal environment for proper growth where breast-fed children were used as the norm for growth and development (WHO, 2006). The conclusions remain the same, however, with the WHO Child Growth Standards being deemed appropriate for use internationally and among different ethnic groups1. This finding was based in part on an evaluation of the length of participants in the WHO reference study from birth to two years of age

(WHO, 2006b). This study showed that 70% of the total variance in length was the result of interindividual differences, whereas only 3% of the variance was the result of intersite differences (WHO, 2006b). This finding is also consistent with a large number of

1 The WHO (1995) does not define the word ethnic group. For the purposes of this thesis ethnic groups are defined as a social group with distinctive social and cultural traditions that have been maintained from generation to generation. These groups are assumed to have a common history and origin. Members often have distinctive features in their way of life and often a shared genetic heritage (Online Medical Dictionary, 2008). 2 independent studies (i.e. Bhandari et al., 2002; Graitcer and Gentry, 1981; Habicht et al.,

1974).

The WHO (1995) does acknowledge that some variation exists in the growth patterns of children from different ethnic groups raised in optimal environments. These include such findings as lower weight-for-height status for children of the Indian subcontinent, lower birth length and weight for children of African descent, and the influence of parental size on birth weight and growth. Despite these differences, they are considered to be minor in comparison to the world-wide variation in environmental factors such as: health, nutrition, and socioeconomic status (WHO, 1995). Differences resulting from ethnicity are consequently ignored. That said, the WHO (1995) does stress the need to include a consideration of variability within all definitions of the reference population. It is also worth noting here that variation also exists between the WHO growth standards and the CDC growth standards. Recent work by de Onis et al. (2007) has found that for all age groups stunting rates are higher when using the WHO standards than when using the CDC standards. Children in the WHO standards were also found to be taller, on average than those children in the CDC standards (de Onis et al., 2007).

These differences between the WHO and CDC standards are thought to be the result of differences in study design and sample characteristics (de Onis et al., 2007). Overall, this study concludes that the WHO standards provide a better means of monitoring the growth of breast-fed infants. Although the WHO standards have been found to provide a better means of monitoring the growth of breast-fed infants, Lampl and Thompson (2007) caution that these growth curves do not describe individual growth, as individual children grow at very different rates. Researchers should thus be aware when using these growth curves that they represent the distribution of size within a population at discrete points and thus are poor representations of individual children's growth (Lampl and Thompson,

2007).

A number of populations, particularly in developing countries, fall below the optimal growth standards published by the WHO. Studies have implicated a number of risk factors for growth faltering including socioeconomic status (poverty) (Oyhenart et al., 2003; Foster et al., 2005; Bustos et al., 2001), poor nutrition (Oyhenart et al., 2003;

Foster et al., 2005; Wilson et al, 1999), compromised intestinal permeability (tropical enteropathy) limiting nutrient absorption (Salazar-Lindo et al., 2004; Lunn et al., 1991), high parasite loads and rates of infection (Moore et al., 2001; Bravo et al., 2003). Despite these studies and the fact that genetic factors are not thought to account for the variability seen in human growth (Habicht et al., 1974), Sara Stinson (1996) found Afro-Ecuadorian children were significantly taller than Chachi Amerindian children. Both of these populations live in the tropical forest of northwest Ecuador and practice similar lifestyles and subsistence strategies, which suggests that genes may be playing a role in the observed height differences (Stinson, 1996).

The purpose of this study is to further explore the possibility of a genetic contribution to variation in growth among two ethnic groups in South America's lowland tropics. In order to accomplish this task the rates of stunting (falling greater than 2 SD below the mean for height for age) among Amerindian children (children with no non-

Amerindian ancestry in past three generations) were compared with those of children of mixed ancestry (children with non-Amerindian ancestry in past three generations) living in villages on the Rupununi Savannah of Guyana. These children share similar lifestyles 4 and subsistence strategies, despite their different ancestry. Amerindians from South

America's lowland tropics are among the shortest people in the world (Wilson and

Bulkan, 2004; Godoy et al., 2006). This study is designed to evaluate the hypothesis that given the similarities in subsistence and lifestyle of the children of Amerindian ancestry and mixed ancestry, there is no difference in their rates of stunting. It should be noted here that specific genetic testing was not possible in this study, and therefore the assumption that differences in ancestry were equivalent to differences in genes was used.

Chapter two provides a profile of the country of Guyana as well as the Makushi

Amerindians. The selection criteria for the seven villages included in this study will be given and their differences described. Lastly, this chapter will briefly describe the history of the Makushi Amerindians and Guyanese Amerindians to present, with specific emphasis on their health and welfare through time. Chapter three provides a literature review concerning the present evidence for a genetic contribution to stature. As well, evolutionary explanations for this genetic variation will be considered. Chapter four describes the techniques, interviews, and statistical analyses undertaken. Chapter five provides the results of these analyses. Chapter six gives an interpretation of the results and their implications.

This study is designed to enhance our understanding of the role that genes may be playing in the observed patterns of growth among the Makushi of Guyana, and as a direct consequence stimulate new research to better understand the role, if any, of genes in the variation seen in childhood growth. For the field of human population biology, a better understanding of all the factors influencing childhood variation in growth will ideally lead to multidisciplinary work in search of the etiology of this observed variation. For the 5

Makushi, a better understanding of the factors influencing their growth will ideally lead to a better means of assessing their health and wellbeing. 6

Chapter Two

Guyana and the Makushi

It was August 1, 1498, when Columbus and his crew became the first Europeans to set eyes on the coast of South America. The first European settlement in Guyana, however, was not established until the late 16th century by the Dutch (Hope, 1985). The country of Guyana originally consisted of three Dutch colonies Essequibo, Demerara, and

Berbice (Singh, 1988). Control of Guyana was, for the most part, retained by the Dutch until 1796 when Britain began to exercise control over the country; however the Dutch did not officially cede the area to the British until 1814 and it became officially known as

British Guyana in 1831 (Hope, 1985).

The name Guyana comes from an Amerindian word meaning "land of waters"

(Schomburgk, 1970; Hope, 1985). It lies between eight degrees 40"N latitude and three degrees 30'S latitude and the 50th and 68th degree of longitude, west of Greenwich

(Schomburgk, 1970; Hope, 1985). It is located on the northeast coast of South America, bounded by the Atlantic Ocean, Brazil, Suriname and Venezuela (Hope, 1985). Four main rivers traverse Guyana: the Essequibo, Demerara, Berbice, and Corentyne (Hope,

1985). The largest and capital city is Georgetown, which is located on the northeast coast at the mouth of the Demerara River and is the major commercial center and port (Hope,

1985). Guyana experiences a tropical climate inland and a subtropical climate on the coast where northeast trade winds mitigate the heat (The Economist Intelligence Unit,

1995; Hope, 1985). Temperatures vary between 22°C and 32°C along the coast, with the mean temperature in Georgetown being approximately 26°C (Hope, 1985; Singh, 1988). In the Rupununi savannah, where this study was conducted, within the interior of the country, the average temperature is around 35°C in the dry season and 22 °C in the wet season (Iwokrama, 2003). The coastal rainy seasons are April to July and November to

January, whereas the Rupununi savannah experiences its rainy season from May through

August (Iwokrama, 2003; Hope, 1985). Both Georgetown and the interior experience an excess of 300-400 mm of rainfall during the rainy season (Iwokrama, 2003).

Guyana covers 83,000 square miles and can be divided into three general areas: inland forests which cover approximately 85% of the country, the grass covered savannahs of the hinderlands which cover approximately 10% of the country, and the coastal plain which covers approximately 4% of the country (Hope, 1985). There are two major savannahs in Guyana; the Rupununi savannah, which lies in the southwest close to the Brazilian Border and the Intermediate savannah, which lies in Berbice behind the northeastern coastlands (Hope 1985). The main economic activity in both of these areas, since the mid-nineteenth century, has been cattle grazing (Hope, 1985; Colson, 1971).

Guyana is the only English speaking country in South America and has a population estimated at just over 700000 (Singh, 1988; U.S. Department of State, 2007).

About 90%o of the population lives on 4% of the land along the narrow, low-lying coastal plain where the majority of the agricultural and industrial activity occurs (Hope, 1985;

Hollett, 1999). The dense forests which cover most of the country are largely uninhabited with the exception of Amerindian settlements, a few small agricultural settlements along riverbanks, and some mining and timber activity (Hope, 1985; Singh, 1988). It has been called the "land of the six races" as 43.4% of its population has East Indian origins,

30.2% African origins, 16.7% mixed origins, 9.2% Amerindian origins, and 0.3% 8

Chinese and White origins (U.S. Department of State, 2007). There are ten Amerindian groups native to Guyana, with three living on the coast and seven living in the interior

(Federal Research Division Library of Congress, 1993). The Coastal Amerindians consist of the Carib, Arawak, and Warao (Federal Research Division Library of Congress, 1993;

Premdas, 1995). The Interior Amerindians consist of the Akawaio, Arekuna, Barama

River Carib, Makushi, Patamona, Waiwai, and Wapisiana (Federal Research Division

Library of Congress, 1993; Premdas, 1995). The Akawaio, Arekuna, Barama River

Carib, and Patamona live in river valleys in Western Guyana, while the Makushi live in the Northern Rupununi and the Wapisiana in the Southern Rupununi. The Waiwai live in the far south of Guyana near the head waters of the Essequibo River (Federal Research

Division Library of Congress, 1993). Linguistically, all Interior Amerindians appear to have originated from the Carib, with the exception of the Wapisiana (Federal Research

Division Library of Congress, 1993).

The infant mortality rate for the Country of Guyana is 49/1000 births and life expectancy is 59 years for males and 64 years for females (U.S. Department of State,

2007). Education is compulsory in Guyana from the ages of approximately 5 to 14 years, with school attendance being in the 90th percentile, and the literacy rate is 96.5% in those adults who attended school (U.S. Department of State, 2007). Religious affiliations show

57.4% to be Christians, 28.3% Hindu, 7.2% Muslim, and 7.1% other (U.S. Department of

State, 2007). The large number of Christians is thought to be a consequence of colonization, where Christian beliefs and observances were prerequisites for social acceptance (Federal Research Division Library of Congress, 1993). 9

Guyana gained independence from Britain on May 26, 1966 and declared itself a

Cooperative Republic on February 23,1970. At that time the government began a process of nationalization of the country's resources (Singh, 1988; Hope, 1985). As is true of many low-and-middle income countries, Guyana exports primarily raw materials,

76% of which consists of sugar, gold and bauxite (The Economist Intelligence Unit,

1995; Munslow, 1998). Rice, timber, fish and diamonds are also exported by Guyana. As of 2006 Guyana's exports totalled $601 million USD.

Guyana is dependent on a number of imported goods in order to rehabilitate and expand its infrastructure. Consequently, its imports greatly exceed its exports at $885 million USD (U.S. Department of State, 2007; The Economist Intelligence Unit, 1995).

As a result, Guyana is a heavily indebted, poor country (U.S. Department of State, 2007).

Its high debt limits its access to foreign exchange and reduces its ability to import needed raw materials and equipment (U.S Department of State, 2007). Increasing fuel prices have increased the country's debt and decreased production and economic growth (U.S.

Department of State, 2007). Overall, nationalization, mismanagement, and other economic programs compromised the productivity of Guyana's economy leading to a sharp decline in Guyana's gross domestic product (GDP) in the 1970's and 1980's

(Colchester, 1997). In 2006, Guyana had a real annual growth rate of 4.7%, and per capita GDP of $974 million USD, rendering it the 9th poorest country in the Western

Hemisphere (U.S. Department of State, September, 2007; CIA, 2007).

In 1989 the Economic Recovery Program (ERP) was launched in conjunction with the World Bank (WB) and the International Monetary Fund (IMF) in which the role 10 of the government in the economy was significantly reduced and foreign investment encouraged (U.S. Department of State, 2007). As a result of these actions Guyana was able to clear its outstanding debts on loan repayments to foreign banks (U.S. Department of State, 2007). Fifteen out of forty-one government owned businesses were sold including the Telephone Company, and assets in timber, rice, and fishing industries (U.S.

Department of State, September, 2007). International corporations purchased the state sugar company and the largest bauxite mine. As well, both the United States and Canada were permitted to open large mines in the country (U.S. Department of State, 2007). This foreign investment brought both money and benefits to Guyana (Colchester, 1997).

Despite these benefits, the economic restructuring initiated by the WB and IMF have, in many ways, made things worse for the Guyanese (Colchester, 1997). Removal of price controls on almost all commodities, as well as a decrease in wages by 18% from 1986, increased the hardships experienced by the Guyanese (Colchester, 1997). As well, government spending on infrastructure decreased leading to deteriorating roads as well as sewage and water system collapses (Colchester, 1997). Health spending was cut in half, and spending on education fell by 66% (Colchester, 1997). So today, despite the changes made in the 1990's, Guyana is still struggling economically (CIA, 2007).

Research Participants

The Makushi are the most numerous Amerindians of the six Carib-speaking peoples of Guyana, numbering approximately 9000 (Forte, 1996). There are 27 principal

Makushi communities in Guyana and they are located primarily between latitudes three and five degrees north, and longitudes 58 and 60 degrees west (Forte, 1996). Guyanese- 11

Makushi territory is bounded by the east-west-running Kanuku Mountains to the south, the Siparuni River to the north, the Essequibo River to the east, and the Takatu and Ireng

Rivers to the west (Wilson et al., 2006).

Traditionally the Makushi were exogamous matrilineal (Myers, 1993). They often live with their extended family in their villages on the open savannah, but plant their farms in the nearby forests (Forte, 1996). Although the Makushi have had contact with colonial powers since the 17th century, as of 2001 many remained divorced from the market economy and continued to subsist via swidden horticulture (Wilson et al., 2006).

The main crop cultivated by the Makushi is high-cyanide manioc (Manihot esculenta Crantz) (Forte, 1996). Manioc is the traditional dietary staple of many

Amerindian groups living within the Amazon and grows as a perennial woody shrub

(Dufour, 1995). Consumption of manioc in the tropical regions of South America averages 150-160 calories per person, per day (Cock, 1985). Manioc grows well in highly acidic, low fertility soil, and is also tolerant of drought and high rainfall as long as good drainage exists (Dufour, 1995; Cock, 1985). The plant is grown mostly for its edible roots, although the leaves can also be eaten (Dufour, 1995). Manioc is often classified as

having either low-cyanide (sweet) or high-cyanide (bitter) content (Dufour, 1995). Low-

cyanide manioc varieties can be eaten without intensive processing, whereas high-

cyanide varieties require intensive processing (Dufour, 1995). The toxic elements of

bitter manioc are the result of cyanogenic glucosides that breakdown into glucose and

hydrogen cyanide during hydrolysis, which occurs whenever the roots are damaged

(Dufour, 1995). The use of high-cyanide manioc, rather than low-cyanide varieties,

appears to be due to the fact that gardens that are planted with high-cyanide manioc have 12 larger yields then those gardens with low-cyanide manioc most likely due to increased disease and insect resistance (Wilson and Dufour, 1999). Among the Makushi, manioc is predominately made into farine (loose particled cereal), but can also be made into bread and fermented drinks (Forte, 1996). To make farine, the manioc roots are trimmed of stem ends, and soaked in stream water for a number of days until] soft (Dufour, 1995;

Cock, 1985). The roots are then peeled, grated, and allowed to ferment for a further few days (Dufour, 1995; Cock, 1985). Finally the grated manioc is dewatered by use of a tipiti (plaited cylinder), sifted and then toasted (Dufour, 1995; Cock, 1985). This process is very labour intensive, especially for women who are in charge of the farms and must spend many hours processing the manioc in order to remove the harmful cyanide and turn it into farine, bread, or fermented drinks (Wilson et al., 2006; Forte, 1996). Children nine years and older will often assist in manioc cultivation (Wilson et al., 2006). Dietary protein is supplied mostly by fishing and hunting both of which are predominately male activities (Forte, 1996). The amount of time dedicated to food production does not differ significantly from rainy to dry season (Forte, 1996). Drinking water is collected from streams, rivers and wells and is almost never boiled (Wilson et al., 2006). The majority of these water sources are contaminated by organic wastes (Guyana Water, Inc., 2003 In:

Wilson et al., 2006; KPMG, 1999 In: Wilson et al. 2006).

The research sample of this study consisted of children under the age of eleven years, selected from six nearby villages in Guyana's North Rupununi region. These villages were Woweta, Kwatamang, Annai, Aranaputa, Toka, and Massara. The

inhabitants of these villages subsist predominately via traditional swidden horticulture

and are close to the road which runs from Georgetown on the coast to Lethem on the 13

Brazilian border. These villages were selected for two reasons. First, they share similar environments and subsistence strategies which improves the control of several potentially confounding variables. Second, two of these villages, Aranaputa and Toka, were originally settled by Afro-Guyanese and Indo-Guyanese immigrants from Guyana's coast (W. Wilson, Personal Communication 2006). The percentage of heads of households from various ethnic groups in both Aranaputa and Toka can be seen below in

Table 1. Of the 22 households headed by persons of mixed descent in Aranaputa (Table

1), 18 were born in Aranaputa, with the majority having one Afro-Guyanese father and

Amerindian mother, two with Indo-Guyanese fathers and Amerindian/mixed mothers, two with Portuguese fathers and Amerindian mothers, one Spanish mixed father and

Arawak mother, and one Carib father and Arawak mother (Iwokrama, 1999). Aranaputa and Toka are both known as mixed settlements of Amerindian and non-Amerindian settlers (Forte, 1996; Iwokrama, 1999). In all of the unstructured interviews conducted in

Aranaputa and Toka in 2007, all informants reported that all households were of mixed ancestry. The villages Massara, Rupertee, Annai, Kwatamang and Woweta, as of 1999, contained no households headed by Coastlanders (people born and raised on the coast of

Guyana) or peoples of mixed descent (Iwokrama, 1999). As of 2007, although there is some intermarriage between the Makushi and different Amerindian groups (i.e. Arawak,

Wapishani), these five villages still appear to have very little, if any, Afro-Guyanese,

Indo-Guyanese, or White inhabitants or ancestry. In the following chapters, the data collected in 2007 concerning ancestry in all of these villages will be elaborated upon.

Henceforth, for simplicity and on the basis of the data which will be described below, the villages Aranaputa and Toka will be classified as mixed villages, whereas the villages 14

Massara, Rupertee, Annai, Kwatamang, and Woweta will be classified as Amerindian villages.

Village Makush i Arawa k Coastlande r Britis h Brazilia n Wapishan a Warr a Portugues e Mixe d Aranaputa 42 4 5 13 1 3 0 4 28

Toka 83 0 3 11 0 0 3 0 0

TABLE 1. Percentage of Heads of Household From Each of the Above Ethnic Groups Living in Aranaputa and Toka (Iwokrama, 1999).

In the remaining sections of this chapter, it is the hope of the author to enlighten the reader as to the impact of colonialism on the Makushi. Literature concerning the impact of colonialism on the Amerindians of South America's lowland tropics is reviewed. Literature concerning impacts in regions outside of Guyana are also considered for two reasons: first, there exist relatively few publications concerning the impact of colonization on the Amerindians of Guyana and fewer still on the Makushi; second, although not all parts of South America's lowland tropics were colonized simultaneously and the impacts varied by region, the literature suggests that most

Amerindian groups faced many of the same challenges and consequences of colonization.

Of specific note, Coimbra et al.'s (2005) work with the Xavante horticulturalists of Brazil will be referenced extensively. Like the Makushi, the Xavante live in an upland savannah and face many of the same environmental challenges (Moran, 1993; Coimbra et al.,

2005). Also similar to the Makushi, the Xavante grow their staple crops in farms located 15 a short distance away from their homes in the surrounding forest, and dietary protein is supplied mostly by fishing and hunting (Coimbra et al., 2005). The Makushi and

Xavante have had contact with colonial powers and were subject to slave raids as early as the 18th century (Coimbra etal. 2005; Myers, 1971; Butt-Colson, 1971). Finally, both the

Makushi (Butt-Colson, 1971) and the Xavante (Coimbra et al., 2005) fell under the administration of their respective colonial governments in the 1940's. Since the 1960's the Xavante have been the focus of anthropological research which has focused upon population genetics, epidemiology and tropical medicine which has generated a tremendous amount of data on the effects of colonial contact on the Xavante (Coimbra et al., 2005).

History

The exact methods of colonization and exploitation of South America, after its discovery in 1498, varied depending largely upon which European country claimed ownership of the land. The Spanish, for example had a policy of seeing the Amerindians as enemies whom they sought to conquer and use in their system of forced labour

(Colchester, 1997). They often forced Amerindians into large centralized villages where they were put under mission control (Colchester, 1997). The Dutch and Portuguese on the other hand, were originally more interested in alliances and treaties with the

Amerindians, realizing that in order to maintain a foothold on their lands they needed

Amerindian support, which is what originally occurred in Guyana (Early et al., 1998;

Colchester, 1997). 16

In the beginning, Dutch colonization and exploitation of Guyana was generally limited to the coast, where conditions were ideal for sugar cultivation (Early et al., 1998).

These conditions produced a strong plantation economy (Early et al., 1998). Getting a reliable labour force however, was a major problem (Early et al., 1998). The need for a labour force prompted most of European exploration into the interior of Guyana, although the search for hidden treasures (i.e. "lost city of Eldorado") also led some

Europeans into the interior (Early et al., 1998). In order to obtain a labour force to produce supplies for the forts and incoming ships the Dutch in Guyana used treaties and negotiations with the Carib Amerindians on the coast to encourage slave raiding of other

Amerindian groups and to encourage guerrilla actions against Spanish settlements

(Colchester, 1997). The Makushi Amerindians were significantly impacted by slave raiding, being one of the prime targets of the Caribs, who raided from the north, and the

Brazilian slaving expeditions which came from the west (Forte, 1996). This slave raiding is thought to have accounted for the wide dispersion of the Makushi as they tried to escape their enemies (Forte, 1996). Accounts tell of Makushi groups camping and hiding in well defended caves among the cliff tops at night and only venturing out into the savannahs by day (Forte, 1996). In 1686 the Warau, Carib, Arawak, and Akawaio

Amerindians on the coast were all given immunity from slavery by the Dutch, which only increased inter-tribal raiding on non-immune Amerindian groups such as the Makushi

(Forte, 1996). Despite the great numbers of Amerindians enslaved, plantations were still unable to satisfy their labour requirements (Early et al., 1998). This was due, in part, to the fact that the Amerindians were unaccustomed to the demands of plantation work and were susceptible to European diseases (Early et al., 1998). Also because they knew the 17 land, the Amerindians would often escape back into the bush (Hollett, 1999). The

Dutch thus turned to acquiring and enslaving Amerindian children to be brought up as slaves (Hollett, 1999). However, the labour demands on these children combined with the newly introduced European diseases again decimated the Amerindians (Hollett, 1999).

Plantation owners, as a result, turned largely to African slaves and in some cases indentured labourers from China and India (Early et al., 1998; Colchester, 1997). Despite the finding that Amerindians did not make good labourers, Amerindian slave raids did not cease until 1793 when the Dutch Government instructed the colonial government to enact a law prohibiting the purchase of Amerindian slaves (Forte, 1996). Guyana fell into the hands of the British in 1796 and by 1834 slavery had been abolished (U.S.

Department of State, 2007). With the abolition of slavery thousands of indentured

labourers were needed to replace the slaves on the sugarcane plantations (U.S.

Department of State, 2007).

