Impact of Desertification on the Livelihood and Health of the Wayúu People of the La Guajira Department, Colombia

Department of Evolutionary Biology and Environmental Studies

Master Thesis Zurich, 10 January 2019

Douglas Fernandes Gomes Da Silva

Supervisors: o PD Dr. Marcus Hall, Department of Evolutionary Biology and Environmental Studies, University of Zurich (Switzerland) o Dr. Isabelle Schluep, Center for Corporate Responsibility and Sustainability (CCRS), at the University of Zurich (Switzerland)

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Imprint

Author

Douglas Fernandes Gomes Da Silva Bahnhofstrasse 27 8303 Bassersdorf Switzerland Email: [email protected] Phone: +41 78 700 24 42

Supervisors

PD Dr. Marcus Hall University of Zurich Professor (Privatdozent) Department of Evolutionary Biology and Environmental Studies Winterthurerstrasse 190 8057 Zürich Switzerland Email: [email protected] Phone: +41 44 635 4807

Dr. Isabelle Schluep Center for Corporate Responsibility and Sustainability (CCRS) at the University of Zurich Head of Sustainable Impact Zähringerstrasse 24 8001 Zürich Switzerland Email: [email protected] Phone: +41 79 812 45 51

Collaborators:

Marta Cecilia De Fatima Jaramillo-Mejía, M.D., Ph.D. Professor at Universidad ICESI, Cali (Colombia)

Soraya Escobar Arregoces, J.D. Lawyer at Defensoría del Pueblo, Riohacha (Colombia)

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Acknowledgements

Primarily I must thank my direct supervisor Dr. Isabelle Schluep from the Center for Corporate Responsibility and Sustainability (CCRS) for coming up with the main idea for the project and for graciously sharing the vast network of contacts she built in Colombia. Her positive energy, knowledge, compassion and friendship supported me through my work in Switzerland and Colombia. Our close collaboration has brought many fruits, and I hope that we’ll be able to continue our work in Colombia in the future.

Secondly, my Environmental Sciences professor and advisor, Dr. Marcus Hall (from my Alma Mater University of Wisconsin – Madison. Go Badgers!). He provided me a much- needed support inside and outside of Colombia, and his positive attitude and ideas have helped me much throughout this experience.

Thirdly, a warm thanks to Dr. Marta Jaramillo-Mejía for introducing me to the Colombian health system and sharing much of the information from her longitudinal health research in underdeveloped areas of Colombia. Her love and care for the children in her country are inspiring, and it was a great pleasure learning from her during this process.

A big thanks goes to my Master of Environmental Sciences “Master Blasters” fellow students, who were there with me through classes, assignments and social events. Without their support and friendship, it would have been a much harder ride.

My Colombian counterparts are many, but the most important thank you goes to Dr. Soraya Pérez Escobar, Dr. Soraya Escobar Arregoces and their extended family (Dr. Laurentino, Miss Remedios, Mr. Alvaro, Miss Yaniris and Ms. Esmeralda) for being my liaisons and hosts in the city of Riohacha. Without their support, assistance and welcome into their home, this project may not have been possible. A special thanks goes to Miss Yaniris Patricia Arregoces Mesa for being my guide and interpreter while in Manaure, and an extended thanks goes to her immediate family for hosting and driving me around the Rancherías in the Manaure region.

A warm thanks goes to Ms. Katherine Klemenz for connecting me to her Mama Tierra NGO, and for the beautiful work that her crew does in Nazareth, especially Mr.

Francisco Iniciarte, Mr. Medardo Polanco, Ms. Luz Mila, Ms. Kayla Polanco, Miss Steffi Iguaran and all the extended Mama Tierra crew, who truly believed in my work and did everything possible to make it happen.

My special thank you goes to the families who participated in the study. Learning about their life struggles and seeing their compassion under so much adversity was truly inspiring. I thank them for their willingness and taking the time to answer my questions, and I hope that soon we can use this knowledge to start projects for some improvements in their lives.

All the background data I obtained included maps, environmental (e.g., climate, desertification), demographic (e.g., population distribution), socio-economic (e.g., economic activities) and health data (e.g., Body Mass Index and Mid-Upper Arm Circumference world standard measurements). This data comes from a body of world experts who willingly share their knowledge for the advancements of science. My thanks go to the following institutions:

o Colombian National Administrative Department of Statistics (DANE) o Colombian Ministry of Environment and Sustainable Development (MINAMBIENTE) o Colombian Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) o Geographic Institute Agustin Codazzi (IGAC) o World Health Organization (WHO) o World Meteorological Organization (WMO) o Food and Agriculture Organization (FAO) of the United Nations o Institute for Health Metrics and Evaluation (IHME) o U.S. National Aeronautics and Space Administration (NASA) Earth Observatory o U.S. National Oceanic and Atmospheric Administration (NOAA) o Alphabet Inc. (Google Earth)

Lastly to my parents who say no matter what I do, I will always have a place to call home, and to my better half and wife Constanze. Without her I have no idea where I’d be right now, and so that I don’t write a full dissertation on her contributions to this work, I’ll just say: I love you to the mountains and back!

Personal Declaration

I, DOUGLAS FERNANDES GOMES DA SILVA (henceforth, “Author”) hereby declare and confirm that this submitted written work, graphs and statistical analysis is the result of my own learning and independent work. I have appropriately acknowledged and credited all persons who were significant facilitators of this work before and during field research. All external sources are explicitly acknowledged and referenced within. Other than myself, only my supervisors PD Dr. Marcus Hall from University of Zurich (UZH) and Dr. Isabelle Schluep from the Center for Corporate Responsibility and Sustainability (CCRS) have seen this work before final submission. Parts of this work may have been corrected for layout and/or content by these supervisors, but the integrity of the work, layout and contents remains truthfully within my creation and authorship.

Bassersdorf, 10.01.2019 ______PLACE / DATE AUTHOR’S SIGNATURE

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Table of Contents

Abstract ...... I Resumen Ejecutivo en Español ...... II List of Abbreviations ...... V List of Figures ...... VI 1. Introduction ...... 1 1.1 Drought ...... 2 1.2 Desertification ...... 3 2. Background ...... 5 2.1 The La Guajira Department, Colombia ...... 5 2.2 The Wayúu People ...... 7 2.3 Drought and Desertification in La Guajira ...... 9 2.4 Desertification and Health ...... 12 2.5 Research Question and Hypothesis ...... 15 3. Research Design ...... 16 3.1 Research Area: Manaure and Uribia Municipalities ...... 16 3.2 Standard Precipitation Index and El Niño Southern Oscillation Events ...... 20 3.3 Temperature ...... 23 3.4 Precipitation and Evaporation ...... 24 4. Materials and Methods ...... 28 4.1 Development of the Household Questionnaire ...... 30 4.2 Household Interview Process ...... 31 4.2.1 Interviews in the Manaure Municipality ...... 31 4.2.2 Interviews in the Uribia Municipality ...... 32 4.2.3 Body Mass Index and Mid-Upper Arm Circumference .... 33 5. Statistical Analyses ...... 33 5.1 Ordinal Data ...... 34 5.1.1 The Likert Scale ...... 34 5.1.2 Non-Parametric Analysis: The Kruskal-Wallis Test ...... 36 5.1.3 Post Hoc Analysis: The Dunn Test ...... 37 5.2 Continuous Data ...... 38

6. Results ...... 39 6.1 Adult Population ...... 40 6.1.1 Education ...... 40 6.1.2 Living conditions ...... 41 6.1.3 Health ...... 45 6.1.4 Occupation and Income ...... 48 6.1.5 Land, Agriculture and Pastoralism ...... 54 6.1.6 Perceptions ...... 55 6.2 Children Population ...... 75 6.2.1 Malnutrition and Child Mortality ...... 75 6.2.2 Body Mass Index (BMI) ...... 81 6.2.3 Mid-Upper Arm Circumference (MUAC) ...... 87 6.2.4 Food Score, Malnutrition and Desertification Levels ...... 94 7. Discussion ...... 103 7.1 Socio-Economic Aspects ...... 104 7.1.1 Education, Infrastructure and Health ...... 104 7.1.2 Economic Opportunities ...... 106 7.2 Perceptions ...... 107 7.2.1 Safety ...... 107 7.2.2 Water ...... 107 7.2.3 Food ...... 109 7.2.4 Environment ...... 111 7.3 Children ...... 113 8. Conclusions ...... 115 9. Directions for Future Research ...... 116 References ...... 118 Appendix ...... 126 A.1 Individual Household Questionnaire ...... 126 A.2 Health Facility Baseline Assessment Questionnaire ...... 146 A.3 Health Worker Questionnaire ...... 160

Abstract

Background and Significance: It is well acknowledged that indigenous groups around the world suffer high rates of poverty due to isolation and marginalization, low levels of education and high rates of malnutrition and infectious diseases. The sources of these problems can vary from region to region. Addressing such gaps is part of the Sustainable Development Goals (SDG 2015) of the United Nations. This empirical research focuses on the Wayúu people living in the La Guajira Department of Colombia, where reports have shown high levels of childhood mortality, and that food, water, sanitation and health securities are lacking due to drought, desertification and economic isolation of the region. Methods: A mixed methods approached is used for this research. It consists of a set of questionnaires with factual and perception types of questions to assess the livelihood and opinions of the Wayúu people living in areas that experience different levels of desertification: low, medium and high desertification areas, as defined by Colombia’s Institute of Hydrology, Meteorology and Environmental Studies (IDEAM). Perception data are gauged against factual data. Results: Socio-economic and health disparities have been found among Wayúu people living in regions with different desertification levels. Individuals living in regions experiencing high desertification have the lowest accessibility to income (F(2,128)=21.3, p<0.00001), lowest levels of education (F(2,85)=14.35, p<0.00001), highest levels of food 2 insecurities (F(2,290)=82.17, p<0.00001) and water scarcity (X =24.057, df=2, p<0.00001), as well as a higher incidence of malnutrition (X2=49.291, df=2, p<0.00001) and childhood mortality (F(2,29)=6.766, p=0.003), in comparison to individuals living in regions experiencing medium or low levels of desertification. Conclusions: Wayúu people living in areas with the highest levels of desertification are at an increased risk for extreme poverty, food and water insecurities and health issues. This research is a call for government and private entities to be aware of this humanitarian crisis and hopes to motivate such institutions to establish effective policies and interventions in order to reduce poverty, food and water insecurities, malnutrition and childhood morbidity and mortality among the Wayúu people. Keywords: Colombia, La Guajira, Wayúu, drought, desertification, food and water insecurity, health, malnutrition, childhood, morbidity, mortality.

Resumen Ejecutivo en Español Los estudios científicos demuestran que los grupos indígenas sufren con las altas tasas de pobreza debido al aislamiento y la marginación, a los bajos niveles de educación y las altas tasas de desnutrición y enfermedades infecciosas. Los orígenes de estos problemas varían entre regiones, y abordar estas carencias es parte de los Objetivos de Desarrollo Sostenible (ODS 2015) de las Naciones Unidas. La investigación en esta tesis de maestría se enfoca en el pueblo Wayúu que vive en la región de Alta Guajira, específicamente en los municipios de Manaure y Uribia del departamento de La Guajira, Colombia, donde los informes muestran altos niveles de mortalidad infantil y gran falta de alimentos, de agua, de sanidad y servicios de salud debido al aislamiento económico, la sequía y desertificación de la región. Utilizamos un método mixto para esta investigación, que consiste en un conjunto de cuestionarios con preguntas objetivas y de percepción para evaluar la vida y las opiniones de las personas Wayúu que viven en áreas que experimentan diferentes niveles de desertificación en Manaure y Uribia: áreas de desertificación baja, media y alta, según lo define el Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM) de Colombia.

Realizamos una extensa entrevista a 131 hogares (63 en Manaure y 68 en Uribia) que incluyó a 230 adultos y 293 niños. Como resultados, encontramos disparidades socioeconómicas y de salud entre los Wayúu que viven en regiones con diferentes niveles de desertificación. Las personas que viven en regiones con las tasas más altas de desertificación tienen la menor accesibilidad a ingresos, los niveles más bajos de educación (80% nunca fueron a una escuela y no pueden leer ni escribir), los niveles más altos de inseguridad alimentaria y escasez de agua, así como una mayor incidencia de desnutrición y mortalidad infantil en comparación con individuos Wayúu que viven en regiones que enfrentan menores niveles de desertificación. La evaluación de 293 niños de varias regiones de dos municipios de la Alta Guajira muestra que 45% de estos niños presentan signos claros de desnutrición, y la mayoría de estos niños desnutridos viven en áreas con alta desertificación. Estas áreas también presentan los porcentajes más altos de enfermedades transmitidas por el agua (como diarrea y vómitos), una alta incidencia de infecciones que provocan fiebre (como en el caso de

II infecciones bacterianas), y 85% de los adultos entrevistados sufren con dolores de cabeza diarios. Los problemas relacionados con la baja disponibilidad y mala accesibilidad a los centros de salud empeoran aún más su situación.

Estos resultados muestran que las personas Wayúu de Alta Guajira que viven en áreas con los niveles más altos de desertificación presentan las condiciones socioeconómicas y de salud más pobres en comparación con las personas que viven en áreas con los niveles más bajos de desertificación. Sin embargo, existe una cierta ambigüedad en tal afirmación, ya que los factores socioeconómicos y ambientales que afectan a la población Wayúu pueden estar estrechamente relacionados entre sí. ¿Son las áreas que presentan una alta desertificación también las áreas más pobres y aisladas? ¿Las personas más pobres y desfavorecidas tienden a ser empujadas a áreas con mayor desertificación? Basándome en tal enigma, deduzco que la principal pregunta de seguimiento sería: ¿cuál es la principal causa de pobreza y problemas de salud entre los Wayúu: se identifica una causa socioeconómica (sociedad) o, mas bien el clima (naturaleza)? Siendo ese el caso, diríamos que es una combinación de ambos.

En general, el pueblo Wayúu de Alta Guajira se enfrenta a grandes problemas como la falta de alimentos y de agua, de infraestructura, de servicios médicos y educación para enfrentar las sequías y la desertificación de la península. Sin embargo, las poblaciones que viven en áreas con los niveles más altos de desertificación parecen estar sufriendo mucho más si se comparan con otras áreas, debido a falta de lluvia y, consecuentemente, a una baja productividad del suelo, lo que agrava sus prácticas agrícolas y aumenta sus inseguridades de alimentos y de agua, y empeora aún más su estatus socioeconómico. Debido a tales problemas, los segmentos más afectados de la sociedad Wayúu son los niños. En exámenes detallados de los niños Wayúu, se pueden observar signos de deficiencia de proteínas como retención de agua en las extremidades y abdomen distendido (mal de Kwashiorkor), y diversos signos de deficiencia de vitaminas como erupciones y protuberancias en la piel, uñas hundidas y quebradizas, y úlceras bucales. La dieta de los niños Wayúu es alta en grasas procesadas y en azúcares, y es baja en nutrientes, en vitaminas y en minerales. Los niños no están necesariamente sufriendo hambruna, pero están sufriendo con las

III consecuencias de las disparidades socioeconómicas influenciadas por el proceso de desertificación, lo que reduce la cantidad, disponibilidad, variedad y accesibilidad de alimentos frescos. En las áreas con alta desertificación no pueden cultivar, por eso los alimentos más económicos y ampliamente disponibles que pueden obtener son la grasa de cerdo, harina blanca, maíz seco y panela (barra de caña caramelizada). La escasez en la variedad de alimentos, la contaminación del agua y la falta de sanidad los hacen vulnerables a los altos riesgos de desnutrición, enfermedades y mortalidad infantil.

Sobre la base de los resultados de esta tesis de maestría, los mejores lugares para iniciar cualquier tipo de iniciativas de desarrollo se encuentran en las zonas que presentan los niveles más altos de desertificación. Los habitantes de dichas áreas necesitan acceso a agua potable para consumo personal y también para la irrigación agrícola, caminos adecuados y transporte público para vincularlos a los mercados (para que puedan comercializar sus artesanías), aumentar la capacidad de atención médica al redor de sus rancherías y, lo que es más importante, deben ser educados sobre sus derechos, la salud, la economía y las prácticas innovadoras para brindarles oportunidades de desarrollo sostenible a largo plazo. Una vez que esas necesidades básicas puedan ponerse en práctica, las personas Wayúu pueden tener la oportunidad de prosperar y continuar desarrollándose dentro de su propia manera cultural.

List of Abbreviations

BMI Body Mass Index

CCRS Center for Corporate Responsibility and Sustainability

CHF Swiss Franc

COP Colombian Peso

DANE National Administrative Department of Statistics

ENSO El Niño Southern Oscillation

GDP Gross Domestic Product

GPS Global Positioning System

IGAC Geographic Institute Augustín Codazzi

IDEAM Institute of Hydrology, Meteorology and Environmental Studies

MINAMBIENTE Ministry of Environment and Sustainable Development

MUAC Mid-Upper Arm Circumference

NDVI Normalized Difference Vegetation Index

NOAA National Oceanic and Atmospheric Administration

ONI Oceanic Niño Index

SIGOT System for Geographic Information and Territory Organization

SPI Standard Precipitation Index

SST Sea to Surface Temperature

WHO World Health Organization

WMO World Meteorological Organization

List of Figures

Figure Name Page Figure 01 Drought 3 Figure 02 The Desertification of La Guajira 4 Figure 03 Satellite Map of La Guajira Department, Colombia 5 Figure 04 Municipalities of the La Guajira Department 6 Figure 05 Ranchería in the Manaure Municipality 7 Figure 06 The Wayúu Bags 8 Figure 07 The La Guajira GDP (2016) 10 Figure 08 The Desertification Levels of La Guajira 12 Figure 09 Wayúu Children Begging for Food and Bottled Water 13 Figure 10 The La Guajira Land Coverage 14 Figure 11 Study Population and Communities Map 17 Figure 12 The Wayúu Population in Colombia 18 Figure 13 Wayúu Populated Points in Manaure and Uribia 19 Figure 14 Standard Precipitation Index (SPI) and Oceanic Nino Index (ONI) 22 Figure 15 Average Maximum and Minimum Temperature in Manaure and Uribia 24 Figure 16 Average Precipitation in Manaure and Uribia Reference Communities 26 Figure 17 Average Evaporation in Manaure and Uribia Reference Communities 27 Figure 18 Framework for Analyzing the Impact of Desertification 29 Figure 19 Manaure Municipality Interviews 31 Figure 20 Uribia Municipality Interviews 32 Figure 21 Household Questionnaire 35 Figure 22 Summary of Communities Visited in Manaure and Uribia 39 Figure 23 Adult Education 41 Figure 24 Water and Human Waste 42 Figure 25 Household Infrastructure 43 Figure 26 Round Trip Walk to the Nearest Water Source 44 Figure 27 Adult Health 46 Figure 28 Time to Reach Nearest Health Facility at Different Desertification Levels 47 Figure 29 Adult Population Occupation Distribution 48 Figure 30 Monthly Household Income by Desertification Level 49 Figure 31 Individual Monthly Income and Education 50/51 Figure 32 Individual Monthly Income by Gender 52/53 Figure 33 Land, Agriculture and Pastoralism 55 Figure 34 Perceptions on Community Safety 57/58 Figure 35 Perceptions on Drinking Water Quantity 59/60

Figure 36 Perceptions on Drinking Water Quality 61/62 Figure 37 Perceptions on Food Availability 63/64 Figure 38 Perceptions on Food Variety 65/66 Figure 39 Perceptions on Precipitation 67/68 Figure 40 Amount of Wind and Dust in the Air 69/70 Figure 41 Temperature 71/72 Figure 42 Quality of Roads and Nearest Health Center 73/74 Figure 43 Children’s Demographics and Malnutrition Assessment 76 Figure 44 Children’s General Health 77 Figure 45 Malnutrition, Diarrhea and Food Availability 78/79 Figure 46 Child Mortality 80 Figure 47 Boys BMI and Malnutrition 82 Figure 48 Boys BMI and Desertification Levels 83/84 Figure 49 Girls BMI and Malnutrition 85 Figure 50 Girls BMI and Desertification Levels 86/87 Figure 51 The MUAC Tape 88 Figure 52 Boys MUAC and Malnutrition 89 Figure 53 Boys MUAC and Desertification Levels 90/91 Figure 54 Girls MUAC and Malnutrition 92 Figure 55 Girls MUAC and Desertification Levels 93/94 Figure 56 Food Guidelines for Colombia 95 Figure 57 Food Score Table 96 Figure 58 Food Score Example 97 Figure 59 Total Food Score, Desertification and Malnutrition 98/99 Figure 60 Food Score for Individual Food Groups 100 Figure 61 Food Score for Individual Food Groups, Malnutrition and Desertification 101 Figure 62 Street Water Vendor in Manaure 108 Figure 63 Water Well Powered by a Windmill 108 Figure 64 A Man-Made Lake (Jagüey) 109 Figure 65 Small Food and Drinks Shop 110 Figure 66 The Colombian Chicha 111 Figure 67 Hiding from the Sun 112 Figure 68 Panela Sugar Production 114

VII

1. Introduction

The aim of this empirical observational research is to investigate the impact of desertification on human health, within a specific case study of the Wayúu indigenous people of the La Guajira Department, Colombia. This study began from a partnership established between my supervisor, Dr. Isabelle Schluep, and Dr. Marta Jaramillo-Mejía from Universidad ICESI in Colombia. Dr. Jaramillo-Mejía published 2 articles showing that childhood mortality in La Guajira is among the highest in the country, especially in areas predominantly inhabited by indigenous people [Jaramillo-Mejía et al. 2016 and 2018]. In light of such information, this master thesis work is an attempt to disentangle the effects of drought and desertification on the livelihood and health of the Wayúu people, to perhaps clarify why the childhood mortality rates in La Guajira are so high. In order to accomplish this goal, this study is divided into two parts. The first part presents the historical aspects related to La Guajira, its desertification process and the livelihood of the Wayúu people. This first part is laid out in Section 2 (Background) and Section 3 (Research Design) of this manuscript. This first part consists of a literature review and background data acquisition in order to provide an overview of the environmental aspects of La Guajira, and to have an overall look at the living conditions of the Wayúu people dwelling in these areas. The second part presents the field research and results from a questionnaire designed to analyze the effects the environment may have on the livelihood and health of the Wayúu people. This second part is laid out in Section 4 (Materials and Methods), Section 5 (Statistics) and Section 6 (Results). Finally, a discussion, conclusions and future directions sections bring this work to a close.