Guyana's Amerindians were also impacted by trading posts set up by the Dutch throughout the interior and along the coast, with annual presents often being given to the

Amerindians in order to maintain good relations (Colchester, 1997). Even those

Amerindian groups which were victims of enslavement, such as the Makushi, would travel great distances to trade or work for Western goods such as metal tools and firearms

(Colchester, 1997). These interactions with different ethnic groups led to many

intermarriages between Amerindians and such groups as the Portuguese and Africans

(Forte, 1996). This gene flow into Amerindian groups decreased the genetic homogeneity

of the Amerindians, potentially decreasing disease mortality; the mixture of African

genes into Amerindian groups over the long run is thought to have provided some 18 immunity to malaria and smallpox, while admixture with Spaniards would have brought some immunity to smallpox, measles, and other childhood diseases (Newson,

2001).

As the plantation economy grew in Guyana and labour was being supplied from abroad, the colonial authorities had little need for the Amerindians (Colchester, 1997).

Trade and gift giving that existed with the Amerindians declined and the Amerindians were largely forgotten unless they made themselves useful to the colonial authorities in situations such as aiding in the defence of Guyana's borders (Colchester, 1997). In other places in South America, such as Brazil, the Amerindians became nuisances, raiding

European settlements/camps within the interior (Coimbra et al., 2005). In both Brazil and

Guyana responsibility for Amerindian affairs was largely left to Christian missions

(Colchester, 1997; Coimbra et al., 2005). As a result, many Amerindian populations that had not moved deeper into the interior to escape the slave-raiders and European society in general, succumbed to pressures to settle in mission villages largely due to epidemics, which left many Amerindian communities in ruin (Colchester, 1997; Coimbra et al.,

2005). Evidence from Brazilian Amerindian groups suggests that most of these mission villages were short lived, with Amerindians leaving with both knowledge of white man's culture and weaponry, or eventually succumbing to the disease prevalent in these villages

(Coimbra et al., 2005).

Throughout Guyana and its neighbouring countries, governments have sought to put Amerindians under the control of the state through varying methods, so that both the

Amerindian people and the lands which they occupy could be exploited (Colchester,

1997; Coimbra et al., 2005). To reach these ends, governments have often relied upon 19 warfare to destroy and break-up indigenous groups (Colchester, 1997; Munslow,

1998), assimilation into the general population via encouraging the mixing of Afro-

Guyanese, Portuguese, and Spanish with Amerindians (Newson, 2001; Forte 1996;

Colchester, 1997; Brown, 2005), and forced settlement (Colchester, 1997; Coimbra,

2005; Brown, 2005). The results of these measures often left Amerindian groups decimated (Hollett, 1999). This decimation is evident in Guyana when one looks at the data from when the colonists first arrived of 19 Amerindian tribes numbering 700,000, whereas in 1969 the Amerindian Land Commission estimated there to be only 32,203

Amerindians in 138 communities (Saunders, 1969 In: Hollett, 1999; Hollett, 1999).

Mining, Logging and Ranching

The exact dates upon which mining, logging, and ranching started in Northern

South America differ, but the processes and consequences which occurred as a result of these actions remain generally consistent throughout. The author has chosen to focus, for the purposes of this chapter, on how these activities emerged and maintained themselves, as well as their consequences, rather than the exact times in which each occurred in each specific country. The section below will focus on the environmental consequences of these activities, whereas the direct effects these activities have had on Amerindians, specifically their health, will be covered in the following section entitled Amerindian health.

With the discovery of precious metals such as gold and silver in Guyana in the

1840's, European miners and their laborers flocked into the interior. Mining camps were 20 set up wherever there were believed to be precious metals, with little regard for those who actually lived on the lands (Colchester, 1997). The mining camps and those miners who traveled extensively around South America were and are vectors for disease, unsanitary conditions, and lawlessness (Colchester, 1997; Coimbra et al., 2005). Indeed, around mining camps one would find stagnant water as well as decaying vegetation and animals (Coimbra et al., 2005). As mining technology advanced, the environmental impacts of mining have also increased (Colchester, 1997). Extensive areas of forest have been ripped up in order to make room for mines and their expansion (Munslow, 1998).

Mud and debris fill rivers increasing their turbidity, in some cases clogging rivers, producing stagnant pools, and making navigation along the rivers increasingly difficult

(Colchester, 1997; Munslow, 1998). Mercury has also been found polluting the waterways in South America as a result of mining activities (Colchester, 1997; Munslow,

1998). In 1995, the Omai mine of Guyana, the largest single metal mine in Latin America

(operated by Cambior, a Canadian mining corporation), spilled tons of cyanide laced toxic sludge into the Essequibo, a major river flowing into the Atlantic Ocean

(Colchester, 1997). Guyana is the world's largest producer of calcined bauxite, the highest grade of bauxite available, used for aluminum (Munslow, 1998). Long periods of exposure to dust particles emitted through the mining of bauxite have been known to cause lung irritation and promote bronchial infections (Munslow, 1998). In addition, the lack of attention concerning the reclamation of land, has resulted in overburden from new mines being dumped into waste mountains surrounded by pools of ground water, rather than being used to fill in old mines (Munslow, 1998). This leaves the land both sterile and toxic. Attempts to try and use these lands for fisheries experiments proved futile as the 21 waters on these lands were found to be too acidic (Munslow, 1998). Despite its negative environmental effects, mining plays a very important role in the economy of

Guyana representing approximately one quarter of Guyana's GDP, second only to agriculture (Munslow, 1998).

The movement of ranchers and loggers into the interior of Guyana and Brazil has also had dramatic effects on both the Amerindians and the environment. Ranchers moved into the interior of Guyana in the mid-nineteenth century (Butt-Colson, 1971). As a result, ranching took over vast expanses of land (savannahs and former rainforest).

Because veterinary bills and fencing are expensive and political ties are often needed to obtain rights or priority to lands, ranching has, for the most part, been concentrated in the hands of a few wealthy non-Amerindians (Colchester, 1997). Ranching also competes with farms for the best soils (Moran, 1993; Fearnside, 1987). Logging has extracted trees in South America at a much faster rate than they can regenerate, which has drastic effects on both the environment and those peoples who live and subsist off of the resources of the region (Colchester, 1997). Roads, for example, created in order to remove products such as logs, require clear felling, meaning whole areas of forest are removed with detrimental impacts on fauna (Munslow, 1998). Road development in the Amazon often blocks existing streams resulting in artificial dams, a perfect breeding ground for mosquitoes and subsequent malaria (Moran, 1993). It is proposed that in Brazil one out of five new cases of malaria is the result of deforestation in order to make new roads or from mining activities (Moran, 1993). In the process of felling trees, about 25% of the surrounding fauna is also damaged, thus although a great number of trees may not be 22 cleared in one area, the process of logging can still leave a significant environmental impact (Munslow, 1998).

Profits and commissions from logging have also been much less than had been expected by many villages and Amerindians (Colchester, 1997; Moran, 1993).

Surprisingly, at least in Guyana, logging also appears to have resulted in few economic benefits for the country. This is due; in part to the fact that the government handed out permission to foreign companies to log there country's forests (Colchester, 1997). An example of this can be seen in Guyana with the concession of 4.127 million acres over 50 years, near the border of Venezuela, to the Korean and Malaysian company Barama

(Munslow, 1998). The company was granted a five year tax break that could be renewed for a further five years (Munslow, 1998). This means that the only profit the country of

Guyana is seeing is from the royalties (Munslow, 1998). The part of the contract protecting the rights of the Amerindians in the area was also left very vague and the company would be exempt from any future laws that would improve the rights of the

Amerindian peoples in the area (Munslow, 1998). It is also notable that these foreign companies are getting concessions and inducements that are not available to local

Guyanese companies (Munslow, 1998).

Despite the costs, mining, ranching and logging within Guyana and its neighbouring countries, is not a simple black or white issue. All logging and mining contracts, and some ranching offer foreign investments in these countries whose economies are often hindered by large debts, and who are plagued with a lack of organization and trained government officials (i.e. biologists, environmentalists, toxicologists, etc) thus making regulation difficult (Colchester, 1997). Guyana's National 23

Forestry Action Programme, created in 1989, has openly admitted that it does not have the qualified personnel, resources, legislation, instruments or financial funding to allow for effective natural resource management (Munslow, 1998). Overall, for the

Amerindians, invasions by miners, ranchers and loggers into Amerindian group territories has led to both increased mobility, when groups had places to move to (Colson, 1997 In

Munslow, 1998), as well as to increased warfare, as Amerindians raided settlers' cattle and crops in attempts to gain back resources lost, or to make themselves heard by governments, who, for the most part, have ignored their problems and needs (Colchester,

1997; Coimbra et al., 2005; Myers, 1993). In Guyana, it was the inaccessibility of the interior forests that both protected the forests and the Amerindian peoples who depend on these forests for their livelihood (Munslow, 1998). With the development of roadways into the interior and foreign concessions of large areas of forest, the threat of massive deforestation is a true possibility in Guyana (Munslow, 1998).

Amerindian Health

The aforementioned history of colonization in Guyana and its neighbouring countries has had direct effects on the health of the Amerindians who inhabit this diverse continent. Although it has been found that transitions experienced by indigenous groups, particularly in terms of their health, are directly related to their individual histories, which

include interactions with local systems and larger social, economic and political

institutions and processes, as well as how the society itself has reacted throughout history to change in particular (Coimbra et al., 2005), there are some general trends that can be

seen in the health and welfare of the Amerindians of Guyana and its neighbouring 24 countries. The purpose of this section is to summarize these general trends. By understanding the history of health among the Amerindians of Guyana and its neighbouring countries, and highlighting their current health status, it should become possible to highlight where problems exist. In terms of the research question of this thesis it is important to determine how the history of their health may be affecting the etiology of variation in linear growth among the Makushi.

Pre-contact

When Columbus landed in South America it was not the pristine, garden of Eden that has been captured in art, literature and the movies (Denevan, 1992; Denslow, 1988;

Larsen, 1994). Although Dobyns (1983:34) states that, ".. .before Europeans initiated the

Columbian Exchange of germs and viruses, the peoples of the Americas suffered no smallpox, no measles, no chickenpox, no influenza, no typhus, no typhoid or parathyroid fever, no diphtheria, no cholera, no bubonic plague, no scarlet fever, no whooping cough, and no malaria", it would be naive to believe that the Americas were free of disease before European contact. Researchers have indeed found that Amerindians in South

America appear to have suffered from spirochetal infections (treponematosis , leptospirosis, pinta, yaws, syphilis, typhus, and two types of relapsing fevers one carried by ticks and the other by lice), respiratory infections (tuberculosis, pneumonia, and blastomycosis ), protozoan infections (i.e. Chagas' disease, Leishmaniais, giardiasis, toxoplasmosis, and amebiasis), bacterial infections (Carrion's disease, and Oroya fever) as well as a number parasites (i.e. hookworm, intestinal helminthes, pin worms, and whip worms) well before European contact (see Table 2 for summary)(Alchon, 1991; 25

Confalonieri et al., 1991; Allison et al., 1974; Coimbra, 1995; Harper et al., 2008).

Most of these conditions were, however, chronic and endemic rather than acute and epidemic (Newson, 1995). As well, high mobility, which is common among many groups within South America, particularly those living within tropical rainforests, is believed to cause periodic outbreaks of zoonotic infections (Coimbra, 1995). These outbreaks were the result of populations moving into areas containing strains of parasites to which they had not yet developed immunity (Coimbra, 1995). It is also worth noting that the increased mobility of indigenous groups to escape European expansion likely also resulted in the increased occurrence of zoonotic outbreaks as they entered into new regions (Coimbra, 1995).

Post-Contact

Despite the presence of disease in South America before Europeans set foot on the continent, it can not be denied that European contact and subsequent acculturation has had a dramatic effect on the health of Amerindians (see Table 2 for summary). European diseases took a devastating toll on the indigenous peoples of South America. In Meso- and South America, population estimates of 100 million before European contact dwindled to a mere 10 million by 1650 (Brown, 2005). In Ecuador, Old World diseases over the long term are thought to have wiped out up to 85% of the indigenous highland peoples, and as much as 30% for those indigenous groups who lived in the cold, high plains of Puno and the Altiplano (Brown, 2005). The lower percentage of deaths in the cold high plains is believed to be caused by the colder weather, as epidemics are thought to be more virulent in tropical, warm environments (Brown, 2005). Indeed, in many places in South America, particularly in the lowlands, whole native cultures were wiped 26 out within a few generations due to Old World diseases that had been brought to South

America by the Europeans (Brown, 2005; Ribeiro, 1967). According to Ribeiro (1967), between 1900 and 1957, 87 out of 230 indigenous groups in Brazil became extinct, leaving Brazil's native population at a mere 5% of what it was estimated to have been in the 1500's. As previously mentioned in Guyana, when the colonists first arrived they reported the presence of 19 Amerindian tribes numbering 700,000, whereas in 1969 the

Amerindian Land Commission estimated there to be only 32,203 Amerindians in 138 communities (Saunders, 1969 In: Hollett, 1999). According to Denevan (1976), one of the leading authorities on pre- and post-contact population sizes in the New World, before contact one would expect to see 0.5 Amerindians per km2 on the upland savannahs of Guyana and Brazil. This indicates that there were approximately 7500 Amerindians in the Rupununi Savannah (15000km2) at contact. In 1946 the total Makushi population in this same region was estimated at 1,676 (Peberdy, 1948 In: Forte, 1996), a decrease of over 78% of the population, if Denevan's estimates are correct.

Wherever there was sustained contact with Europeans, such as at missions or other settlements, lethal outbreaks of epidemic diseases among Amerindians were also evident (Coimbra et al., 2005; Alchon, 1991). Outbreaks of Old World diseases were exacerbated by malnutrition and hard labour (Coimbra et al., 2005). Smallpox, measles, influenza, scarlet fever, and diphtheria appear to have been some of the main epidemics which decimated Amerindian populations (Alchon, 1991; Newson, 1995). Between 1524-

1585, smallpox, measles, pneumonic plague, typhus, and influenza epidemics all hit the north-central highlands of Ecuador, with each individual epidemic killing between 15-

50% of the native population (Alchon, 1991). In Quito between 1604-1618, indigenous 27 peoples suffered outbreaks of diphtheria, measles, typhus, and scarlet fever, in Peru between 1546-1597 there were outbreaks of pneumonic plague, typhus, measles, and smallpox among the indigenous peoples. In 1558 smallpox, measles, and possibly influenza hit Ecuador and neighboring regions (Newson, 1995). Smallpox was brought into the Runpununi in 1842 by an Amerindian returning from the coast (Butt-Colson,

1971). Smallpox as well as cholera, yellow fever, dysentery, tuberculosis, whooping cough, pneumonia, bronchitis, and the common cold decimated the Amerindian populations within Guyana's interior (Butt-Colson, 1971). The forced movement of

Amerindians into missions, or their movement into mission villages due to the devastating effects of epidemics and in the hope of peace, served as perfect reservoirs for the Old World diseases (Coimbra et al., 2005). As a result, numerous natives succumbed to both stress and disease (Coimbra et al., 2005). In Guyana for example, missions experienced epidemics of smallpox in 1728 and 1742, and measles in 1744 in which many Amerindians died (Rippy and Nelson, 1936).

20th Century

In many cases, indigenous groups which had survived the initial disease outbreaks appeared to bounce back over time as they developed sufficient immunity (Alchon, 1991;

Newson, 2001); however periodic outbreaks still took a large number of indigenous lives, and to this day indigenous populations remain vulnerable to many diseases (Brown,

2005; Ribeiro, 1967; Alchon, 1991). Among the Makushi for example, despite rising population numbers in the 1940's, successive epidemics during the 20th century, such as the influenza pandemic of 1918 and the measles outbreak in 1950, had drastic crippling effects on their society (Forte, 1996 ). As of the 1960's, measles, the common cold, 28 whooping cough, flu, and tuberculosis still represented serious threats to the lives of

Amerindians in Guyana's interior (Butt-Colson, 1971). Life expectancy for Brazilian and

Venezuelan Amerindians was found to be lower than life expectancy in the United States in 1900, and even lower than Serra Leone, the country with the lowest life expectancy in the world, in 2000 (Hurtado et al., 2005; Hurtado and Salzano, 2004) . Human remains in

Ecuador, also suggest that before the Spanish conquest, 27% of the indigenous peoples lived past the age of 40, while only 12% did after 1534 (Alchon, 1991). Infant and child mortality rates are much higher among Amerindian populations, as can be seen among the Xavante of Brazil where between 1972-1990 approximately 102 out of 1000 Xavante infants died before their first birthday as compared to the national average for Brazil in

1981 of 68.7/ 1000 (Coimbra et al., 2005). An even larger discrepancy in infant mortality can be seen when the Xavante statistics are compared to the regional infant mortality rate

(IMR) of non-indigenous peoples living in the same region as the Xavante of 54.3/1000 births , again being compared to the Xavante's IMR of 102/ 1000 births (Coimbra et al.,

2005). The IMR among the Makushi of Guyana of 93/1000 births (Wilson et al., 2006) is almost double that for the rest of Guyana at 49/1000 (U.S. Department of State, Bureau of Western Hemisphere Affairs, September, 2007). Child mortality rate (CMR) for the

Makushi is 169/1000 (Wilson et al., 2006) which is more then two and a half times greater than the overall CMR of Guyana at 64/1000 (WHO, 2006). This large discrepancy between both life expectancy, IMR and CMR between non-indigenous and indigenous peoples has, for the most part, been blamed upon infectious diseases and deficient health care which exists to this day (Coimbra et al., 2005). 29

Unfortunately, the Old World epidemics such as smallpox and measles were not the only diseases that the Amerindians of South America have had to cope with. In addition to epidemics, mining communities brought endemic diseases such as goiter, syphilis, dysenteries, and dropsies (possibly beriberi) (Coimbra et al., 2005).

Environmental changes due to mining in Guyana have led to outbreaks of malaria, typhoid, dysentery and increases in sexually transmitted diseases such as HIV (Munslow,

1998). Mining also brought about substantial changes to Amerindian lifestyle as many

Amerindian men joined the mining frenzy, which resulted in male labour being absent from Amerindian villages (Colchester, 1997; Munslow, 1998). As a result, heavy burdens were left on the shoulders of the women. With no men to clear new garden plots in order to grow food, many women were forced to look elsewhere for a means of survival

(Colchester, 1997). Amerindian women were and are sometimes forced to work as prostitutes and labourers outside of their villages (Colchester, 1997; Brown, 2005; Butt-

Colson, 1971). Excessive alcoholism has also been found to be an increasing problem with some tribes in Guyana's interior due to indiscriminate contact with miners and other

Creoles (Butt-Colson, 1971). Greater wealth differentiation has occurred within villages,

contributing to increased tensions as well as falling standards of living, as more

expensive and scarce western foods begin to replace local and traditional foods

(Colchester, 1997). In addition, debt bondage to coastal miners and malnutrition have

become increasingly prevalent in Guyana (Colchester, 1997; Butt-Colson, 1971).

As might be apparent in the Omai mine disaster mentioned previously, pollution

from mining activities also contributes to health problems facing Amerindian

populations. Mercury, which is used to leach gold from ore, has been found to have 30 serious health effects for both the animals on which the Amerindians subsist, as well as for the Amerindians themselves (Colchester, 1997). It can cause Minamata disease and a range of side effects, some debilitating, others fatal, such as trembling, headaches, blurred vision and other neurological symptoms which can lead to unconsciousness and death (Colchester, 1997; Yorifuji et al., 2008; Ekino, et al., 2007). Mercury also enters into the food chain, accumulating to greater degrees as one move's up the food chain due to biological magnification (Colchester, 1997; MacMillan, 1995). In French Guiana for example, among the Wayana Amerindians, 57% of the population had mercury levels above the limits declared safe by the WHO (Frery et al., 2001). Fish samples taken from the rivers utilized by the Wayana, revealed that four carnivorous fish eaten by the

Wayana accounted for 72% of the mercury they ingested (Frery et al., 2001). In

Venezuela, mercury concentrations in fish from the Guri dam are so high that a number

of fish species have been banned for human consumption due to mercury poisoning from

mining activities (Colchester, 1997). The waste from mining activities in rivers in

Guyana has also led some Amerindian groups to trek into the forests to find clean water that can be carried back to their homes (Munslow, 1998). Currently, the number of miners working in Guyana is almost equal to the total Amerindian population (Munslow,

1998).

Logging and ranching have also had similar effects on the health of Amerindian populations. Increased hunting, illegal wildlife trade and logging have put stress upon

traditional food sources and resources (disruptions of traditional subsistence economies)

(Colchester, 1997). Conflicts over jobs and prices, and the contamination of indigenous

environments from toxic spills of such things as wood preservatives, insecticides and 31 fungicides have also placed stress on Amerindians (Colchester, 1997). Logging and ranching, like mining, have taken Amerindian lands (conflicts over land between

Amerindians and ranchers have been intensifying over time, causing increased tensions between Whites and Amerindians). Indeed, Amerindians in Guyana did not receive land titles until 1976 and many Amerindian groups have yet to receive proper land rights

(Colchester, 1997; Munslow, 1998). It appears unlikely that the government will realize

Amerindian claims to lands rich in mineral deposits and potential foreign investments, as they are important areas for economic development for one of the poorest countries in the world (Munslow, 1998). In addition to the loss of lands, many men have left their villages to work in logging and ranching, leaving heavy burdens on women (Colchester, 1997;

Munslow, 1998). As a result, both family diet and relations have suffered (Colchester,

1997). Roadways being built through the interior of Guyana for easier shipment of logging products and cattle to the coast, are suspected to increase problems for local

Amerindian communities (Colchster, 1997). As well traditional knowledge is being lost as men go off to work outside their villages, rather then passing along important parts of their Amerindian culture to their children (Munslow, 1998). Hence disease has not been the only predicament that Amerindian populations have had to face as a result of

European colonization.

Recent Bio-Medical Research

Twentieth century research has found the Amerindians of South America to display both very short stature, as well as low weight-for-height ratios in comparison to

NCHS standards (Wilson and Bulkan, 2005). Stunting rates among South American 32

Amerindians are around 36%, whereas wasting rates (low weight for height) are around 1.8% (Oyhenart et al., 2003). Research done among the Makushi Amerindians of

Guyana in 2000-2001 found that 35% of the Makushi are stunted and 7% are wasted when compared to the NCHS standards (Wilson and Bulkan, 2004). Similar results have been found among the Tsimane Amerindians of lowland Bolivia, where growth retardation was found to be a severe problem in both males and females between the ages of 18 months and 9 years of age (Foster et al., 2005). Tsimane Children were found to consistently fall below the 5th percentile for height-for-age. Indeed, it is predicted that approximately half of the children living in lowland South America are stunted (Foster et al., 2005).

A number of variables appear to affect the large rate of stunting presently seen among Amerindian populations of South America, including socioeconomic status

(poverty) (Oyhenart et al., 2003; Foster et al., 2005; Bustos et al., 2001; Chacin-Bonilla et al., 2000), poor nutrition (Oyhenart et al., 2003; Foster et al., 2005), high parasite loads and rates of infection (Foster et al., 2005, Moore et al., 2001; Bravo et al., 2003) and even a genetic cause has been proposed (Stinson, 1996), which will be discussed in more detail in the following chapter. Compromised intestinal permeability (tropical enteropathy), limiting nutrient absorption (Salazar-Lindo et al., 2004; Lunn et al., 1991), has also been proposed as another possible cause of stunting, but presently all work on intestinal permeability has been done in Africa, and thus whether or not this problem applies to

South American Amerindians has yet to be seen. Clearly, many of these variables are related. For example, someone with a low socioeconomic status would be much more 33 likely to have poor nutrition and live in environments with poor sanitary conditions and health care, thus increasing the chances of parasitic infections.