As an introduction, I would like to provide the background on the main environmental factors in question: drought and desertification. In general, drought and desertification are closely related phenomena that can affect large areas of the world and may have serious environmental, economic, social and health impacts on human populations [Guha-Sapir et al. 2012]. While drought is a natural occurrence, the process of desertification and land degradation can also be influenced by human activities such as deforestation, unsuitable agricultural and pastoral practices, or overexploitation of water and mineral resources [NDMC 2012]. Although the environmental and economic

1 impacts have been well described [Below et al. 2007], literary investigations have shown that there is limited empirical evidence on the social and health impacts of drought and desertification [Stanke et al. 2013], especially in regard to indigenous groups [Bramley el al. 2004].

1.1 Drought

Meteorologists generally define drought as a prolonged period of time where precipitation in a certain region is lower than usual, causing a shortage in water supplies [Paepe et al. 1990]. It has been further categorized based on the method of how it is measured. The following types of drought are found: meteorological drought, which considers the degree of dryness and the duration of the dry period because of low precipitation; hydrological drought, which investigates the impacts of precipitation shortages on surface and/or underground water supplies; agricultural drought, which links characteristics of meteorological and/or hydrological drought to agricultural impacts, where the amount of moisture in the soil no longer meets the needs of particular plantations; and socio-economic drought, which explores when water shortages affect the livelihood of a population [NDMC 2012]. Meteorological and hydrological droughts are a critical consideration for rain-dependent areas because of their impact on fresh and ground water ecosystems, whilst agricultural drought is critical for plants and crops development [Eltahir and Yeh 1999]. These types of drought are non-independent, focusing on the physical processes that affect the complexity, scale and speed of the desertification process. Ultimately, they impact the long-term water availability and food production of a region, which leads to what is called a “socio- economic drought” [Redmond et al.1991]. This socio-economic drought is defined as the time-dependent impact of drought and desertification on the supply and demand of economic goods and services (such as food, water, grains, fish, and hydroelectric power), and health services to a region [Wilhite and Glantz 1985]. During drought conditions, the lack of water supplies and sanitation services can lead to increased population morbidity and mortality due to malnutrition and high vulnerability to communicable infectious diseases such as cholera, typhoid fever, diarrhea and measles [WHO 2012]. Furthermore, with the increased risk for wildfires and dust storms during drought periods, particulate matter suspended in the air can irritate the bronchial

2 passages and lungs, making chronic respiratory illnesses worse and increase the risk for respiratory infections like bronchitis and pneumonia [CDC 2017]. As drought conditions can economically and geographically isolate a certain area, the availability and distribution of medical services and supplies may become scarce and have a negative impact on the welfare of a population.

Figure 01: Drought.

Because of the drought, a lake has completely dried in the Wayúu village of Tomasitomana, Manaure Municipality of the La Guajira Department, Colombia. (Photo by Author, 2018)

1.2 Desertification

The term “desertification” was first coined in 1927 by the French explorer Louis Lavauden, and popularized by the French forester Andre Aubreville, as he explored the expansion of the Sahara Desert in the late 1940’s [Darkoh 2003]. In 1978, Priscilla Reining proposed the first comprehensive definition of desertification as the permanent degradation of previously fertile land, typically as a result of extended drought in combination with pressure from human influences such as exploration, deforestation, overgrazing, and improper agricultural activities [Reining 1978]. In recent years, scientists expanded the initial definitions to say that desertification has a multidimensional interpretation with ecological, meteorological, and human variables, defining it as a reduction in the productivity of the land, where it may no longer support the same plant growth it had in the past [Reynolds 2001].

Beyond the scope of definitions, the loss of productive land for even one season can pose a serious economic threat to many populations in the world. Desertification occurs in high concentrations in developing countries [GEF & GM 2006], where an estimated 40 percent of people in Africa and Asia live in areas constantly threatened by desertification [Stather 2006]. Due to desertification, the primary annual loss of income is estimated at US$65 billion (measured in terms of lost productivity, including reduced crop yields and pastoral activities [Sherbinin 2002]), a figure that does not include secondary costs incurred by the social and health sectors of society [Kannan 2012]. The combination of these primary and secondary costs affects the economy, introduces negative changes in society and lifestyles, endangers community safety and degrades public health [Ambalan 2014]. The public health implications of areas presenting desertification are numerous: some can be directly observed, measured and remedied (e.g., bacterial infections), whilst others come from indirect health implications that are not always easy to anticipate, monitor and treat (e.g., chronic illnesses from malnutrition) [CDC 2017]. These implications come from the compromised availability of food, quantity and quality of drinking water, effects on air quality, diminished living conditions related to energy, sanitation, hygiene and increased incidence of disease [Amin 2004]. For example, severe drought and desertification conditions can pollute both underground and surface water sources with viruses, protozoa, and bacteria, which increase the risk for gastrointestinal infectious diseases that can easily spread from person to person when hygiene is not maintained [WHO 2012]. Figure 02: The Desertification of La Guajira.

The extended drought and lack of infrastructure is likely influencing the continuous desertification process of La Guajira. (Photos by Author, 2018) 4

Part 1: Historical Overview

2. Background

2.1 The La Guajira Department, Colombia

La Guajira is located on the northernmost tip of South America, it covers an area of approximately 20’800 Km2 and it’s surrounded by the Caribbean Sea (Figure 03).

Figure 03: Satellite Map of La Guajira Department, Colombia.

(Sources: Google Maps, 2018 and Wikimedia Commons, 2012)

It is one of the 32 departments that compose the Republic of Colombia, and its capital city is Riohacha. According to the last census report from the Colombian National Administrative Department of Statistics (DANE) in 2005, La Guajira has a population of approximately 680’000 where 46 percent belongs to indigenous populations (majority from the Wayúu tribe), eight percent of Afro-Colombian descent, one percent from other ethnicities (mostly Middle Eastern descent), while the remaining 45 percent not considering themselves to be part of a particular ethnic group, but can be generally

5 regarded as European descendants [DANE 2005]. Since then, the only available population data comes from projections at national and regional levels, by sex and age for the 2005-2020 period, showing that the population of La Guajira currently stands at over one million, but the ethnic division percentages remained roughly the same [DANE 2018]. These numbers also do not account for the migration crisis happening from Venezuelans fleeing misery and poverty from their socialist-government run country. It is estimated that over half a million Venezuelans have crossed into La Guajira, where there is a widespread lack of suitable and effective border controls [Cobb et al. 2018].

The main languages spoken in the region are Spanish and Wayúunaiki. The population genders are about evenly split at 50.5 percent males and 49.5 percent females [DANE 2005]. The Department is divided into 15 municipalities, each with its own capital and each administered by a popularly elected mayor and a city council, as well as municipal- level courts [Curvelo et al. 2015]. Figure 04 shows the municipality divisions and the three major sub regions: Alta, Media y Baja Guajira (High, Middle and Lower Guajira).

Figure 04: Municipalities of the La Guajira Department.

(Source: Wikimedia Commons, 2008) 6

2.2 The Wayúu People

The Wayúu people are a native South American ethnic group formed from various ancient groups from the Amazon and Antilles, who are believed to have begun forming their roots in La Guajira around the year 200 AD, as a way to escape from the harsh and hostile environments of the rainforests (Picon 1983). Culturally, they are divided into matrilineal clans, where their last names define the lineage where they come from. They do not formally get married but engage in what is called a union libre (free union), where partners live together and establish their own rules. The downside of such union is that a man or woman can leave the relationship (even if there are children involved) without any attachments or consequences. A traditional Wayúu settlement is called a Ranchería (Figure 05) and is made up of a few houses (usually from the same family, or from the same clan). Each Ranchería is named after a plant, animal or anything particular about the region where they live – all in their original Wayúunaiki mother tongue. Rancherías are usually isolated from each other in order to avoid territorial conflicts or mixing of their animal herds. According to sociologist and Manaure native Cecilia Schucker Bula, the Wayúu “love their land and consider it as their mother; they are born and fed from it, and when they die, the land opens its arms to receive them once again. The Wayúu believe their true land is where their cemeteries are – where they will always find their ancestors. They respect this land, they demand respect for their land and will fight to defend it” [Bula 2000].

Figure 05: Ranchería in the Manaure Municipality

(Photo by Author, 2018) 7

The Wayúu people are reserved, but welcoming and friendly once they know and accept your intentions for being in their land. They are also well known for making Wayúu bags, or as they are usually called, mochilas. The selling of the mochilas constitutes as one of their important sources of income. As the women predominantly make the mochilas, they are actively participating in the income generation of families, and usually the women themselves travel to nearby cities to sell them. The mochilas are very characteristic for their decorating patterns inspired by nature and the culture they see around.

Figure 06: The Wayúu Bags.

Women gathering to design and make traditional Wayúu bags (mochilas). (Photos by Author, 2018)

2.3 Drought and Desertification in La Guajira

The Republic of Colombia is located entirely within the area of the Inter-Tropical Confluence Zone (ICZ), where it is subject to the fluctuation of trade winds from the Northeast and Southeast, and the El Niño Southern Oscillation (ENSO) events that causes the cycle of warm and cold temperatures of the tropical central and eastern Pacific Ocean [CPC 2005]. The combination of trade winds, ENSO’s warm and cold- water patterns, and the altitude gradients (as nearly 70 percent of Colombia’s territory is located in the Andean region) leads to extreme weather fluctuations, resulting in lower humidity and less precipitation in the flat northern Caribbean regions (predominantly in the La Guajira Department, where not many mountains are present to trap the humidity) and floods in the mountainous south western Pacific areas [IDEAM 2016]. The La Guajira peninsula has been going through the desertification process likely due to climatic influences from the ICZ and ENSO events [CPC 2005].

However, climate may not be the only factor to be considered in the desertification process. From the 1960’s through the 1990’s, the country experienced the direct destabilizing effects of the conflict between the drug-trafficking organizations, left extremists and the armed forces of the Colombian government, which slowed the country’s development, but had an unprecedented benefit of preserving the country's unique ecosystems due to increased land abandonment and slow colonization patterns [Etter et al. 2006]. However, the fall of the large drug cartels in the late 1990’s brought certain stability to the political environment and allowed for a boom in extraction activities such as crude oil, minerals (especially coal) and natural gas harvesting. These activities became increasingly important to the development of the modern Colombian economy but have provided many scars to the ecosystems of the country [Bushnell 2010]. According to a report from the Colombian National Department of Planning (DNP), the economy of the La Guajira Department is based on the exploitation of mineral resources, such as coal, salt and natural gas, which constituted nearly 45 percent of the total revenues of the region in 2016 [DNP 2017]. Other sectors include social services (education, health, community and domestic services), small businesses especially in the tourism industry (as La Guajira is home to Punta Gallinas, the northernmost point of South America) and privately-owned construction and real estate

9 companies. Agriculture, pastoralism and fishing account for only five percent of the total Gross Domestic Product (GDP) of the region (Figure 07).

Figure 07: The La Guajira GDP (2016).

(Source: DNP, DANE and Central Bank of Colombia)

Since the early 2000’s, the extent of mining activities and oil drilling extractions have increased nearly three-fold in the country, as more international investors began taking part of these profitable activities [Steiner and Vallejo 2010].

Possible consequences of this boom in exploratory mining activities are the damages they can inflict on the environment. Independent reports have denounced the construction of dams that have diverted waters towards mining industries, and consequently drained rivers that provided water to the lands of Wayúu people in La Guajira [Graavgard and Morales 2010, Rosso 2016, Chaparro 2017]. The Universidad Javeriana in Bogota has done research on the climate, topography, globalization, technological advancements and land exploration activities, and showed that the process of land degradation in La Guajira has progressively increased in the past 30 years by means of climate change, deforestation, erosion, compaction, salinization and contamination [Etter et al. 2008]. However, despite studies and reports showing that mining industries may have an influence on environmental degradation and the

10 desertification process of a region, such topic is beyond the scope of this research. The specific regions of the La Guajira Department that I have visited to collect data are located between 150 to 300km north from the closest major coal-mining site (El Cerrejón mine). From personal observations, there are no apparent indications that the mining industry directly affects the surrounding environment of the study population, although further research would be needed to establish the effects, if any, of the mining industry in the study region. Furthermore, I have not taken into account other issues such as human over-population (for example, due to the migrant crisis from Venezuela) or uncontrolled pastoral activities (that can lead to land degradation from over-grazing), which may also be important considerations for future investigations. This research has solely taken into consideration the ongoing natural desertification process from climatic events such as drought and influences from ENSO events.

The La Guajira Department presents the highest levels of desertification in the country, where approximately 75 percent of its land coverage presents a certain level of desertification [Minambiente 2015]. The decrease in rainfall in recent years in La Guajira has increased the desertification rates and has had serious negative effects on the agricultural and livestock sectors, with consequently decreasing levels of food and nutritional security, and affecting the overall health of the population [Minambiente 2005, WFP 2014]. In order to better understand the impact of desertification on the La Guajira Department, the Institute of Hydrology, Meteorology and Environmental Studies of Colombia (IDEAM), together with the Ministry of Environment and Sustainable Development of the Government of Colombia (MINAMBIENTE), and in partnership with the Geographic Institute Augustín Codazzi (IGAC), have collected data and investigated the La Guajira Department area to define the levels of desertification throughout the region. Figure 08 shows a map created from the System for Geographic Information and Territory Organization (SIGOT) online tool from IGAC, displaying the five identified levels of desertification in La Guajira based on Normalized Difference Vegetation Index (NDVI) land coverage satellite images and calculated aridity index from the superficial hydric balance (which includes the measurement of precipitation and evaporation ratio) for specific key regions in the La Guajira Department.

Figure 08: The Desertification Levels of La Guajira.

(Source: SIGOT online tool from IGAC)

2.4 Desertification and Health

Desertification studies have demonstrated that as one moves from areas with more natural resources to those with less, the poverty levels increase, female adult literacy decreases, and childhood morbidity and mortality increases [UNDP 2009]. Such findings point out that patterns of poverty are highly influenced by the lack of resources, but one must also account for the climate, political marginalization, remote location, and geographical isolation of these regions, and the possible lack of infrastructure that may lead to limited access to markets, education and health care facilities [Safriel and Zafar 2005].

In Colombia, a 2010 demographic and health national survey shows a 13 percent nationwide average rate of chronic malnutrition (based on age, height and weight) in children below five years of age, and that the La Guajira Department is one of the main drivers of this malnutrition rate with a 28 percent level [Profamilia 2011]. Furthermore, the areas of La Guajira presenting the highest desertification rates are shown to have twice the risk of childhood mortality due to malnutrition in comparison to other areas [Jaramillo-Mejía et al. 2018]. Addressing indigenous health gaps are part of the Sustainable Development Goals of the United Nations [SDG 2015], but international indigenous affairs reports have decried that, despite independent local efforts, Wayúu’s crops and livestock have wasted, and children are dying of preventable causes such as malnutrition and diseases (especially from diarrhea and pneumonia), and that these disparities may not to be a priority on the agenda of local government authorities [IWGIA 2015].

In summary, the effects of desertification are very apparent in terms of food insecurities and poor quality/shortage of water, leading to economic losses and health deterioration due to malnutrition and disease [Minsalud 2015]. Therefore, socio-economic factors such as food and water insecurities, low health care capacities, high morbidity and childhood mortality have a direct link to desertification, especially when considering indigenous populations [The Lancet 2009, Jaramillo-Mejía et al. 2016]. However, there is a lack of empirical studies showing how these individual and particular sets of risk factors in the La Guajira Department interact with each other.

Figure 09: Wayúu Children Begging for Food and Bottled Water.

At a remote region of Alta Guajira presenting high desertification levels. (Photo by Author, 2018) 13

In the arid, semi-arid and dry sub-humid areas that cover the majority of the La Guajira Department (Figure 10), drought and desertification are directly linked to food and water shortages, increased risk of fires, interpersonal conflicts and limited access to health care [Minsalud 2015]. Furthermore, women and children are especially vulnerable to droughts, in accordance to the different productive roles they carry out in society [Tichagwa 1994]. As the natural resources in close proximity to their community begin to disappear, Wayúu women become exposed to higher stress in searching for food, water and firewood because of the increased walking distances, often in dangerous conditions [Rosso 2016].

Figure 10: The La Guajira Land Coverage.

(Source: SIGOT online tool from IGAC)

2.5 Research Question and Hypothesis

Based on these background data reports from La Guajira’s drought and desertification (e.g., Etter et al. 2008), and the Wayúu people’s background reports on socio-economic and health challenges (e.g. Jaramillo-Mejía et al. 2018), the following research question and hypothesis have been formulated:

Research Question Do different levels of desertification affect the livelihood and health of the Wayúu people?

As this is a general question, these are the specific sub-questions that require answers:

1) What are the socio-economic situations (work, income and education) of people living in regions presenting different desertification levels?

2) What are the Wayúu people’s perceptions in regard to their access to food and water in regions presenting different desertification levels?

3) What are some of the major health issues that the Wayúu people experience in regions presenting different desertification levels, and what are their main challenges in regard to receiving proper medical care?

4) In regard to children, is malnutrition a problem and do rates of malnutrition differ in regions presenting different desertification levels?

5) Does childhood mortality differ in regions presenting different desertification levels?

Hypothesis Wayúu people living in areas with the highest levels of desertification present the poorest socio-economic and health conditions in comparison to people living in areas with lesser levels of desertification.

3. Research Design

As this is an empirical observational study, there are several variables to consider for evaluating the impact of desertification on the livelihood and health of the Wayúu people. In order to account for external variables, both the Media and Alta Guajira present different desertification levels within its boundaries, therefore the chosen areas for this research presented High, Medium and Low desertification levels, based on data previously described from IGAC and IDEAM (shown in Figure 8).

3.1 Research Area: Manaure and Uribia Municipalities

The initial challenge for choosing the communities is the fact that the Wayúu are reserved and do not usually accept the presence of strangers without a certain reason and/or connection to their Ranchería. Through the contacts I received from my supervisor Dr. Isabelle Schluep, I was able to network with the necessary entities and individuals who were able to facilitate my entry and acceptance into the Wayúu people’s communities.

The details of the community selection process are as follows: In the Manaure and Uribia municipalities, I pre-selected areas that presented different desertification levels. Then my guides (Miss Yaniris Mesa and Mr. Francisco Iniciarte) contacted community leaders and asked for their permission to enter communities within the selected areas. Once I received confirmation that it was ok to go into these selected areas for interviews, I designated a Reference Community to serve as a starting point for the randomization of other communities. The Reference Communities were a city, village or Ranchería within my catchment area that were chosen based on the presence of meteorological data (Standard Precipitation Index, total precipitation and evaporation data) from the IDEAM database. I then conducted a meteorological investigation to account for any major weather-related differences between Reference Communities within the same desertification level in the Manaure and Uribia regions (this meteorological investigation is explained in more detail in section 3.4). Random communities were then selected within an approximate 3km radius from the Reference Community, as to ensure I would stay within the same desertification level.

After the selection and interview process was finished, with the help of Global Positioning System (GPS) technology, I was able to generate a map of the study areas and selected communities as a reference guide (Figure 11).

Figure 11: Study Population and Communities Map.

A second relevant piece for background information is in regard to the Wayúu population density in the Manaure and Uribia municipalities. Considering that reports show significant levels of desertification, general poverty and health deterioration, why won’t the Wayúu people migrate to other areas? There may be various economic or social reasons for them being sedentary, but the most compelling answer I received from their own mouths is because of the attachment they have for their land. As previously stated, no matter the social, economical or environmental conditions, the

Wayúu love their land and will tend to it until the day they will be buried in it. From the 2005 Colombian national census, it shows that from the estimated 270’000 Wayúu living in Colombia, 98 percent of the population predominantly lives in the La Guajira department and the great majority living in Uribia (39 percent) and Manaure (17 percent) municipalities (Figure 12).

Figure 12: The Wayúu Population in Colombia.

(Source: DANE National Population Census, 2005)

The Government of La Guajira (in partnership with DANE and GPS satellite imaging) was able to pinpoint individual Wayúu communities within the municipalities in 2014, showing that the areas in Manaure and Uribia continue to be densely populated by the Wayúu (Figure 13).

Figure 13: Wayúu Populated Points in Manaure and Uribia.

7’743 Wayúu Populated Points (Manaure)

22’037 Wayúu Populated Points (Uribia)

(Source: DANE and Government of La Guajira, 2014)

3.2 Standard Precipitation Index and El Niño Southern Oscillation Events

The first step of the investigation as to why the La Guajira Department is currently going through a desertification process is to look at the precipitation patterns in the region. The lack of rainfall over a certain period of time can lead to various degrees of drought conditions, affecting water resources, agriculture and socio-economic activities. As rainfall varies significantly between different regions, the World Meteorological Organization (WMO) recommends examining the Standardized Precipitation Index (SPI) to monitor the severity of rain and drought events [WMO 2018].

The method used for SPI measurements was developed by McKee et al. [1993] to study relative departures of precipitation from normality. It uses monthly precipitation aggregates at various time scales (1, 3, 6, 12, 18, and 24 months, etc.). The SPI Z- score is the number of standard deviations a data point is from the overall mean at which an event occurs [Vicente-Serrano et al. 2010]. For example, in the Manaure municipality, a 12-month SPI Z-score is calculated based on the local precipitation accumulated over a 12-month period and compared with the mean total precipitation of Colombia during the same period. For precipitation, high positive values correspond to wet sequences and high negative values correspond to drought periods. For study purposes, I want to investigate if there are differences in precipitation rates between Manaure and Uribia, so that I can ensure both areas have compatible weather patterns to serve as replicates for analysis.

I also want to take into account other influences such as the El Niño Southern Oscillation (ENSO) events in the region. ENSO is an irregularly periodic variation in winds and sea surface temperatures (SST) over the tropical eastern Pacific Ocean, affecting climate of much of the tropics and subtropics [CPC 2005]. The warming phase of the sea temperature is known as El Niño (which can lead to very dry conditions in northern Colombia, as well as very wet condition in southern Colombia), the cooling phase as La Niña (which can lead to heavy rains in northern Colombia), and the Southern Oscillation is the accompanying low (El Niño) and high (La Niña) atmospheric pressure components, coupled with the sea temperature change [Trenberth et al. 2007]. 20

Figure 14 shows the results for the Standard Precipitation Index (SPI) and Oceanic Niño Index (ONI) analysis. Graph 14-A shows the SPI values for Manaure and Uribia Municipalities between 1982 and 2016. The graph was designed by the Author from SPI raw data obtained from IDEAM. The monthly precipitation aggregates were analyzed on a 12-month time scale. The “0-Score” represents the mean precipitation in Colombia over the time period. Lines represent the measured SPI Z-Scores for Manaure and Uribia Municipalities in relation to Colombia mean precipitation values. A One-Way ANOVA test reveals there are no significant differences between the municipalities

(F(1,820)=0.093, p>0.05), therefore indicating that it is safe to assume that the precipitation rates in Manaure and Uribia follow a similar pattern.

Graph 14-B shows the ENSO event intensities, based on the Oceanic Niño Index (ONI), between 1982 and 2016. Graph was designed by the Author with ONI raw data obtained from the US National Weather Service’s Climate Prediction Center (2018). ONI is the standard utilized by the US National Oceanic and Atmospheric Administration (NOAA) for identifying El Niño (warm) and La Niña (cool) events in the tropical Pacific. It’s the running 3-month mean SST anomaly for the Niño 3.4 region (located at 5oN-5oS, 120o-170oW). Events are defined as five consecutive overlapping 3-month periods at or above the +0.5oC anomaly for warm (El Niño) events and at or below the - 0.5oC anomalies for cold (La Niña) events. The threshold is further broken down into “Weak” (with a 0.5oC to 0.9oC SST anomaly), “Moderate” (1.0oC to 1.4oC), “Strong” (1.5oC to 1.9oC), and “Very Strong” (≥ 2.0oC) events (e.g., an event to be categorized as weak, moderate, strong or very strong must have equaled or exceeded the threshold for at least 3 consecutive overlapping 3-month periods) [NWC-CPC 2018].

Figure 14: Standard Precipitation Index (SPI) and Oceanic Nino Index (ONI).

Graph 14-A: SPI for Manaure and Uribia Municipalities between 1982 and 2016. (Source: IDEAM - http://www.ideam.gov.co/web/tiempo-y-clima/meteorologia-agricola). Graph 14-B: ENSO event intensities, based on ONI, between 1982 and 2016. (Source: http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php)

In summary, the SPI in the Manaure and Uribia municipalities are not significantly different from each other, meaning that precipitation indexes in both municipalities are relatively similar and compatible to one another, and therefore may be used as sites for collecting data point replicates. Furthermore, the ONI graph shows that both the Manaure and Uribia’s SPI values follow the ENSO events (e.g., a very strong El Niño event in 2016 corresponded with a highly negative SPI Z-score, which indicates a lack of rain). It suggests that ENSO events, which research has shown it can be influenced by climate change [Fasullo et al. 2018], may have an effect on the desertification of the region.

3.3 Temperature

The second step in the environmental analysis is to investigate the differences in temperature between Manaure and Uribia (i.e., if the temperature fluctuations in Manaure are similar or different to the temperature fluctuations in Uribia), so that I can further ensure that both areas have compatible weather patterns to serve as replicates for analysis.

Figure 15 shows the average maximum and minimum temperatures for Manaure and Uribia municipalities. Graph 15-A shows the monthly average maximum temperatures for the Manaure and Uribia between 1981 and 2010. The graph was designed by the Author from average temperature raw data obtained from IDEAM. A Chi-Square test reveals there are no significant differences between the average monthly maximum temperatures between the municipalities (X2=0.042, df=11, p>0.1), indicating that it is safe to assume that the maximum temperatures in Manaure and Uribia follow a similar pattern on a yearly basis.

Graph 15-B shows the monthly average minimum temperatures for the Manaure and Uribia between 1981 and 2010. The graph was designed by the Author from average temperature raw data obtained from IDEAM. A Chi-Square test reveals there are no significant differences between the average monthly minimum temperatures between the municipalities (X2=0.041, df=11, p>0.1), indicating that it is safe to assume that the minimum temperatures in Manaure and Uribia follow a similar pattern on a yearly basis.

Figure 15: Average Maximum and Minimum Temperature in Manaure and Uribia.

Graph 15-A: Average Maximum Temperature for Manaure and Uribia (1981 – 2010). Graph 15-B: Average Minimum Temperature for Manaure and Uribia (1981 – 2010). (Source: IDEAM - http://www.ideam.gov.co/web/tiempo-y-clima/meteorologia-agricola)

In summary, the average temperature graphs show that both average maximum and minimum temperatures in the Manaure and Uribia municipalities are not significantly different from each other, and therefore these areas may be used as sites for collecting data point replicates.

3.4 Precipitation and Evaporation

The third step in the environmental analysis is to investigate the differences in precipitation and evaporation in the Reference Communities in the Manaure and Uribia municipalities so that I can ensure that these specific sites have compatible weather patterns to serve as replicates for analysis. Precipitation and evaporation levels are measured by IDEAM via standard procedures and instruments (such as a rain gauge for precipitation and an atmometer for evaporation). As previously explained in Section 3.1, 24 one of the prerequisites for the selection of the Reference Communities is the presence of meteorological information available from the IDEAM database. I selected the communities of Manaure, Arruyaca and Cuririri in the Manaure municipality, and Nazareth, Huimparash and Karasua in the Uribia municipality to be my Reference Communities because they all present average total annual precipitation and evaporation rate measurements.

Figure 16 shows the average total annual precipitation measurements for Manaure and Uribia municipalities. Graph 16-A shows the average monthly and total annual precipitation for the Manaure municipality’s Reference Communities (Manaure, Arruyaca and Cuririri) between 1981 and 2010. The graph was designed by the Author from average total annual precipitation raw data obtained from IDEAM. A Chi-Square test reveals there are significant differences between the average total annual precipitation levels among the communities (X2=41.859, df=22, p=0.006), therefore indicating that all three Reference Communities have significantly different precipitation rates. Additionally, it is safe to assume that the results show the total annual precipitation rates are compatible to the desertification levels (i.e., high desertification = less precipitation, low desertification = more precipitation).

Graph 16-B shows the average total annual precipitation for the Uribia municipality’s Reference Communities (Nazareth, Huimparash and Karasua) between 1981 and 2010. The graph was designed by the Author from average total annual precipitation raw data obtained from IDEAM. A Chi-Square test reveals there are significant differences between the average total annual precipitation levels among the locations (X2=46.776, df=22, p=0.001), therefore indicating that all three Reference Communities also have significantly different total annual precipitation rates.

When comparing the Reference Communities from different municipalities that are located within similar desertification levels, a Wilcoxon rank sum test reveals there are no significant differences between the average total annual precipitation rates between the communities (Manaure vs. Nazareth: p=0.93, Arruyaca vs. Huimparash: p=0.66, Cuririri vs. Karasua: p=0.47). This indicates that Reference Communities located in similar desertification levels do not have significantly different annual precipitation rates.

Figure 16: Average Precipitation in Manaure and Uribia Reference Communities.

Graph 16-A: Average Precipitation for Manaure Municipality Reference Communities. Graph 16-B: Average Precipitation for Uribia Municipality Reference Communities. (Source: IDEAM - http://www.ideam.gov.co/web/tiempo-y-clima/meteorologia-agricola)

Figure 17 shows the average total annual evaporation measurements for Manaure and Uribia municipalities. Graph 17-A shows the average total annual evaporation for the Manaure municipality’s Reference Communities (Manaure, Arruyaca and Cuririri) between 1981 and 2010. The graph was designed by the Author from average total annual evaporation raw data obtained from IDEAM. A Chi-Square test reveals there are significant differences between the average total annual evaporation levels between the cities (X2=43.733, df=22, p=0.00003), therefore indicating that the Reference Communities have significantly different total annual evaporation rates. Additionally, it is safe to assume that the results show that the total annual evaporation rates are compatible to the desertification levels (i.e., high desertification = less evaporation, low desertification = more evaporation). Graph 17-B shows the average total annual evaporation for the Uribia municipality’s Reference Communities (Nazareth,

Huimparash and Karasua) between 1981 and 2010. The graph was designed by the Author from average total annual evaporation raw data obtained from IDEAM. A Chi- Square test reveals there are significant differences between the average total annual evaporation rates between the cities (X2=45.519, df=22, p=0.0007), therefore indicating that all three Reference Communities have significantly different evaporation rates.

When comparing the Reference Communities from different municipalities that are located within a similar desertification level area, a Wilcoxon rank sum test reveals there are no significant differences between the average total evaporations between the cities (Manaure vs. Nazareth: p=0.27, Arruyaca vs. Huimparash: p=0.08, Cuririri vs. Karasua: p=0.83). This indicates that Reference Communities located in similar desertification levels do not have significantly different annual evaporation rates.

Figure 17: Average Evaporation in Manaure and Uribia Reference Communities.

Graph 17-A: Average Precipitation for Manaure Reference Communities (1981 - 2010). Graph 17-B: Average Precipitation for Uribia Reference Communities (1981 - 2010). (Source: IDEAM - http://www.ideam.gov.co/web/tiempo-y-clima/meteorologia-agricola)

In summary, the precipitation and evaporation graphs show that both total annual precipitation and evaporation rates in the Reference Communities of Manaure and Uribia located within similar desertification levels, do not have significantly different weather patterns from each other; therefore, this further ensures the selected areas are compatible to be the sites for data collection.

Part 2: Field Research and Analysis

4. Materials and Methods

International [SDG 2015] and local efforts from the high courts of Colombia [Torrez 2018], have recognized that the precarious situation in La Guajira in terms of water and food insecurities, lack of proper health care and the population not participating in decision making processes is in direct violation of the constitutional rights of the Wayúu people. However, reports still show that the Wayúu populations of the Alta Guajira (which includes the Manaure and Uribia municipalities) are still suffering from the lack of infrastructure and, because of their lack of education, they are unable to play a role in the decision-making processes that affect their lives, making them even more vulnerable [Fajardo 2017]. Furthermore, the process of desertification may present a serious impact on their health and well-being [Jaramillo-Mejía et al. 2018]. Therefore, the Wayúu people depend on climate, government and natural ecosystem services for their basic needs. Their livelihood and health is dependent on a complex mix of factors involving the climate, geographic location, desertification level, education, age, gender, job opportunities and access to basic goods and services such as food, water, sanitation and health care. In order to begin to better understand the complexity of such a system, I designed a flowchart to scrutinize the different variables and possible outcomes that may be considered for analysis (Figure 18).

Figure 18: Framework for Analyzing the Impact of Desertification.

(Designed by Author, 2018 - Adapted from the United Nations Convention to Combat Desertification, 2010)

In order to analyze the effects of the environment on population livelihood and health, I have developed a household questionnaire to quantify and understand the effect the environment has on these different variables. The investigation involves looking at the environmental independent variables (i.e., desertification levels) and the influence they may pose on individual dependent variables such as access to income and education, water and food securities, rate of diseases and incidence of malnutrition in selected regions of the Manaure and Uribia municipalities of the La Guajira Department, and evaluate if they have a direct association to each other. The impact desertification on the wildlife migration and biodiversity of the region are important considerations (as they can relate to the livelihood of the Wayúu populations), but such topics have not been evaluated within this specific research study due to time constraints.

4.1 Development of the Household Questionnaire

The aim of the household questionnaire is to gain insight into the La Guajira population and to find out about key issues present in the selected regions. The questionnaire was developed based on questionnaire models utilized by the World Health Organization [WHO 2018], previous embeddedness investigations from CCRS [Sostizzo-Graf 2017], and earlier work I have performed in Uganda [DaSilva et al. 2013]. The questions were formulated with the help of Dr. Marta Jaramillo-Mejía according to predictions that are emphasized in the region. Questions include (but are not limited to) the evaluation of demographics, ethnicity, education, and income, water, food and energy sources, availability of goods and services, health history, environmental influences and livelihood satisfaction. The questions were either open-ended (continuous) or based on close-ended (ordinal) answers. It is important to point out that all respondents were free to give further comments to all questions if they chose to do so. All selected families needed to have at least one child, so they could be evaluated for malnutrition signs. Upon completion of the questionnaire, all 131 families received an appreciation package containing rice, lentils, plantains, corn and coffee (thanks to kind support from the University of Zurich Department of Evolutionary Biology and Environmental Studies and Prof. Marcus Hall). The questionnaire (in English, although it has been translated to Spanish) is found in the Appendix section of this manuscript.

Additionally, two other questionnaires were developed for this study. A health facility questionnaire was developed in order to understand the health capacity of clinics and hospitals located within the catchment area of the study, and a health worker questionnaire was designed to capture the perspective and opinions of health workers. The original aim was to also: 1) Quantify the local health care infrastructure, training of health workers and their satisfaction, and the impacts of health clinics on the well-being and livelihood of the local population; 2) Evaluate the perceptions from health professionals in regards to health problems that may be directly caused by environmental factors; 3) To determine if any local outreach initiatives (e.g., educational activities) were present in the regions. However, due to time and budget constraints, I was not able to apply these questionnaires. For publication and future research purposes, both questionnaires are found in the Appendix section of this manuscript. 30

4.2 Household Interview Process

4.2.1 Interviews in the Manaure Municipality

The household interview process in the Manaure municipality was carried between the 14th–15th (high desertification areas), 21st–22nd of May (medium desertification areas) and 11th–12th of June (low desertification areas), 2018. With the help from local contacts of the government-run Defensoría Del Pueblo (Ombudsman of the People - Riohacha office) spearheaded by Dr. Soraya Escobar Arregoces, I was granted access to different Rancherías in the Manaure municipality. Dr. Soraya’s niece, Yaniris Arregoces Mesa (a native-born Wayúu, 3rd year psychology student at University of La Guajira) was assigned to be my guide and interpreter for the interview process. Yaniris’s mother hosted me in her home at the Tomasitomana Ranchería (which served as my base in the Manaure region) and I hired some of her family members as drivers (her brother Nene Arregoces, uncle Rafael Arregoces and aunt Yasmin Romero). They would take me to a previously selected Reference Community (a suburban village or Ranchería), and from there to random villages within an approximate 3 km radius from this reference community (to ensure I would stay within the same desertification level during data collection). Most communities were very accepting of my presence because of my Wayúu guides and interpreter. In two instances I was denied entry into Rancherías for undisclosed reasons.

Figure 19: Manaure Municipality Interviews.

(Left Photo) The interview process conducted by my interpreter Yaniris and myself. (Right Photo) Without a knowledgeable local person driving, it would be nearly impossible to navigate the vast deserted region. (Photos by Author, 2018) 31

4.2.2 Interviews in the Uribia Municipality

The household interview process in the Uribia municipality was carried between June 4th–5th (medium desertification), 6th–7th (low desertification) and 8th–9th (high desertification) of 2018. With the help from local contacts of the Swiss non-profit organization Mama Tierra (http://www.mama-tierra.com), led by Katherine Klemenz and Lourdes Grollimund, I was able to interview communities in the northern part of the La Guajira peninsula. I stayed in the community of Panterramana with the family of Mama Tierra’s lead driver and Wayúu community leader Mr. Medardo Polanco, and was assisted by his sister Kayla Polanco and niece Steffi Iguaran, who are teachers at a local Wayúu school. My personal guide was Mr. Francisco Iniciarte, an anthropologist from Maracaibo, Venezuela, who is the chief of operations for Mama Tierra. The process of interviews was similar to the ones conducted in Manaure. In this location, all communities were very accepting of my presence.

Figure 20: Uribia Municipality Interviews.

(Left Photo) The Mama Tierra Team from left to right: Kayla Polanco, Francisco Iniciarte, Steffi Iguaran, Douglas DaSilva (Author), and Medardo Polanco. (Right Photo) Interview process with the help of the Mama Tierra staff. I personally trained and did the oversight for all staff to conduct the interviews in a standardized form. (Photos by Author, 2018)

4.2.3 Body Mass Index and Mid-Upper Arm Circumference

Body Mass Index (BMI), calculated from height and weight, and Mid-Upper Arm Circumference (MUAC) were obtained from all 293 children. BMI is used for classifying adults and children into groups, defined by their weight and height measurements (BMI = kg/m2) in relation to their age. The common interpretation for BMI is that it represents an index of an individual’s corpulence and used as a risk factor for the development or prevalence of several overweight and underweight health issues [WHO 1995]. Additionally, it has been useful in population-based studies by virtue of its wide acceptance in defining specific categories of body mass as a health issue [Bonilla et al. 2018]. MUAC measurements are used to quantify and identify children at a high risk of severe malnutrition. This is based on a series of longitudinal population studies, which took place in the 1980’s and early 1990’s, before the advent of modern community management of severe acute malnutrition [WHO-UNICEF 2009]. Studies demonstrated that MUAC has the highest receiver operating characteristic curve, which is an indication of good performance as a diagnostic tool [Myatt et al. 2006]. The advantage of MUAC is that it’s simple to use by trained community members and in health facilities, and easy for service providers and caretakers of children to understand [Briend et al. 2012].

5. Statistical Analyses

In surveys and observational studies, many variables can be investigated, and participants can be grouped in several different ways. For this specific master thesis, in addition to One-Way ANOVA (for continuous variables) and Chi-Square test for equality of proportions (for percentage differences), the Likert scale analysis was abundantly utilized to look at different variables in regard to the study participant’s living conditions (such as infrastructure, water and food availability) and their perceptions in regard to the surrounding environmental factors (such as temperature and rain). The participants were grouped either by gender, education level, a specific condition (e.g., the presence or absence of malnutrition), or based on the desertification level of the area where they dwell (high, medium and low desertification areas). As the results from this survey can have an effect on public perceptions and health issues, it is important to analyze the

33 results in a way that will minimize misleading conclusions. The following is a justification for the statistical analysis tools that are used on this master thesis.

5.1 Ordinal Data

Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories are not known [Agresti 2013]. The ordinal scale is distinguished from the nominal scale by having a ranking, and it also differs from interval and ratio scales by not having category widths that represent equal increments of the underlying attribute [Stevens 1946]. The most well- known example of ordinal data is the Likert scale [Cohen et. al. 1996].

5.1.1 The Likert Scale

The Likert scale was developed in the 1930’s by Rensis Likert to measure attitudes in psychology studies [Likert 1932]. The usual Likert scale is a 3-, 5- or 7-point ordinal scale used by respondents to rate the degree to which they can for example agree/disagree, like/dislike or evaluate the level of good/bad or benefit/detriment of a variable within a statement. Because it is represented in an ordinal scale, responses can be rated or ranked, thus you can determine the frequency differences between, for example, “Very Bad”, “Bad”, “Regular”, “Good” and “Excellent” on a frequency response scale. Unlike interval data (where the difference between responses can be calculated and the numbers refer to an actual measurement), on the Likert scale one cannot assume that the difference between responses is equidistant, even though the numbers assigned to those responses are [Jamieson 2004]. As a majority of interviewed individuals have no formal education and are not able to write or read, a chart scale of “smile faces” (Figure 21) was utilized to represent the Likert scale (e.g., red “sad face” representing “Very Bad” to the green “happy face” representing “Excellent”).