Rates of parasitic infection among Amerindians in South America are extremely high, particularly in lowland South America, and are worth elaborating on here (Hurtado et al., 2005). Chronic exposure to infections, for instance those caused by parasites resulting from such things as poor nutrition and poor hygiene, cause over-stimulation of both the inflammatory and immune system, which are known to lead to growth faltering

(Hurtado et al., 2005; Scrimshaw and SanGiovanni, 1997). Among the Makushi gastrointestinal parasitic infections are endemic (Abbot and Burns personal communication In Wilson et al., 2006) and the diarrhea rate for children less than five years of age in this region is 26.9% (UNICEF, 2003 In: Wilson et al., 2006). This diarrhea rate is 60% higher than for any other region in Guyana (UNICEF, 2003 In:

Wilson et al., 2006). Parasites have been found in a number of studies to not only effect stature, but a number of other health factors as well. In a study done among Colombian boys of low socioeconomic status, light-to-moderate loads of gastrointestinal parasites were positively correlated not only with lower stature and higher rates of stunting, but also with lower hemoglobin levels, reduced physical work capacity, and iron-deficiency anemia (Wilson et al., 1999). Gastrointestinal parasites have also been linked to reduced cognitive function and physical fitness levels (Moore et al., 2001; Stephenson, 1994;

Nokes et al., 1992; Ndamba et al., 1993). Infection with certain parasites, such as nematode parasites, also appears to predispose the infected person to subsequent infection with nematode parasites, leading to chronic infections (Hurtado et al., 2005). Chronic parasitic infections can lead to chronic malnutrition by lowering the infected person's 34 nutritional status (Hurtado et al., 2005). This, in turn, leads to things such as growth faltering and greatly impairs quality of life (Hurtado et al., 2005).

Although, parasitic infections were likely endemic in South America before

European contact (Alchon, 1991), the socio-economically disadvantaged state of most

Amerindian's in South America has likely resulted in a significant decline in Amerindian health, and likely worsened the parasite loads and infections (Foster et al., 2005; Chacin-

Bonilla et al., 2000; Stinson, 1996). Research has documented heavier loads of intestinal parasites as well as deteriorated health among Amerindians of South America as a result of acculturation (Chacin-Bonilla et al., 2000).

Parasitic infections, research also suggests, may be partially responsible for the

Amerindian's increased susceptibility to the bacterial and viral infections brought by

Europeans (Hurtado et al., 2005; Borkow et al., 2001). The reason proposed for this increased susceptibility to bacterial and viral infections among Amerindian populations is based on the fact that macroparasitic infections results in the dominance of T-helper 2

(Th2) immune cells which specialize in fighting macroparasitic infections (Hurtado et al.,

2005). With Th2 dominance, however, a person's body produces less Thl immune cells which specialize in fighting bacterial and viral infections (Hurtado et al., 2005). By measuring immunoglobulin E (IgE) levels within the blood, Th2 dominance can be measured (Hurtado et al., 2005). Studies that have measured Th2 levels among South

American Amerindians have found some of the highest IgE levels ever reported in healthy individuals without allergies or autoimmune disorders/diseases (Hurtado et al.,

2005; Hurtado et al., 2003; Kron et al., 2000). These findings suggest that if parasite 35 loads and infections could be decreased in Amerindian populations they would likely become less susceptible to bacterial and viral infections.

An interesting study by Marini et al. (2007) contradicts the above findings and suggests that intestinal parasites are not detrimental to the nutritional status of

Amerindians and some, such as Helicobacter pylori, may even improve nutritional status, particularly among children. While working among the Guahibos of Venezuela, Marini et al. (2007) noticed that although the Amerindians showed low height for age, they had an adequate body mass index. This was found despite the fact that 99% of the participants had intestinal parasites. Helicobacter pylori infection in children was also found to be correlated with better nutritional status. The authors suggest that gastrointestinal parasites might actually result in a more robust immune system in children and thus would provide a selective advantage over those children who did not have gastrointestinal parasites.

Indeed, gastrointestinal parasites, at least in this population, may actually have a mutualistic relationship with humans (Marini et al., 2007). Overall, this study suggests that lower energy intake, slower growth rates and short stature may not imply poor

development (Marini et al., 2007). It also suggests that height and weight alone may not

be good indicators of nutritional status (Marini et al., 2007).

Although colonization and subsequent acculturation has had a large number of

negative effects on the health and lives of the Amerindians of South America, efforts

have been made to bring some of the health benefits of western medicine to the

Amerindians. Acculturation has been associated by some with improvements in diet,

health, and education among some Amerindians (Gracey, 2000; Edelweiss et al., 2003).

Before independence, Guyana's British government were genuinely concerned about 36 health of the Amerindian populations (Myers, 1993; Butt-Colson; 1971). As such the

British instigated a policy from 1946-1966 which aimed to integrate the Amerindians into the national economy and society (Butt-Colson, 1971). Many of the measures were designed to protect the Amerindians, whereas others set out to encourage development of

Amerindian communities, for example, training Amerindian teachers and setting up local self governments (Butt-Colson, 1971). By 1947 Medical Rangers had been stationed in the interior as well as a Government Dispenser in Lethem (Myers, 1993). Medical

Rangers were trained to administer anti-malarial drugs, worm medicine, and other preventative measures (Janette Bulkan, personal communication, 2008). As of 1949, there was a Medical Officer for the Interior who made successive tours throughout the area (Myers, 1993). An anti-malarial campaign was also initiated which entailed DDT spraying of Amerindian communities (Myers, 1993). These actions lead to dramatic increases in Amerindian populations within the interior (Butt-Colson, 1971). More recently, South American countries, including Guyana, have set up educational programs in Amerindian languages (Colchester, 1997). In Brazil, missionaries have provided health care to some Amerindian groups (Coimbra et al., 2005). In Guyana and Brazil, governments have set up health posts and government funded programs to aid in preventative medicine on reservations (Wilson et al., 2006). They have even trained

Amerindians to work as health assistants (Coimbra et al., 2005; Wilson et al., 2006).

Among the Makushi, for example, each village has a health post with a community health worker trained in such things as first aid, microscopy, leprosy detection etc. (Wilson et al., 2006). As a result of these measures Makushi numbers have increased substantially from an estimated 4680 in 1969 to an estimated 9000 in 1996 (Forte, 1996). 37

Studies have also found an increase in the numbers of older Amerindians (over the age of 50) in South America and increases in total fertility rates (TFR) (Coimbra et al., 2005). Among the Xavante Amerindians of Brazil, the number of people over the age of 50 increased from 2.8% in 1977 to 6.1% in 1990 (Coimbra et al., 2005). This rise in the number of older Amerindians in Brazil is believed to be the result of decreasing epidemics, which most severely affected the elderly and very young (Coimbra et al„

2005). It should be noted, however, that life expectancy at birth is still not high in many

Amerindian villages. As of 1996, among the Makushi of the village Massara, there were only 5 persons over the age of 60 out of a population of 250, and in the village Yakarinta with a population of approximately 500 there were only 13 persons over the age of 60

(Forte, 1996). Increases in TFR can be seen among the Xavante where they were found to increase from 5.88 between 1957-1971 to 7.86 between 1972-1990, although this value is still below the 1942-1956 TFR of 8.10 (Coimbra et al., 2005). Also among the Xavante infant and child mortality rates have also decreased, but are still much higher than what is seen for the rest of Brazil (Coimbra et al., 2005).

Despite efforts that have been made on the part of governments and non­ governmental organizations (NGOs) to offer medical assistance to Amerindians, their success has been compromised by poor organization, insufficient funding, and, notably, a lack of cultural understanding of the Amerindians (Coimbra et al., 2005). Projects aimed at training or providing health care services to Amerindians are often planned without seeking the help of anthropologists who have studied and lived with the Amerindians, nor with the Amerindians themselves, and thus these programs eat up vast amounts of money and fail without aiding the Amerindian peoples (Coimbra et al., 2005). An exception to 38 this may be the British policy put into place in Guyana in 1946, described above.

Despite the success of this program early on, it too was plagued with a number of problems, particularly near the end of the program. As of 1965 there was only one doctor stationed in the Rupununi for a population of over 11000 people and one in the North

West district for a population of 15000 (Butt-Colson, 1971). It has been argued however, that this doctor was highly mobile and that the Medical Ranger service was highly successful despite there only being one doctor in the region (Janette Bulkan, personal communication, 2008). Although this may be the case, as of 1965, the hospitals in the region were found to be antiquated, and a dentist had not been seen in the region in three years (Butt-Colson, 1971).

With Guyana's independence in 1966, conditions got significantly worse for the

Amerindians of the interior (Myers, 1993). The Medical Ranger service declined and the anti-malarial campaign was shut down (Myers, 1993). This resulted in the return of malaria to the region (Myers, 1993). As of 1984, 2500 malaria cases were reported in the

Rupununi and North West district (Myers, 1993). The malaria parasite had also become resistant to the malarial drugs available at the time (Myers, 1993). In the 1970's, in the

Guyana hinderlands (North West, Upper Mazaruni and Rupununi, which cover 70,025 square miles), there was still only one doctor stationed in the North West district, and one in the Rupununi district (Fredericks et al., 1986). The Mazaruni district had no doctors, and was cared for only by a number of dispensaries and one nursing station (Fredericks et al., 1986). Quality dental care was also sorely needed in these regions (Fredericks et al.,

1986). Presently, many medical facilities found on Amerindian reservations are

abandoned, or carry very limited medical supplies (Coimbra et al., 2005). The Makushi 39 for example, have a health post in each of their villages, but most lack sufficient medical supplies (Wilson et al., 2006). The regional health post which is often better supplied, has individuals trained at the level of a nurse, but may be anywhere from 1-80 km away (Wilson et al., 2006). In addition, although the regional health worker is supposed to visit each village once a month in order to vaccinate children and address cases of acute illness, in practice these visits do not occur monthly in the more isolated villages (Wilson et al., 2006). The poor condition of many medical facilities on

Amerindian lands leaves Amerindians uneducated regarding public health, in need of health care, and and in need of health care, and forces many to travel long distances in search of health services (Colchester, 1997; Coimbra et al., 2005)..

Having to travel off of their reservations for health care also subjects

Amerindians to discrimination from non-Amerindians who sometimes refuse to treat or serve Amerindians, or who give them less than standard service (Colchester, 1997;

Coimbra et al., 2005). In Brazil, for example, Amerindians are often turned away from private hospitals with excuses that they do not have good manners or hygiene (Coimbra et al., 2005). In some cases special wards are built for Amerindian patients so that they do not interact with the non-native patients in the hospitals (Coimbra et al., 2005).

The poor condition of many educational facilities on Amerindian lands not only leaves Amerindians uneducated, but in order to receive these basic needs they are often forced to travel long distances (Colchester, 1997; Coimbra et al., 2005). On the

Rupununni, although there is a nursery and primary school in each of the major villages, there is only one secondary school stationed at Bina Hill. Many students, if they can afford and choose to attend secondary school, must move away from their homes and board at the school. In Guyana as a whole, although the government has provided free education to its citizens from nursery school to University since 1975 (although parents are still responsible for supplies and uniforms), they have not put aside sufficient funds to maintain a high standard of education (U.S. Department of State, 2007). As a result many schools are in poor condition and there is a shortage of textbooks and exercise books, as well as an insufficient number of teachers within the country (U.S. Department of State,

2007).

Acculturation has also brought new diseases to South America in addition to the

Old World epidemics brought with the first colonists. With forced sedentary lifestyles on reservations (lowered physical activity), increased access to Western foods and technology, combined with a decrease in traditional diets and activities there has been a startling increase in non-contagious diseases (obesity, type II diabetes, hypertension, etc), which up until recently have been foreign to indigenous groups (Coimbra et al., 2005;

Flemming-Moran et al., 1991; Palatnik et al., 2002; Edelweiss et al., 2003). Research among the Parkateje Amerindians of Brazil, who have recently experienced both rapid and extensive cultural changes, found that 67.8% were overweight, 14.4% obese, 4.4% have hypertension, and 44.4% had dyslipidemia (abnormal amounts of lipids or lipoproteins in the blood) leading researchers to conclude that this population is at high risk for developing diabetes and cardiovascular disease (Edelweiss et al., 2003). Even among the Aymara of Northern Chile, who have yet to experience a complete transition to modern industrialized lifeways, there are already reports of relatively high rates of obesity and dyslipidemia, which are only expected to increase as they become more and more accultured (Santos et al., 2001). Moreover, Amerindians who still live relatively 41 traditionally, with traditional diets high in cassava and plantains/bananas (which have been found to be nutritionally adequate for adults) (Dufour, 1992), do not display any non-contagious diseases such as diabetes, and obesity (Coimbra et al,. 2005; Palatnik et al., 2002).

The result of the rise in non-contagious diseases among Amerindian groups, combined with the remaining high prevalence of parasitic and other infectious diseases, puts even more strain on Amerindian health and their need for medical treatment, which is already lacking. These substantial changes in the lifeways of the Amerindians of South

America, due to acculturation, have led many Amerindians to declare that their quality of life and health have actually declined with acculturation. Psychosocial stress, for example has been found to increase with modernization due to the advent of new social interactions, greater work demands, increased efforts to obtain new goods, and the altered relevance of psychological stimuli (McGarvey, 1999). As early as the 1940's, the

Makushi were reported to be in despair over their situation and their inability to stop their tribe from quickly dying out (Myers, 1993). These feelings of despair and powerlessness were reported to have resulted in increased drinking and malnutrition which predisposed the Makushi to disease (Myers, 1993). In the 1960's it was noted that reports on

Guyanese Amerindians showed a decline in morals as a result of the tribal breakdown of system organization and ideology (Butt-Colson, 1971). Tsupto Buprewen Wairi, village chief for the Xavante village of Etenitepa in Brazil, also expresses concern for the loss of traditional lifeways and the affects it has had on his people (Coimbra et al., 2005). He stresses the need for research among his peoples in order to find solutions to these 42 diseases and health problems that the White people have brought upon his people

(Coimbra et al., 2005).

Conclusion

From the first Dutch settlement in Guyana in the late 16th century, to its present day acculturation and industrialization, dramatic imprints have been left upon the health and lifeways of the native Amerindians. It has been found that transitions experienced by indigenous groups, particularly in terms of their health, are directly related to their individual histories (interactions with local systems and larger social, economic and political institutions and processes as well as how the society itself has reacted throughout history to change in particular). General trends however, can be seen in the health and welfare of South Americas Amerindians, including the Makushi, both in the past and in the present (Coimbra et al., 2005). By understanding the history of the

Amerindians of South America, and highlighting their current health status, it should become possible to point out where problems exist, and to perhaps deduce causation. It is the purpose of this study, with the help of the Makushi of Guyana, to look at one area of

Amerindian health, childhood growth. In principal this study hopes to aid in the development of a better understanding of the causation of growth faltering among

Amerindian children. This will potentially lead to solutions which will not only benefit the Amerindians themselves, but the world in general, by ensuring that the traditions and cultures of these important and unique peoples live on to be experienced by future generations. 43

Time Period Diseases/Infections/ Consequences Impact of Time Period

of Time Period

Pre-Contact § spirochetal infections: § Generally chronic and

treponematosis , pinta, yaws, syphilis, endemic rather then acute

leptospirosis, typhus, and two types of and epidemic (Alchon,

relapsing fevers one carried by ticks and 1991)

the other by lice (Alchon, 1991;

Coimbra, 1995) § Zoonotic outbreaks were

thought to have occurred as

§ respiratory infections: tuberculosis, groups moved into areas

pneumonia, and blastomycosis (Alchon, containing unfamiliar

1991). parasites in which the

groups had not

§ protozoan infections:. Chagas' subsequently developed

disease, Leishmaniais, giardiasis, immunity (Coimbra, 1995)

toxoplasmosis, and amebiasis (Coimbra,

1995; Alchon, 1991). § Parasitic infections may

be responsible for the

§ bacterial infections: Carrion's Amerindians increased

disease, and Oroya fever (Alchon, susceptibility to bacterial

1991). and viral infections brought

by the colonists from the 44

§ parasites: hookworm, intestinal Old World. T-helper 2

helminthes, roundworm, pin worms, (Th2) immune dominance,

and whip worms. which appears during

macroparasitic infections,

results in a decrease in Thl

immune cells which help

protect the body against

bacterial and viral

infections. Amerindians

have some of the highest

documented Th2 levels

(Hurtado et al., 2005).

Post-Contact § Smallpox: Very contagious disease, § Meso- and South

characterized by pustules on the skin America, population

estimates of 100 million

§ Measles: acute infection caused by before European contact

the measles virus. Symptoms include dwindled to a mere 10

fever and cold-like symptoms as well as million by 1650 (Brown,

a red rash on the body and tiny white 2005)

spots lining the inside of the cheeks.

§ Scarlet Fever: Acute illness caused § Particularly in the

by the bacterium Streptococcus lowlands, whole native

pyogenes. It is characterized by a cultures were wiped out reddish skin rash within a few generations

due to Old World diseases

§ Influenza: Acute viral infection (Brown, 2005; Ribeiro, involving the respiratory tract. 1967)

§ Pneumonic plague: rapidly § Between 1900 and 1957, progressive, often fatal plague in which 87 out of 230 indigenous solid masses develop in the pulmonary groups in Brazil became system with bloody fluids forming in extinct, leaving Brazil's the air passageways and being expelled native population at a mere through coughing. A high fever is also 5% of what it was predicted common. to have been in the 1500's

(Riberio, 1967)

§ Diphtheria: Acute infectious disease caused by Corynebacterium diphtheriae. § Forced movement of

Bacterium forms a tough false indigenous peoples into membrane which can lead to bronchial missions, or their

obstruction and death from hypoxia. movement into these

Exotoxins released by the bacterium can mission villages due to the

also lead to systemic effects such as devastating effects of

myocarditis. epidemics, served as perfect

reservoirs for Old World

diseases (Coimbra et al.,

2005). Numerous natives ______succumbed to disease in

these mission villages

(Coimbra et al., 2005).

Acculturation § Endemic Diseases: goiter, syphilis, § Heavy burdens left on the

dysenteries, and dropsies, venereal shoulders of the women

disease (Colchester, 1997;

Munslow, 1998). With no

§ Men leave villages for long periods men to clear new garden

of time in search of work plots in order to grow food,

many women were forced

§ Women forced into prostitution and to look elsewhere for a

laborers for White men means of survival

(Colchester, 1997).

§ Debt bondage

§ Indigenous communities

§ Rate of malnutrition increases that once flourished on

sharing and cooperation

§ Pollution, Water contamination have turned into

communities with cash

§ Land loss values (Colchester, 1997).

§ Disruptions of traditional § Greater wealth

subsistence economies differentiation within 47

villages (and increased

tensions), as well as falling

standards of living as more

expensive and scarce

western foods begin to

replace local and traditional

foods (Colchester, 1997).

§ Stunting rates (low height

for age) among South

American Amerindians are

around 36%, whereas

wasting rates (low weight

for height) are around 1.8%

(Oyhenart et al., 2003)

TABLE 2. Summary of the Results and Impacts of Pre- and Post European Contact with South American Amerindians as well the Results and Impacts of Acculturation. 48

Chapter Three

Growth and the Environment

A number of populations, particularly in developing countries, fall below the optimal growth standards published by the WHO. As discussed in chapter one, a number of variables have been proposed to explain this occurrence. Some studies however, have found that despite adequate nutrition and the absence of infection, growth faltering has continued to persist (Prentice at al., 1993; Rousham and Gracey, 1997; Poskitt et al.,

1999). According to Wells (2000), the poor growth often seen in developing countries may be due to: 1) inadequate nutrition 2) high disease loads, and 3) warm climates. Over time, most research concerning the etiology of growth retardation has predominately focused on diet and disease, largely because genetic factors have not been found to account for the variability in human growth (Wells, 2000; Habicht et al., 1974). Some studies however, suggest that genes may play a more important role in growth variation than has been found elsewhere. As noted in chapter one, Stinson (1996) found Afro-

Ecuadorian children were significantly taller then Chachi Amerindian children. The purpose of this chapter is to explore the possibility that genes may be contributing to the variation seen in stature. In doing so, this chapter, will consider several environmental variables which may have influenced the development of this possible genetic variation. Thermoregulation

Homeotherms, such as humans, maintain their core body temperature between roughly 36-38°C, despite varying environmental temperatures, in order to maintain physiological homeostasis. They generate heat through metabolism, and undergo heat exchange between their body surfaces and their environment to maintain their core temperature or set point (Kormondy and Brown, 1998). Generally, humans living in hot climates must adapt to heat stress and vice versa for those who inhabit cold climates.

Given the region inhabited by the Makushi this consideration of thermoregulation will focus on heat stress.

There are four processes through which heat exchange can occur between the body and the environment: conduction, convection, radiation and evaporation.

Conduction is the transfer of heat between two solid objects in physical contact, such as between a warm rock and the person sitting on it. Convection refers to heat exchange between an object and a fluid or gas, such as hot water heating the cup in which it is sitting. The exchange of heat through electromagnetic energy between objects which are close to each other is called radiation. Sunbathing would be an example of solar radiation heating up the sunbather's body. Lastly, evaporation is when atoms in the liquid state absorb sufficient energy in the form of heat to enter into the gaseous state. Sweating is an example of evaporation resulting in body cooling. Of significant note the mechanism of heat loss from the body via each of these four processes varies depending on environmental temperature. As temperature increases within an environment from comfortable (~25°C) to hot (~35°C) the amount of heat lost via evaporation increases from 23% to 90%, whereas heat lost via radiation decreases from 67% to 4% (Frisancho, 50

1996). Convection is responsible for 33% of the heat lost at a warm temperature (30°C)

(41% and 26% lost in warm environment via radiation and evaporation respectively), however, it is only responsible for 10% of the heat lost at a comfortable temperature, and

6% at a hot temperature (Frisancho, 1996). Conductive heat loss has been found to only represent a small percentage of total heat exchange, as areas of skin contact with environmental objects is small, and contact with very conductive materials is generally avoided (Frisancho, 1996). As a result conduction is generally not discussed as a separate identity in studies of adaptation* (Frisancho, 1996). It is usually included in discussions of radiation and convection.

Vascular adaptations also play a role in thermoregulation. In cold conditions the vasoconstriction of the superficial blood vessels constricts blood flow to the shell of the body thereby keeping the essential internal organs at an optimal functioning temperature

(Kormondy and Brown, 1998). Consequently, in hot environments the superficial blood vessels will dilate, shunting heat from the core towards the shell where it can be dissipated via conduction convection, evaporation, and radiation (Kormondy and Brown,

1998). Full vasodilation can increase heat conductance by eightfold when compared to the vasoconstricted state (Frisancho, 1996). Piloerection (erection of hair on limbs) and

* Adaptation is the process of developing or enhancing structural, physiological or behavioral/cultural characteristics that improve an organism's chance of survival and reproduction in a particular environment (Kormondy and Brown, 1998). There are three forms: 1) Genetic Adaptations: adaptations which arise from the conventional mechanisms of natural selection acting on genetic variation within a population or via genetic drift. Genetic adaptations are hard-wired, inflexible, and heritable (Frisancho, 1996). 2) Phenotvpicallv Plastic Adaptations: adaptations which allow an organism to mold itself to its current environment. That is they change in response to the environment. They can be either short term (acclimatization), which are reversible, or long term (developmental), which results from long term exposure to a particular environmental stress from birth. These developmental adaptations are often irreversible or only partially reversible (Frisancho, 1996). 3) Behavioral/ Cultural Adaptations: voluntary actions taken by organisms to deal with current environmental conditions. They are flexible and conscious 51 shivering also occur during cold stress. Shivering significantly increases the basal metabolic rate causing the release of more heat to warm the body (Kormondy and Brown,

1998).

Cultural or behavioural adaptations exhibited by human populations such as clothing, activity levels, food, housing, etc. also combat thermal stress (Kormondy and Brown,

1998). It is worth noting here that it is often much easier to adapt behaviourally to cold stress, particularly through cultural adaptations such as clothing, than it is to adapt to heat stress. Many layers of fur or clothing can be added to an organism, but there are a limited number of layers that can be taken off.