Choosing the best statistical test for the data collected for this master thesis was challenging. The choices I have made came from investigating various publications and message board forum readings (e.g., http://www.researchgate.net) in regard to choosing the most appropriate analysis for questionnaire-based ordinal Likert scale

34 data. There is a large debate in regard to dealing with Likert scale data. Some have argued that it can be treated as interval scale, and hence handled by parametric statistics [Lubke & Muthen 2004]. However, most agree that because Likert scale is ordinal, one should use non-parametric statistics for analysis [Pett 1997, Blaikie 2003, Hansen 2003, Jamieson 2004]. In light of such investigation, I use non-parametric analysis for all Likert scale type of data.

Figure 21: Household Questionnaire.

Household questionnaire accompanied by the smile faces chart representing the Likert scale. For example, “Very Bad” was represented by the red sad face, “Regular” the yellow indifferent face and “Very Good” by the green smile. The Mid-Upper Arm Circumference (MUAC) tape is also depicted. (Photo by Author, 2018)

5.1.2 Non-Parametric Analysis: The Kruskal-Wallis Test

The Kruskal-Wallis test is a rank-based, non-parametric test to determine whether samples originate from the same distribution [Kruskal & Wallis 1952]. It is considered the nonparametric alternative to the one-way ANOVA, and an extension of the Mann- Whitney U test to allow the comparison of more than two independent groups of equal or different sample sizes [Corder & Foreman 2009]. It is therefore used to determine if there are significant differences between two or more groups of an independent variable on an ordinal dependent variable. A significant Kruskal–Wallis test indicates that at least one group stochastically dominates at least one other group [Siegel & Castellan 1988]. However, it is important to realize that the Kruskal-Wallis test cannot tell you which specific groups are stochastically different; it only tells you that at least two groups are different. In order to further analyze the specific sample pairs for stochastic dominance, one must utilize a post hoc testing method such as the Dunn's test [Conover 1979].

As far as other non-parametric tests, the Chi-squared test is inappropriate for the Likert scale type data because the range used in the response coding influences the chi- squared calculation. For example, in some groups the number of answers to the “Very Bad” category is zero, and because of it the R software displays a warning message that “Chi-squared approximation may be incorrect”. The Kruskal-Wallis is specifically designed for Likert scales, and accommodates such data for a more comprehensive calculation of the Chi-squared value [Siegel & Castellan 1988]. Another non-parametric test available for Likert scale analysis is the Friedman test. It is similar to the parametric repeated measures ANOVA; therefore it is used to detect differences in treatments across multiple test attempts [Friedman 1940]. As the data in this master thesis does not follow such design (treatment differences across multiple tests), the Kruskal-Wallis is the most appropriate test for my data analysis.

5.1.3 Post Hoc Analysis: The Dunn Test

In order to determine the individual differences between the groups in question, one must do what is called a post hoc (after event) analysis for the Kruskal-Wallis test. The Dunn test computes the stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among the groups being considered in the analysis [Dunn 1964, Dunn 1968]. The Dunn test makes multiple pairwise comparisons based on Dunn’s Z-test statistic by approximating exact rank-sum test statistics by utilizing the mean rankings of the outcome in each group and basing inference on the differences in mean ranks in each group, in order to determine if there is a stochastic dominance [Dinno 2015]. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals to one-half [Dinno 2017]. It’s noteworthy to point out that the Dunn’s test null hypothesis analysis corresponds to that of the Wilcoxon rank sum test. However, when using the Kruskal-Wallis test you cannot use the Wilcoxon rank sum test without multiple comparison adjustments. The problems arise because (1) the ranks that the Wilcoxon pair-wise rank sum tests use are not the same ranks used by the Kruskal-Wallis test; and (2) Dunn's test preserves a pooled variance for the tests implied by the Kruskal- Wallis null hypothesis [Dinno 2017]. Therefore, I have chosen the Dunn test as post hoc based on suggestions by literature [Dinno, 2017, Agresti 2013, Corder & Foreman 2009, Siegel & Castellan 1988, Dunn 1964] and relevant discussions from various online forums regarding post hoc for the Kruskal-Wallis method (http://www.researchgate.net).

As far as other post hoc tests, I have encountered the Siegel-Tukey and Nemenyi tests. According to the 2006 book by Erich Lehmann on non-parametric statistics, the Siegel- Tukey is a non-parametric test that can be applied to data measured at least on an ordinal scale (e.g., Likert scale) and it tests for differences in scale between groups. The test is used to determine if one of two (or more) groups of data tends to have more widely dispersed values than the other. In other words, the test determines whether one of the two (or more) groups tends to move, sometimes to the right, sometimes to the left, but away from the center (of the ordinal scale). If the null hypothesis is true, it is

37 expected that the sum of the ranks (taking into account the size of the groups being compared) will be roughly the same. If one of the two groups is more dispersed its sum will be lower, due to receiving more of the low ranks reserved for the extreme tails, while the other group will receive more of the high scores assigned to the center (this is analogous to the Wilcoxon rank sum test) [Lehmann 2006]. The Nemenyi test is a post hoc test intended to find the groups of data that differ after a statistical test of multiple comparisons (such as the Friedman test) has rejected the null hypothesis that the performance of the comparisons on the groups of data is similar [Nemenyi 1963].

However, both the Siegel-Tukey and Nemenyi tests are not appropriate for analyzing groups with unequal numbers of observations [Hollander et. al. 1999], therefore these tests are not utilized in this master thesis because my groups slightly differ in the number of observations.

5.2 Continuous Data

An Analysis of Variance (ANOVA) tests two or more groups (i.e., the independent variables) for mean differences based on the measured values (i.e., the dependent variables). In the case of this master thesis, independent variable examples are the desertification levels or gender, and dependent variables examples are Body Mass Index (BMI) or Mid-Upper Arm Circumference (MUAC). Also, Chi-Square test for equality of proportions (with continuity corrections) between independently grouped populations was utilized to compare the incidence of percentages between dependent variables in these different groups.

6. Results

A summary of the communities visited can be found in the table below (Figure 22). The communities highlighted in green are located in areas with low desertification, the blue in medium desertification and red in the high desertification. My attempt was to keep the data point numbers balanced between both municipalities and desertification levels, but was not possible due to randomization. In Manaure, I visited one extra community in the high desertification area because after visiting four communities, I did not believe I had collected enough data points. All coordinates were obtained from GPS.

Figure 22: Summary of Communities Visited in Manaure and Uribia.

6.1 Adult Population

The first results are from the adult population. From a total of 230 adults, 131 are female (57 percent) and 99 are male (43 percent). The difference in gender numbers come from the fact that single mothers led 32 households. From the households that presented both parents, 89 percent of the time both were present in the household during the interview and were able to answer questions individually. In the remaining eleven percent, only the mother was present during the interview, but they were able to answer basic questions such as father’s education, salary and basic health related questions such as the number of colds and complaints of chronic headaches.

6.1.1 Education

Figure 23 shows the study adult population distribution and their education levels. Graph 23-A shows the distribution of males and females between the different desertification groups. Graph 23-B shows the general education distribution of the adult study participants among the different desertification levels. It is relevant to point out that nearly 80 percent of participants in the high desertification group have no formal education, and most educated participants have a primary school level of education only. Graph 23-C shows the percentage of adults who speak the official language of the country (Spanish). It is important to point out that less than 30 percent of adults living in the high desertification areas speak Spanish. Graph 23-D shows the percentage of adults who are able to read and write. Similarly, less than 30 percent of the adult population living in the high desertification areas can read or write. Such education disparity between desertification areas may be due to the isolation of communities living in areas with high desertification levels, and people living in medium and low desertification areas seem to have better access to educational opportunities. Additional analyses (not shown in graphical form) indicate there are no education disparities between genders.

Figure 23: Adult Education.

Chi-squared test for equality of proportions (with continuity corrections) used for analysis. Graph 23-A: Distribution of males and females between different desertification groups. Graph 23-B: Education distribution of adults among the different desertification groups. Graph 23-C: Percentage of adults who are able to speak Spanish. There are significant differences between High and Medium (X2=69.833, df=1, p<0.00001) and High and Low (X2=54.442, df=1, p<0.00001) groups. Graph 23-D: Percentage of adults who are able to read and write. There are significant differences between High and Medium (X2=95.191, df=1, p<0.00001), High and Low (X2=32.251, df=1, p<0.00001) and Medium and Low (X2=20.195, df=1, p=0.00007) groups.

In summary, from the adult education data, results suggest that there is a significantly large disparity in education levels from people who live in high desertification areas in comparison to medium and low desertification areas.

6.1.2 Living conditions

In order to give an overall picture of living conditions, data from household utilities and facilities (e.g., toilet, water source and garbage management) was obtained. Figure 24

41 illustrates some of the basic infrastructure conditions of study participants in high and medium desertification areas. Figure 24: Water and Human Waste.

Top left: even if they answered that the garbage is being burned, this is the situation of a burning location. Top right: a local 12-meter deep well. Bottom right: the water that came out of that well is turbid, salty and is consumed by locals without filtration or treatment. Bottom left: families have made attempts to build latrines, but they would soon be abandoned because it is more practical to go behind the bush. (Photos by Author, 2018)

Figure 25 shows the distribution of the living conditions of study participants. Graph 25- A shows that over 90 percent of households in the high and medium desertification areas do not have a proper toilet or latrine. Graph 25-B shows the different methods of garbage management. Despite showing that households in high and medium desertification areas mostly burn their garbage, there is no proper site or standard methods for burning, causing much of the waste to be thrown around (see Figure 24).

Graph 25-C shows different sources of water. “Bottled” water means an ambulant vendor pushes a cartwheel with 20-liter jugs of untreated water and sells them for approximately 1000 COP (0.30 CHF). For a 20-Liter jug of treated water one pays 5000 COP (1.50 CHF), but usually people do not buy these because of the price difference. In high and medium desertification areas, the majority of the people obtain their water from wells. From observations, the wells are not deep (circa 12-15 meters) and water is dirty and likely contaminated. Graph 25-D reveals that 100 percent of the households in high desertification regions do not use any type of water treatment (boiling, filtering or chemical). Such practices can increase the risk of water borne diseases that can cause diarrhea in adults and children.

Figure 25: Household Infrastructure.

Graph 25-A: Types of Toilet Facilities. Graph 25-B: Types of Garbage Management Strategies. Graph 25-C: Sources of Water. Graph 25-D: Water Treatment.

Figure 26 shows a graph of the average walking time (round trip) for families to collect water in high and medium desertification areas. People living in low desertification areas usually purchase water from ambulant vendors or have a public tap within their property, therefore they are not considered in these analyses. Results suggest that both residents of high and medium desertification areas need to walk relatively similar distances to collect water on a daily basis. However, it is important to point out that the mean round trip time to make a water run is approximately 50 minutes, and many families reported doing the water trip at least two or three times per day.

Figure 26: Round Trip Walk to the Nearest Water Source.

Colored box represents the interquartile range. Bottom of the box is the lower quartile (25th percentile), the middle line is the median, and top of the box is the upper quartile (75th percentile). Upper and lower whisker ends represent largest and smallest sample values (excluding outliers). Dots outside of these ranges are outliers. The yellow bars show the means. A One-Way ANOVA test reveals there are no significant differences between the groups (F(1,93)=0.002, p>0.1). Mean values: High: M=51.4, SE=10.22; Medium: M=50.9, SE=5.99. 44

In summary, in regard to toilet facilities, over 90 percent of the answers in the high and medium desertification households said “voy al monte”, meaning that they go into the bush or behind a tree to urinate or defecate. Garbage is usually burned in the medium and high desertification areas, but there is usually no designated place or standard practice for the burning process. Water is usually dirty, as it comes from shallow wells, and families can spend a few hours per day walking in a 35 degrees Celsius heat to get to the nearest water source. Also, most people don’t have the practice of at least boiling the water before consuming it, which can increase their chances of acquiring water- borne diseases.

6.1.3 Health

In regard to health, Figure 27 shows different health markers for adults participating in the study. Graph 27-A shows the incidence of cold symptoms (general flu-like symptoms such as cough, runny nose and malaise) in the past six months. Results suggest that high and medium desertification populations experience more colds in comparison to low desertification populations. Graph 27-B shows the incidence of chronic headaches (at least four times per week) experienced by the adult population in the past six months. Results suggest that high and medium desertification populations currently experience more chronic headaches in comparison to low desertification populations. Graph 27-C shows the current (at least in the past 30 days) intake of medications by the adult population. Results suggest that high desertification populations significantly take more medications in comparison to medium and low desertification populations. From all adults who reported currently taking medications (n=131), 85.5 percent take generic Acetaminophen, as it is the cheapest and easiest medication one can find in the region.

Figure 27: Adult Health.

Chi-squared test for equality of proportions (with continuity corrections) used for all analysis. Graph 27-A: Cold Symptoms. There are significant differences between High and Low (X2=5.663, df=1, p=0.01) and Medium and Low (X2=6.634, df=1, p=0.01) groups. Graph 27-B: Chronic Headaches. There are significant differences between High and Low (X2=22.076, df=1, p<0.00001) and Medium and Low (X2=11.776, df=1, p=0.0006) groups. Graph 27-C: Current Medication Intake. There are significant differences between High and Medium (X2=12.431, df=1, p=0.0004) and High and Low (X2=3.917, df=1, p=0.04) groups.

Figure 28 shows the average time it takes a family to reach the nearest health center, in relation to the desertification level of where they live. Results suggest a correlative relationship in that it takes significantly longer for families who live in high desertification areas to reach the nearest health center in comparison to families living in medium and low desertification levels. Further analysis show that people who live in high desertification areas need an average of two hours by public transportation to reach the nearest health center, but it can take as long as three to five hours for those who cannot afford to take a motor vehicle and need to walk to the health center. In medium

46 desertification areas, health centers were distributed within an hour by public transport or two to three hours walking. In low desertification areas, most people can usually walk to a clinic in about 20 to 30 minutes.

Figure 28: Time to Reach Nearest Health Facility at Different Desertification Levels.

The yellow bars show the means. A One-Way ANOVA test reveals there are significant differences between the groups (F(2,128)=39.8, p<0.00001), and a post hoc Tukey multiple pairwise comparison reveals there are significant differences between High and Medium (p<0.00001), High and Low (p<0.00001) and Medium and Low (p=0.0002) groups. Mean values: High: M=119.0, SE=9.70; Medium: M=63.5, SE=7.44; Low: M=18.6, SE=2.42.

In summary, colds and headaches are the most prominent types of complaints in terms of health issues among adults. For such conditions, adults usually take Acetaminophen, as it is the least expensive and most widely available (and easily attainable) medication in the region. Adults living in high desertification regions seem to experience more headaches and take more medications in comparison to adults living in medium and low desertification regions. Furthermore, it is significantly more difficult and longer for people living in high desertification areas to reach the nearest health center in comparison to medium or low desertification areas.

6.1.4 Occupation and Income

Figure 29 shows the distribution of occupations among the adult interviewees (n=230). It’s important to notice that nearly 65 percent of the population interviewed (n=148) has some type of remunerated occupation (majority work with crafting and selling Wayúu bags). From the total number of unemployed individuals (n=82), 62 percent are female housewives who take care of the household and children full-time, and the remaining 38 percent are unemployed males actively searching for a job. In the study population, both male and female have similar opportunities of earning income, as 68 percent of the male interviewees and 61 percent of female interviewees have a remunerated activity.

Figure 29: Adult Population Occupation Distribution.

Created by Author from collected data, 2018

Figure 30 shows the financial situation, in terms of total monthly income in Colombian Pesos (COP), with Swiss Francs (CHF) shown between parentheses, of households in relation to the desertification level of where they live.

Figure 30: Monthly Household Income by Desertification Level.

The yellow bars show the means. A One-Way ANOVA test reveals that there are significant differences between the groups (F(2,128)=21.3, p<0.00001), and a post hoc Tukey multiple pairwise comparison reveals that there are significant differences between High and Low (p<0.00001) and Medium and Low (p=0.00001) groups. Mean values: High: M=196’605, SE=27’059; Medium: M=337’212, SE=24’478; Low: M=713’750, SE=104’222.

Results suggest that despite an average range of opportunities for earning income, populations living in areas with high and medium desertification levels present the lowest average monthly household income in comparison to areas with low desertification levels.

Furthermore, Figure 31 shows how education also plays part in the individual’s capacity of obtaining income. Graph 31-A shows the financial situation (monthly income) of individuals based on their education level and graph 31-B shows the financial situation (monthly income) of individuals based on education and the desertification level of the region where they live.

Figure 31: Individual Monthly Income and Education.

Graph 31-A: Monthly income of individuals based on their education level. The yellow bars show the means. A One-Way ANOVA test reveals that there are significant differences between the groups (F(4,225)=19.32, p<0.00001), and a post hoc Tukey multiple pairwise comparison reveals that there are no significant differences between No Education, Primary and Secondary education levels (henceforth: NPS, p>0.05), but there are significant differences between High School and NPS (overall p=0.0001), College and NPS (overall p<0.00001) and College and High School (p=0.00003).

Graph 31-B: Monthly income of individuals based on education and desertification level. A One- Way ANOVA and post hoc Tukey multiple pairwise comparisons are used for analysis. When taking desertification levels into account, the most striking difference comes from the monthly incomes of the population with no education (F(2,85)=14.35, p<0.00001), revealing that the High group income is significantly lower than Medium (p=0.04) and Low (p<0.00001) groups.

Results suggest that having a High School or College education may provide you with a significantly higher monthly income, but it’s worth noting that these levels of education only accounted for approximately 10 percent of the adult population interviewed. It also suggests that individuals without formal education living in areas with high and medium desertification levels are at the most at risk for poverty in comparison to low desertification regions. It is important to note that such trend is evident because the low desertification study areas are located near urban centers, where there may be more possibilities for income generation even for individuals without formal education.

Analyses of income for different genders are depicted in Figure 32. Graph 32-A shows that there seem to be no significant disparities between genders in their capacity of earning income. However, when looking at the different desertification levels, graph 32- B reveals that males and females living in high desertification regions equally earn less in comparison to people from the same gender living in medium and low desertification regions. Also, in the low desertification region, males on average seem to earn significantly more than females.

Figure 32: Individual Monthly Income by Gender.

Graph 32-A: Monthly income among genders. The yellow bars show the means. A One-Way ANOVA test reveals there are no significant differences between the groups (F(1,228)=3.36, p>0.05), indicating there are no income disparities between genders within the study population.

Graph 32-B: Monthly income based on gender and desertification level. One-Way ANOVA and post hoc Tukey multiple pairwise comparisons are used for analysis. When taking desertification levels into account, there were significant differences within the gender groups [(Male: F(2,96)=26.32, p<0.00001), Female: F(2,128)=6.26, p=0.002)]. Pairwise comparisons for Males reveal there are significant differences between High and Low (p<0.00001) and Medium and Low (p<0.00001) groups. Comparison for Females reveal there are significant differences between High and Low (p=0.002) and Medium and High (p=0.04) groups.

Similar analyses were performed for the combined ability to speak Spanish and read/write and their capacity for earning income (represented by the individual monthly income). A One-Way ANOVA test reveals that there are significant differences between the groups (F(1,228)=20.7, p<0.00001), suggesting that people who do not speak Spanish and cannot read/write may have a lower capacity to earn money in comparison to those who can both speak Spanish and read/write.

6.1.5 Land, Agriculture and Pastoralism

The Wayúu live from their land, but the lack of technology and infrastructure mostly allow them to practice a subsistence type of plantations, and trading in small scale among neighboring Rancherías. People living in high and medium desertification areas have enough land available for plantations and raising animals. Pastoralism is an important activity for the subsistence of the Wayúu because animals (predominantly goats, pigs and chickens) provide meat, milk and eggs for their personal consumption, and on a small scale, it serves as a complementary source of income for households (as shown in Figure 29, less than two percent of the families interviewed lived solely from pastoral income). Populations living in low desertification areas dwell in suburban settings, where family land plots are small and are not conducive for raising animals or subsistence plantations. Most families in the low desertification regions have access to local markets and a certain level of infrastructure. Based on this information, I asked people living in high and medium desertification areas if they are able to cultivate their land, and their carrying capacity for raising farm animals (such as goats and pigs). Figure 33 shows the results from these analyses. Graph 33-A shows the percentage of households that can cultivate their land; graph 33-B shows the percentage of households that currently own farm animals; and graph 33-C reveals the results of animal loss in the past year. Results suggest that high desertification areas have a significantly lower capacity of sustaining crops due to deficits in water and rain. It also shows that farm animals are present on the majority of households and are an important part of their subsistence. Considering herds are not large (average 27 animals in medium desertification households and 16 in high desertification households), they are used for the family consumption and can be sold to generate supplemental income during times of necessity. Furthermore, results show that farm animals are at a high risk for mortality. It is important to note that approximately 85 percent of the households in the high desertification areas reported having lost at least one animal in the past year. The reported reasons for animal loss in high desertification areas include hunger and thirst (90%), theft (5%) and killed by predators (5%). The reported reasons for animal loss in medium desertification areas include hunger and thirst (52%), theft (28%), unknown disease (16%) and killed by predators (4%).

Figure 33: Land, Agriculture and Pastoralism.

Chi-squared test for equality of proportions (with continuity corrections) used for analysis. Graph 33-A: Shows the ability to cultivate the land. There are significant differences between High and Medium (X2=52.983, df=1, p<0.00001) groups, suggesting that high desertification regions have significantly lower capacity for sustaining crops. Graph 33-B: Shows that the majority of households raise farm animals. Average number of animals per household: Medium group = 27, High group = 16. Graph 33-C: When asked if they had lost at least 1 animal in the past year, there are significant differences between High and Medium (X2=13.340, df=1, p=0.0002) groups, suggesting that high desertification households significantly lose more animals. Average number of animals lost per household: Medium group = 4, High group = 7.

6.1.6 Perceptions

As part of this research, the views and opinions of the Wayúu people are extremely important in order to account for their perceptions on various sectors of their livelihood and surrounding environment. All the following results were obtained from Likert scale analyses and reflect the perceptions of the Wayúu people themselves.

Community Safety

I will utilize the “Community Safety” analysis as an in-depth example of how all the subsequent analyses were performed and explain the layout of the graphs. Families were asked how safe they felt living in their communities. As follows, an example of the question extracted directly from the questionnaire located in the Appendix:

In general, do you feel safe Not Little Somewhat Very Extremely in your home and your Safe Safe Safe Safe Safe community? (circle one) 1 2 3 4 5

Figure 34 shows the graphs obtained based on this Likert scale analysis on the population’s perceptions of safety in their communities. Graph 34-A shows the different desertification level groups on the y-axis, and Likert scale responses in different colors. The x-axis represents the percentage of responses from the total number of interviewees for each individual category (“Not Safe” in dark brown, “Little Safe” in light brown, “Somewhat Safe” in grey, “Very Safe” in light green, “Extremely Safe” in dark green). On the graph, the left “%” values for each group represent the percentage of “negative” (i.e., “Not Safe” and “Little Safe”) responses combined, the middle “%” values represent the percentage of the mid category (i.e., “Somewhat Safe”) responses, and the right “%” values represent the percentage of “positive” (i.e., “Very Safe” and “Extremely Safe”) responses combined. The brackets ( [ ) and stars ( * ) show that statistically significant differences are found between the groups. Graph 34-B shows a simple Histogram representing the frequency of distribution of the answers and the line shows the overall mean (for data visualization purposes only). Graph 34-C shows the Mean Plots, which are the means and standard errors for the Likert scale analysis. Histograms and Mean Plots were obtained on all analysis, but both graphs will not be shown on subsequent results because the Likert graphs and descriptions will contain all necessary information such as the Kruskal-Wallis and post hoc Dunn results, means (M) and standard error (SE) values.

Figure 34: Perceptions on Community Safety.

Graph 34-A: Population’s perceptions of safety in their communities. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and distribution of responses (X2=6.882, df=2, p=0.03). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.01) groups.

Graph 34-B: Histogram (for visualization purposes only) 57

Graph 34-C: Mean Plots (for visualization purposes only) with mean values (M) and standard errors (SE). Mean values: High: M= 3.86, SE=0.178; Medium: M=4.44, SE=0.111; Low: M=4.00, SE=0.195.

Overall results indicate that the majority of interviewees feel safe living in their homes and communities. Some of the negative answers reflect individuals who have engaged in personal conflicts with neighbors or have had their animals stolen, but there are no reports of random violence, crimes or disarray within the communities.

Drinking Water Quantity

Figure 35 shows the graphs on perceptions on drinking water quantity (Graph 35-A), and how much they believe water quantity has changed in the past year (Graph 35-B). Results suggest that populations living in high desertification areas may have significantly lower quantity of drinking water available in comparison to medium and low desertification regions. Also, despite a great majority within the groups believe the water quantity in all three regions have remained the same in the past year, high desertification regions may have experienced a significant worsening in water quantity in comparison to medium and low desertification regions.

Figure 35: Perceptions on Drinking Water Quantity.

Graph 35-A: Shows the population’s perceptions on drinking water quantity. A Kruskal- Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=24.057, df=2, p<0.00001). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.00001) and High and Low (**p=0.00005) groups. Mean values: High: M= 2.30, SE=0.158; Medium: M=3.38, SE=0.158; Low: M=3.36, SE=0.155.

Graph 35-B: Shows the population’s perceptions on drinking water quantity change in the past year. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=13.079, df=2, p=0.001). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.01) and High and Low (**p=0.0004) groups. Mean values: High: M= 2.23, SE=0.144; Medium: M=2.69, SE=0.093; Low: M=2.89, SE=0.053.

Drinking Water Quality

Figure 36 shows the graphs on perceptions on drinking water quality (Graph 36-A), and how much they believe this water quality has changed in the past year (Graph 36-B). Results suggest that populations living in high desertification regions may have worse drinking water quality in comparison to medium and low desertification regions. Also, despite a great majority within the groups believe the water quality in all three regions have remained the same in the past year, the water quality in high desertification regions may have become slightly worse in comparison to medium desertification regions.

Figure 36: Perceptions on Drinking Water Quality.

Graph 36-A: Shows the population’s perceptions on drinking water quality. A Kruskal- Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=13.538, df=2, p=0.001). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.0002) and High and Low (**p=0.05) groups. Mean values: High: M= 2.58, SE=0.198; Medium: M=3.50, SE=0.127; Low: M=3.22, SE=0.150.

Graph 36-B: Shows the population’s perceptions on drinking water quality change in the past year. A Kruskal-Wallis test reveals there may be no significant differences between the desertification groups and the distribution of responses (X2=4.652, df=2, p=0.09). However, a post hoc Dunn test reveals there is a slight significant difference between High and Medium (*p=0.03) groups. Mean values: High: M= 2.30, SE=0.147; Medium: M=2.71, SE=0.092; Low: M=2.64, SE=0.098.

Food Availability

Figure 37 shows the graphs on perceptions on food availability (Graph 37-A), and how much they believe this food availability has changed in the past year (Graph 37-B). Results suggest that populations living in high desertification regions may have significantly lower availability of food in comparison to medium and low desertification regions. Also, despite a great majority within the groups believe the food availability in all three regions have remained the same in the past year, high desertification regions may have experienced a significant worsening in food availability in comparison to medium and low desertification regions.

Figure 37: Perceptions on Food Availability.

Graph 37-A: Shows the population’s perceptions on food availability. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=43.709, df=2, p<0.00001). A post hoc Dunn test reveals significant differences between High and Medium (*p<0.00001) and High and Low (**p<0.00001) groups. Mean values: High: M= 1.95, SE=0.129; Medium: M=3.29, SE=0.108; Low: M=3.42, SE=0.212.

Graph 37-B: Shows the population’s perceptions on food availability change in the past year. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=20.647, df=2, p=0.00003). A post hoc Dunn test reveals significant differences between High and Medium (*p<0.00001) and High and Low (**p=0.009) groups. Mean values: High: M= 2.35, SE=0.141; Medium: M=3.02, SE=0.043; Low: M=2.81, SE=0.087.

Food Variety

Figure 38 shows the graphs on perceptions on food variety (Graph 38-A), and how much they believe this food variety has changed in the past year (Graph 38-B). Results suggest that populations living in high desertification regions may have significantly lower variety of food in comparison to medium and low desertification regions. Also, despite a great majority within the groups believe the food variety in all three regions have remained the same in the past year, high desertification regions may have experienced a significant worsening in food variety in comparison to medium and low desertification regions.

Figure 38: Perceptions on Food Variety.

Graph 38-A: Shows the population’s perceptions on food variety. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=39.622, df=2, p<0.00001). A post hoc Dunn test reveals significant differences between High and Medium (*p<0.00001) and High and Low (**p<0.00001) groups. Mean values: High: M= 1.91, SE=0.132; Medium: M=3.15, SE=0.121; Low: M=3.36, SE=0.204.

Graph 38-B: Shows the population’s perceptions on food variety change in the past year. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=17.886, df=2, p=0.0001). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.00002) and High and Low (**p=0.01) groups. Mean values: High: M= 2.44, SE=0.130; Medium: M=3.00, SE=0.038; Low: M=2.83, SE=0.074.

Precipitation

Figure 39 shows the graphs on perceptions on the amount of precipitation (Graph 39- A), and how much they believe the amount of precipitation has changed in the past year (Graph 39-B). Results suggest that populations living in high desertification regions experience significantly less precipitation in comparison to medium and low desertification regions. Furthermore, populations living in high desertification regions believe that precipitation levels have significantly decreased in the past year in comparison to medium and low desertification regions.

Figure 39: Perceptions on Precipitation.

Graph 39-A: Shows the population’s perceptions on precipitation levels. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=86.798, df=2, p<0.00001). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.0007) and High and Low (**p<0.00001) and Medium and Low (***p<0.00001) groups. Mean values: High: M= 1.53, SE=0.090; Medium: M=2.06, SE=0.042; Low: M=2.97, SE=0.048.

Graph 39-B: Shows the population’s perceptions on precipitation levels change in the past year. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=43.863, df=2, p<0.00001). A post hoc Dunn test reveals significant differences between High and Medium (*p<0.00001), High and Low (**p<0.00001) and Medium and Low (***p=0.02) groups. Mean values: High: M= 1.98, SE=0.122; Medium: M=2.69, SE=0.070; Low: M=2.97, SE=0.027.

Amount of Wind and Dust in the Air

Figure 40 shows the graphs on perceptions on the amount of dust in the air (Graph 40- A), and amount of wind (Graph 40-B) they experience. Results suggest that populations living in high and medium desertification regions believe the amount of dust in the air is significantly higher in comparison to low desertification regions. Also, populations living in high and medium desertification regions believe they experience significantly higher amounts of wind in comparison to low desertification regions. The wind increases the amount of dust in the air, however it also has a cooling effect from the intense heat, and therefore the overall opinion of the population is that it is not bothersome.

Figure 40: Amount of Wind and Dust in the Air.

Graph 40-A. Shows the population’s perceptions on the amount of dust in the air (includes dust from unpaved roads and sand). A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=19.757, df=2, p=0.00005). A post hoc Dunn test reveals significant differences between High and Low (*p=0.002) and Medium and Low (**p=0.00001) groups. Mean values: High: M= 2.33, SE=0.128; Medium: M=2.08, SE=0.113; Low: M=2.86, SE=0.107. 69

Graph 40-B. Shows the population’s perceptions on the amount of wind. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=39.107, df=2, p<0.00001). A post hoc Dunn test reveals significant differences between High and Low (*p<0.00001) and Medium and Low (**p<0.00001) groups. Mean values: High: M= 4.33, SE=0.109; Medium: M=4.54, SE=0.075; Low: M=3.47, SE=0.116.

Temperature

Figure 41 shows the graphs on perceptions on temperature (Graph 41-A), and how much they believe the temperature has changed in the past year (Graph 41-B). Results suggest that populations living in high desertification regions believe the heat they experience is significantly higher in comparison to medium and low desertification regions. Also, despite a great majority within the groups believe the temperature in all three regions have remained the same in the past year, populations living in high desertification regions believe the heat they experience has become significantly hotter in comparison to medium and low desertification regions.

Figure 41: Temperature.

Graph 41-A: Shows the population’s perceptions on temperature. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=35.136, df=2, p<0.00001). A post hoc Dunn test reveals significant differences between High and Medium (*p<0.00001) and High and Low (**p=0.02) and Medium and Low (***p=0.001) groups. Mean values: High: M= 2.40, SE=0.149; Medium: M=3.60, SE=0.107; Low: M=3.00, SE=0.138. 71

Graph 41-B: Shows the Likert scale for the population’s perceptions on the temperature change in the past year. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=17.954, df=2, p=0.0001). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.00005) and High and Low (**p=0.002) groups. Mean values: High: M= 2.56, SE=0.117; Medium: M=3.00, SE=0.027; Low: M=2.92, SE=0.061.

Quality of Roads and Nearest Health Center

Figure 42 shows the graphs on perceptions on the quality of roads to reach the nearest health center (Graph 42-A), and their perceptions on the quality of this health center (Graph 42-B). Results suggest that populations living in high desertification regions may have significantly lower quality roads available to reach the nearest health center in comparison to medium and low desertification regions. Also, populations living in high desertification regions may have significantly lower quality health centers available to assist them in comparison to medium and low desertification regions.

Figure 42: Quality of Roads and Nearest Health Center.

Graph 42-A: Shows the population’s perceptions on the road quality to the nearest health center. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=27.278, df=2, p<0.00001). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.009), Medium and Low (**p=0.002) and High and Low (***p<0.00001) groups. Mean values: High: M= 1.63, SE=0.120; Medium: M=2.19, SE=0.158; Low: M=2.81, SE=0.111.

Graph 42-B: Shows the population’s perceptions on the quality of the nearest health center. A Kruskal-Wallis test reveals an overall significant difference between the desertification groups and the distribution of responses (X2=13.531, df=2, p=0.001). A post hoc Dunn test reveals significant differences between High and Medium (*p=0.0003) and High and Low (**p=0.007) groups. Mean values: High: M= 3.26, SE=0.186; Medium: M=4.04, SE=0.101; Low: M=3.91, SE=0.176.

6.2 Children Population

The second sets of results obtained were from the young population. From a total of 293 children, 149 were female (51 percent) and 144 were male (49 percent). I personally evaluated children for malnutrition signs based on training I have received from the University of Wisconsin – Madison (while working for ten years as a pediatric allergy and nutrition researcher) and from training at the Ugandan Ministry of Health Village Health Team program, where I researched malnutrition and health danger signs in rural communities for a period of three years. As the assessment of malnutrition is quite difficult and subject to error, I additionally obtained the height and weight from all children, calculated their Body Mass Index (BMI), and measured the children’s mid- upper arm circumference (MUAC). Both BMI and MUAC are part of the child growth standards stipulated by the WHO [2009] and can identify the risk for malnutrition. Furthermore, I estimated the children’s daily intake of food by creating a Food Score system (based on recommended nutritional intake standards from the Colombian Ministry of Health) to further evaluate the malnutrition assessment.

6.2.1 Malnutrition and Child Mortality

Figure 43 highlights the malnutrition status of the children who participated in the study. Graph 43-A introduces the population by showing the distribution of male and female children between the different desertification groups. Graph 43-B shows the percentage of children who were assessed positive for malnutrition (all children combined and by gender differentiation). Graph 43-C shows percentage of the positive assessment of malnutrition signs in children among the different desertification levels. Results suggest that children living in high desertification areas are at a higher risk for malnutrition signs. Graph 43-D shows examples of observed malnutrition signs among the children. Presences of any of these visual danger signs, and/or physician-diagnosed malnutrition (from medical records, if available) were taken into account to determine the positive assessment of malnutrition for all children.

Figure 43: Children’s Demographics and Malnutrition Assessment.

Chi-squared test for equality of proportions (with continuity corrections) used for analysis. Graph 43-A: Shows the distribution of male and female children between the different desertification groups (n=293). Graph 43-B: Shows the percentage of children assessed positive for malnutrition (all children combined and gender differentiation). Graph 43-C: Shows the percentage of positive assessment for malnutrition signs in children among the different desertification levels. There are significant differences between the groups (X2=49.291, df=2, p<0.00001). Differences between High and Medium (X2=23.528, df=1, p<0.00001) and High and Low (X2=41.65, df=1, p<0.00001). Graph 43-D: Illustrates examples of observed malnutrition signs among the children.

Figure 44 shows the children’s general health status. Graph 44-A shows the percentage of children that had at least one diarrhea episode in the past six months. Results suggest that children living in medium and high desertification areas are at a high risk for diarrhea. Graph 44-B shows percentage of children that had at least one episode of fever in the past six months. Results suggest that children living in medium and high

76 desertification areas are at a high risk for fever. Graph 44-C shows percentage of children that had at least one episode of vomiting in the past six months. Results suggest that children living in medium and high desertification areas are at a higher risk for vomiting. Graph 44-D shows percentage of children’s medication intake in the past six months. Results suggest that children living in medium and high desertification areas significantly take more medications than children in low desertification regions.

Figure 44: Children’s General Health.

Chi-squared test for equality of proportions (with continuity corrections) used for analysis. Graph 44-A: Shows the percentage of children with diarrhea. There are significant differences between High and Low (X2=17.002, df=1, p=0.00003) and Medium and Low (X2=21.257, df=1, p<0.00001) groups. Graph 44-B: Shows the percentage of children with fever. There are significant differences between High and Low (X2=3.843, df=1, p=0.04) and Medium and Low (X2=13.637, df=1, p=0.0002) groups. Graph 44-C: Shows the percentage of children with vomiting. There are significant differences between High and Low (X2=27.861, df=1, p<0.00001) and High and Medium (X2=24.118, df=1, p<0.00001) groups. Graph 44-D: Shows the percentage of children’s medication intake. There are significant differences between High and Low (X2=6.602, df=1, p=0.01) and Medium and Low (X2=7.822, df=1, p=0.005) groups.

Figure 45 shows a parallel between the presence of malnutrition and diarrhea, and the presence of malnutrition and parental report of the perception that the family does not have enough food to eat on a daily basis. These analyses serve as a way to further validate the assessment of malnutrition in the study population (because diarrhea is a well-known chronic symptom of malnutrition), and to evaluate the food availability and the incidence of malnutrition in this population. Graph 45-A shows percentage of malnourished children and the presence of diarrhea. Results suggest that children with malnutrition have a higher incidence of diarrhea. It is important to point out that nearly 90 percent of the children assessed positive to malnutrition presented diarrhea in the past six months. Graph 45-B shows a parallel between malnutrition and parental report of not having enough food on a daily basis. It is important to point out that 100 percent of children assessed to be malnourished had a parental report of not having enough food available on a daily basis. Parental opinions on what “having enough food” means can widely vary, therefore I do not suggest that the opinion of not having enough food can lead to malnutrition - it is simply a marker for further investigation.

Figure 45: Malnutrition, Diarrhea and Food Availability.

Graph 45-A: Shows the percentage of malnourished children and the presence of diarrhea. Chi-squared test for equality of proportions (with continuity corrections) used for analysis. There are significant differences between groups (X2=48.663, df=1, p<0.00001).

Graph 45-B: Shows the percentage of malnourished children and parental report of not enough daily food availability. Chi-squared test for equality of proportions (with continuity corrections) used for analysis. There are significant differences between groups (X2=97.244, df=1, p<0.00001).

Figure 46 shows the childhood mortality rates within the study population. The mortality definition does not take into account pre-birth complications (such as miscarriages) or birth complications that lead to death. All responses involved death due to illness after a healthy birth and no major chronic illnesses during early infancy or childhood. Most families referred to these fatalities as a mal de ojo (bad eye), representing the belief of envy and witchcraft that caused their child to fall ill and die as a consequence. Graph 46-A shows the percentage of households that have lost at least one child in the past. Results suggest that high desertification areas have a higher incidence of child mortality in comparison to medium and low desertification areas. It is important to note that in the high desertification areas, nearly every other family I interviewed had lost at least one child in the past due to illness. Graph 46-B shows the child mortality average age of death for the study population. Results suggest that the average age of death in high and medium desertification regions are significantly lower than in the low desertification regions. In summary, from Figures 43 through 46, results show that children living in areas with high desertification levels are at an increased risk for malnutrition, illness and early childhood mortality.

Figure 46: Child Mortality.

Chi-squared test for equality of proportions (with continuity corrections) used for analysis. Graph 46-A: Shows the percentage of households that have lost at least 1 child in the past. There are significant differences between High and Medium (X2=10.892, df=1, p=0.0009) and High and Low (X2=13.344, df=1, p=0.0002) groups. Graph 46-B: Shows the child mortality average age of death for the study population. The yellow bars show the means. A One-Way ANOVA test reveals that there are significant differences between the groups (F(2,29)=6.766, p=0.003), and a post hoc Tukey multiple pairwise comparison reveals that there are significant differences between High and Low (p=0.002) and Medium and Low (p=0.01) groups. Mean values: High: M=10.4, SE=2.21; Medium: M=12.9, SE=3.55; Low: M=42.3, SE=22.5.

6.2.2 Body Mass Index (BMI)

The following analysis shows the Body Mass Index (BMI) measurements from boys and girls in separate sections because they present gender specific standards. The standards utilized come from the World Health Organization (WHO) and are based on worldwide averages. Colombia presents BMI averages comparable to countries such as Switzerland, Brazil, South Africa and Russia [WHO 2009], and their growth curves for height, weight and BMI are not significantly different from these world standards [Duran et al. 2016]. The BMI Z-scores (also called BMI standard deviation scores) are measures of relative height and weight adjusted for child age and sex, and calculated relative to the WHO international reference. Individual BMI Z-scores follow the formula:

BMI Z-Score = (measured BMI – mean measured BMI) / mean standard BMI

On the BMI Standard chart from the WHO, the “SD0 (Normal)” line represents the reference BMI Z-score for the 0th Standard Deviation (a.k.a., the mean). The lines above the SD0 mean represent the number of positive standard deviations from this mean, and the lines below represent the number of negative standard deviations from the mean. The WHO stipulates that one standard deviation (SD1) above the mean is defined as risk for overweight, two standard deviations (SD2) above the mean is defined as overweight, and finally three standard deviations (SD3) is defined as obesity. Similar patterns follow for the negative standard deviations (SD-1, SD-2, SD-3), and their respective definitions (risk of underweight, underweight and risk of malnutrition).