In concluding this section the author shall discuss some of the special physiological changes that occur during pregnancy, which increase heat stress and subsequently affect maternal thermoregulation. Maternal heat stress is thought to be intensified during pregnancy due to three major physiological occurances. First, fat deposition increases the specific heat of the body in such a way that an increase in core body temperature, as a result of a specific level of thermal stress, is greater in women with higher adipose levels (Falk, 1998). Body temperature during pregnancy is normally higher then in non-pregnancy, and is an example of programmed rheostasis (condition or state in which homeostatic mechanisms are still present, but at a certain point in the life cycle there is a change in the regulated level or set point) (Fewell, 1995). Second, the change in body weight and shape during pregnancy decreases the surface area to mass ratio (SA/M ratio), leading to decreased efficiency in losing heat to the environment

(Frisancho, 1996). 52

(Wells, 2002). Third, maternal weight gain and fetal growth lead to an increase in basal heat production, likely also playing a role in the increased body temperature seen during pregnancy (Wells and Cole, 2002).

Climate and Body Shape, Size and Proportions

In 1847 Bergmann proposed what has become known as Bergmann's rule. This rule states that within homeotherms (warm-blooded organisms); mean body mass increases with a decrease in mean environmental temperature (Bergman, 1847 In:

Katzmarzyk and Leonard, 1998). For example, in a cold environment an organism would be expected to have a shorter, stockier body (increased volume), with a low SA/M ratio, which would decrease the efficiency of heat loss via conduction, convection, radiation and evaporation (Kormondy and Brown, 1998). In a hot environment one would expect a longer, leaner bodied organism, with a high SA/M ratio (Kormondy and Brown, 1998).

A number of studies have supported these findings in non-human mammals (Ashton et al., 2000; Meiri and Dayan, 2003; Sand et al., 1995). Ashton et al. (2000), for example, analyzed the relationship between size and latitude, as well as size and environmental temperature, for 110 and 64 mammalian species respectively, found within the published literature. They found that 78 out of 110 species displayed a positive correlation between size and latitude, 48 out of 64 species showed a negative correlation between size and temperature, and conclude that the data appear to strongly support Bergmann's rule.

Notably, it is not evident what Ashton et al. (2000) mean by environmental temperature

(if it refers to mean environmental temperature or summer temperatures, etc) and there is no evidence that humidity was considered, which may compromise these results. 53

Allen's rule proposed by Allen (1877 In: Katzmarzyk and Leonard, 1998) states that the relative length of the extremities, in homeotherms, decreases as mean environmental temperature decreases. In other words, a species which occupies environments ranging from always warm to seasonally cold will exhibit variation in limb

length: those populations in seasonally cold climates should have relatively shorter limbs.

Once again this is related to SA/M ratio; shorter limbs decrease SA/M ratio and

consequently decreases the amount of heat lost to the surroundings in cold environments.

There have been relatively fewer mammalian studies focusing on Allen's rule and the

findings are mixed. Some studies, such as Clarke and O'Neil (1999) found that male and juvenile female Chinese-origin rhesus monkeys [Macaca mulatto) were heavier, longer

and taller (in both body and limbs) than Indian-origin rhesus monkeys. These results

appear to be opposed to both Bergmann's and Allen's rule, as the Chinese-origin rhesus

monkeys in the colder environment would be expected to have shorter limbs and shorter

bodies than those of Indian-origin. Notably, the fact that the Chinese-origin rhesus

monkey's are heavier is consistent with Bergmann's rule, however, the authors of this paper are focusing on the fact that the Chinese-origin monkey's have longer bodies,

which does not follow with Bergmann's predictions, as they would not have a decreased

SA/M ratio. This same study, however, found that female Louisiana-origin rhesus

monkeys did conform to Bergmann's and Allen's rules by having longer limbes and

bodies than those of Chinese-origins. Studies on tail length in macques have also found a

trend in tail length which shows decreasing length with increasing latitude (Fooden and

Albrecht, 1999). Exceptions to the tail length trend, such as rhesus monkeys, are

explained through possible migration events induced by such things as glacial advance 54

(Fooden and Albrecht, 1999). Overall these studies suggest that their may be some exceptions to Allen's rule within mammals.

Roberts (1953) was the first to look at Bergmann's rule, and later Allen's rule

(Roberts 1978) on a world-wide scale in humans (Katzmarzyk and Leonard, 1998).

Through the statistical analysis of anthropometric data from 116 males and 33 females, taken from the literature available at the time, Roberts (1953) set out to see if Bergmann's rule could be applied to modern humans. He broke down his sample into ten groups:

African, European, Indian, Australian, South Asian, Eastern Mongoloid, Melanesian,

American, Central Asian, and Polynesian. From his analyses, Roberts (1953) concluded that there was a negative correlation between body mass and mean annual temperature in both men and women. These results suggested that humans, like mammals, appeared to

conform to Bergmann's rule. Roberts (1978) next looked at relative sitting height as well

as limb and trunk size relative to mean annual temperature. His analysis supported

Allen's rule; populations living in colder regions have shorter limbs (particularly the

lower limbs) than those populations inhabiting hotter environments.

Roberts (1953, 1978) conclusions were subsequently called into question by

several authors (see Steegmann, 2007). Studies have found that individuals who migrate

from developing countries, to grow up in developed countries, consistently grow larger then previous generations (Bogin, 1999). This suggests that there may not be a genetic

adaptation to climate in humans, that environmental factors, such as nutrition and disease

load, may play a bigger role in determining body size and shape (Bogin, 1999). It has

also been argued that other physiological adaptations (i.e. vascular changes, metabolic

rate, etc) to thermal stress are much more efficient and useful to humans than the 55 advantages brought about by the changes in SA/M ratios suggested by Bergmann's and

Allen's rules (Ruff, 1994). The ability to adapt behaviourally to heat stress has also been suggested to act as a buffer against the environment, and thus alleviate the need for the genetic or phenotypically plastic adaptations proposed by Bergmann's and Allen's rules

(Ruff, 1994).

In 1998, Katzmarzyk and Leonard set out to re-evaluate Roberts' (1953, 1978) findings by using data that had been published since Roberts' 1953 paper. The impetus for this study was to see if Roberts' findings had changed as a result of the large transformations that had occurred in human growth in the 40 years since Roberts' original study. If changes were evident, they hoped that these findings could help determine the roles of other environmental factors in determining body size and morphology. Katzmarzyk and Leonard looked at data from 225 adult males and 195 adult females from the anthropologic literature published since 1953, and classified them into the same ten ethnic groups as were used by Roberts'. Katzmarzyk and Leonard's data, supported Roberts' (1953) findings, but had weaker correlations. The authors suggest that this may be a consequence of the increases in body mass that have occurred world-wide as a result of improvements in diet and health due to acculturation. These changes appear to be most evident among tropical populations. In terms of Allen's rule, Katzmarzyk and

Leonard did not find the same relationship between relative sitting height and mean annual temperature as Roberts (1978). They conclude that there have been some significant changes to body proportions since the collection of the data used by Roberts' in 1953. This finding for Allen's rule is also consistent with the findings of Dembo et al.

(2007), in which reanalysis of Roberts original data showed that variation in human limb 56 proportions did not appear to be consistent with Allen's rule. As the critiques of

Bergman's and Allen's rules suggest, there are more factors involved in body mass and body proportions than just climate. Katzamarzyk and Leonard do show however, that despite the addition of more factors, climate still appears to have significant effects on both body mass and size.

Wells (2000) adds to the body of literature concerning climate and growth by focusing on infants (0-12 months) and children (1-8 years). Wells (2000) assesses stunting and wasting as possible adaptations to thermal stress. Infants and children have a greater SA/M ratios than adults, but have lower sweating rates, likely due to smaller sweat glands and a lower sweat rate per gland (Falk, 1998). They also do not have the greater blood volume that adults utilize to increase heat loss from the peripheral tissues

(Falk 1998). This is potentially a problem in hot climates as infants and children also have the highest metabolic rate per unit mass (Wells and Davies, 1998). As a result,

infants and children have a hard time acclimatizing to heat stress. Wells (2000) consequently suggests that infants and children must have some sort of adaptation to heat

stress in tropical environments.

According to Wells (2000), there are two possible ways to deal with heat stress.

First, as stated in Bergmann's and Allen's rules, in a hot environment heat loss can be amplified by increased SA/M. This is thought to be a long-term genetic adaptation, or possibly a phenotypically plastic adaptation due to findings that people from the

developing world who move and grow up in a developed country grow considerably

larger then previous generations (Bogin, 1999). Second, heat stress may be decreased via

decreased food intake (Wells, 2000).The digestion of food increases metabolism (Wells, 57

2000). Studies have found that mammals adjust their food intake in relation to environmental temperature (Brobeck, 1960; Kleiber 1961): in hot environments some mammals decrease food intake (Kleiber, 1961), whereas in colder environments some mammals increase food intake in order to maintain heat production (Kleiber, 1961).

Wells (2000) considers both of these adaptations in his consideration of infant and child growth. Infants with moderate growth faltering and wasting (defined as the 10th percentile) as well as normal growth (50* percentile) were taken from the American

NCHS reference (Wells, 2000). The 10th percentile was used as a cutoff for moderate wasting and moderate stunting in order to control for energy expenditure, as there is some evidence that severely malnourished infants have higher energy expenditures per unit body mass, due to most of their weight being internal organs, which have a higher metabolic rate then other tissues (Holliday, 1971). For each of the infants in the study,

Wells (2000) calculated surface area, as well as energy expenditure. For infants up to 12 months of age energy expenditure was determined from a previously conducted doubly labelled water study (Wells and Davies 1998 In: Wells, 2000), whereas data on the energy expenditures of children was taken from Tourun et al. (1996). Wells (2000) found that moderate growth faltering, predominately moderate wasting, was related to both decreased food intake and increases in SA/M ratios. Although moderate stunting did not increase SA/M ratios until after the age of two, moderate wasting increased SA/M ratios by > 15% in infancy, and by 10% in childhood. As a result, moderate growth faltering appears to increase infants' and childrens' ability to deal with heat stress. Wells (2000) cautions that he is not dismissing disease and inadequate nutrition as important players in growth variation among the people living in hot tropical environments. He states that his 58 models assumed that energy expenditure was not raised by infection (infection is known to increase energy expenditure) and that in his study moderate activity levels were assumed to be applicable to all categories of growth (increased or decreased activity would change energy expenditure values). Infection and changes in energy expenditures could thus alter the conclusions drawn in Wells (2000). Wells (2000) thus concludes that these preliminary data indicate that the environment is another factor influencing growth in tropical environments.

Ruff (1991, 1994, 2002), considers the potential relationship between climate, and body size and proportions by looking at hominid evolutionary trends and relating these to modern populations. Ruffs (1994, 2002) study of early hominids leads him to conclude that the variation seen in both body shape and proportions today are also evident in our ancestors. Ruff (2002) focuses on bi-iliac breadth, as it is a good indicator of body breadth. Body breadth is important, as in order to change the SA/M ratio of an object, or organism, its absolute breadth must change (Ruff, 1994). Bi-iliac breadth also shows 25% variability worldwide and appears to follow set latitudinal gradients, while being less affected by environmental variation in soft tissues (Ruff, 1994). In addition to bi-iliac breadth Ruff (2002) looked at body proportions. Many studies have found that variability in body size and proportions was even greater in ancient hominids than modern Homo sapiens, although they appear to follow the same latitudinal trends (Aiello,

1992; McHenry, 1992). According to Aiello (1992), fossil evidence suggests that by 1.6 million years ago some of the ancient hominids from Africa (i.e. Homo erectus) may have had weight-for-height ratios which are equivalent to modern human populations.

This is significant for a number of reasons: First, body size is often used to assess 59 evolutionary trends in the hominid lineage; Second, body size and shape of early hominids can serve as a baseline for accessing recent trends in geographic variation

(Ruff, 2002). For example, knowing long-standing differences in body size and proportions among populations inhabiting different environments, may help determine the best methods to determine the health and nutritional status of indigenous populations within these environments (Ruff, 2002). In order to determine the evolutionary trends in body size and proportions evident in ancient hominids, Ruff (2002) determined body mass based on femoral head size and bi-iliac breadth, as well as stature from the literature. Modern, world-wide indigenous population means, for these same features, were also used from Ruff (1994), in order to determine if the trends seen in ancient hominids were also apparent in present indigenous populations. By plotting ulna length against bi-iliac breadth, it was found that the Middle Pleistocene specimens from

Yinnuishan and Atapuerca, which are hyper-arctic (having a climate equivalent to modern-day Alaska and Siberia) specimens that lived during the Pleistocene, had wide bodies and short arms. The two Neanderthals samples (Kebara 2, and La Chapelle 1) also showed a similar trend of short arms and wide bodies. The two African Pleistocene samples showed the opposite trend, having longer arms and narrower bodies. When modern indigenous populations from the high arctic (Eskimos/Aleuts) and East Africa were compared to these data it was also seen that the high arctic populations had short arms and wide bodies, compared to the modern indigenous East Africans who had long arms and narrow bodies.

In addition, Ruffs (2002) data generated some interesting findings concerning changes in hominid body mass over time. Although there appears to have been a net 60 decrease in average body mass in hominids approximately 50,000 years ago (possibly due to technological advancements, climate change, nutritional stress, and reduced gene

flow), hominids living in northern latitudes later increased in body mass (Ruff, 2002).

The hominids in southern latitudes, on the other hand, never regained any of their lost

body mass (Ruff, 2002). Overall, among these data show that among those high latitude

early hominid populations considered, they appear to have similar body masses to their

Pleistocene relatives (Neanderthals, Yinnuishan and Atapuerca), whereas lower latitude

populations have smaller body masses in comparison to their Pleistocene relatives (H.

egaster/ erectus).

Ruff (2002) also makes some interesting conclusions concerning how these

latitudinal trends in body mass may be used when studying modern populations. He notes

that factors other than just climate can influence body proportions and height. For

example, improved nutrition and health status have been found to increase relative limb

length (Tanner et al., 1982). Despite this, Ruff (2002) notes that tropical populations,

which usually suffer from poorer nutritional status when compared to high latitude

populations, still show relatively longer limbs, suggesting a potential genetic etiology. Bi-

iliac breadth does not appear to be affected by nutritional, or other developmental

influences, possibly due to the effects of stabilizing selection (Ruff, 1994). Ruff (2002)

suggests that the use of bi-iliac breadth, which directly affects SA/M ratio, and shows

latitudinal trends, may therefore be a better indicator of health within a population than

height.

To show the usefulness of this technique, Ruff (2002) compares the usefulness of

BMI (body mass index= kg/m ) and TFI (trunk frame index = body mass/bi-iliac breadth 61 x 1000) for assessing under and overweight individuals within a population. He specifically uses arctic populations, the Inupiat and Inuit. Their BMI data are compared, as is standard, against a reference sample taken from U.S. children and adults (Denver

Growth Study in this case). Modern arctic populations have short limbs and wide bodies as predicted by Bergman's and Allen's rules. In comparison to the U.S. standards, these arctic populations have high BMI's and upper-arm muscle circumferences, although their triceps skinfold thickness is consistent with U.S. standards (Johnston et al, 1982). The fact that BMI and upper-arm circumference data suggest arctic populations are overweight, whereas their triceps skinfold thicknesses are consistent with average U.S. values, has led to the suggestion that U.S. reference samples may not be relevant for all populations (Schaefer 1977). By taking BMI and TFI data for both 9 year old Inupiat children from Alaska, and U.S. children, Ruff (2002) found that as expected, the Inupiat children had higher mean BMI values than the U.S. children. However, with respects to

TFI, Inupiat means were equivalent to U.S. means. The distribution values for TFI were also very similar. Triceps skinfold data were collected, and as had been previously reported, they were not large in the Inupiat, but equal to those of U.S. children. Ruff

(2002) therefore concludes that studies in which BMI is used as a measure of nutritional status, should be looked at with caution as BMI, unlike TFI, fails to account for variation in body build/shape. Consequently, Ruff (2002) suggests that by taking body shape into consideration when accessing nutritional status in many indigenous populations, including tropical populations, researchers may obtain more accurate estimates of overall health. 62

In conclusion there appear to be a significant number of studies which find at least some support for the applicability of Bergmann's and Allen's rules to humans, both past and present. These studies suggest that genetic or phenotypically plastic adaptations to climatic stress developed through human evolution and are thus at least partly responsible for the differences seen in human growth, body shape, size and proportions. Although none of these studies suggest that climate is the only factor influencing growth, they do indicate that it is a factor one should consider when studying the etiology of variation in human growth.

Genes, Climate and Body Mass

While climate appears to explain at least part of the variation in human growth, none of the studies cited above could denote a specific gene which might help to explain the observed patterns. Geneticists, however, have uncovered a gene in humans thought to be directly involved in genetic adaptations to climate. The acid phosphatase 1 (ACPI) locus, located on the short arm of chromosome 2, has been proposed to have emerged in humans as an adaptation to thermal stress (Greene et al, 2000). The gene codes for a low molecular weight phosphotyrosine protein phosphatase (low Mr PTP) (Bottini et al.,

1995; Fauman and Saper, 1996; Ramponi and Stefani, 1997). Two co-dominant alleles* exist at the ACPI locus in all populations, and three occur in polymorphic frequencies in certain geographical regions (Spencer et al., 1964; Jenkins and Corfield, 1972; Yoshihara and Mohrenweiser, 1980; Mohrenweiser and Novotny, 1982; Cavalli-Sforza et al., 1994).

ACPI *A and ACPI *B alleles exist in all populations (Greene et al., 2000). The

* refers to alleles (i.e. ACP1*A), whereas the absence of this symbol refers to phenotypes (i.e. ACPI A) 63

ACP1*R allele is generally only found in South African populations, and the ACP1TIC-

1 and ACP1*GUA-1 alleles are only present in South and Central American indigenous populations (Jenkins and Corfield, 1972; Yoshihara and Mohrenweiser, 1980;

Mohrenweiser and Novotny, 1982). The last allele found at this locus is the ACP1*C allele, which is only found in European populations (Greene et al., 2000). Enzyme activity levels (micromoles of p-nitrophenol liberated in 30min per gram of hemoglobin at 37 °C) differ for each of the different ACPI phenotypes as follows from lowest to highest enzyme activity: ACPI GUA-1= 70.8, ACPI R= 109.3, ACPI A= 122.4, ACPI

TIC-1 = 144.0, ACPI AB= 153.9, ACPI AC= 183.6, ACPI B= 188.3, ACPI BC= 212.3,

ACPI C = 240.0 (Spencer et al., 1964 In: Greene et al., 2000). These enzyme activity differences are important as diminished levels of low Mr PTP have been linked to increased insulin-mediated metabolic activities and increased cell proliferation, whereas increased levels of low Mr PTP have been linked with down-regulated metabolic processes and decreased cell proliferation (Greene et al., 2000). It has also been found that those ACPI phenotypes associated with low enzyme activity are associated with high glutathione reductase (GR) activity and vice versa for the high activity ACPI phenotypes

(Mohrenweiser and Novotny, 1982; Kirjarinta, 1976 In: Greene et al., 2002). Differences in the levels of low Mr PTP and GR activity between different ACPI phenotypes means that differences in ACPI enzyme activity may influence both the efficiency of energy metabolism in the body, as well as the body's ability to utilize nutrients to support somatic growth (Greene et al., 2002). As will be discussed in more detail below, these activity differences in ACPI variants may be advantageous in certain environments. 64

Ananthakrishnan and Walter (1972) found a significant relationship between

ACPI *A allele and mean annual temperature among 65 native populations. As mean annual temperature increased, the frequency of the ACPI* A allele decreased and vise versa. Regression analysis showed that for every 13°C drop in temperature, the frequency of the ACP1*A allele increased by 0.25. Subsequently Anathakrishnan and Walter (1972) found that the frequency of the ACP*B allele increased with mean annual temperature, and decreased with lower mean annual temperatures. These findings were later confirmed by Cavalli-Sforza et al. (1994), who found the ACP*A allele to be highest in northern latitudes, whereas the ACP*B allele was highest in Africa, South America, and Australia.

Data taken from newborns has shown a significant relationship between newborn

ACPI phenotypes and body size (Gloria-Bottini et al., 1988; Amante et al., 1990; Bottini et al., 1990). Newborns born with the ACPI A or ACPI AB phenotypes have increased frequency of macrosomia (birthweight greater then the 90th percentile) (Greene et al.,

2000). This appears to be a less frequent occurrence in newborns with the ACPI B,

ACPI AC, ACPI BC, and ACPI C phenotypes (Greene et al., 2000). The ACP1*C

allele may decrease the chances of macrosomia in populations with high ACP1*A allele

frequencies, and only exists in Europe (Greene et al., 2000). Newborns with ACPI A and

ACPI AB phenotypes, even if they do not display macrosomia, appear to have higher

birth weights (Amante et al., 1990). Subsequently, ACPI A and ACPI AB phenotypes,

appear to be much more common in groups of severely obese people (Bottini et al.,

1990). This suggests that the low activity ACPI phenotypes are associated with increased

body mass (Greene et al., 2000). As increased body mass is an advantage in cold

environments, it is suggested by Greene et al. (2000), that the heat producing effects of 65

ACPI A and ACPI AB phenotypes, through metabolic advantages, would be strongly

selected for in cold environments. Greene et al. (2000) also suggest that larger newborns would contain more stored nutrients and lower SA/M ratios then smaller newborns, which would also be an advantage in colder environments.

There is also some evidence of pre-birth selection for certain ACPI phenotypes.

Gloria-Bottini et al. (1996, 1997) found that in Italian populations there appeared to be

selection (thought to be fertility selection or intrauterine selection) during conceptus for the ACP1*A allele during the coldest months of the year, whereas during the hottest months there appeared to be selection for the ACP1*C allele. Thus, it would appear that the ACPI allele frequencies are, in part, determined by the frequencies of cold and hot periods experienced during pregnancy (Greene et al., 2000). This occurrence has been

suggested to be the result of maternal metabolic cues, resulting from the amount of

calories available to the developing fetus during the pregnancy (Greene et al., 2000). It is

believed that these cues influence the survival of the fetus, as the mothers body will select

for a fetus whose growth trajectories are optimally suited to the external environment at

the time of conception (Greene et al, 2000).

In summary the ACPI alleles appear to have been maintained in human

populations via balancing selection, with different genotypes having increased

frequencies in different thermal and nutritional environments (Greene et al, 2000). People

who have the low activity ACPI A and ACPI AB phenotypes are more likely to be

macrosomic, have lower SA/M ratios, and as a result be better adapted to cold

environments (Greene et al, 2000). High activity ACPI B and ACPI C alleles may be

maintained within populations inhabiting cold environments to counteract extreme 66 macosomia and the need for excessive amounts of nutrients, as people with low activity

ACPI alleles have larger nutritional requirements (Greene et al., 2000). Acquisition of the high activity ACPI B, ACPI AC, and ACPI BC phenotypes appears to decrease body mass, increase the SA/M ratio, and lead to individuals which are better adapted to hot environments (Greene et al., 2000). The presence of the low activity ACP1*TIC1,

ACP*GUA-1 and ACP1*R alleles may be maintained within tropical populations due to the fact that these alleles may have a greater ability to convert energy, and nutrients into needed body mass, in times of food scarcity (Greene et al., 2000). It also appears that selection at the ACPI locus is due to the combination of many selective pressures, including pre and post-natal selection events (Greene et al, 2000).