Boys BMI Figure 47 shows the results of BMI analysis for the young male study participants (“Boys”, n=144) ages 0-19 years old grouped by malnutrition status. Graph 47-A shows the distribution of the measured Boys BMI and their malnutrition status (blue dots = no malnutrition, red dots = malnutrition) in comparison to the world standard Boys BMI Z- scores from the WHO (lines). Graph 47-B shows the Boys BMI Z-Scores grouped by malnutrition status. Results reveal there are no significant differences between the malnutrition groups suggesting that BMI measurements may not be the best method for assessing the risk of malnutrition in this young male population.

Figure 47: Boys BMI and Malnutrition.

Graph 47-A: Shows the lines representing the WHO standards for Boys BMI Z-scores for 0-19 years of age, and the measured BMI for each young male study participant with no observed malnutrition signs (blue dots), and observed malnutrition signs (red dots). (Standard data source: https://www.who.int/childgrowth/standards/bmi _for_ age)

Graph 47-B: Shows the comparison between Boys BMI Z-Scores for No Malnutrition and Malnutrition groups. The yellow bars show the means. A One-Way ANOVA test reveals that there are no significant differences between the groups (F(1,142)=3.738, p=0.06). Mean values: No Malnutrition: M=-0.55, SE=0.10; Malnutrition: M=-0.84, SE=0.12.

Figure 48 shows the results of BMI analysis for the young male study participants (“Boys”, n=144) ages 0-19 years old grouped by the desertification level of where they live. Graph 48-A shows the distribution of measured Boys BMI, color-coded by the desertification level groups (red = High, blue = Medium and green = Low), in comparison to the world standard Boys BMI Z-scores from the WHO (lines). Graph 48- B shows the Boys BMI Z-Scores grouped by the desertification level of the region where they live. Results reveal that, despite significant differences between the High and Medium groups (but no other significant differences between the other groups), overall BMI measurements may not be the best method for evaluating desertification effect differences in malnutrition within this young male population.

Figure 48: Boys BMI and Desertification Levels.

Graph 48-A: Shows the lines representing the WHO standards for Boys BMI Z-scores for 0-19 years of age, and dots represent the measured BMI for each young male study participant and the color represents the desertification level of the region where they live. (Standard data source: https://www.who.int/childgrowth/standards/bmi _for_ age)

Graph 48-B: Shows the comparison between Boys BMI Z-Scores for young males living in different desertification regions. The yellow bars show the means. A One-Way ANOVA test reveals that there are significant differences between the groups (F(2,141)=5.451, p=0.005), and a post hoc Tukey multiple pairwise comparison reveals that there are significant differences between High and Medium (p=0.003) groups. Mean values: High: M=-0.95, SE=0.12; Medium: M=-0.42, SE=0.10; Low: M=-0.73, SE=0.18.

Girls BMI Figure 49 shows the results of BMI analysis for the young female study participants (“Girls”, n=149) ages 0-19 years old grouped by malnutrition status. Graph 49-A shows the distribution of the measured Girls BMI and their malnutrition status (blue dots = no malnutrition, red dots = malnutrition) in comparison to the world standard Girls BMI Z- scores from the WHO (lines). Graph 47-B shows the Girls BMI Z-Scores grouped by malnutrition status. Results reveal there are no significant differences between the malnutrition groups suggesting that BMI measurements may not be the best method for assessing the risk of malnutrition in this young female population.

Figure 49: Girls BMI and Malnutrition.

Graph 49-A: Shows the lines representing the WHO standards for Girls BMI Z-scores for 0-19 years of age, and the measured BMI for each young female study participant with no observed malnutrition signs (blue dots) and observed malnutrition signs (red dots). (Standard data source: https://www.who.int/childgrowth/standards/bmi _for_ age)

Graph 49-B: Shows the comparison between Girls BMI Z-Scores for No Malnutrition and Malnutrition groups. The yellow bars show the means. A One-Way ANOVA test reveals that there are no significant differences between the groups (F(1,147)=0.075, p>0.05). Mean values: No Malnutrition: M=-0.45, SE=0.13; Malnutrition: M=-0.40, SE=0.10. 85

Figure 50 shows the results of BMI analysis of the young female study participants (“Girls”, n=149) ages 0-19 years old grouped by the desertification level of where they live. Graph 48-A shows the distribution of the measured Girls BMI, color-coded by the desertification level groups (red = High, blue = Medium and green = Low), in comparison to the world standard Girls BMI Z-scores from the WHO (lines). Graph 48- B shows the Girls BMI Z-Scores grouped by the desertification level of the region where they live. Results reveal there are no significant differences between desertification groups, suggesting that BMI measurements may not be the best method for evaluating desertification effect differences in malnutrition within this young female population.

Figure 50: Girls BMI and Desertification Levels.

Graph 50-A: Shows the lines representing the WHO standards for Girls BMI Z-scores for 0-19 years of age, and dots represent the measured BMI for each young female study participant and the color represents the desertification level of the region where they live. (Standard data source: https://www.who.int/childgrowth/standards/bmi _for_ age)

Graph 50-B: Shows the comparison between Girls BMI Z-Scores for young females living in different desertification regions. The yellow bars show the means. A One-Way ANOVA test reveals that there are no significant differences between the groups (F(2,146)=0.121, p>0.05). Mean values: High: M=-0.42, SE=0.14; Medium: M=-0.48, SE=0.12; Low: M=-0.37, SE=0.17.

6.2.3 Mid-Upper Arm Circumference (MUAC)

The following analysis shows Mid-Upper Arm Circumference (MUAC) measurements from boys and girls in separate sections because they present gender specific standards. The standards utilized come from the World Health Organization (WHO) and are based on worldwide averages. Colombia does not have a specific standard for MUAC, therefore WHO international standards are used. The MUAC Z-scores (also called MUAC standard deviation scores) are measures of relative arm circumference for a child’s age and sex and calculated relative to the WHO international reference. Individual MUAC Z-scores follow the formula:

MUAC Z-Score = (measured MUAC – mean measured MUAC) / mean standard MUAC

On the MUAC Standard chart from the WHO, the “SD0” line represents the reference MUAC Z-score for the 0th Standard Deviation (a.k.a., the mean). The lines above the SD0 mean represent the number of positive standard deviations from this mean, and

87 the lines below represent the number of negative standard deviations from the mean. The MUAC standards [WHO-UNICEF 2009] stipulates that the mean and positive standard deviation values are in no risk for malnutrition (green zone), one standard deviation below the mean (SD-1) is defined as risk for moderate malnutrition (but still within the green zone), two standard deviations below the mean (SD-2) is defined as moderate malnutrition (yellow zone) and three standard deviations below the mean (SD- 3) is defined as acute malnutrition (red zone). The MUAC tape was designed to account for these differences, and provides and easy to use, fast and comprehensive measurement for the risk of malnutrition (Figure 51).

Figure 51: The MUAC Tape.

(Source: WHO and UNICEF, 2009)

Boys MUAC Figure 52 shows the results of MUAC analysis for the young male study participants (“Boys”, n=144) ages 0-19 years old grouped by malnutrition status. Graph 52-A shows the distribution of the measured Boys MUAC and their malnutrition status (blue dots = no malnutrition, red dots = malnutrition) in comparison to the world standard Boys MUAC Z-scores from the WHO (lines). Graph 52-B shows the Boys MUAC Z-Scores grouped by malnutrition status. Results reveal there are significant differences between the malnutrition groups, which suggests malnourished boys may have lower MUAC measurements. However, most of the measurements are above SD0, and a few between SD0 and SD-1 (representing only a risk for moderate malnutrition). This can suggest that MUAC measurements may not be the best method for accurately assessing the true risk of malnutrition in this young male population.

Figure 52: Boys MUAC and Malnutrition.

Graph 52-A: Shows the lines representing the WHO standards for Boys MUAC Z-scores for 0- 19 years of age, and the measured MUAC for each young male study participant with no observed malnutrition signs (blue dots) and observed malnutrition signs (red dots). (Standard data source: https://www.who.int/childgrowth/standards/ac _for_ age)

Graph 52-B Shows the comparison between Boys MUAC Z-Scores for No Malnutrition and Malnutrition groups. The yellow bars show the means. A One-Way ANOVA test reveals that there are significant differences between the groups (F(1,142)=5.172, p=0.02). Mean values: No Malnutrition: M=0.13, SE=0.11; Malnutrition: M=-0.22, SE=0.11. 89

Figure 53 shows the results of MUAC analysis for the young male study participants (“Boys”, n=144) ages 0-19 years old grouped by the desertification level of where they live. Graph 53-A shows the distribution of the measured Boys MUAC, color-coded by the desertification level groups (red = High, blue = Medium and green = Low), in comparison to the world standard Boys MUAC Z-scores from the WHO (lines). Graph 53-B shows the Boys MUAC Z-Scores grouped by the desertification level of the region where they live. Results reveal there are no significant differences between the groups, suggesting that MUAC measurements may not be the best method for evaluating desertification effect differences in malnutrition within this young male population.

Figure 53: Boys MUAC and Desertification Levels.

Graph 53-A: Shows the lines representing the WHO standards for Boys MUAC Z-scores for 0- 19 years of age, and dots represent the measured MUAC for each young male study participant and the color represents the desertification level of the region where they live. (Standard data source: https://www.who.int/childgrowth/standards/ac _for_ age)

Graph 53-B: Shows the comparison between Boys MUAC Z-Scores for young males living in different desertification regions. The yellow bars show the means. A One-Way ANOVA test reveals that there are no significant differences between the groups (F(2,141)=1.013, p>0.05), and a post hoc Tukey multiple pairwise comparison also reveals no significant differences. Mean values: High: M=-0.14, SE=0.10; Medium: M=0.10, SE=0.12; Low: M=-0.06, SE=0.21.

Girls MUAC Figure 54 shows the results of MUAC analysis for the young female study participants (“Girls”, n=149) ages 0-19 years old grouped by malnutrition status. Graph 54-A shows the distribution of measured Girls MUAC and their malnutrition status (blue dots = no malnutrition, red dots = malnutrition) in comparison to the world standard Girls MUAC Z- scores from the WHO (lines). Graph 54-B shows the Girls MUAC Z-Scores grouped by malnutrition status. Results reveal there are no significant differences between the malnutrition groups, suggesting that MUAC measurements may not be the best method for accurately assessing the risk of malnutrition in this young female population.

Figure 54: Girls MUAC and Malnutrition.

Graph 54-A: Shows the lines representing the WHO standards for Girls MUAC Z-scores for 0- 19 years of age, and the measured MUAC for each young female study participant with no observed malnutrition signs (blue dots) and observed malnutrition signs (red dots). (Standard data source: https://www.who.int/childgrowth/standards/ac _for_ age)

Graph 54-B Shows the comparison between Girls MUAC Z-Scores for No Malnutrition and Malnutrition groups. The yellow bars show the means. One-Way ANOVA test reveals that there are no significant differences between the groups (F(1,147)=1.397, p>0.05). Mean values: No Malnutrition: M=-0.0009, SE=0.08; Malnutrition: M=-0.14, SE=0.09. 92

Figure 55 shows the results of MUAC analysis for the young female study participants (“Girls”, n=149) ages 0-19 years old grouped by the desertification level of where they live. Graph 55-A shows the distribution of the measured Girls MUAC, color-coded by the desertification level groups (red = High, blue = Medium and green = Low) in comparison to the world standard Girls MUAC Z-scores from the WHO (lines). Graph 55-B shows the Girls MUAC Z-Scores grouped by the desertification level of the region where they live. Results reveal there are significant differences between the Low and Medium desertification groups only. However, most of the measurements are above SD0, and a few between SD0 and SD-1 (representing only a risk for moderate malnutrition). This can suggest that MUAC measurements may not be the best method for evaluating desertification effect differences in malnutrition within this young female population.

Figure 55: Girls MUAC and Desertification Levels.

Graph 55-A: Shows the lines representing the WHO standards for Girls MUAC Z-scores for 0- 19 years of age, and dots represent the measured MUAC for each young female study participant and the color represents the desertification level of the region where they live. (Standard data source: https://www.who.int/childgrowth/standards/ac _for_ age)

Graph 55-B: Shows the comparison between Girls MUAC Z-Scores for young females living in different desertification regions. The yellow bars show the means. A One-Way ANOVA test reveals that there are significant differences between the groups (F(2,146)=3.053, p=0.05), and a post hoc Tukey multiple pairwise comparison reveals that there are significant differences between Medium and Low (p=0.04) groups. Mean values: High: M=-0.11, SE=0.10; Medium: M=-0.22, SE=0.09; Low: M=0.14, SE=0.11.

6.2.4 Food Score, Malnutrition and Desertification Levels

Food guidelines are important because they provide standards for the recommended proportions of food that should be consumed on a daily basis, so people can fulfill their nutritional requirements and have a healthy life. Based on the Colombian Ministry of Health and FAO Colombia [FAO 2018] recommendations for daily food intake (Figure 56), I created a simple “Food Score” system to evaluate the eating habits of the children and to have an approximation of how close they are to the recommended food intake (Figure 57). Instead of looking at actual food consumption and its recommended daily servings, the Food Score extrapolates on the daily consumption and represents the weekly percentage of how often the children consume a certain food group. Because I

94 could not measure how many grams of food they consume on a daily basis, I simply asked parents how many days in the week the children consumed a certain type of food. For example, I asked how many days they consumed vegetables (green leaf salads, tomatoes, etc.) in a period of 7 days. The Food Score is the ratio between the estimated daily consumption by the maximum number of days in the week. As a disclaimer, the Food Score is based on food intake approximations from parental reports, and not from a study of the actual food consumption from each child participating in the study. This scoring system was created as a simple tool to quantify (approximately) the food intake of children, identify the gaps and determine the risk of childhood malnutrition within the study population.

Figure 56: Food Guidelines for Colombia.

Example of a comprehensive food guide from the Plato Saludable de La Familia Colombiana (The Colombian Family’s Healthy Plate), based on nutrient requirements and dietary guidelines from the Ministry of Health of Colombia. Clockwise: water; fruits and vegetables; milk and dairy products; physical activity; meat, eggs and dry legumes; fats; sugars; cereals, roots, tubers and plantains. (Source: FAO 2018)

Figure 57: Food Score Table.

A Food Score system was created for different food groups, which loosely represents the percentage of how much the study participants consume per week. The “Colombian Average Recommended Daily Intake for Children” column is the recommended daily intake for Colombians based on FAO data. The “Food Score Daily Consumption Estimates” column does not take into account how many grams of a certain food the study subjects eat per day – it only assumes that they eat an unspecified amount of the food at least once per day. The “Food Score Weekly Consumption Estimates” column is the maximum Food Score value one can obtain for a specific Food Group. The Total Score is the maximum amount of points one can achieve, meaning that if you are close to or reach a maximum score, you eat a well-balanced diet on a weekly basis and shouldn’t be at risk for malnutrition. (Source for Colombian Average Recommended Daily Intake for Children: FAO 2018)

As an example, Figure 58 shows the food intake approximations and Food Score results obtained from a random child in the study. It shows that the child: 1) never eats fruits or carbohydrates, 2) eats vegetables only once per week, 3) eats meat and legumes twice per week, and 4) eats dairy (milk), grain (corn), fats and sugars every day. Therefore, on average, this subject eats only 52.3 percent of the recommended weekly intake of foods. The subject’s diet is not rich in vitamins and proteins, and is highly composed of processed fats and sugars. This specific child did not present malnutrition signs but based on the Colombian recommendations and overall results from study population (Figure 59-B), such diet may infer a risk for malnutrition.

Figure 58: Food Score Example.

(Created by Author, 2018)

Food Score Results Figure 59 shows the results of the children’s total Food Score analysis grouped by the desertification level of where they live and by malnutrition status. Graph 59-A shows the distribution of Food Scores for the different desertification level groupings. Graph 59-B shows the distribution of Food Scores among the children who presented signs of malnutrition versus the children without malnutrition signs. Results suggest that children living in high desertification regions have the lowest total Food Score in comparison to medium and low desertification regions. Furthermore, children with signs of malnutrition have a significantly lower Total Food Score in comparison to children without signs of malnutrition. It is important to point out that many families reported that sometimes they eat only once in the day (either lunch or dinner), and sometimes once every other day, as they do not have enough food available every day for consumption.

Figure 59: Total Food Score, Desertification and Malnutrition.

Graph 59-A: Shows the children’s total Food Score grouped by desertification levels. The yellow bars show the means. A One-Way ANOVA test reveals there are significant differences between the groups (F(2,290)=82.17, p<0.00001), and a post hoc Tukey multiple pairwise comparison reveals there are significant differences between High and Low (p<0.00001), High and Medium (p<0.00001) and Medium and Low (p<0.00001) groups. Mean values: High: M=36.4, SE=1.22; Medium: M=52.1, SE=1.28; Low: M=64.1, SE=2.16.

Graph 59-B: Shows the children’s total Food Score values grouped by presence or absence of malnutrition. The yellow bars show the means. A One-Way ANOVA test reveals there are significant differences between the groups (F(1,291)=453.2, p<0.00001). Mean values: No: M=61.9, SE=1.06, Yes: M=33.5, SE=0.71.

These overall results suggest that a Food Score system can be used as a simple and effective tool to help identify the risk of malnutrition in children and quantify the deficits of the recommended food and nutritional intake requirements of a population.

Furthermore, it is important to identify which Food Groups may be in deficit. While interviewing the subjects, I noticed that they do not eat certain types of foods because they are simply not available (either cannot be purchased or cannot be cultivated grown due to the drought and desertification process of the region). Therefore, the foods they eat are not a matter of personal choice – they are a matter of availability and accessibility. Figure 60 shows the distribution of Food Scores among the individual Food Groups. Results show that in general, there are significant food disparities in the diets of the children participating in the study (especially fruits and vegetables), which can have a large effect in their recommended daily nutrition intake, leading to problems such as malnutrition

Figure 60: Food Score for Individual Food Groups.

Graph shows the Food Score values for all children, divided into individual food groups. The black bars show the means and individual answers are shown with the “jitter” function (for better visualization of data points). A One-Way ANOVA test reveals there are significant differences between the groups (F(5,1752)=329.4, p<0.00001). It is relevant to point out that the consumption of Fruits and Vegetables are significantly lower than all other food groups (p<0.00001) and that Fats and Sugars are significantly higher than all other food groups (p<0.00001).

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Figure 61 looks at the Food Score for individual Food Groups, malnutrition status of the children and desertification levels. Graph 61-A shows the Food Score for individual Food Groups, grouped by malnutrition status. Results show that children with malnutrition signs eat significantly less of all food groups in comparison to children without malnutrition signs. Graph 61-B shows Food Score for individual Food Groups, grouped by the desertification level of the region where they live. Results show that other than sugars, the high desertification group eats significantly less fruits and vegetables (p<0.00001), carbohydrates and grains (p<0.00001), dairy products (p=0.0001) and processed fats (p<0.00001) in comparison to medium and low groups, and significantly less meat and legumes than the low desertification group (p<0.00001). Figure 61: Food Score for Individual Food Groups, Malnutrition and Desertification Levels.

Graph 61-A: Shows the Food Score values for individual Food Groups, grouped by malnutrition groups. One-Way ANOVA and post hoc Tukey multiple pairwise comparisons were used for analysis. When taking malnutrition status into account, for example “Fruits and Vegetables”, shows that children with malnutrition signs eat significantly less from this food type (F(1,291)=85.85, p<0.00001) in comparison to children without malnutrition signs.

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Graph 61-B: Shows the Food Score values for individual Food Groups, grouped by the desertification level of the region. One-Way ANOVA and post hoc Tukey multiple pairwise comparisons were used for analysis. When taking desertification levels into account, for example “Fruits and Vegetables”, analysis shows that the High desertification group eat significantly less from this food type (F(2,290)=62.73, p<0.00001) in comparison to Medium (p=0.0006) and Low (p<0.00001) groups. Other than sugars, the high desertification group eats significantly less carbohydrates and grains (p<0.00001), dairy products (p=0.0001) and processed fats (p<0.00001) in comparison to medium and low groups, and significantly less meat and legumes than the low desertification group (p<0.00001).

Results reinforce the suggestion that a Food Score system can help identify and quantify food consumption habits of different communities and help assess the risk of malnutrition of a population.