Birth Weight and Climate

According to the WHO (1992), low birth weight (LBW) (infants weighting less then 5.5 pounds at birth) is one of the leading causes of infant morbidity and mortality in both developed and developing countries. In developed countries LBW is often the result of pre-term infants and smoking (Wells and Cole, 2002). In developing countries the majority of LBW infants are attributed to intrauterine growth retardation (IGR), which may result from many factors including ethnicity, low energy intake, low pre-pregnancy birth weight, and malaria (WHO, 1992; Wells and Cole, 2002). Wells (2002) suggests thermal stress as another factor which may contribute to IGR. As discussed previously, the physiological changes that occur during pregnancy in hot environments are thought to intensify maternal heat stress (Wells and Cole, 2002). Thermodynamic theory predicts that a decrease in body size may decrease SA/M (Wells and Cole, 2002). In hot 67 environments, the size of both the mother and offspring will therefore be reduced in order to decrease the infant's heat stress (Wells and Cole, 2002). Decreased birth weight in hot environments is proposed to occur through two possible mechanisms. First, increased heat stress upon the mother during pregnancy may have direct effects on fetal growth

(Wells and Cole, 2002). Second, heat stress may only have small effects on the fetus, and thus result in a decrease in population mean birth weight, but not on all individuals within a population (Wells and Cole, 2002). The first mechanism is supported by the findings of

Gloria-Bottini et al. (2000), discussed in the previous section. Gloria-Bottini et al. (2000) found evidence within Italian populations for the selection (thought to be fertility selection or intrauterine selection) of different ACPI alleles depending on the month of conception, particularly between the coldest and hottest months of the year. This result is thought to occur from metabolic cues which result from the amount of calories available to the developing fetus during pregnancy (Greene et al., 2000). It has even been suggested that the mothers body may be selecting for a fetus whose growth trajectories would be most optimally suited to the external environment at the time of conception

(Greene et al., 2000).

Wells and Cole (2002) tested the hypothesis that heat stress affects infant birth weight, in order to see if environmental heat load could explain the worldwide variation in birth weight. The authors collected birth weight and environmental heat stress information from the literature on 140 populations, as well as data on other possible confounding variables (i.e. maternal height and size, gross domestic product, etc). They found that 46.1% of the between population variation in birth weight could be explained by gross domestic product and maternal height. Heat index explained an additional 9.6% 68 of the between population differences. Almost half of the variation in birth weight could not be explained in this study, and the authors attribute this to their inability to find data on such factors as pregnancy weight gain, malaria, maternal nutrition status, and ethnic group. If these factors were taken into consideration, the authors believe that the role of heat, in explaining the variation between populations with respect to birth weight, would be increased, as these factors are thought to be influenced by heat. For example, as mentioned above, theoretical models predict lower pregnancy weight gain, and less body fat in pregnant women in environments with greater heat stress (Wells, 2002). Overall, this study finds that there is a very slight inverse relationship between birth weight and thermal stress, suggesting that low birth weight may be an adaptation to thermal stress in hot environments.

Wells (2006) considers birth weight in a more evolutionary light. He notes that human evolution is one long line of dispersal, colonization, and migratory events. Adults colonize new areas, and it is their offspring who must deal with the consequences of these migratory events. As a result, it would be advantageous to be phenotypically plastic in early growth and birth weight. Indeed, modern studies have supported this. Studies looking at migration have found that migration greatly decreases the between group variation in post-natal size, thus highlighting the importance of the environment (Wells,

2006; Bogin, 1999). It has been found however, that it can take several generations for the inherited environmental effects on birth weight and size to disappear completely; that

is, a small mother in a newly prosperous environment may still give birth to small

children as her body size would constrain the size of her offspring (WHO, 1992; Wells,

2006; Bogin, 1999). Wells (2006) suggests, assuming no gene flow, that this results from 69 the evidence of a genetic component slowly decreasing over time, with the contribution of plasticity subsequently increasing. It is this plasticity which Wells (2006) believes to be critical for the adaptation of particular birth weights to particular environments. The development of the fetus is a developmental game in which the fetus tries to pick a strategy based on a number of environmental cues (i.e. temperature, nutrition, etc), which afford it the best chances for survival in its post-natal environment (Wells, 2006).

According to Wells (2006), this game plan can be manipulated further after birth, but only to a certain point, as some processes, such as organogenesis (formation of organs during development), are already set by the time an infant is born (Barker et al, 1998).

Overall the plasticity of birth weight appears to be part of our evolutionary legacy; an adaptation which humans acquired as they spread to colonize the diverse environments of the five continents.

The Pygmies

The exact factors which control the normal variation seen in growth and stature among human populations, for the most part, are largely unknown (Davila et al., 2002), as may be evident from the previous sections of this chapter. The pygmies, who by definition are those populations with an average height of less then five feet, represent one extreme on the height spectrum, comprising the world's shortest human populations.

The Efe Pygmies of Africa are the shortest pygmy population of all (Bailey, 1991). The mean adult stature of male Efe pygmies is only four feet six inches (Bailey, 1991). The oldest written documentation mentioning African Pygmies comes from a 4000 year old letter Pharaoh Nefrikare of Egypt wrote instructing his soldiers to take good care of the 70

"dancing dwarf captured along the Upper Nile (Diamond, 1991). Despite the long interest in pygmy populations, the etiology of their short stature is equivocal.

The real breakthrough in research looking into the etiology responsible for Pygmy short stature began with Van De Koppel and Hewlett's (1986) work from 1975 to 1980.

They measured the weight and height for Aka pygmies in the Central African Republic and determined the age of their participants by asking parents to relate their children's births to important local events. These local events could be dated at least to the year in which they occurred. This made it possible for the researchers to determine the approximate month in which children were born. When comparing the growth curves of the Pygmies to non-Pygmy farmers in the area they found that the growth curves were almost identical up until puberty. Van De Koppel and Hewlett (1986) thus concluded that the Pygmies grow similarly to non-pygmy children up until the age of puberty where, unlike other children, they stop growing. To explain this finding physiologically Merimee and Rimoin (1986) assessed the Pygmies ability to secrete and respond to growth hormone (GH). What they found was that Pygmies secreted normal amounts of GH, however, they were resistant to the insulinotropic, lipolytic, and nitrogen-retaining properties of GH. In 1980-1981 the serum samples from both the Pygmies and the controls were reanalyzed for IGF-1 (insulin-like growth factor 1) and IGF-2 (insulin-like growth factor 2) (Merimee et al., 1981, Merimee and Rimoin, 1986). They did so as GH is known to primarily stimulate human growth through IGF peptides, indeed most of the actions of GH work through IGF peptides (Jain et al., 1998; Van Wyk et al., 1974). IGF-1 in vitro effects cell cycle progression, cell prolification, cell death, cell differentiation, as well as many other cell specific functions via specific receptors present on various cells 71

(Zapf et al., 1984; Jones and Clemmons, 1995). IGF-2, on the other hand, appears to have the same effects as IGF-1 on the fetus, but its exact function in adults is unknown (Zapf et al., 1984). Recent research, suggests however, that it may partially compensate for cells with IGF-1 receptor defects (Hattori et al., 1996). Merimee and colleagues proposed that the growth retardation in Pygmy populations was due to an isolated deficiency in

IGF-1, which is most notable during puberty, and results in the absence of the normal growth spurt during puberty (Merimee et al., 1981, Merimee and Rimoin, 1986). They also determined that this IGF-1 deficiency was genetically based and not related to environmental factors such as diet (Merimee and Rimoin, 1986). These findings not only provided an explanation for Pygmy short stature, but also suggested that IGF-1 was what triggered the normal growth spurt associated with puberty (Merimee et al., 1981,

Merimee and Rimoin, 1986).

Between 1980 and 1987, while working on the Ituri project in northeast Zaire with the Efe Pygmies, Bailey and colleagues set out to look at Pygmy growth by measuring the heights of children observed from birth, rather than measuring children of estimated ages (Bailey and Devore, 1989; Bailey, 1990). As many of the original children as could be located were measured every six months for height (Bailey and Devore,

1989; Bailey, 1990). What Bailey and his colleagues found was that at less then one year of age the Pygmy children were below the third percentile for height when compared to

U.S. females and they became increasingly stunted in the following years. In conclusion, the average five year old Efe Pygmy only attained the same height as a two and a half year old American girl. Even when compared to the neighbouring Lese farmers, the Efe

Pygmies were consistently shorter then the Lese in the first five years of life, with the 72 difference in stature between the two groups increasing as the children grew older

(Bailey, 1991). These findings raised considerable doubt about the findings of Van De

Koppel and Hewlett's (1986) as well as those of Merimee and colleagues. It appeared that the Pygmies did not have normal growth up until puberty, but actually had decreased growth starting from birth (Bailey and Devore, 1989, Bailey, 1990, Bailey, 1991).

Bailey's studies urged caution on studies concluding that Pygmies lacked an adolescent growth spurt from data which only estimated the ages of its subjects.

Starting from the works of Van De Koppel and Hewlett, Merimee, and Bailey, recent studies have found that pygmy stature is the result of growth hormone (GH) resistance. This conclusion is based on four main findings: 1) low serum IGF-1 in adult pygmies; 2) the inability of short term GH treatments to increase serum IGF-1 levels, nitrogen retention, insulin secretion, and urinary excretion of calcium, as well as its inability to decrease urinary excretion of phosphate; 3) reduced in-vitro GH-induced secretion of IGF-1 in Epstein-Barr virus-transformed B lymphocyte cell lines from six

Pygmies; 4) low levels of high affinity growth hormone binding protein (GHBP) in

Pygmy blood serum (Geffner et al., 1995; Jain et al., 1998). These findings, however, have lead to a debate between those who think Pygmy height is due to IGF-1 resistance leading to secondary GH resistance and those who believe Pygmy height is due primarily to GH resistance.

The idea that Pygmy height is the result of IGF-1 resistance leading to secondary

GH resistance, predominately comes from work on T cells taken from Pygmy populations (mostly the Efe Pygmies of Zaire). T cell lines taken from Efe Pygmies have been found to be completely resistant to the growth promoting actions of IGF-1 at 73 concentrations of less then 250(ig/L and GH concentrations of less then 500|ig/L

(Geffner et al., 1993; Geffner et al., 1995; Jain et al., 1998). Control T cell lines showed a normal bimodal response to IGF-1 and a unimodal response to GH, whereas the Lese farmers, who are neighbours of the Efe Pygmies, and are known to have intermarried with Efe Pygmies, showed an intermediate response to both IGF-1 and GH (Jain et al.,

1998). The lack of IGF-1 responsiveness in pygmy T cell lines represents a genetic condition as T cell lines are not influenced by blood-borne influences such as undernutrition and other environmental circumstances (Geffner et al., 1995). Merimee and Rimoin's (1986) work with Lese farmers, who have intermixed with the Pygmies, also suggests that the Pygmies short height is not inherited as a simple dominant trait.

More in-depth studies of T cells showed significant decreased levels of IGF-1 receptor gene transcription and IGF-1 receptor signalling between Pygmy T cells and controls

(Hattori et al., 1996). This has lead to the conclusion that it is a defect involving the IGF-

1 receptor that is resulting in the short stature seen in Pygmies and that human stature in general may be controlled by the IGF-1 receptor (Hattori et al., 1996).

Those who believe that Pygmy short stature is caused by decreased serum levels of growth hormone binding protein (GHBP), the circulating ectodomain of the growth hormone receptor (GHR) leading to direct GH resistance, argue that it is difficult to know whether the fact that T cells are resistant to IGF-1, due to reduced expression of IGF-1 receptors, can be extrapolated to other tissues (Davila et al., 2002). They also point out that no structural abnormities have been found in the coding region of the IGF-1 receptor gene (Davila et al., 2002; Hattori et al., 1996). IGF-1 receptor knockout mice have been found to die shortly after birth suggesting it to be a fatal mutation, while heterozygous 74

IGF-1 mice do not appear to have any major abnormalities (Jain et al., 1998; Davila et al.,

2002). Despite this argument research has shown that the structure and DNA sequence of the GHR gene is also normal in Pygmies (Jain et al., 1998). It has also been found that at least African Pygmies show no notable morphological or biochemical similarities to individuals with genetic GH resistance due to mutations in the GH receptor gene (Jain et al., 1998). Thus at this point neither side can pinpoint a specific genetic mutation or abnormality as the cause of Pygmy short stature (Merimee et al., 1990). The IGF-1 resistance hypothesis has also been attacked for being unable to explain the low levels of

IGF-1 and GHBP serum levels in pygmy populations, as with IGF-1 receptor inactivation one would expect normal levels of IGF-1 (Dalvila et al., 2002; Jain et al., 1998; Geffner et al., 1995). This apparent contradiction has been partially explained with mice and rat models which have shown that during protein malnutrition IGF-1 levels remain low despite combined IGF-1 and GH resistance (Thissen et al., 1990, Thissen et al., 1991;

Jain et al., 1998). This finding has been speculated to be the result of either coexisting

GH resistance or the ability of undernutrition, specifically protein malnutrition, to decrease IGF-1 levels (Jain et al., 1998). This can not be the full explanation for the low levels of IGF-1 seen in pygmy populations as if this were the case one would expect Lese subjects, with their higher BMI's, to have higher or even normal IGF-1 levels, which is not the case (Geffner et al., 1995). Merimee and Rimoin (1986) also note that the pygmy diet is not severely deficient in protein and their fasting GH levels are normal. In addition they point out that when a two week high protein, high carbohydrate diet was given to

Pygmy subjects there was no change in serum IGF-1 levels, GH unresponsiveness, or 75 insulinopenia. Lastly, Dalvila et al. (2002) found at least one Pygmy population, the

Mountain Ok peoples from Papua-New Guinea that had normal IGF-1 levels.

As can be seen from the above studies and debate, there is strong evidence for a genetic explanation for short stature among Pygmies. Interestingly, this evidence was even evident in the 1880's when Pygmies who were raised in Italy and fed a nutritionally adequate diet, were found as adults to be as short as their African relatives (Giglioli, 1880

In Davila et al., 2002). In conclusion, although evidence suggests Pygmy short stature has a genetic basis, the exact genetic mutation and mechanism resulting in their short stature has yet to be found and fully understood. As a result the debate continues.

The evidence of a genetic contribution to Pygmy stature has led to speculation that their stature may represent a genetic adaptation. The stimulus however, that led to this phenotype, and its possible benefit to the Pygmies, is currently unknown, although many theories exist. Thermal reasons have been proposed for the same reasons as have been discussed previously, but they have been attacked as it has been found, that unlike

Bergmann's rule, in places like New Guinea height actually declines with elevation

(Diamond, 1991). Pygmy populations in the Philippines and Malaysia live in the mountains (the Philippines maximum of 6800 feet, (Diamond, 1991). This may not completely discredit a thermal explanation for short stature in all populations, however, as these groups may be subject to different stresses. One of these potential stressors for example, in high altitude populations, could be hypoxia, particularly with its negative effects on birth weight and length from which children appear to express no catch up growth (Leonard et al., 2000; Frisancho and Baker 1970; Krampl et al., 2000; Parraguez et al. 2005, Leonard et al., 1995). Most of the difference in height observed between high 76 altitude populations and coastal populations can be seen in the first six months of life

(Leonard et al., 1995). It has also been argued that pygmies short stature, particularly in mountainous areas, is an adaptation to dense vegetation (Diamond, 1991). Even among

African forest dwelling pygmies the advantage of short height has been noted (Turnbull,

1986). This theory, however, does not explain the short stature of all Pygmy populations either, as noted by Diamond (1991). The Kalahari Bushmen live, as Diamond points out, in a desert environment with very little, if any vegetation. This has lead to the hypothesis that pygmy short stature may actually be an adaptation to starvation in environments with low and fluctuating productivity (Diamond, 1991). It has also been proposed that thousands of years of cyclical undernutrition (resulting from events such as periodic droughts or floods) combined with limited gene flow from other populations, may have led to the evolutionary adaptation of short stature (Jain et al., 1998).

Lastly, in 2006 an interesting hypothesis explaining the reasons for Pygmy short stature was proposed by Walker et al. (2006). They point out a common trend in bioanthropological studies in which those populations which live in better environmental conditions tend to have children with faster child and juvenile growth, an earlier adult growth spurt resulting in larger and taller adults, earlier menche and age of first reproduction. Despite this they suggest that it would be an important advantage for populations in poor environments, particularly those where high mortality risk from violence, disease, and accidents outweighs the mortality due to malnutrition, to have faster growth and earlier reproductive development. In order to test this hypothesis

Walker and colleagues (2006) looked at 22 small-scale societies from Africa, Philippines,

and Venezuela, where life-history data were available. They used adult body size to 77 suggest overall nutrient availability and probability of survivorship to age 15 was used to help measure the force of selection for earlier and faster maturation. Overall, this study found that the majority of populations conformed to what has been found in most biological anthropology studies, where populations in poor environments with low energy availability, displayed slower growth with later maturation. The exception to this appeared to be among the African Pygmy populations as well as the Philippine Negritos

(Aeta/Batak/Agta) and the Hiwi of Venezuela where there appears to be accelerated maturation despite their small size. These populations appear to have faster more linear growth during their development despite poor environmental conditions. For example child/juvenile growth for Baka children from Cameroon was 7.1cm/year and 2.9kg/year.

This growth rate for linear growth exceeds that of U.S. children (6.5cm/year) and approaches the same weight gain per year (3.3kg/year), despite the fact that Baka adults are on average less then 2/3 the weight of U.S. adults. The height of Baka children at age

10 also represents approximately 70% of their adult height, whereas in other societies such as the Maya, and Hadza at 10 years of age their height only represent approximately

40-45% of their adult size. What is notable about the African Pygmy, Philippine

Negritos, and Hiwi of Venezuela is that they display high juvenile mortality. For example the Pygmies of West Africa and the Batak and Agta of the Philippines have only a 55%,

51% and 42% (regardless of sex), chance of living to age 15, respectively. Non pygmy populations such as Turkana of Kenya, Tsimane of Bolivia, and Ache of Paraguay have

76% (female and male), 76% female, 80 male% and 68% female, 79% male, chance of living to age 15, respectively. As a result, the authors propose that in these Pygmy populations, more energy is invested in current reproduction rather than future 78 reproduction, as future survival is less of a certainty. It is also worth noting that the authors of this paper found that growth was much less plastic in males then it was in females, which seems plausible due to the high energy requirements of reproduction on females. Walker and colleagues also note that in these populations, having a larger body size at a young age may be important for combating parasites and disease through a more robust immune system. In order to explain why populations living in mountainous or drier environments, such as the New Guinea Highlanders, do not display this accelerated growth, the authors suggest that the death risk from such things as parasites and infectious disease is reduced in these environments. On the other hand, the risk of starvation increases in these environments, and thus selection would favour slower growth and later reproduction as a maintenance-cost effective life strategy. In conclusion it would appear that high juvenile mortality may be contributing to Pygmy short stature.

While there appears to be a genetic explanation for Pygmy short stature, the exact genetic mechanism(s) causing their short stature has yet to be identified. Genes however, do not appear to be the only contributing factor. Pygmy short stature may also be influenced by other factors such as nutrition, and the environment.

Conclusion

The purpose of this chapter was to explore climatic and genetic factors which may influence body shape and size. Until recently, climate has largely been ignored due to a belief that its contribution to body size and shape is negligible in comparison to other proposed factors such as nutrition and socioeconomic status. The data presented in this 79 chapter provides a counterpoint; climate and, consequently, genes appear to influence growth in a number of populations. 80

Chapter Four

Methods

As stated in chapter one, the purpose of this study is to determine whether or not genetic variation may be contributing to the observed variation in growth identified among the Makushi. In order to complete this task, this study utilizes some of the anthropometric measurements, specifically height, and interview data collected by the project "Culture Change and Health Among the Makushi of Guyana" in 2000, 2001,

2005, and 2007 under the direction of Warren M. Wilson, and described in detail below.

From these data, only children under the age of 11 years were used as they would be classified as prepubescent, having not yet experienced the effects of pubescent growth spurts (Marshall and Tanner, 1986).

Ancestry, as discussed in chapter two, was initially determined via the village in which these children lived: it was assumed that children from Toka and Aranaputa were of mixed ancestry, whereas children from Wowetta, Kwatamang, Massara, Annai, and

Rupertee were of Amerindian ancestry based on data from Iwokrama (1999) and Forte

(1996). In order to test ancestry data in Iwokrama (1999) and Forte (1996), the author conducted interviews in these villages in May-June 2007 concerning the ancestry of families with children under the age of 11 years. As well, the author collected height data for those children in these villages who were not measured in 2005, and 2007. This was done so that the study would have a small subset of families in which ancestry was not assumed, but known.

As socio-economic status (SES) is known to influence growth (Oyhenart et al.,

2003; Foster et al., 2005; Bustos et al., 2001), this author conducted SES interviews in 81

May -June 2007 on the same subset of families as the pedigrees (discussed in detail below). This was done so that SES differences between families, and possibly villages, could be taken into consideration during the analysis of these data. If SES was a significant contributor to growth variation upon analysis of this small subset, then the results of the analyses on the data collected in the larger subsets in 2000, 2001, 2005, and

2007, where SES data were not available, may be called into question. The research presented in this study was conducted with approval from the Conjoint Faculties

Research Ethics Board at the University of Calgary (Appendix B), Guyana's Ministry of

Amerindian Affairs, Guyana's Environmental Protection Agency, and the North

Rupununi District Development Board, an organization which represents the villages in the region.

Anthropometric and Interview Data Collection

Following standard procedures (Lohman et al. 1988), anthropometric data were collected opportunistically in each village in May and June 2000, 2001, 2005, and 2007.

As school was in session during these visits, equipment was often set up in the village school, and all children attending school over the days of data collection were measured.

Participants were asked to remove shoes and any heavy pieces of clothing prior to measurement. Children were measured for height, weight, and sitting height. Height and sitting height were measured to the nearest 0.5 cm using a portable anthropometer (Seca

Road Rod). Weight was measured in light clothing to the nearest 500g with a mechanical scale (Seca model 761). After measurements were completed at the schools, equipment was set up in the village health post. The Makushi assistant (MRU) in each village then 82 went door to door asking families to come by the clinic to be measured. Infants and children younger then two years of age were measured for height in the recumbent position to the nearest 0.5 cm using a portable anthropometer (Seca Road Rod in 2000 and 2001, Seca 210 Soft Touch Baby Measure Mat in 2005 and 2007).

The date of birth for all individuals was determined by asking the participant, teacher, MRU, and parent, and, where possible, confirmed with village health post records. Parents, teacher and the MRU were asked to specify the number of children in the families of the participants, as well as the participant's birth order. The name of the child's mother was also recorded. In 2005 and 2007, parents, teacher and MRU were also asked whether or not the children's families had wage labour and, if yes, which family member or members had wage labour.

As discussed briefly in chapter two and above, this current study utilizes only the height measurements of children under the age of 11 years from seven villages: Toka,

Aranaputa, Rupertee, Annai, Wowetta, Kwatamang, and Massara. These seven villages were chosen, as mentioned above, because they share similar environments and

subsistence strategies, thus helping to control for potentially confounding variables. The

selection criteria resulted in the following sample sizes: 222 children in the 2000, 2001

data set, and 525 children in the 2005, 2007 data set.

Pedigrees and SES Interview

Prior to conducting the pedigree and SES interviews and the collection of

additional height data in each village, the author met with the village's MRU and

requested that she find families with children under the age of 11 years to participate in 83 these surveys. The original intent was to have measured children of mixed and

Amerindian descent in each of the villages. While working in Toka and Aranaputa, however, it became evident that there were either no families of purely Amerindian descent or only a few who were not of Makushi descent, but rather that of another

Amerindian group native to Guyana (i.e. Wapishana, Arawak, Aracuna). When asking the MRU's in Toka and Aranaputa about families with only Amerindian ancestry, the

MRU's invariably replied that everyone was mixed. This is consistent with the literature in which Aranaputa and Toka are stated to be mixed villages (Iwokrama, 1999; Forte,

1996). On the other hand, anecdotal evidence and the literature indicate that in Rupertee,

Annai, Wowetta, Kwatamang, and Massara the families are of Amerindian descent

(Iwokrama, 1999). The MRU's in the mixed villages of Toka and Aranaputa were instructed to find 13 families in Toka and 12 families in Aranaputa of mixed descent who had children under the ages of 11 years and were willing to participate in this study. In the Amerindian villages, MRU's were asked to find approximately five families with pure Makushi or Amerindian descent, who had children under the age of 11 and were willing to participate in the study. In total, in 2007 pedigrees were created and SES interviews conducted for 149 children from 48 families. All families were visited at their homes and paid 300 Guyanese dollars, approximately $1.50 US, for the pedigree and SES

interviews which generally lasted 15 to 20 minutes. Families were not told in advance that they would be compensated for their participation in the study.

The MRU was present for all interviews, facilitated the interview, and reminded the participants that they could withdraw from the study at any time without penalty.

Upon arrival at the house, the MRU and author explained the purpose of the study to the 84 participants. The participants were encouraged to ask questions if they wanted to know why certain questions were being asked, or why the study was being done. All questions asked in the SES interview can be found in Appendix A. In some cases, the MRU had to translate questions into Makushi, or rephrase questions to clarify them for the participant.

Participants were asked for their full name and that of their spouse and children. Those children who were under the age of 11 years and who had not previously had their height measured in 2005 or 2007 were measured as described above and their birth dates recorded. Where birth certificates were not available, birth dates were subsequently confirmed at the village health post. All answers were immediately written down by the author in her notebook and rewritten that evening. In 23% (n=l 1) of the interviews, the female and male heads of household were present. In 65% (n=31) of the interviews, only the female head of household was present. In 8% (n=4) of the interviews, only the male head of household was present. In 4% (n=2) of the cases only a grandparent of either the husband or wife was present.

For the pedigrees, the heads of the household were asked about their ethnicity, that of their mother and father, and their grandparents on both their mother's and father's side. When the male head of the household was not present, the female head of the household was asked for this information. In those instances when only the grandparent(s) or the male head of the household was present, they were asked the questions. Whenever the ethnicity of someone in the pedigree was not known, it was left blank.

SES interviews were conducted directly after the pedigree information had been obtained. These questions utilized the protocol designed by King and Mascie-Taylor 85

(2002) which was modified as to be relevant for the Makushi. When the husband was present he was also asked the question that pertained specifically to him and joined in answering the questions based on the family and household. Likewise, when only the grandparent(s) or male head of household was home they were asked all of the questions.

Answers to the SES questions were subsequently categorized to facilitate analysis

(Appendix A).

Data Analysis

NHANES III (Kuczmarski et al., 2002) was used to assess the growth patterns seen among Makushi children of Amerindian and mixed descent who were under the age of 11. All z-scores were calculated via the Standardized Height Calculator (2008) available online. An individual is classified as stunted if they fall more than two standard deviations below the mean height for their age and sex cohort (Keller and Fillmore, 1983;

WHO 1995). For the purpose of this study it was necessary to determine whether or not a relationship existed between ancestry and stunting. Chi-square tests are used to access whether or not two variables in a contingency table are independent from one another

(Schlotzhauer, 2007), and is used here to compare the frequency of stunting between the

Amerindian and mixed children. Chi-square was performed on this data using a two by two contingency table available online from GraphPad Software: Analyze, Graph, and

Organize Your Data (2007). Yates chi-square value was used in this study as it contains the Yates continuity correction designed to make the chi-square approximation more conservative. Directional p values were given for all chi-square tests as stunting only refers to one side of the normal distribution for height. Odds ratios were determined for each of the chi-square tests in order to determine whether children of Amerindian descent 86 or children of mixed descent had increased odds of being stunted. As well, the chi-square test was used to determine whether or not rates of stunting varied among the seven villages.

To facilitate data analysis, the dependent variable, height for age, was categorized as follows: not-stunted, stunted. Independent variables were categorized as follows: ancestry (Amerindian, mixed), village (Aranaputa, Toka, Massara, Annai, Kwatamang,

Wowetta, Rupertee), wage labor (none, father only, mother only, both parents, other), birth order (ordinal) and number of kids in the family (interval) where applicable. On the basis of Iwokrama (1999) and Forte (1996), the home village of the participant served as a proxy for ancestry in the 2000 and 2001, and 2005 and 2007 data, whereas the data generated in pedigrees were used to determine ancestry for the subset of 2007 data. Data generated in the 2007 SES interviews were categorized as shown in Appendix A.

A binary, step-wise, backwards logistic regression was used to determine the relative relationship between the independent and dependent variables for five reasons.

First, the dependent variable in this study is a binary classification of height for age; a child is either stunted or not-stunted. Second, logistic regression enables the researcher to ask ".. .which [independent] variables can be used to predict whether or not a person will exhibit the given dependent variable" (Simonoff, 2003). The purpose of this study is to determine which independent variables collected among the Makushi best predict

stunting, with a specific interest in whether or not ancestry is a significant predictor

variable. This regression test assists in this endeavor by producing a model of the

probability of success; that is, a model that best aids the researcher in predicting the

dependent variable (Simonoff, 2003). Third, this allows for the incorporation of 87 categorical independent variables (Simonoff, 2003). This is a necessity as several of the independent variables in this study are categorical. Forth, as a backwards, stepwise regression was used, this test tracks which variables are removed in each step of the regression and which predictor variables significantly aided in predicting stunting in each of the steps (e.g. Table 9). Fifth, this test shows changes in the predictive ability of the model as insignificant variables are removed in each step (e.g. Table 8).

Variables modeled in the binary, backwards step-wise logistic regression model for the 2000, 2001 data included the villages, ancestry, birth order, and number of kids in the family. Variables modeled for the 2005 and 2007 data included villages, ancestry, wage labour, birth order, and number of kids in the family. Before analysis of the 2007 data, a binary, backwards, step-wise logistic regression was performed on the SES variables in order to determine which SES variables best aided in predicting stunting.

Once this was determined, these variables were used to classify each of the children as being from low, medium or high SES families. As discussed below and seen in Tables

23-27 (chapter five and Appendix B), families did not end up having to be classified as such. This meant that the SES of the children was not included as a variable in subsequent analysis of the 2007 subset data. The binary, backwards, step-wise logistic regression for the 2007 subset data thus included the following variables: villages, ancestry, birth order, and number of children in the family. Unlike the 2000 and 2001 data and 2005 and 2007 data, the data collected by the author in 2007 concerning ancestry were based on the results of the pedigrees taken for each of the children's families rather than being based on the villages in which they lived. Two families from the Amerindian village of Rupertee were dropped out of the study as a result of the 88 pedigree analysis. Each of these families contained one great-grandparent who was only half Amerindian. If only one great-grandparent was half Amerindian, the present children who participated in this study would only contain 1/8 of the mixed genes and thus any affects of these non-Amerindian genes is likely to be lost. It was for this reason that families which fell into this category were dropped from the study, as they do not truly fit into either the mixed or Amerindian categories.

Independent t-tests were performed on all variables that were not categorical and deemed significant in the binary, backwards, step-wise, logistic regressions, in order to determine whether or not these variables were significantly different between children of

Amerindian descent and children of mixed descent. Independent t-tests were also performed on z-score data to determine whether or not the mean z-score differed between children of Amerindian and mixed descent in each of the data sets. The purpose of an independent t-test is to assess whether or not there is a statistically significant difference between the means of two unrelated groups (Schlotzhauer, 2007). In this study this test helps to ensure that any differences in the stunting rates between children of Amerindian descent and children of mixed descent were not actually caused by differences in variables other than ancestry.

Alpha was set at 0.05. The Chi-square tests for rates of stunting between children of Amerindian descent and mixed descent were conducted using GraphPad Software

(2007). All other tests were conducted using SPSS Graduate Pack 15.0 (2006). 89

Chapter Five

Results

2000 and 2001 Data

Twelve and a half percent of the children of mixed ancestry are stunted and 30.5 percent of the children of Amerindian ancestry are stunted (%2= 5.332, p= 0.011). Odds- ratio analysis shows that children of Amerindian descent are 3.07 times more likely to be stunted than those children of mixed descent (Figure 1). Mean z-scores differ significantly for children of Amerindian and mixed descent (t—2.356, p<0.05, Table 3), with children of Amerindian descent having a mean z-score of-1.46, and children of mixed descent having a mean z-score of-0.90 (Figure 2).

100% Stunting • Not Stunted iH Stunted

75% -i Bars show percents

C P 50% a

25% "1

Amerindian Ancestry

Figure 1: Percentage of Children Who are Stunted and Not Stunted in Children of Both Mixed and Amerindian Descent for 2000 and 2001 Data. 90

Independent Samples Test Levene's Test for [quality of Variances t-test for Equality of Means 95% Confidence Interval of the Mean Std. Error Difference F Sig. t df Jig. (2-tailed) Difference Difference Lower Upper z-score Equal variana .068 .795 -2.356 220 .019 -.55629 .23613 •1.02166 -.09091 assumed Equal variancf not assumed -2.621 88.562 .010 -.55629 .21220 -.97796 -.13461 TABLE 3. Results of Independent t-test for Mean z-score of Children of Mixed and Amerindian Descent for 2000 and 2001 Data.

-0.90-

-1.00-

-1.10- 1 O n z-scor e

E

-1.30-

-1.40-

-1.50-J -----'•""' —^f •..-.viivy-,;:-•:- i Amerindian Mixed Ancestry

Figure 2: Mean z-score in Children of Mixed and Amerindian Descent for 2000 and 2001 Data.

For the backwards, stepwise, logistic regression, 176 (79,3%) of the 222 children surveyed were included in this analysis. Of the variables analyzed: ancestry (mixed or

Amerindian), village, birth order, and number of children in the family (expressed as No. of Kids in Family in all tables and figures), data were missing for one or more of these 91 variables for 46 children, or 20.7% of the total sample. Those children with missing variables were removed from the analysis.

From the Omnibus tests of model coefficients (Table 4) the exclusion of the variable, villages, in step 2 were justified as their significance of change value was greater then 0.10 (i.e. 0.154 - 0.003 > 0.10). The significance of change value for birth order in step 3 is not quite 0.10 and therefore it may or may not be justified. The Hosmer and Lemeshow Test (Table 5) shows that step 1 produces a chi square of 6.578 (p=

0.583), step 2: 10.562 (p=0.228) and step 3: 14.598 (p= 0.067) meaning that none of the models produced in step 1 through step 3 produce significantly good models for predicting stunting at p<0.05. It should be noted here that step 3 (Table 6) produced the best model as the percentage of stunting correctly predicted dropped less then a percent from step 2, when the variable birth order was removed, therefore model three, which contains only the independent variables ancestry and number of children in the family is the best model as it maintains its accuracy with the least number of independent variables.

Omnibus Tests of Model Coefficients

Chi-square df Sig. Step 1 Step 23.162 8 .003 Block 23.162 8 .003 Model 23.162 8 .003 Step 2 a Step -8.041 5 .154 Block 15.120 3 .002 Model 15.120 7 .034 Step 3 a Step -1.340 1 .247 Block 13.780 2 .001 Model 13.780 2 .001 a. A negative Chi-squares value indicates that the Chi-squares value has decreased from the previous step.

TABLE 4. Determining if Removal of Variables in Each Step was Justified for 2000 and 2001 Data 92 Hosmer and Lemeshow Test Step Chi-square df Sig. 1 6.578 8 .583 2 10.562 8 .228 3 14.598 8 .067 TABLE 5. Significance of Regression Model in Each Step for 2000 and 2001 Data

Classification Table Predicted

Stunting Percentage Observed Not Stunted Stunted Correct Step 1 Stunting Not Stunted 127 4 96.9 Stunted 33 12 26.7 Overall Percentage 79.0 Step 2 Stunting Not Stunted 129 2 98.5 Stunted 36 9 20.0 Overall Percentage 78.4 Step 3 Stunting Not Stunted 129 C M O 98.5 Stunted 37 17.8 Overall Percentage 77.8 a. The cut value is .500

TABLE 6. Ability of Regression Model to Predict Stunted and Not Stunted Children in Each Step for 2000 and 2001 Data.

The elimination of the independent variables, villages and birth order, and the inclusion of the independent variables, ancestry and number of children in the family, were also supported by the regression model (Tables 7-9, Table 9 in Appendix B). The variables ancestry and number of children in the family give a statistically significant

Wald statistics (p<0.05) in all steps (1-3) (Table 7), meaning that these variables are useful in all the models produced in these three steps. The variables, villages and birth order, did not give statistically significant Wald statistics, and are thus not useful variables in any of the models produced in these three steps. Of the statistically significant variables, the Exp(B) column in Table 7 shows that the number of children in 93 the family had values greater then 1.000 in all steps, indicating that increased values of

this variable correspond with increased odds of being stunted. The variable ancestry had

values less then 1.000 in all steps and therefore children of Amerindian descent had

increased odds of being stunted. Table 8 confirms that only the variables ancestry and

number of children in the family significantly contribute to the model, whereas the

variables villages and birth order should be removed from the model as they had

significance levels greater then p<0.05 in all steps.

Variables in the Equation

B S.E. Wald df Sig. Exp(B) S^ep Ancesty(1) 1.904 .978 3.792 1 .051 6.715 1 Villages 7.344 5 .196 Villages(1) -.752 .897 .703 .402 .471 Villages(2) -2.018 .971 4.320 .038 .133 Villages(3) -1.445 .911 2.518 .113 .236 Villages(4) -.761 .963 .624 .430 .467 Villages(5) -.682 .877 .603 .437 .506 BirthOrder -.257 .174 2.191 .139 .774 No.KidsinFamily .484 .183 7.017 .008 1.623 Constant -3.062 .719 18.142 .000 .047 Sjep Ancesty(1) 1.091 .495 4.848 .028 2.976 2 BirthOrder -.188 .163 1.338 .247 .829 No.KidsinFamily .381 .165 5.298 .021 1.463 Constant -3.097 .639 23.466 .000 .045 SJep Ancesty(1) 1.157 .492 5.534 .019 3.181 3 No.KidsinFamily .212 .076 7.764 .005 1.236 Constant -3.043 .635 22.953 .000 .048 a. Variable(s) entered on step 1: Ancesty, Villages, BirthOrder, No.KidsinFamily. TABLE 7. Variables in the Model and Their Usefulness in Each Step of the Regression for 2000 and 2001 Data. 94 Model if Term Removed

Change in Model Log -2 Log Sig. of the Variable Likelihood Likelihood df Change Step Ancesty -90.305 3.662 1 .056 1 Villages -92.494 8.041 5 .154 BirthOrder -89.576 2.205 .138 No.KidsinFamily -92.090 7.232 .007 Step Ancesty -95.312 5.635 .018 2 BirthOrder -93.164 1.340 .247 No.KidsinFamily -95.183 5.378 .020 Step Ancesty -96.433 6.536 .011 3 No.KidsinFamily -97.224 8.120 .004 TABLE 8. Variables Removed in Each Step of the Regression and Their Significance to the Model for 2000 and 2001 Data

The mean number of children in the families of non-stunted and stunted children of Amerindian descent was approximately 4 children and 5 children respectively (Figure

2). The mean number of children in the families of non-stunted and stunted children of mixed descent was approximately 5 children and 6 children respectively (Figure 3). 95

Stunting • Not Stunted H Stunted

Amerindian Ancestry

Figure 3: Mean Number of Children in the Families of Stunted and Non-Stunted Children of Mixed and Amerindian Descent for 2000 and 2001 Data.

From the logistic regression discussed above and seen in Tables 4-9, the number of children in the family is a variable that is useful in predicting stunting. It is thus possible that the observed variation in the incidence of stunting between children of mixed and Amerindian descent may not actually be a result of genes, but rather due to the fact that households of children of Amerindian descent have, on average, more children.

However, there was not a significant difference in the mean number of children in the families of the children of mixed and Amerindian descent (t=-0.357, p>0.05, Table 10).

Equality of variance could not be assumed between children of mixed and Amerindian descent in this data set, as is evident in the results of the Levene's test (F=4.16, p<0.05,

Table 10). This may be the result of the difference in the larger number of children of 96 Amerindian descent versus children of mixed descent in this sample, as Equality of

Variance was assumed in the 2005 and 2007, and 2007 subset to be discussed below.

Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Sig. Mean Std. Error Difference F Sig. t df (2-tailed) Difference Difference Lower Upper No. Kids Equal variances 4.160 .043 -.357 174 .722 -.148 .414 -.966 .670 in Family assumed Equal variances -.330 69.440 .742 -.148 .447 -1.040 .745 not assumed

TABLE 10. Results of Independent t-test for Number of Kids in the Families of Children of Mixed and Amerindian Descent for 2000 and 2001 Data.

Of the children in Rupertee, 45.5% were stunted, 22.7% in Kwatamang, 32.6% in Annai, 41.4% in Wowetta, 14.8% in Massara, 12.9% Aranaputa and 11.8% in Toka

(Figure 4). The frequency of stunting between villages differs significantly (x = 15.401, p<0.05, Table 11). 97

Stunting • Not Stunted • Stunted

Bars show percents

Annai Kwatamang Rupertee Wowetta Arunaputa Massara Toka Village

Figure 4: Percentage of Stunted and Not Stunted Children in Each of the Seven Villages for 2000 and 2001 Data.

Chi-Square Tests

Asymp. Sig. Value df (2-sided) Pearson Chi-Square 15.401a 6 .017 Likelihood Ratio 15.725 6 .015 N of Valid Cases 222 a. 1 cells (7.1%) have expected count less than 5. The minimum expected count is 4.52. TABLE 11. Chi-Square Tests for Frequency of Stunting in Each of the Seven Villages 98

2005 and 2007 Data

Eight point four percent of children of mixed ancestry are stunted and 17.3 percent of children of Amerindian ancestry are stunted (%2= 5.811, p= 0.008). Odds-ratio analysis shows that children of Amerindian descent are 2.28 times more likely to be stunted than those children of mixed descent (Figure 5). Mean z-scores differ significantly for children of Amerindian and mixed descent (t=3.014, p<0.05, Table 12), with children of Amerindian descent having a mean z-score of-1.00, and children of mixed descent having a mean z-score of-0.59 (Figure 6).

ioo%- Stunting • Not Stunted • Stunted

75%' Bars show percents

C 0) H 50%' 0)

25% "I

0%J Mixed Amerindian Ancestry

Figure 5: Percentage of Children who are Stunted and Not Stunted of Mixed and Amerindian Descent for 2005 and 2007 Data. 99 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Mean Std. Error Difference F Sig. t df Sig. (2-tailed) Difference Difference Lower Upper z-score Equal variances 1.530 .217 3.014 524 .003 .40779 .13531 .14198 .67360 assumed Equal variances 383.989 .000 not assumed 3.592 .40779 .11353 .18457 .63102 TABLE 12. Results of Independent t-test for Mean z-score of Children of Mixed and Amerindian Descent for 2005 and 2007 Data.

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: : ::: y : : -1.20- :iii -;^;.*iS:'^S^ kS:afes:iiSfj j^^^ra^;:^^ ^: - '^ •-'•'•••:' i i Mixed Amerindian Ancestry Figure 6: Mean z-score of Children of Mixed and Amerindian Descent for 2005 and 2007 Data.

For the backwards, stepwise, logistic regression, 511 (97.3%) of the total sample size of the 522 children surveyed were included in this analysis. Of the variables analyzed: ancestry (mixed or Amerindian), village, birth order, and number of children in the family, data were missing for one or more of these variables for 14 children, or 2.7% 100 of the total sample. Those children with missing variables were removed from the analysis.

From the Omnibus tests of model coefficients (Table 13) the exclusion of the variable birth order in step 2 is justified as its significance of change value was greater then 0.10 (i.e. 0.983-0.000> 0.10). The significance of change value for wage labor in step 3 is not quite 0.10 and therefore it may or may not be justified. The Hosmer and

Lemeshow Test (Table 14) shows that step 1 produces a chi square of 6.295 (p= 0.614), step 2: 5.998 (p=0.647) and step 3: 8.712 (p= 0.367) meaning that once again none of the models produced in step 1 through step 3 produce significantly good predictor models at p<0.05. It should be noted here that step 3 (Table 15) produced the best model as the percentage of stunting correctly predicted did not drop from step 2 or from step 1 when the variable birth order and wage labor were removed. This confirms that model three, which contains only the independent variables ancestry, village and number of children in the family, appears to produce the best model for predicting stunting as it maintains its accuracy with the least number of independent variables.

Omnibus Tests of Model Coefficients

Chi-square df Sig. Step 1 Step 43.367 12 .000 Block 43.367 12 .000 Model 43.367 12 .000 Step 2a Step .000 1 .983 Block 43.366 11 .000 Model 43.366 7 .000 Step 3a Step -1.277 4 .865 Block 42.090 7 .000 Model 42.090 6 .000 a. A negative Chi-squares value indicates that the Chi-squares value has decreased from the previous step. TABLE 13. Determining if Removal of Variables in Each Step was Justified for 2005 and 2007 Data 101

Hosmer and Lemeshow Test

Step Chi-square df Sig. 1 6.295 8 .614 2 5.998 8 .647 3 8.712 8 .367 TABLE 14. Significance of Regression Model in Each Step for 2005 and 2007 Data

Classification Table Predicted

Stunting Percentage Observed Not Stunted Stunted Correct Step 1 Stunting Not Stunted 434 2 99.5 Stunted 71 4 5.3 Overall Percentage 85.7 Step 2 Stunting Not Stunted 434 2 99.5 Stunted 71 4 5.3 Overall Percentage 85.7 Step 3 Stunting Not Stunted 434 2 99.5 Stunted 71 4 5.3 Overall Percentage 85.7 a. The cut value is .500 TABLE 15. Ability of Regression Model to Predict Stunted and Not Stunted Children in Each Step for 2005 and 2007 Data.

The elimination of the independent variables, birth order and wage labor, and the inclusion of the independent variables, ancestry, village and number of children in the family were also supported by the findings of Tables 16-18 (Table 18 in Appendix B).