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7. Discussion

The first consideration in order to answer my main research question, if different levels of desertification affect the livelihood and health of the Wayúu people, is in the historical context of the Wayúu population. Many can argue that the Wayúu are poor because they live in a remote, arid to semi-arid region, and therefore this is how they have lived for hundreds of years. My argument enlightens the fact that La Guajira is experiencing a sample of what climate change and global warming may eventually do to the entire planet: suddenly influence and change the way people are commonly used to live. Climate change is slowly showing its facets as it increases the frequency and magnitude of droughts and hurricanes, causing destruction and social insecurities even in the most developed countries such as the United States. The Wayúu have dwelled in La Guajira for over 1000 years and have survived and stayed within their land in the face of the adversities of living within desert-like surroundings. However, just in the past ten years they have experienced extended droughts that correlate to strong El Niño events (which are known to be affected by climate change), causing their rivers and lands to dry, crops and animals to die, and consequently increasing the incidence of malnutrition and childhood mortality. The Wayúu have resided in the Alta Guajira for a long time, but only within the past decade one can see Colombian government decrees that the poverty and vulnerability of the Wayúu is unconstitutional [Torrez 2018], and credible international news articles (such as Cosoy 2015, Rosso 2016, Fajardo 2017 and Chaparro 2017) decrying the problems caused from the drought and the ensuing humanitarian crisis among the Wayúu people. My investigation shows that even though the Wayúu have dwelled under arid and unfavorable socio-economic conditions for many generations, the current intensification of the desertification process due to drought has exacerbated the challenges they usually have in terms of livelihood and health.

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7.1 Socio-Economic Aspects

7.1.1 Education, Infrastructure and Health

I personally believe that development is based on three initial basic factors: education, infrastructure and access to health care, so populations can thrive and innovate (and I recommend a brilliant timeless article by Ester Boserup [1976] as backup for my beliefs). My results show that people living in the regions with the highest desertification levels, present the lowest levels of education. More specifically, nearly 80 percent of adults I interviewed (61 out of 77 adults) have never set foot in a school. The current desertification process of course does not cause this lack of education; the remoteness of these communities and the lack of infrastructure are likely the factors that inhibit the opportunities for education and development. Consequently, populations become less resilient to change and, almost unwillingly, they make the problem even worse. For example, the fact that over 90 percent of households in rural areas do not have a proper toilet or latrine shows how the lack of education and awareness can be detrimental to the environment and the population health. Garbage and human feces could be found in almost every surrounding area of the Rancherías, and many of the children I encountered were running around barefoot. Such practices exponentially increase the risk for acquiring intestinal worms and other diseases. Furthermore, the lack of the practice of at least boiling the water they drink can increase the risk for acute diarrheal diseases, which is considered by the WHO to be one of the lead sources of childhood mortality, next to malnutrition and acute respiratory diseases such as pneumonia.

Also, the limited locations and sources of water can have a large social and health impact on the Wayúu society. Many mothers and their children need to procure water on a daily basis, and the walking trip to the well (which can take longer than one hour) is perilous because of the heat. There’s only so much one can say they are “accustomed” to the heat of a region, as the extreme effort of carrying 10 to 15 kilos of water in a 35 degrees Celsius temperature can cause heat exhaustion and lead to heat stroke, that is, when the body temperature raises to over 40 degrees Celsius, causing internal organ complications and death if not treated with urgency.

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One of the major discoveries I had not taken into consideration before this investigation is the frequency of chronic headaches among the adult population. Over 85 percent of the interviewees living in high desertification areas, and over 75 percent living in medium desertification areas, reported having chronic headaches (i.e., at least four continuous episodes per week). If I were to try and ascertain the possible causes, my main presumption would be dehydration from the limited sources of water and intense heat. I later found out that, according to the Institute of Health Metrics and Evaluation (IHME), one of the leading causes of disability in adults in Colombia are headache disorders [IHME 2017]. Therefore, even the incidence of headaches cannot be ignored when looking at the gaps in infrastructure, education and the effects they have on the health of these specific populations.

Lastly, the lack of road infrastructure to link communities to health centers can hinder the access to health services. Aside from a few urban centers in Alta Guajira, there are no paved or maintained roads. Families living within regions presenting the highest desertification levels may need approximately two hours to reach the nearest health center by vehicle, but it can take as long as three to five hours to walk to a clinic when they do not have the means to pay for transport (and sometimes there is no nearby public transport available to them). Furthermore, there aren’t enough clinics and hospitals within the Uribia and Manaure municipalities. The Uribia municipality has an area of 8’200 km2, and out of an approximate 117’000 total population, 105’000 (90%) are Wayúu. Uribia has two hospitals (one in Nazareth and one in Uribia city) and six clinics sparsely located to attend the entire population. The Manaure municipality has an area of 1’900 km2, and out of an approximate 68’000 total population, 46’000 (68%) are Wayúu. Manaure has one hospital and five clinics available to the entire population. In comparison, the Riohacha municipality (located southwest of Manaure) has an area of 3’100 km2, and out of 150’000 total population, 30’000 (20%) are Wayúu. Between clinics and hospitals, Riohacha has 164 health facilities available to the population [DANE 2005, GDC 2018]. Therefore, there is a clear disparity between the municipalities, and the specific areas densely populated by the Wayúu seem to be at a disadvantage in terms of health infrastructure.

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Therefore, overall results show that desertification, lack of education and infrastructure, together with the marginalization of the Wayúu population in Uribia and Manaure, can directly affect the health and well-being of the communities, especially the ones living within regions presenting the highest levels of desertification.

7.1.2 Economic Opportunities

A strong suit of the Wayúu comes from their capacity to find ways to obtain some money and goods within a nearly barren landscape. Families living within the high and medium desertification areas may not be able to cultivate their land and the chances for their livestock to die from drought conditions are extremely high. In a region with little opportunities, the Wayúu turn to their trademark product: the mochilas (bags). The crafting of the bags is a work of art, and a person can produce one large bag worth 30’000 COP (10 CHF), or two small bags worth 18’000 COP each (6 CHF) within a week. The person then pays approximately 15-20’000 COP (5-6.50 CHF) for transport to the nearest large city (Riohacha, Uribia or Manaure) and they sell the bag to street vendors, who will then at least double its price for a profit margin. Then the person purchases the materials for the next set of mochilas (worth another 15-20’000 COP) and they return home with approximately half of the total profit they could have made if no extra costs were involved. In addition to the mochilas, many Wayúu families have livestock such as chicken and goats, which are utilized predominantly for milk and eggs, and occasionally a family will sacrifice an animal for personal consumption and/or to sell for extra money.

With such scenarios in mind, the calculated average monthly household income for the study participants living in high desertification areas is approximately 200’000 COP (60 CHF - usually earned when both parents work). From personal investigations of food item prices, in an ideal world, I calculated that if you are to feed a family of five people (for example: mother, father and 3 children) with basic food items from the recommended food guidelines such as rice, beans, fruit, vegetables and meat, one can spend approximately 15’000 COP (5 CHF) per day to buy a half kilo of each of these items. Doing some simple math, one realizes that such habits would only feed their family of five people for half a month on a 200’000 COP salary. The prices of food and

106 monthly income comparison begin to explain the low rates of food intake and malnutrition among the Wayúu living in rural areas, especially within the high desertification areas where they cannot cultivate their land. Despite their ability to generate some income from traditional practices, it is still not enough revenue to provide a basic healthy living to their families.

7.2 Perceptions

7.2.1 Safety

The great majority of interviewees (especially in rural areas) speak very humbly and yet quite sincerely. Despite all the challenges they encounter to simply survive on a daily basis, they feel safe living in their land because they are free from what they hear on the news from their amplitude modulation (AM) Radios: murder, stealing, kidnappings, fraud and bureaucracy, which are some of the staple newscast announcements in La Guajira. They know the recent history of the drug cartels and the mass violence it inflicted throughout the country from the 1970’s through the early 2000’s, and many families are happy to have been left alone through this period. Some of the respondents who seemed unsure about their safety have never experienced any type of violence or negative occurrences but are afraid that it may happen one day in light of what they hear on the radio or from mouth-to-mouth. Others have had their livestock stolen or have entered in conflict with neighbors over personal issues or land disputes. Overall, the Wayúu may indeed be isolated from the modern world’s negative influences, but at the same time their isolation deprives them from infrastructure and technologies that could make their lives better (such as electricity, water and sanitation innovations).

7.2.2 Water

One of the major issues for the Wayúu is the availability and quality of water. The suburban areas presenting low desertification levels have the advantage of being near large centers that enjoy a certain level of infrastructure. They may have at least one water tap in their homes, or street vendors usually sell water directly in front of everyone’s homes (Figure 62). The water is not treated and sometimes people complained it was too salty, but overall their perception on the water situation was regular (not good, but also not too bad). 107

Figure 62: Street Water Vendor in Manaure.

(Photo by Nicolo Filippo Rosso, 2016)

Populations living in the medium desertification levels did not have tap or street water vendors, but they had water wells powered by windmills (Figure 63). Those windmills could be sporadically found in some regions, and they pumped water up from a depth of at least 200 meters. Some families had to walk one to two hours to reach the windmills, but the water quality was much better than other sources that could be reached within an hour walk (a shallow 12 to 15 meter-deep well or a Jagüey lake, which usually presented very dirty water).

Figure 63: Water Well Powered by a Windmill.

(Photo by Author, 2018)

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Populations living in remote regions presenting high desertification levels didn’t have much of a choice for their water. The nearest sources (1-2 hours walk) were either shallow wells or from a Jagüey, which is a man-made lake (Figure 64). Water trucks show up infrequently and dump its contents (approximately 30’000 liters) into the Jagüey. The water is openly exposed to the environmental elements; people and animals drink and bathe in it, so needless to say it is dirty and certainly contaminated.

Figure 64: A Man-Made Lake (Jagüey).

In the region of Alta Guajira, near Nazareth (Photo by Author, 2018)

7.2.3 Food

Large markets in Alta Guajira are exclusive to urban centers such as the cities of Manaure and Uribia (and a small market in Nazareth). Other than that, small vendors procure food in the large centers and resell them (at an inflated price) in local small shops (Figure 65). These shops usually sell dry goods such as rice, beans, flour, salt, sugar, cooking oil, soft drinks, beer and batteries for flashlights. The quality of the roads is very bad, in that there are no paved roads and no regular maintenance on dirt roads. The transport is expensive (because of high petrol prices) and distances are nearly impossible to cover by simply walking due to the intense daytime heat (on average 35 degrees Celsius all year long). Therefore, populations in remote areas are confined to their Rancherías and immediate surroundings, and they only have these small local shops for basic goods.

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Figure 65: Small Food and Drinks Shop.

Small shop located in the Uribia municipality (near Nazareth) and the shop owner’s pet goat Marcello. (Photo by Author, 2018)

Populations living in medium desertification levels usually can cultivate their land between September and October when it usually rains. They plant as much as they can and gather their crops around December and February, utilizing some of the yield for personal consumption and the remaining for selling or trading. However, due to the lack of education on financial planning, a few families reported experiencing the “hunger months” (usually between August and October) when they have exhausted the crops and money from the past year and can experience a shortage of daily food on their plates.

The most significant (and unfortunately downhearted) interviews involved a few families living in high desertification areas, which reported eating only one meal per day every other day. One family with five children reported they sometimes go two days without a meal. As striking as it sounds, I asked if there are ways that they can satiate the hunger. The answer comes from the most ancient and traditional of the Wayúu 110 practices: the Chicha. Chicha is simply dry corn, manually ground and boiled in water (most of the time with sugar added, if available). The Wayúu drink Chicha a few times per day, everyday, and it seems that even the most disadvantaged of the families can acquire a handful of dry corn to make the Chicha. Such practice helps the most underprivileged families with a safe source of liquid and a small amount of carbohydrates to provide them a bit of energy until their next meal (Figure 66).

Figure 66: The Colombian Chicha

The process of cooking the Chicha, which is use for human daily consumption and also used for feeding small animals. (Photos by Author, 2018)

However, even with having the Chicha as a small quencher, the difficulties in growing and/or procuring food for many Wayúu households is a daily struggle, and a few families were sincere enough to report going to sleep not being sure if they will be able to eat the next day. Even though this is beyond the scope of this investigation, I can’t help but to imagine the possible psychological implications this may have on adults and their children.

7.2.4 Environment

The Wayúu people living in medium and high desertification level regions are usually quite resilient to their living conditions. Days are usually hot, it barely rains all year long and the strong winds always lift up the sand that many times hits them directly in the eyes. However, despite the results from these analyses, most study participants reported being used to those conditions. Normally they appreciate the wind, as it provides a cooling effect from the intense heat. During the hottest parts of the day

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(usually between 11am and 3pm), the Wayúu gather under the shade, laying on their chinchorros (hammocks) and use the time for resting, socializing or working on the mochilas (Figure 67). Such practices come from adaptability to the heat, as they are aware that working under such conditions is detrimental for their health.

Figure 67: Hiding from the Sun.

Wayúu gathering to rest after lunch. Time of photo: 2:34pm. Measured heat at the time of photo: 37.8 degrees Celsius. (Photo by Author, 2018)

Despite their resilience, the lack of rain causes rivers and Jagüeys to dry, prevents the growth of crops due to land degradation, and increases the chance of livestock mortality due to shortages of water and lack of grass growth, which usually serves as food for animals. There is a shortage of incentives and initiatives from government and the private sector for the implementation of innovations and technologies, such as irrigation projects, which could improve the nutrient content and water retention of the soil.

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7.3 Children From the socio-economic challenges to the ongoing environmental desertification of the La Guajira peninsula, the population that truly suffers the most are the children. Parents try their best to provide to their families, but the lack of education, economic capacity and awareness of family planning causes most Wayúu families to be large (usually four to six children per household). The lack of resources, safe water and food can increase the rates of malnutrition, which in turn can escalate the rates of childhood mortality. I evaluated 293 children from various random regions from two municipalities in Alta Guajira, and 45 percent of these children presented clear signs of malnutrition. Furthermore, the majority of these malnourished children live in areas presenting high desertification. Medium and high desertification areas have the highest percentages of water-borne illnesses (such as diarrhea and vomiting) and high incidence of fever- inducing infections, which can also come from contaminated water and lack of proper nourishment. If one wants to explain why childhood mortality is so high in La Guajira, the answer goes back to the basics which have been addressed in the beginning of this discussion: education, infrastructure and access to health care. The government entities of La Guajira are aware of these problems, but resources and manpower can suffer with the lack of support from the federal government. Despite being a multi- faceted issue, providing the basic infrastructure (public water, proper roads and public transport, schools and health clinics) to the densely populated areas where the Wayúu live would be a start to give them a chance to learn and thrive.

I have been trained to identify individual signs of malnutrition, but there are standardized measurements that can be obtained to numerically measure an individual’s body status, such as BMI and MUAC (predominantly in children). The higher a person’s BMI, the increased the chances for health problems such as diabetes and cardiovascular diseases. Conversely, the lower BMI numbers correlate to malnutrition and risk of morbidity and mortality. Similarly, low MUAC measurements can further confirm this increased risk for malnutrition. From my results, there are no differences in BMI or MUAC for both boys and girls. Malnutrition status or the desertification of the area where they live provided no trends, and therefore my suggestion that both BMI and MUAC may not be the best tools to identify the risk of malnutrition in these populations.

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But why is this the case? If 45 percent of the children have physical signs of malnutrition, why didn’t BMI or MUAC capture such trends? The answer of course, comes from their diet.

The creation of a simple food scoring system enabled the evaluation of the population’s eating habits and results show that malnourished children eat significantly less than children with no evident malnutrition signs. To me this is an obvious statement, but it has been demonstrated in numerical form, so that we can establish a foundation in order to know where to begin addressing the malnutrition problem. However, stating that BMI and MUAC are not the best way to identify malnutrition comes from identifying the food scoring system for individual food groups (as shown in Figure 60). The Wayúu children’s diet is high in processed fats and sugars, and low in nutrients, vitamins and minerals. They are not necessarily experiencing famine; they are experiencing the consequences of socio-economic disparities influenced by the desertification process, which reduces the quantity and variety of fresh food accessibility and availability. In the high desertification areas, they cannot grow crops and the most inexpensive and widely available food items the Wayúu can obtain are animal lard, white flour, dried corn and Panela, which is a bar of unrefined, caramelized pure sugar cane (Figure 68).

Figure 68: Panela Sugar Production.

A low-tech sugar mill near Bogota Colombia, producers of the sugar bars (Panela) (Photos by J. Kenji Lopez-Alt @ Seriouseats.com)

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Therefore, driven by necessity, the Wayúu children mostly eat lots of animal fat and processed oils, a little carbohydrate from making Arepas (fried white flour and water) and drinking Chicha (ground corn boiled in water), the Panela sugar they use to sweeten the Chicha, and sometimes they drink goat milk. Some individuals even laughed when I asked them if they ever eat fruits, vegetables or legumes. To the untrained eye, such dietary habits do not make them look physically malnourished, and furthermore BMI and MUAC could not capture the signs of malnutrition. However, in cautious examinations, one can see widespread signs of protein deficiency (such as water retention in extremities and Kwashiorkor distended abdomen) and various vitamin deficiency signs (such as skin rashes and bumps, brittle sunken nails, mouth sores), and the Food Score system was able to assist in identify and quantify the risk of malnutrition among the Wayúu population of Alta Guajira.

8. Conclusions

Overall results allow me to accept the hypothesis that the Wayúu people living in areas with the highest levels of desertification present the poorest socio-economic and health conditions in comparison to people living in areas with lesser levels of desertification. However, there is a certain ambiguity to such statement, as the socio-economic and environmental factors affecting the Wayúu may be intricately intertwined with each other. Are the areas presenting high desertification also the poorest and more isolated areas? Do the poorer, more disadvantaged people tend to be pushed into areas with higher desertification? Based on such conundrum, I deduce that my main follow up question would be: which one is the main cause of poverty and health problems among the Wayúu – socio-economic (society) or climate (nature)? That being the case, I would hypothesize that it is a combination of both. In general, the Wayúu people of Alta Guajira are facing major problems in terms of food and water securities, lack of infrastructure, medical services and education to deal with the ongoing droughts and subsequent desertification of the peninsula. Additionally, the populations living in areas with the highest desertification levels seem to be suffering significantly more in comparison to other areas, with an increased lack of rain and ensuing low soil

115 productivity, which aggravates their food and water insecurities, and further exacerbates their already low socio-economic status.

Because of such issues, the most affected segments of the Wayúu society are the children. The shortages and variety of food, water contamination and lack of sanitation makes them vulnerable to high risks of malnutrition, disease and early childhood mortality. These risks are further increased in young populations living in areas with the highest desertification levels.

International entities, local government and private institutions must acknowledge that the Wayúu may be in the brink of a humanitarian crisis and should come together to establish effective policies and interventions in order to reduce poverty, food and water insecurities and malnutrition, and consequently reduce childhood morbidity (rate of diseases) and mortality among these populations.

Based on the results from this master thesis, the best places to start any type of development initiatives are in the zones presenting the highest levels of desertification. The inhabitants of such areas need access to public water (for personal consumption and agricultural irrigation), proper roads and public transport to link them to markets (so they can sell and/or trade their crafts), increase the health care capacity within the nearby surroundings of their settlements, and very importantly they need to be educated about their rights, health, economics and innovative practices in order to provide them with longitudinal sustainable development opportunities. Once such basic needs can be put in place, the Wayúu people may have a chance to thrive and continue developing within their own cultural fashion.

9. Directions for Future Research Based on the results and ideas developed for this master thesis, there is an ample field of research and development that can be explored in the Manaure and Uribia regions. Such expansion would be beneficial to the Wayúu people, as it may provide them with technologies and education for development. The first approach for the continuation of

116 this research would be to increase the catchment area of the current analysis to evaluate if the results of this thesis holds true. Furthermore, it is important to assess in detail the healthcare delivery systems. Utilizing the questionnaires that have been developed for this master thesis, the plan is to conduct the evaluation of the capacity of each individual health center in the Manaure and Uribia regions, and also appraise the satisfaction of the health workers employed by these facilities. Such analyses can give an advanced understanding of the deficits in the healthcare system that can further influence the high rates of childhood mortality.

This apparent marginalization of the Wayúu people can influence the lack of incentives for development. The shortage of education and opportunities to improve their livelihood may be one of the key issues that can be addressed without depending on the initial intervention from the federal institutions (although federal support would be crucial for the longevity of such programs). The idea is to establish a model village to teach locals about simple and sustainable technological innovations (such as drip irrigation for agriculture) that can address their basic food security needs. In addition, provide general education for children and vocational training for adults. Such initiatives can be researched, and efficacy can be measured by direct comparison to other regions that experience similar drought and desertification implications. Furthermore, the beneficiaries of such program would be recruited to form an outreach program, where they are taught to disseminate the information, tools and knowledge they have learned to other communities. Topics of outreach education include infrastructure, finance and health. Such initiative can also be measured to determine its efficacy based on learning, retention and utilization of such knowledge by other communities.

Providing the Wayúu people with basic needs, education, ideas and incentive for their own development is key for the sustainability of this future research initiative. Based on this master thesis research, one must first provide water to populations, and secondly utilize these water resources for agricultural food securities to improve their livelihood and well-being, and thirdly focus on education and simple technologies so they can carry on their development strategies within their own cultural practices.