The variables ancestry, village, and number of children in the family give a statistically significant Wald statistics (p<0.05) in all steps (1-3) (Table 16), meaning that these variables are useful in the models produced in these three steps. The variables, birth order and wage labor, did not give statistically significant Wald statistics, and are thus not 102 useful variables in the models produced in these three steps. Of the statistically significant variables, the Exp(B) column in Table 16 shows that the number of children in the family had values greater then 1.000 in all steps, indicating that increased values of this variable corresponds with increased odds of being stunted. The variable ancestry had values less then 1.000 in all steps and therefore children of Amerindian descent had increased odds of being stunted. Table 17 confirms that only the variables ancestry, village and number of children in the family significantly contribute to the model, whereas the variables birth order and wage labor should be removed from the model as they had significance levels greater then p<0.05 in all steps. B S.E. Wald df Sig Exp(B)

Satep Birth_Order .002 .098 .000 .983 1.002 1 No._Kids_ln_Family .223 .099 5.076 .024 1.250 Wage_Labour 1.235 4 .872 Wage_Labor(1) .412 1.096 .141 .707 1.509 Wage_Labor(2) .037 1.120 .001 .974 1.038 Wage_Labor(3) .361 1.162 .096 .756 1.434 Wage_Labor(4) .249 1.169 .045 .831 1.283 Ancestry(1) -1.74E .690 6.402 .011 .175 Village 17.89^ .003 Village(1) 1.174 .708 2.750 .097 3.235 Village(3) -1.706 .634 7.250 .007 .182 Village(4) .564 .441 1.634 .201 1.757 Village(5) -.063 .485 .017 .897 .939 Village(6) -.074 .467 .025 .873 .928 Constant -2.872 1.155 6.185 .013 .057

SatepNo._Kids_ln_Family .225 .059 14.587 .000 1.252 2 Wage_Labor 1.236 4 .872 Wage_Labor(1) .410 1.095 .140 .708 1.507 Wage_Labor(2) .036 1.119 .001 .974 1.037 Wage_Labor(3) .359 1.159 .096 .757 1.432 Wage_Labor(4) .248 1.167 .045 .832 1.281 Ancestry(1) -1.74E .690 6.403 .011 .175 Village 17.89: .003 Village(1) 1.174 .708 2.751 .097 3.235 Village(3) -1.707 .632 7.287 .007 .181 Village(4) .563 .439 1.642 .200 1.756 Village(5) -.062 .484 .016 .898 .940 Village(6) -.075 .467 .026 .873 .928 Constant -2.872 1.155 6.185 .013 .057

SatepNo._Kids_ln_Family .233 .058 16.147 .000 1.263 3 Ancestry(1) -1.720 .687 6.265 .012 .179 Village 17.544 5 .004 Village(1) 1.180 .705 2.799 .094 3.253 Village(3) -1.560 .611 6.517 .011 .210 Village(4) .659 .423 2.424 .119 1.932 Village(5) .000 .473 .000 1.000 1.000 Village(6) .036 .452 .007 .936 1.037 Constant -2.684 .468 32.837 .000 .068 Vaaiable(s) entered on step 1: Birth_Order, No._Children_ln_Family, Wage_Labour,

TABLE 16. Variables Removed in Each Step of the Regression and Their Significance to the Model for 2005 and 2007 Data 104

Model if Term Removed

Change in Model Log -2 Log Sig. of the Variable Likelihood Likelihood df Change Step BirthjDrder -191.438 .000 1 .983 1 No._Kids_ln_Family -193.932 4.988 1 .026 Wage_Labour -192.075 1.275 4 .866 Ancestry -195.393 7.911 1 .005 Village -203.420 23.965 5 .000 Step No._Kids_ln_Family -199.002 15.129 1 .000 2 Wage_Labour -192.076 1.277 4 .865 Ancestry -195.394 7.911 1 .005 Village -203.421 23.967 5 .000 Step No._Kids_ln_Family -200.435 16.717 1 .000 3 Ancestry -195.945 7.736 1 .005 Village -203.741 23.328 5 .000

TABLE 17. Variables Removed in Each Step of the Regression and Their Significance to the Model for 2005 and 2007 Data.

Of children in Rupertee, 18.8% were stunted, 18.5% in Kwatamang, 26.4% in

Annai, 18.9% in Wowetta, 4.5% in Massara, 10.7% in Aranaputa and 5% in Toka (Figure

7). The frequency of stunting between villages differs significantly (x2= 25.125, p<0.05,

Table 19). 100% Stunting • Not Stunted • Stunted

75% Bars show percents

C Q.

o%- Aranaputa Massara Kwatamang Rupertee Toka Annai Wowetta Village

Figure 7: Percentage of Stunted and Not Stunted Children in Each of the Seven Villages for 2005 and 2007 Data.

Chi-Square Tests

Asymp. Sig. Value df (2-sided) Pearson Chi-Square 25.125a 6 .000 Likelihood Ratio 27.247 6 .000 Linear-by-Linear 1 .003 Association 8.720 N of Valid Cases 526 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 8.90. TABLE 19. Chi-Square Tests for Frequency of Stunting in Each of the Seven Villages 106 The mean number of children in the families of non-stunted and stunted children of Amerindian descent was approximately 4 children and 6 children respectively

(Figure 7). The mean number of children in the families of non-stunted and stunted children of mixed descent was approximately 5 children and 5 children respectively

(Figure 7).

Stunting B Not Stunted • Stunted

Amerindian Ancestry

Figure. 7: Mean Number of Children in the Families of Stunted and Non-Stunted Children of Mixed and Amerindian Descent for 2005 and 2007 Data.

From the logistic regression discussed above and seen in Tables 13-18, the number of children in the family is a variable that is useful in predicting stunting. Once again, as was evident in the 2000 and 2001 data, it is thus possible that the observed variation in the incidence of stunting between children of mixed and Amerindian descent may not actually be a result of genes, but rather due to the fact that households of 107 children of Amerindian descent have, on average, more children. However, there was not a significant difference in the mean number of children in the families of children of mixed and Amerindian descent (t= 1.626, p>0.05, Table 20). Equality of variance was assumed, as is evident in the results of the Levene's test (F=0.787, p>0.05, Table 20).

Independent Samples Test Levene's Test for Equality of Variance t-test for Equality of Means Vlean 95% Confidence Difference 3td. Error Interval of the Sig. Difference Difference F Sig. t df (2-tailed) Lower Upper No._Kids_ Equal variances .787 .37E 1.626 513 .104 .368 .226 -.076 .812 ln_ assumed Family Equal variances 1.576 237.2; .116 .368 .233 -.092 .827 not assumed

TABLE 20. Results of Independent t-test for Number of Kids in the Families of Children of Mixed and Amerindian Descent for 2005 and 2007 Data.

2007 Subset Data

Seven point six percent of the children of mixed ancestry are stunted and 19.3 percent of the children of Amerindian ancestry are stunted (x2= 5.811, p= 0.008). Odds- ratio analysis shows that children of Amerindian descent are 2.91 times more likely to be stunted then those children of mixed descent (Figure 8). Mean z-scores differ significantly for children of Amerindian and mixed descent (1=2.194, p<0.05, Table 21), with children of Amerindian descent having a mean z-score of-1.15, and children of mixed descent having a mean z-score of-0.74 (Figure 9). 100%-l Stunting • Not Stunted • Stunted

75%" Bars show percents

C Q) O 50%' 0 a.

25%"

0%-" Mixed Amerindian Ancestry

Figure 8: Percentage of Children Who are Stunted and Not Stunted in Children of Mixed and Amerindian Descent for 2007 Subset Data.

Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Mean Std. Error Difference F Sig. t df Sig. (2-tailed) Difference Difference Lower Upper z score Equal variance .173 .678 2.194 147 .030 .40697 .18547 .04044 .77351 assumed Equal variance 2.231 .027 .40697 .18238 .04650 .76745 not assumed 144.673 TABLE 21. Results of Independent t-test for Mean z-score of Children of Mixed and Amerindian Descent for 2007 Subset Data. 109

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£ O -0.90- o W N c re o S

-1.00-

-1.10-

Mixed Amerindian Ancestry

Figure 9: Mean z-score of Children of Mixed and Amerindian Descent for 2007 Subset Data.

Tables 22 through 26 show the results of a backwards, step-wise, logistic regression performed on the SES variables collected from the parents of each of the 149

Makushi children measured. There were no missing data and thus no children were excluded from the analysis.

The Hosmer and Lemeshow Test (Table 22) shows that step 1 produces a chi- square of 2.691 (p= 0.952), and step 2: 0.814 (p=0.999) meaning that none of the models produced in step 1 through step 2 produce significantly good predictor models at p<0.05.

With a significance of 0.952 and 0.999, it becomes evident that both models produced in this analysis are very poor and are thus of little, if any, use in predicting stunting. From

Table 23 we can see that the models produced in this logistic regression predicted 110 stunting correctly 93.3%. When this percentage is taken into consideration with the results of Table 24 (Appendix B), it suggests that this high percentage is likely due more to chance then to either of the steps being good models for predicting stunting. Table 25 and Table 26 (Appendix B) show that none of the SES variables measured significantly aided in predicting stunting, as no variables are significant at p<0.05.

Step Chi-square df Sin- 1 2.691 .952

2 .814 0 .999 TABLE 22. Significance of Regression Model in Each Step for 2007 Subset SES Data

Predicted Stunting Percentage Observed Not Stunted Stunted Correct Step 1 Stunting Not Stunted 129 1 99.2 Stunted 9 10 52.6 Overall Percentage 93.3 Step 2 Stunting Not Stunted 128 2 98.5 Stunted 8 11 57.9 Overall Percentage 93.3 a The cut value is .500 TABLE 23. Ability of Regression Model to Predict Stunted and Not Stunted Children in Each Step for 2007 Subset SES Data.

Backward, step-wise, logistic regression to determine the best model for predicting stunting, among Makushi children from whom specific pedigree data was available, can be seen in Tables 27-31. There were no missing data and thus no children were excluded from the analysis. The Hosmer and Lemeshow Test (Table 28) shows that step 1 produces a chi square of 8.452 (p= 0.391), meaning that the model produced is not a significantly good predictor of stunting (p<0.05). Ill Omnibus Tests of Model Coefficients

Chi-square df Sig. Step 1 Step 25.241 0 C O .001 Block 25.241 .001 Model 25.241 .001 TABLE 27. Determining if Removal of Variables in Each Step was Justified for 2007 Subset Data

Hosmer and Lemeshow Test

Step Chi-square df Sig. 1 8.452 8 .391 TABLE 28. Significance of Regression Model in Each Step for 2007 Subset Data

Classification Table

Predicted

Stunting Percentage Observed Not Stunted Stunted Correct Step 1 Stunting Not Stunted 125 3 97.7 Stunted 15 6 28.6 Overall Percentage 87.9 a. The cut value is .500 TABLE 29. Ability of Regression Model to Predict Stunted and Not Stunted Children in Each Step for 2007 Subset Data.

The inclusion of the independent variables ancestry, village, birth order and number of children in the family into the model was also supported by the findings of

Table 30 and Table 31. The variables ancestry, birth order and number of children in the family all give a statistically significant Wald statistics (p<0.05) (Table 30), meaning that that these variables are useful in the model. Villages did not give a statistically significant

Wald statistic, meaning that the variable is likely not significant to the model. Although the variable villages was not seen as significantly aiding the model, as evident from the

Wald statistic, Table 31, discussed below, does show that the variable villages should still 112 be included in the model. Of the statistically significant variables, the Exp(B) column in Table 30 shows that the number of children in the family had values greater then 1.000 indicating that increased values of this variable corresponds with increased odds of being stunted. The variable ancestry and birth order had values less than 1.000 in all steps and therefore children of Amerindian descent had increased odds of being stunted as well as children with smaller birth orders (first born children). Table 31 confirms that the variables ancestry, village, birth order and number of children in the family significantly contribute to the model and thus none of the variables should be removed.

Variables in the Equation

B S.E. Wald df Sig. Exp(B) S^ep Ancestry(1) -1.833 .963 3.622 1 .057 .160 1 Village 8.534 5 .129 Village(1) .231 .915 .064 .800 1.260 Village(2) .046 .975 .002 .962 1.047 Village(3) -2.711 1.195 5.146 .023 .066 Village(4) -2.077 1.218 2.908 .088 .125 Village(5) -.454 .864 .276 .599 .635 Birth Order -.725 .261 7.704 .006 .484 No._Kids_ln_Family .699 .259 7.292 .007 2.011 Constant -1.492 1.051 2.013 .156 .225 a. Variable(s) entered on step 1: Ancestry, Village, Birth_Order, No._Kids_ln_Family. TABLE 30. Variables Removed in Each Step of the Regression and Their Significance to the Model for 2007 Subset Data

Model if Term Removed

Change in Model Log -2 Log Sig. of the Variable Likelihood Likelihood df Change Step Ancestry -50.003 4.060 1 .044 1 Village -54.044 12.142 5 .033 Birth_Order -52.248 8.550 1 .003 No._Kids_ln_Family -51.870 7.795 1 .005 TABLE 31. Variables Removed in Each Step of the Regression and Their Significance to the Model for 2007 Subset Data. 113 The mean number of children in the families of non-stunted and stunted children of Amerindian descent was approximately 5 children and 6 children respectively

(Figure 10). The mean number of children in the families of non-stunted and stunted children of mixed descent was approximately 5 children and 6 children respectively

(Figure 10).

Stunting • Not Stunted

Ancestry Figure 10: Mean Number of Children in the Families of Stunted and Non-Stunted Children of Mixed and Amerindian Descent for 2007 Subset Data.

From the logistic regression discussed above and seen in Tables 27-31, the number of children in the family is a variable that is useful in predicting stunting. As evident in the 2000 and 2001, and 2005 and 2007 data, it is possible that the observed variation in the incidence of stunting between children of mixed and Amerindian descent may not actually be a result of genes, but rather due to the fact that households of children of Amerindian descent have, on average, more children. However, there was not 114 a significant difference in the mean number of children in the families of the children of mixed and Amerindian descent (t=-0.630, p>0.05, Table 32). Equality of variance is assumed between children of mixed and Amerindian descent in this data set, as it evident in the results of the Levene's test for equality of variance (F= 3.524, p>0.05, Table 32).

Independent Samples Test

Levene's Test for [ quality of Variances t-test for Equality of Means 95% Confidence Interval of the Sig. Mean Std. Error Difference (2-tailed) F Sig. t df Difference Difference Lower Upper No._Kids_ Equal variances ln_Family assumed 3.524 .062 -.630 147 .530 -.249 .395 -1.030 .532 Equal variances -.612 119.74* .542 -.249 .407 -1.055 .557 not assumed

TABLE 32. Results of Independent t-test for Number of Kids in the Families of Children of Mixed and Amerindian Descent for 2007 Subset Data.

The mean birth order of non-stunted and stunted children of Amerindian descent was approximately 4 and 4 respectively (Figure 11). The mean birth order for non-stunted and stunted children of mixed descent was approximately 4 and 4 respectively (Figure

11). 115

Stunting • Not Stunted H stunted

Amerindian Ancestry Figure 11: Mean Birth Order in the Families of Stunted and Non-Stunted Children of Mixed and Amerindian Descent for 2007 Subset Data.

It is possible that observed variation in stunting between children of mixed and

Amerindian descent may actually be the result of children of Amerindian descent being on average of a lower birth order than children of mixed descent. However, there was no significant difference in birth order between children with mixed and Amerindian descent

(t= -0.127, p>0.05, Table 33). Equality of variance is assumed between children of mixed and Amerindian descent in this data set, as determined from the Levene's test for equality of variance (F=3.218, p>0.05, Table 33). 116 Independent Samples Test Levene's Test for Equality of Variance t-test for Equality of Means 95% Confidence Interval of the Sig. Mean Std. Error Difference F Sig. t df (2-tailed) Difference Difference Lower Upper Birth_ Equal variances 3.218 .075 -.127 147 .899 -.050 .394 -.829 .729 Order assumed Equal variances -.124 122.430 .902 -.050 .404 -.850 .750 not assumed

TABLE 33. Results of Independent t-test for the Birth Order of Children of Mixed and Amerindian Descent for 2007 Subset Data.

Of the children in Rupertee 37.5% were stunted, 22.2% in Kwatamang,

5.8% in Annai, 40.0% in Wowetta, 4.3% in Massara, 6.8% Aranaputa and 8.3% in Toka

(Figure 12). The frequency of stunting between villages differs significantly (% = 18.744, p<0.05, Table 34)

Stunting I I Not Stunted • Stunted

75% -i Bars show percents I

mSr t HUH E3ZI Kwatamang Rupertee Annai Aranaputa Village

Figure 12: Percentage of Stunted and Not Stunted Children in Each of the Seven Villages for 2007 Subset Data. 117

Chi-Square Tests

Asymp. Sig. Value df (2-sided) Pearson Chi-Square 18.744a 6 .005 Likelihood Ratio 16.446 6 .012 Linear-by-Linear .782 1 .377 Association N of Valid Cases 149 a. 6 cells (42.9%) have expected count less than 5. The minimum expected count is 1.41. TABLE 34. Chi-Square Tests for Frequency of Stunting in Each of the Seven Villages 118 Chapter Six

Discussion

The purpose of this study is to assess the etiology of variation in growth retardation, specifically low height for age, among children less than 11 years of age, living in similar environments, in seven villages in Guyana's North Rupununi region, who have either Amerindian or mixed ancestry. To address this question, this study has used several statistical models to assess anthropometric, SES, and other data from three recent rounds of data collection. On the basis of both literature and data collected, villages were used as markers of ancestry. Consistent with the majority of the literature on growth in childhood, this study hypothesized that there would be no difference in the rates of stunting between children of Amerindian and mixed ancestry. The analysis presented here fails to support this hypothesis; for all three data sets (2000 and 2001,

2005 and 2007, and 2007 subset) children of Amerindian ancestry had mean height-for­ age z-scores which were significantly lower than those of children of mixed descent and children of Amerindian ancestry were stunted significantly more often than those of mixed ancestry. Moreover, the results suggest that, of all of the independent variables considered here, ancestry is the variable that best explains the variation in stunting rates observed among these cohorts.

In the 2005 and 2007 data as well as the 2007 subset data, villages were also a significant variable in the stunting model, however, as mentioned in chapter five, this result is to be expected due to the way ancestry appears to be split according to village. In the 2007 subset data, birth order proved to be a significant variable in the equation, however, this may be due to the smaller sample size in the 2007 subset data (n=149 119 children), as birth order was dropped as a significant variable in both the 2000 and

2001 data and the 2005 and 2007 data, both of which had larger sample sizes.

There is a significant difference between the stunting rates of all seven villages in all data sets (Tables 11, 19, and 34). This result is to be expected, as the villages children lived in were used to determine ancestry. For example, children from Toka and

Aranaputa were considered to be of mixed descent, whereas children from the other five villages were considered to be of Amerindian descent. Even in the 2007 subset where pedigrees were used to determine ancestry, ancestry followed the above village

designations. Interestingly, however, for the 2000 and 2001 logistic regression, the variable village was not considered a significant variable in the model produced. Due to the fact that ancestry is based on village, one would expect villages to have been included

in the model as can be seen in the logistic regressions of the 2005 and 2007 (Tables 13-

18, Table 18 in Appendix B) and 2007 subset data (Tables 27-31).

Surprisingly, SES did not appear to be playing a role in the stunting of Makushi

children (Tables 22-26, Tables 24 and 26 in Appendix B), and was thus not considered in

the 2007 subset data logistic regression to determine the best model for predicting

stunting. Many studies have linked growth to SES (King and Mascie-Taylor, 2002; Liu et

al., 1998; Vella et al., 1994; WHO, 1995). It may be that the sample size in this study was

too small to detect the influence of SES differences on growth. It may also be that among

the Makushi, SES does not vary significantly between villages. In these villages it was

often evident that having the money to purchase something didn't always mean that the

desired product was available for purchase. For example, although some Makushi have

propane stoves, there is often no propane available within the area. It is also not always 120 clear which possession is indicative of a higher SES. For example, Makushi homes are generally roofed with palm thatch, but some are roofed with zinc. If one were to ask the Makushi the cost of each type of roof, they would most likely state that a zinc roof cost more. Palm thatch can be collected for free from stands of Mauritia flexuosapalms .

However, palm thatch may actually be indicative of higher SES. This is because a thatch roof keeps a house cooler and, during downpours, quieter. As well, in the long run, a thatch roof is more expensive as they need to be replaced more often than zinc in a process that is very labor intensive. Therefore although a zinc roof may be more expensive in the short term, in the long term having a thatch roof could actually be more costly. It therefore seems that the choice to have a thatch or zinc roof is more based on personal preference than on SES in many circumstances; some Makushi like the cooler house which results from a thatch roof, while others prefer the zinc roof which does not need to be replaced as often.

Although number of children in the family was a useful variable in all models for predicting stunting, the difference between the number of children in the families of children of mixed and Amerindian descent was not significant (Tables 10, 20, and 32), meaning that number of children in the family is not accounting for the differences seen in stunting rates between these two groups. This was also true of birth order which was found to be a useful variable for predicting stunting. However, the difference in the mean birth order of children of mixed and Amerindian descent was not significant (Table 33).

This leaves the variable ancestry as the only variable left to explain the variation in stunting rates seen between these groups. Notably, these results are consistent with the findings of Stinson (1996), in which she found Afro-Ecuadorian children to be 121 significantly taller than the Chachi Amerindian children who live in the same region and have very similar lifestyles and subsistence strategies.

While the functional implications of short stature among the Makushi is unclear, one can speculate about this phenotype in light of the potential explanations presented in chapter three. As Bergman (1847) and Allen (1877) suggested, in response to heat stress one might expect a warm-blooded animal occupying a warm climate, such as that found in central Guyana, to have a smaller body size and increased surface area relative to its conspecifics in cooler climates (Newman, 1953). Indeed, Newman's (1953) study of indigenous peoples of the Americas documents that, with the exception of inhabitants of the arctic, stature increased as one moved away from the equator. Despite criticisms of

Bergmann's (1847) and Allen's (1877) rules (Steegman, 2007), Katzmarzyk and

Leonard's (1998) study on human body size and proportions also documents that the body size and proportions of humans is consistent with Bergman's and Allen's rules.

Among 62 groups of South American Indians, however, Stinson's (1990) survey documented a similar cline, but reported that stature showed a significant, inverse correlation with precipitation, but not temperature. Hence, the apparent genetic contribution to growth variation evident in the present study may not reflect an adaptation to heat stress.

It is also possible, as suggested among the Pygmies (Diamond, 1991), that

Makushi short stature may be an adaptation to dense vegetation. The Makushi live on the savannah but plant their farms in the nearby forests (Wilson et al., 2006). Fishing and hunting expeditions also often require travel through the forests (Forte, 1996). Another possibility which has been proposed for the Pygmies (Jain et al., 1998), and may also be 122 true for the Makushi is that thousands of years of cyclical undernutrition (i.e. periodical floods or droughts leading to crop failure) combined with limited gene flow from other populations may have selected for short stature. Anecdotal evidence does suggest that periodic floods and drought, leading to crop failures, do occur among the

Makushi. If this proposed hypothesis were true among the Makushi it would have to be completely due to cyclical events, such as flooding or drought however, as the nutritional intake of the Makushi has been found to not be influenced by seasonal changes in climate

(Ardley, 2005). Alternatively, Walker et al., (2006) suggest that among populations with high juvenile mortality there may have selection for a younger age of sexual maturation and consequently shorter stature. It also remains possible that Makushi children remain short because their parents are short due to some past environmental pressures, which may no longer be present. It has been well documented that a child's birth weight is strongly associated with the birth weight of its mother, meaning that a child may be small because its parents are small (Ounsted et al.,1998; Mullis, and Tonella, 2008). The fetus thus adapts to its environmental conditions in the womb, which influences body size post- natally (Mullis and Tonella, 2008; Arnuna and Zotor, 2008).

Heeding the warning of Gould and Lewontin (1979), it is important to note that traits may arise by means other than natural selection. Short stature among the Makushi may simply be due to genetic drift, specifically founder's effect and genetic bottlenecks.

Relative to Old World populations, it is likely that genetic variation was reduced by founder's effect. Newson (2001) adds that during the immigration from the Old World across the Bering Strait, variation was reduced by genetic bottlenecks. Those peoples who first settled in South America likely experienced a further genetic bottleneck when 123 crossing the inhospitable environment in the southern portion of the Panamanian isthmus (Newson, 2001). A probable third genetic bottleneck was the disease brought to the Americas by European explorers and colonists (Moore et al., 1998) which lead to the death of approximately 95% of the Amerindian inhabitants of Guyana alone (Saunders,

1969 In: Hollett, 1999). Hence, the present genetic and phenotypic variation observed among the Makushi may simply be an artifact of drift among the original inhabitants of the Americas.

If the differences between the Amerindian and mixed children in rates of stunting does, indeed, have a genetic basis, finding the possible gene(s) contributing to the short stature of the Makushi could enhance our understanding of this trait. Unfortunately, this is unlikely to happen anytime soon. While it has long been known that height is a polygenic, multifactorial trait, until recently scientists have known very little about the specific genes involved (Lewis, 2007; Mitchell, 2008). A genome wide association study, has led to the discovery of dozens of genes which appear to influence height (Mitchell,

2008). Of these genes, there appear to be up to 27 different single-nucleotide polymorphisms (SNP's) (Mitchell, 2008). Although this appears to be a good start for determining genes which influence growth, these SNP's appear to explain only four percent of the height variation seen within a population, meaning that there are many more regions and genes to be found. In addition how each of these SNP's affects height has yet to be determined (Mitchell, 2008). As a result, although headway is being made on the genes influencing height, there is still a long way to go before it will be possible to determine whether or not the Makushi have a gene, or multiple genes, which help to explain their short stature. 124

Potential Confounders

It should be noted here that none of the models produced in this thesis were significant (see Table 5, 14, and 28). It is not surprising that a regression model concerning risk factors for stunting which did not include dietary or morbidity data did not produce a significant result. As discussed in chapters one and two, it is well known that other variables such as, poor nutrition (Oyhenart et al., 2003; Foster et al., 2005), compromised intestinal permeability (tropical enteropathy) which limits nutrient absorption (Salazar-Lindo et al., 2004; Lunn et al., 1991), high parasite loads and rates of infection (Foster et al., 2005, Moore et al., 2001; Bravo et al., 2003) also contribute to stunting. Although this study was designed to control for these other variables, they are not included in the model. The purpose of this study is not, however, to come up with a significant predictor equation for stunting, but to evaluate whether or not ancestry, as a proxy for genetic variation, might help us to explain some of the observed variation in growth. Therefore, although the predictor equation was not significant in any of the analyses, one could still predict stunting correctly 77.8% of the time in the 2000 and 2001 data with only the variables ancestry and number of children in the family (Table 6, step

3), 85.7% of the time with the variables ancestry, number of children in the family, and villages in the 2005 and 2007 data (Table 15, step 3), and 87.9% of the time with the variables ancestry, birth order, number of children in the family, and villages in the 2007 subset data (Table 29). This suggests that ancestry, and indirectly genes, along with other factors, are playing a significant role in the observed patterns of growth. 125 These missing variables mentioned above may help to explain Figures 4, 7 and 12, which show the frequency of stunting in each of the seven villages. Interestingly, the village of Massara has relatively low levels of stunting compared to the other

Amerindian villages, with stunting levels that are very similar to the mixed villages of

Aranaputa and Toka. The pattern observed in Massara is difficult to explain. A consideration of dietary intake in Massara and a neighboring village with especially high rates of stunting (not included in this study), suggests that the difference between these two villages, might be at least partially due to differences in dietary intake; caloric intake in both villages falls below recommended levels and the village with the especially high rates of stunting has a significantly lower intake of several micronutrients among which Vitamin A is the most notable (Palmer and Wilson, 2007). Hence, variation between dietary intake in these two villages appears to explain part of the variation in growth. If this is true in the study by Palmer and Wilson (2007), it may well be true of the villages considered in this study. In addition, patterns of morbidity do vary by village.

Notably, two of the Amerindian villages included in this study, lie along the banks of the

Rupununi River (Massara and Kwatamang). Villages in the region which lie along riverbanks, have a significantly higher incidence of malaria (Wilson et al. in preparation).

For example 11 percent of the population in Massara and Kwatamang reported cases of malaria in 1998 compared to 3, 2 and 6 percent in Rupertee, Toka and Wowetta, respectively (Wilson et al. in preparation). As noted by Myers (1993) who worked among the Makushi from 1933 to 1944, chronic malaria drains the energy of the affected individual leaving them more likely to stay at home resting, than walking the long distances to their fields. This in turns leads to hunger and malnutrition, which further 126 exacerbates their health (Myers, 1993). This said, as can be seen in Figures 3, 5 and

10, Kwatamang possesses stunting rates that are fairly equivalent to the other Amerindian villages used in this study, who do not live on the banks of the Rupununi River, yet

Kwatamang has the same prevalence of malaria as Massara. If malaria or living along the banks of the river affects the rate of stunting within a population, one would expect

Kwatamang and Massara to have similar stunting rates, but they do not. Therefore the higher incidence of malaria, and the riverside location of Massara, does not appear to explain Massara's low incidence of stunting compared to the other Amerindian villages used in this study.

Finally, this study could be improved upon by a refined sampling strategy and incorporating several other types of data. For the 2007 subset, in which specific pedigrees were obtained in order to confirm ancestry, a larger sample size from the Amerindian villages would render the findings more accurate. As well, a random, as opposed to opportunistic, sample from each of the villages, which represents a set percentage of the village's inhabitants, would improve the accuracy of this study. Additionally, more detailed data concerning daily activities, the percentage of time spent on each activity, the availability of goods in local shops, the amount of income generated from different jobs and detailed environmental information would all enhance the ability to tease apart the etiology of variation in growth retardation in these groups. 127 Conclusions

Amerindians from South America's lowland tropics are among the shortest people in the world (Wilson and Bulkan, 2004; Godoy et al., 2006). The etiology of this phenotype is not clear, but several studies report that growth retardation is not manifest until weaning, which suggests a role for dietary or disease stress (Benefice et al., 2006;

Dewey, 2001; Padmadas et al., 2002; Simondon et al., 2001). Consistent with these findings, the WHO (1995) explains global variation in growth as a result of differences in

SES, nutrition, and disease stress. Genetic factors are not believed to account for the variability seen in human growth (Habicht et al., 1974). In fact, the WHO Multi-centre

Growth Study (2006) concludes that genes explain only 4% of the variation in growth observed worldwide. As a result, WHO growth standards are deemed appropriate for use internationally among different ethnic groups (WHO, 2006; Bhandari et al, 2002; and

Graitcer and Gentry, 1981). Recognizing the current lack of sufficient data to adequately tease apart the etiology of short stature among the Makushi, this analysis suggests that genes may explain at least part of the variation in growth observed among these groups and that the CDC and WHO growth standards may not actually be applicable to this population. Again, it is notable that the results presented here concur with Stinson's

(1996) among the Chachi. Suggesting that the short stature, which is characteristic of

Amerindians in South America's humid tropics, may have an unusual genetic component.

The idea of a genetic contribution to shorter stature among these populations may help to explain the findings of some studies in which growth faltering persists in populations despite adequate nutrition and the absence of infection (Prentice at al., 1993; Rousham

and Gracey, 1997; Poskitt et al., 1999). Clearly, caution is to be taken when interpreting these results. This study does not rule out the importance of diet and disease in influencing variation in growth; rather, it suggests that among the groups considered here, genes may play a more important role than anticipated. The relative contributions of genes and the environment to the overall phenotype have yet to be determined, and indeed it may differ for different populations.

The point is however, that genes may be playing a role, along with the environment, in stunting, and thus should be taken into consideration in studies looking at growth. 129

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SES Interview

Socioeconomic Status Interview

Mothers Name:

Fathers Name:

Names of Children:

1) Residence of Immediate Family: a) Family in villages only b) Family in town, or city

2) Mothers Education: a) No Education b) Some Primary School c) Finished Primary d) Finished Secondary

3) Fathers Education: a) No Education b) Some Primary School c) Finished Primary d) Finished Secondary

4) Mothers Language: a) Makushi and English b) English Only c) Makushi, English, and Portuguese d) Makushi, English and Other e) English and Other (Non-Makushi)

5) Fathers Language: a) Makushi and English b) English Only c) Makushi, English, and Portuguese d) Makushi, English and Other e) English and Other (Non-Makushi)

6) Wage Labor: a) None b) Father c) Mother d) Both Mother and Father

7) Fathers Employment: a) Never Employed b) Occasional/ Temporary Employment c) Wage-Paying Job d) Seasonal Employment

8) Mothers Employment: a) Never Employed b) Occasional/ Temporary Employment c) Wage-Paying Job d) Seasonal Employment

9) If the Father has had Employment, how Long has he had Employment: a) Not Applicable b) Less then 6 Months c) 6-11 Months d) 1-2 Years e) More then 2 Years

10) If the Mother has had Employment, how Long has she had Employment: a) Not Applicable b) Less then 6 Months c) 6-11 Months d) 1-2 Years e) More then 2 Years 11) If the Father has had Employment, Where has he been Employed: a) Not Applicable b) Iwokrama c) Mining d) Logging e) Store f) Rockview Lodge g) Teacher h) Other

12) If the Mother has had Employment, Where has she been Employed: a) Not Applicable b) Iwokrama c) Mining d) Logging e) Store f) Rockview Lodge g) Teacher h) Other

13) Type of Roof on House: a) Zinc b) Thatch c) Wood Shingles d) Other

14) Wall Structure: a) Adobe b) Baked Brick c) Wood d) Other

15) Predominant Cooking Method: a) Firewood b) Gas Stove

16) Type of Flooring: a) Dirt b) Cement c) Wood d) Other

17) Household Size: a) Entered Numerically

18) Cash Cropping/ Selling Agricultural Products: a) None b) Occasionally c) Regularly d) Frequently

19) Number of Tables in Household: a) None b) 1 c) 2 d) 3 or More

20) Number of Chairs in Household: a) None b) 1-2 c) 3-4 d) 5 or More

21) Number of Radios in Household: a) None b) 1 c) 2 or More

22) Number of Boat Motors Owned by Household: a) None b) 1 c) 2 or More

23) Number of Sewing Machines in Household: a) None b) 1 c) 2 or More

24) Number of Motorcycles a) None b) 1 c) 2 or More

25) Number of Bicycles a) None 164 b) 1 c) 2 d) 3 or More

26) Time to Fields: a) Not Applicable b) An Hour or Less c) More Than an Hour Appendix B

Result Tables and Ethics Approval

Variables not in the Equation

Score df Sig. S^ep Variables Villages 7.833 5 .166 2 Villages(1) 1.232 1 .267 Villages(2) 3.703 1 .054 Villages(3) .821 1 .365 Villages(4) .508 1 .476 Villages(5) 1.163 1 .281 Overall Statistics 7.833 5 .166 SJep Variables Villages 6.999 5 .221 3 Villages(1) 1.408 1 .235 Villages(2) 3.554 1 .059 Villages(3) .673 1 .412 Villages(4) .311 1 .577 Villages(5) .798 1 .372 BirthOrder 1.354 1 .245 Overall Statistics 9.061 6 .170

a. Variable(s) removed on step 2: Villages. b. Variable(s) removed on step 3: BirthOrder. TABLE 9. Variables Not in the Equation 2000 and 2001 Data.

Variables not in the Equation0

Score df Sig. Step 2a Variables Birth_Order .000 1 .983 Overall Statistics .000 1 .983 Step 3b Variables Birth_Order .096 1 .757 Wage_Labour 4 Wage_Labour(1) .000 1 1.000 Wage_Labour(2) .000 1 1.000 Wage_Labour(3) .000 1 1.000 Wage_Labour(4) .007 1 .933 a. Variable(s) removed on step 2: Birth_Order. b. Variable(s) removed on step 3: Wage_Labour. c. Residual Chi-Squares are not computed because of redundancies. TABLE 18. Variables Not in the Equation 2005 and 2007 Data.

I 166

Variables in the Equation

S.E. Wald df Sig. EXP(B) Step Wage_Labour .000 1.000 1(a) Wage_Labour(1) 168939.4 348627423643. 26.577 .000 1.000 24 380 Wage_Labour(2) 161594.9 -6.175 .000 1.000 69 .002 Wage_Labour(3) 229868.8 7.955 .000 1.000 90 2849.090 Freq._Of_Employment .000 1.000 Freq._Of_Employment(2) 306279.3 -13.334 .000 1.000 .000 33 Freq._Of_Employment(3) 159585.8 -37.953 .000 1.000 .000 00 Length_Of_Employment .000 1.000 Length_Of_Employment(2 97985.23 18.623 .000 1.000 122369357.939 ) 8 Length_Of_Employment(3 46411.01 260981633902 42.406 .000 .999 ) 1 5409000.000 Length_Of_Employment(4 88793.70 18.623 .000 1.000 122362855.506 ) 8 Type_Of_Employment .000 1.000 167597.5 -26.577 .000 1.000 .000 Type_Of_Employment(1) 92 282501.5 -114.027 .000 1.000 .000 Type_Of_Employment(2) 82 54229.42 1.470 .000 1.000 4.348 Type_Of_Employment(3) 3 76429.11 38310200412.5 24.369 .000 1.000 Type_Of_Employment(4) 3 36 707331.0 -59.256 .000 1.000 Type_Of_Employment(5) 10 .000 258732.7 .000 1.000 Type_Of_Employment(6) -72.676 41 .000 466458.2 .000 Type_Of_Employment(7) -67.999 05 1.000 .000 Mothers_Education .000 1.000 Mothers_Education(1) 450907.4 39885834199.4 24.409 .000 1.000 56 54 Mothers_Education(2) 200988.4 -33.909 .000 1.000 .000 32 Fathers_Education .000 1.000 532223.0 Fathers_Education(1) -101.704 .000 1.000 .000 93 Fathers_Education(2) 572868.1 -108.206 .000 1.000 .000 02 Fathers_Education(3) 139639.7 -60.090 .000 1.000 .000 96 Location_Of_Family Mem 150730.8 881353053806. 27.505 .000 1.000 bers(1) 26 734 Mothers_Languages .000 1.000 167

Mothers_Languages(1) 135700.9 289286174202 37.904 .000 1.000 82 75800.000 Mothers_Languages(2) 97353.33 153391971705 44.177 .000 1.000 8 72610000.000 Mothers_Languages(3) 231033.5 207596502127 32.967 .000 1.000 90 067.100 Fathers_Languages .000 4 1.000 Fathers_Languages(1) 50608.06 8.070 .000 1.000 3197.007 4 Fathers_Languages(2) 61839.19 7.378 .000 1.000 1599.873 5 Fathers_J_anguages(3) 151161.8 -6.405 .000 1.000 .002 23 Fathers_Languages(4) 467155.0 -57.373 .000 1.000 .000 26 Type_Of_Roof .000 1.000 Type_Of_Roof(1) 279168.3 104.983 .000 1.000 3.921 E+045 35 Type_Of_Roof(2) 461623.9 139.628 .000 1.000 4.362E+060 51 Type_Of_Roof(3) 410534.2 106.538 .000 1.000 1.857E+046 17 Wall_Structure .000 3 1.000 Wall_Structure(1) 111855.0 -5.342 .000 1.000 .005 42 Wall_Structure(2) 155830.5 15575795958.8 23.469 .000 1.000 59 55 Wall_Structure(3) 271148.0 -54.435 .000 1.000 .000 80 Type_Of_Floor .000 3 1.000 Type_Of_Floor(1) 139914.7 -1.689 .000 1.000 .185 36 Type_Of_Floor(2) 258958.3 -15.200 .000 1.000 .000 86 Type_Of_Floor(3) 739825.0 237.641 .000 1.000 1.607E+103 47 Predominant Cooking M 131046.4 13.424 .000 1.000 675821.768 ethod(1) 19 Household_Size .886 1.250 .502 .478 2.425 Cash_Cropping .000 3 1.000 366988.1 Cash_Cropping(1) -47.813 .000 1.000 .000 20 Cash_Cropping(2) 499858.9 -74.831 .000 1.000 .000 15 Cash_Cropping(3) 326989.6 -61.093 .000 1.000 .000 41 Number_Of_Tables .000 3 1.000 Number_Of_Tables(1) 552213.1 2.226 .000 1.000 9.266 52 Number_Of_Tables(2) 161002.4 -10.932 .000 1.000 .000 03 Number_Of_Tables(3) 134740.8 6.273 .000 1.000 530.224 05 Number_Of_Chairs_or_B .000 2 1.000 enches Number_Of_Chairs_or_B 119799.5 652182304296 52.532 .000 1 1.000 enches(2) 29 45000000000.0 168

00

Number_Of_Chairs_pr_B 156572427720 275455.5 enches(3) 69.526 .000 1.000 923700000000 60 0000000.000 Number_Of_Radios .000 1.000 Number_Of_Radios(1) 269342.7 -97.681 .000 1.000 .000 72 Number_Of_Radios(2) 303759.6 -125.535 .000 1.000 82 .000 Number_Of Boat_Motors 134160.4 -22.531 .000 1.000 .000 d) 28 Number_Of_Sewing_Mac .000 hines 1.000 Number_Of_Sewing_Mac 206071.5 138.636 .000 .999 1.618E+060 hines(1) 78 Number_Of_Sewing_Mac 368231.1 548458149873 40.846 .000 1.000 hines(2) 50 943000.000 966624.1 -65.446 .000 .000 Constant 26 1.000 Wage_Labour .000 1.000 101512.4 1.488 .000 4.426 Wage_Labour(1) 73 1.000 114906.3 -37.380 .000 1.000 .000 Wage_Labour(2) 81 120147.0 -97.963 .000 .999 .000 Wage_Labour(3) 12 Freq._Of_Employment .000 1.000 Freq._Of_Employment(2) 77059.54 -1.909 .000 1.000 .148 5 Freq._Of_Employment(3) 181326.1 -9.129 .000 1.000 .000 37 Length_Of_Employment .000 1.000 Length_Of_Employment(2 150296.7 -10.293 .000 1.000 .000 ) 85 Length_Of_Employment(3 41706.77 602788551603 40.940 .000 .999 ) 2 842000.000 Length_Of_Employment(4 160077.5 -8.459 .000 1.000 .000 ) 51 Mothers_Ed ucation .000 1.000 Mothers_Education(1) 157444.8 -69.817 .000 1.000 .000 82 Mothers_Education(2) 189194.5 -26.231 .000 1.000 .000 09 Fathers_Education .000 1.000 Fathers_Education(1) 73581.27 -23.328 .000 1.000 .000 3 Fathers_Education(2) 569252.3 -10.975 .000 1.000 .000 35 Fathers_Education(3) 136773.9 -9.875 .000 1.000 .000 35 Location_Of_Family_Mem 132108.9 -56.367 .000 1.000 .000 bers(1) 89 Mothers_Languages .000 1.000 Mothers_Languages(1) 73.525 288348.5 .000 1.000 853714903376 169

19 787000000000 00000000.000 Mothers_Languages(2) 233300.7 10.132 .000 1.000 16 25132.793 Mothers_Languages(3) 366942.0 116.581 .000 1.000 43 4.272E+050 Fathers_Languages .000 1.000 Fathers_Languages(1) 87278.19 210957482948 30.680 .000 1.000 1 60.050 Fathers_Languages(2) 63767.55 -4.706 .000 1.000 7 .009 Fathers_Languages(3) 92283.84 2.071 .000 1.000 8 7.936 Fathers_Languages(4) 72421.63 -26.526 .000 1.000 3 .000 Type_Of_Roof .000 1.000 Type_Of_Roof(1) 159607.7 1.490 .000 1.000 4.439 23 Type_Of_Roof(2) 352085.8 -16.383 .000 1.000 .000 41 Type_Of_Roof(3) 215072.0 6.090 .000 1.000 441.287 16 Wall_Structure .000 1.000 Wall_Structure(1) 124672.4 -37.123 .000 1.000 .000 53 Wall_Structure(2) 74826.29 -9.709 .000 1.000 .000 4 Wall_Structure(3) 60260.08 -6.798 .000 1.000 .001 4 Type_Of_Floor .000 1.000 Type_Of_Floor(1) 68598.09 -38.204 .000 1.000 .000 9 Type_Of_Floor(2) 110200.0 -54.736 .000 1.000 .000 27 Type_Of_Floor(3) 84476.38 -12.467 .000 1.000 .000 5 Predominant Cooking M 91054.22 -11.646 .000 1.000 .000 ethod(1) 3 Household_Size .886 1.250 .502 .478 2.425 Cash_Cropping .000 1.000 Cash_Cropping(1) 42274.09 863775091223. 27.485 .000 .999 7 579 Cash_Cropping(2) 98072.52 -7.250 .000 1.000 5 .001 Cash_Cropping(3) 95266.25 -26.790 .000 1.000 3 .000 Number_Of_Tables .000 1.000 Number_Of_Tables(1) 157085.6 -31.160 .000 1.000 .000 81 Number_Of_Tables(2) 16.137 17885.27 .000 .999 10194089.651 2 Number_Of_Tables(3) 103157.5 .625 .000 1.000 1.869 70 Number_Of_Chairs_or_B .000 1.000 enches Number_Of_Chairs_or_B 299904.2 4.773 .000 1.000 118.237 enches(2) 39 170

Number_Of_Chairs_or_B 333951.9 -23.443 .000 1 1.000 .000 enches(3) 97 Number_Of_Radios .000 2 1.000 Number_Of_Radios(1) 294886.6 434760769555 42.916 .000 1 1.000 52 6262000.000 Number_Of_Radios(2) 176130.8 429285667450 35.996 .000 1 1.000 58 4322.000 Number Of Boat Motors 282161.5 11.572 .000 1 1.000 106083.543 (1) 22 Number_Of_Sewing_Mac hines .000 2 1.000 Number_Of_Sewing_Mac 496984.7 -60.633 .000 1 1.000 .000 hines(1) 67 Number_Of_Sewing_Mac 286850.0 -114.157 .000 1 1.000 .000 hines(2) 49 Constant 117884636624 192687.6 50.821 .000 1 1.000 21250000000.0 56 00 a Variable(s) entered on step 1: Wage_Labour, Freq._Of_Employment, Length_Of_Employment, Type_Of_Employment, Mothers_Education, Fathers_Education, Location_Of_Family_Members, Mothers_Languages, Fathers_Languages, Type_Of_Roof, Wall_Structure, Type_Of_Floor, Predominant_Cooking_Method, Household_Size, Cash_Cropping, Number_Of_Tables, Number_Of_Chairs_or_Benches, Number_Of_Radios, Number_Of_Boat_Motors, Number_Of_Sewing_Machines. TABLE 24. Variables in the Model and Their Usefulness in Each Step of the Regression for 2007 Subset SES Data. 171

Model if Term Removed Change in Model Log -2 Log Sig. of the Variable Likelihood Likelihood df Change Step Wage_Labour -23.467 .000 3 1.000 1 Freq._Of_Employment -23.467 .000 2 1.000 Length_Of_Employment -25.716 4.499 3 .212 Type_Of_Employment -23.467 .000 7 1.000 Mothers_Education -23.467 .000 2 1.000 Fathers_Education -23.467 .000 3 1.000 Location_Of_Family_ -23.467 .000 1 1.000 Members Mothers_Languages -23.467 .000 3 1.000 Fathers_Languages -23.467 .000 4 1.000 Type_Of_Roof -23.467 .000 3 1.000 Wall_Structure -23.467 .000 3 1.000 Type_Of_Floor -23.467 .000 3 1.000 Predominant_Cooking_ -23.467 .000 1 1.000 Method Household_Size -23.723 .514 1 .474 Cash_Cropping -23.467 .000 3 1.000 Number_Of_Tables -23.467 .000 3 1.000 Number_Of_Chairs_or_ -23.467 .000 2 1.000 Benches Number_Of_Radios -23.467 .000 2 1.000 Number_Of_Boat_Motors -23.467 .000 1 1.000 Number_Of_Sewing_ -23.467 .000 2 1.000 Machines TABLE 25. Variables Removed in Each Step of the Regression and Their Significance to the Model for 2007 Subset SES Data. Variables not in the Equation(b)

Score df Siq. Step Variables Type Of Employment .000 7 1.000 2(a) Type_Of_Employment(1) .000 1.000 Type_Of_Employment(2) .000 1.000 Type_Of_Employment(3) .000 1.000 Type_Of_Employment(4) .000 1.000 Type_Of_Employment(5) .000 1.000 Type_Of_Employment(6) .000 .997 Type_Of_Employment(7) .000 1.000 a Variable(s) removed on step 2: Type_Of_Employment. b Residual Chi-Squares are not computed because of redundancies.

TABLE 26. Variables Not in the Equation 2007 Subset SES.