117

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Appendix

A.1 Individual Household Questionnaire

Individual Household Questionnaire INTRODUCTION: As part of a multi-lateral study about the factors that contribute to childhood health in the La Guajira Department, the following questionnaire aims to assess the local healthcare system and its delivery. Our study is especially concerned with desertification and its effects on a child’s well being. By answering the following questions you help us gain an in-depth overview of the health sector and identify possible challenges it faces. CONFIDENTIALITY STATEMENT: We guarantee that the information and data herewith collected will be handled with the utmost confidentiality, will be analyzed in an anonymous way and no personal information will be forwarded or shared with other parties without your permission. INSTRUCTIONS: This individual household survey includes 8 sections. Check or circle only one answer per question, unless specified that more than one answer can be checked or circled. If you feel uncomfortable answering a specific question in this survey, you may skip it. Examples:

INTERVIEWER NAME: ______

START TIME OF SURVEY: ______END TIME OF SURVEY: ______

Created by: Douglas Fernandes Gomes DaSilva Web: http://www.ccrs.uzh.ch Center for Corporate Responsibility and Sustainability (CCRS) Email: [email protected] Zähringerstrasse 24, 8001 Zürich, Switzerland Phone: +41 78 700 2442

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Section A: Personal Information

A-01. City/Village: ______A-02. Municipality: ______

A-03. City/Village Setting: 1 Rural 2 Urban

A-04. Desertification Level: 1 High 2 Medium 3 Low

HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) A-05. What is your full name? A-06. What is your email or phone? A-07. Date of birth (DD/MM/YYYY) A-08. Gender 1 Male 2 Female 1 Male 2 Female A-09. Height (cm) A-10. Weight (kg)

A-11. What is your 1 No Formal Education 1 No Formal Education highest level of 2 Primary School (1-5 grade) 2 Primary School (1-5 grade) education? 3 Elementary School (6-9 grade) 3 Elementary School (6-9 grade) (check one) 4 Secondary School (10-12 grade) 4 Secondary School (10-12 grade)

5 Vocational School 5 Vocational School

6 University – Undergraduate 6 University – Undergraduate

7 University – Graduate 7 University – Graduate

A-12. What is your 1 Single 99 Other: 1 Single 99 Other: current marital 2 Married 2 Married status? (check one) 3 Separated 3 Separated

4 Divorced 4 Divorced

5 Widowed 5 Widowed

A-13. What is your 1 Indigenous ______1 Indigenous ______ethnic background? 2 European descendent 2 European descendent (check one) 3 Mix European + Indigenous 3 Mix European + Indigenous

4 African descendent 4 African descendent

5 Mix European + African 5 Mix European + African

6 Mix African + Indigenous 6 Mix African + Indigenous

99 Other: 99 Other:

A-14. Can you speak 1 Yes 2 No 1 Yes 2 No Spanish? A-15. Can you read 1 Yes 2 No 1 Yes 2 No and write? A-16. Do you have a 1 Yes 2 No 1 Yes 2 No Colombian ID? A-17. If NO, why not? A-18. If NO, why not?

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Section B: Work Status B-01. HEAD OF HOUSEHOLD: Do you currently have a paid job? 1 Yes 2 No B-02. SPOUSE: Do you currently have a paid job? 1 Yes 2 No 3 N/A

IF NO to B-01 and/or B-02… HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) B-03. If you do not 1 Homemaker (carrying for family) 1 Homemaker (carrying for family) have a job, choose 2 Can’t work due to health problems 2 Can’t work due to health problems the best that 3 Looking for a job 3 Looking for a job describes your 4 Studying 4 Studying current situation: (check all that 5 Retired 5 Retired apply) 99 Other: 99 Other: B-04. If you’re 1 Not at all 4 Very much 1 Not at all 4 Very much

LOOKING FOR A 2 A little 5 Extremely 2 A little 5 Extremely JOB, how difficult 3 Moderately 3 Moderately has it been?

B-05. Do you or your spouse receive unemployment funds or any type of financial aid? 1 Yes 2 No B-06. If YES to B-05, please describe: ______

If you currently have a job… HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) B-07. What is your main occupation? B-08. Do you have a fixed job or only do daily work? 1 Fixed job 2 Daily work 1 Fixed job 2 Daily work B-09. How long have you worked at your current job? Years: ______Months: ______Years: ______Months: ______B-10. What is your current work status? 1 Full-Time 2 Part-Time 1 Full-Time 2 Part-Time B-11. What is your monthly income (approximately)? B-12. Routinely, how do you 1 Car 4 Bicycle 1 Car 4 Bicycle reach your work? 2 Motorcycle 5 Walk 2 Motorcycle 5 Walk (check all that apply) 3 Bus 3 Bus

99 Other: 99 Other:

B-13. How long does it take 1 Hours: ______Minutes: ______1 Hours: ______Minutes: ______for you to reach work from 2 How many kilometers? ______2 How many kilometers? ______your home? B-14. How much do you 1 Not at all 4 Very much 1 Not at all 4 Very much enjoy the type of work you 2 A little 5 Extremely 2 A little 5 Extremely do? (check one) 3 Moderately 3 Moderately

B-15. Do you feel safe that 1 Yes 4 No 1 Yes 4 No you will have this job for at B-16. If NO, why not? B-17. If NO, why not? least the next year?

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Section C: Household

C-01. Total number of people living in your household: 1Children: ______2Adults: ______Housing Infrastructure… C-02. What is the current status of 1 Own (paid off) 3 Rent your home? (check one) 2 Own (making payments) 4 Employer provided

99 Other: C-03. How many ROOMS do you have in your house? C-04. What’s the approximate SIZE of 2 your home (in m ) ______m2 Don’t know

C-05. Do you believe to have enough 1 Yes 2 No space for you and your family? Comments:

C-06. The WALLS of your home are 1 Concrete 2 Iron sheets 3 Stones made from what materials? 4 Wood 5 Fired mud bricks 6 Compact mud (check all that apply) 7 Plant leaves 99 Other:

C-07. The ROOF of your home is 1 Concrete 2 Iron sheets 3 Clay tiles made from what materials? 4 Wood 5 Plant leaves 6 Plastic Sheet (check all that apply) 99 Other:

C-08. The FLOOR of your home is 1 Concrete 2 Tile 3 Sand made from what materials? 4 Wood 5 Mud (check all that apply) 99 Other: C-09. How would you describe the Poor Fair Average Good Excellent QUALITY of your house? (circle one) 1 2 3 4 5 C-10. Over the past few years, how Worsened Worsened Stayed the Improved Improved did the QUALITY of your house a lot a bit same a bit a lot change? (circle one) 1 2 3 4 5

Energy… C-11. Where does the ENERGY for 1 Public electricity 4 Gas lamps your home come from? 2 Petrol generator 5 Firewood (check all that apply) 3 Solar panels 6 Candles

99 Other:

C-12. Where does the COOKING FUEL 1 Electricity 2 Gas 3 Firewood for your home come from? 4 Charcoal 5 Animal dung 6 Crop waste (check all that apply) 99 Other:

C-13. Comments on housing and energy:

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Water… C-14. Where does the WATER for 1 Public piped water 4 Well or borehole your home come from? 2 River or stream water 5 Rain water collection (check all that apply) 3 Commercialized bottled water 6 Water truck

99 Other:

C-15. Do you ever treat the water you drink? 1 Yes 2 No

C-16. If YES to C-15, check all the treatment options that apply:

1 Filter 2 Chemical treatment 3 Boiling 99 Other:

C-17. Do you have to walk outside your property to collect water? 1 Yes 2 No

C-18. If YES to C-17, how long does it take? (Round trip) 1 Hours: ______Minutes: ______

(if known) 2 How many kilometers? ______C-19. If YES to C-17, how often do you have to go collect water? (check one) 1 Everyday 2 5 – 6 times/week 3 3 – 4 times/week 4 1 – 2 times/week

C-20. How would you describe the Poor Fair Average Good Excellent QUALITY of the water? (circle one) 1 2 3 4 5 C-21. Over the past year, how did Worsened Worsened Stayed the Improved Improved the QUALITY of the water change? a lot a bit same a bit a lot (circle one) 1 2 3 4 5 C-22. How would you describe the Very Low Low Average Good Excellent QUANTITY of water available to your 1 2 3 4 5 home? (circle one) C-23. Over the past year, how did Worsened Worsened Stayed the Improved Improved the QUANTITY of water change? a lot a bit same a bit a lot (circle one) 1 2 3 4 5

C-24. In general, do you feel Not at all A little Moderately Very much Extremely safe in your home and your 1 2 3 4 5 community? (circle one)

Waste Disposal… C-25. What type of toilet you have in 1 Flushing toilet 3 No toilet facility your home? 2 Pit latrine 99 Other: (check all that apply) C-26. How do you dispose of your 1 City collection 3 Burial pit household waste? 2 Burning pit 99 Other: (check all that apply)

C-27. Comments on water and waste disposal:

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Section D: Rural Ranches or Farms D-01. Do you and your family live and/or work on a ranch or farm? 1 Yes 2 No (SKIP IF YOU DON’T LIVE AND/OR WORK ON A RANCH OR FARM) Environmental factors… D-02. In the past year, has 1 Drought and/or lack of water your farm or ranch been 2 Low soil productivity affected by any of the 3 Outbreak of a crop or plantation disease: ______following? 4 Outbreak of an animal disease: ______(check all that apply) 5 Heavy rain

6 Fire

7 No problems affected my land

99 Other:

Soil quality… D-03. How would you describe the Poor Fair Average Good Excellent SOIL QUALITY of your land? 1 2 3 4 5 (circle one) D-04. Over the past year, how did Worsened Worsened Stayed the Improved Improved the SOIL QUALITY change? a lot a bit same a bit a lot (circle one) 1 2 3 4 5 D-05. Comments on soil quality:

Water quantity… D-06. How would you describe the Very Low Low Average Good Excellent QUANTITY OF WATER available to 1 2 3 4 5 your land? (circle one) D-07. Over the past year, how did Worsened Worsened Stayed the Improved Improved the QUANTITY OF WATER change? a lot a bit same a bit a lot (circle one) 1 2 3 4 5 D-08. Comments on water quantity:

Crops or personal plantation growth…

D-09. Are you able to grow any crops or personal plantations on your land? 1 Yes 2 No

D-10. If NO to D-09, please explain reasons why:

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If YES to D-09… D-11. Do you currently to grow any crops or personal plantations on your land? 1 Yes 2 No D-12. If YES to D-11, please specify the types of crops or plantations:

D-13. How has the growth of your Worsened Worsened Stayed the Improved Improved crops or personal plantation change a lot a bit same a bit a lot in the past year? (circle one) 1 2 3 4 5

D-14. Do you currently use fertilizer on your crops or plantations? 1 Yes 2 No D-15. If YES to D-14, please specify the types of fertilizer you use:

D-16. How has your general use of Increased Increased Stayed the Decreased Decreased fertilizers change over the past year? a lot a bit same a bit a lot (circle one) 1 2 3 4 5

D-17. Over the past year, have you lost any crops or plantations? 1 Yes 2 No D-18. If YES to D-18, please specify reasons why:

D-19. How much does a loss of crops Not at all A little Moderately Very much Extremely or plantations affect you? (circle one) 1 2 3 4 5

Animals… D-20. Do you have any livestock on your land? 1 Yes 2 No D-21. If YES to D-20, please specify: Animal type: ______How many: ______D-22. Over the past year, have any of your animals died? 1 Yes 2 No D-23. If YES to D-22, please specify reasons why:

D-24. How much does a loss of Not at all A little Moderately Very much Extremely animals affect you? (circle one) 1 2 3 4 5

D-25. Comments on crops/plantations and animals:

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Section E: Health – Head of Household/Spouse E-01. Are you enrolled in a health insurance program? 1 Yes 2 No E-02. If YES to E-01, which program do you have? HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) 1 EPS Contributory Plan 1 EPS Contributory Plan

2 EPS Subsidized Plan 2 EPS Subsidized Plan

3 Prepaid Medicine (Private Plan) 3 Prepaid Medicine (Private Plan)

4 Don’t know 4 Don’t know

99 Other: 99 Other:

E-03. Have you ever had any of the following conditions? (check all that apply) HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) 1 Allergies 7 Hepatitis 1 Allergies 7 Hepatitis

2 Asthma or Bronchitis 8 Kidney disease 2 Asthma or Bronchitis 8 Kidney disease

3 Anxiety or Depression 9 Tuberculosis 3 Anxiety or Depression 9 Tuberculosis

4 Diabetes 10 Malnutrition 4 Diabetes 10 Malnutrition

5 High Blood Pressure 11 Cancer 5 High Blood Pressure 11 Cancer

6 Heart Problems 12 HIV 6 Heart Problems 12 HIV

99 Other: 99 Other:

E-04. In the past 6 months, how many times were you sick? (check one) HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) 1 1 4 4 1 1 4 4

2 2 5 5 or more 2 2 5 5 or more

3 3 6 I was not sick in the past year 3 3 6 I was not sick in the past year

E-05. Please list any illnesses you had in the past 6 months… HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) 1. 1. 2. 2. 3. 3. 4. 4. 5. 5.

E-06. In the past 6 months, did you miss work because of an illness? 1 Yes 2 No E-07. If YES to E-06, how many days have you missed? (check one) HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) 1 1 – 5 days 4 16 – 20 days 1 1 – 5 days 4 16 – 20 days

2 6 – 10 days 5 21 – 30 days 2 6 – 10 days 5 21 – 30 days

3 11 – 15 days 6 More than a month 3 11 – 15 days 6 More than a month

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E-08. Do you currently take medications? HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) 1 YES 2 NO 1 YES 2 NO E-09. If YES to E-08.A, name the medication(s): E-10. If YES to E-08.B, name the medication(s):

E-11. Explain how do you buy or receive any medications you need:

Health care facilities… E-12. Name of the clinic or hospital you usually go to:

E-13. How would you describe the Poor Fair Average Good Excellent QUALITY of this clinic / hospital? 1 2 3 4 5 (circle one) E-14. Normally how do you 1 Car 3 Taxi 5 Bus 99 Other: reach this clinic / hospital? 2 Motorcycle 4 Bicycle 6 Walk (check all that apply) E-15. How long does it take 1 Hours: ______Minutes: ______for you to reach this clinic / 2 How many kilometers? (if known) ______hospital? E-16. How are the road conditions to Poor Fair Average Good Excellent reach the health center (circle one) 1 2 3 4 5

E-17. Does this clinic / hospital ever charges you for health services? 1 YES 2 NO

E-18. In general, can you afford to pay for insurance, health services and 1 YES 2 NO medications? E-19. What you normally do when you or your family are sick and need medical attention?

When you go to the health center, how often does a Always Sometimes Never health worker measure your VITAL SIGNS… (circle one Measure Measure Measure in each category) E-20. Temperature 1 2 3 E-21. Heart Rate 1 2 3 E-22. Respiratory Rate 1 2 3 E-23. Blood Pressure 1 2 3 E-24. Pulse Oximetry 1 2 3 E-25. Weight 1 2 3 E-26. Height 1 2 3

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When you go to the health center, how often does a Always Sometimes Never health worker perform PHYSICAL EXAM components? Perform Perform Perform (circle one in each category) F-27. General Assessment 1 2 3 F-28. Ears (with otoscope) 1 2 3 F-29. Eyes (with light) 1 2 3 F-30. Nose (with otoscope) 1 2 3 F-31. Mouth/Throat (with otoscope/tongue depressor) 1 2 3 F-32. Neck glands (touch) 1 2 3 F-33. Heart (with stethoscope) 1 2 3 F-34. Pulmonary (with stethoscope) 1 2 3 F-35. Gastro-intestinal (touch and stethoscope) 1 2 3 F-36. Genitals (inspection) 1 2 3 F-37. Extremities (inspection) 1 2 3 F-38. Neurologic test 1 2 3

Have you ever felt you were discriminated or treated badly by a health care worker because of your: E-39. Gender 1 Yes 2 No

E-40. Age 1 Yes 2 No

E-41. Lack of money 1 Yes 2 No

E-42. Ethnic group or color 1 Yes 2 No

E-43. Type of illness 1 Yes 2 No

E-44. Other: 1 Yes 2 No

E-45. Do you ever go to a local traditional healer for health problems? 1 Yes 2 No

E-46. If YES to E-45, please describe the practices and traditional medicines utilized:

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Section F: Children and their Health F-01. Have you ever had any children? 1 Yes 2 No (if NO, skip this section) F-02. If YES, how many? ______

F-03. Has a child of yours died in the past? 1 Yes 2 No F-04. If YES to F-03, please list date of birth, date of death and reason of death:

Individual child information (if more than 4 children, use Appendix A at the end of survey) Child 1 (A) Child 2 (B) Child 3 (C) Child 4 (D)

F-05. Name of child 1 Male 1 Male 1 Male 1 Male F-06. Gender of child 2 Female 2 Female 2 Female 2 Female F-07. Date of Birth F-08. Height (cm)

F-09. Weight (kg)

F-10. MUAC F-11. Does your child 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No have a Colombian ID? F-12. Is your child 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No going to school? F-13. If NO to F-12, why not? F-14. Has your child been immunized? 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No F-15. If NO to F-14, why not?

F-16. In general, how 1 Poor 1 Poor 1 Poor 1 Poor is your child’s health 2 Fair 2 Fair 2 Fair 2 Fair now? (circle one) 3 Average 3 Average 3 Average 3 Average

4 Good 4 Good 4 Good 4 Good

5 Excellent 5 Excellent 5 Excellent 5 Excellent

F-17. In comparison to 1 Much worse 1 Much worse 1 Much worse 1 Much worse last year, how would 2 Worse 2 Worse 2 Worse 2 Worse you rate your child’s 3 The same 3 The same 3 The same 3 The same health now? 4 Better 4 Better 4 Better 4 Better (circle one) 5 Much better 5 Much better 5 Much better 5 Much better

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Do your children have any of the following conditions? Child 1 (A) Child 2 (B) Child 3 (C) Child 4 (D)

Name of child

F-18. Allergies 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-19. Respiratory disease 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-20. Diabetes 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-21. Heart problems 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-22. Kidney problems 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-23. HIV 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-24. Cancer 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-25. Behavior problems 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-26. Developmental 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No disability (if YES, list disability)

In the past 6 months, did any of your children have…? Child 1 (A) Child 2 (B) Child 3 (C) Child 4 (D)

Name of child

F-27. Diarrhea 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-28. Pneumonia 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-29. Malnutrition 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-30. Tuberculosis 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-31. Hepatitis 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-32. Meningitis 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-33. Malaria 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-34. Chagas 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-35. Typhoid 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-36. Leishmaniasis 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-37. Tape/round worms 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No F-38. Dengue, Yellow 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No Fever, Zika, Chikungunya F-39. Other: 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-40. In the PAST 6 MONTHS, how many times were your children SICK? (check one) Child 1 (A) Child 2 (B) Child 3 (C) Child 4 (D) Name: Name: Name: Name:

1 1 4 4 1 1 4 4 1 1 4 4 1 1 4 4

2 2 5 5 or more 2 2 5 5 or more 2 2 5 5 or more 2 2 5 5 or more

3 3 6 Never sick 3 3 6 Never sick 3 3 6 Never sick 3 3 6 Never sick

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F-41. Please list any ILLNESSES your children had in THE PAST 6 MONTHS… Child 1 (A) Child 2 (B) Child 3 (C) Child 4 (D) Name: Name: Name: Name:

1. 1. 1. 1. 2. 2. 2. 2. 3. 3. 3. 3. 4. 4. 4. 4. 5. 5. 5. 5.

F-42. Are any of your children taking MEDICATION(S)? Child 1 (A) Child 2 (B) Child 3 (C) Child 4 (D) Name: Name: Name: Name:

1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No If YES, list If YES, list If YES, list If YES, list medication(s): medication(s): medication(s): medication(s):

F-43. In the past year, did you miss work because your child was sick? 1 Yes 2 No F-44. If YES to F-43, how many days have you missed? (check one) HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) 1 1 – 5 days 4 16 – 20 days 1 1 – 5 days 4 16 – 20 days

2 6 – 10 days 5 21 – 30 days 2 6 – 10 days 5 21 – 30 days

3 11 – 15 days 6 More than a month 3 11 – 15 days 6 More than a month

F-45. Overall, how WORRIED are you about your children’s health? (check one) HEAD OF HOUSEHOLD (A) SPOUSE/PARTNER (B) ( None) 1 Not at all 4 Very much 1 Not at all 4 Very much

2 Just a little 5 Extremely 2 Just a little 5 Extremely

3 Moderate amount 3 Moderate amount

F-46. Comments on children’s health

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Section G: Nutrition and Food Security Over the PAST 1 WEEK (7 DAYS), how many days did you and others in your household consumed the following food group items… (check one in each category) FOOD TYPE HOW MANY DAYS IN THE LAST WEEK? 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-01. Cereals and Grains

(Maize, Rice, Bread, Wheat Flour, Pasta) 8 Did not consume this type of food 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-02. Roots and Plantains

(Cassava, Potatoes, Plantains) 8 Did not consume this type of food 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-03. Nuts and Legumes

(Nuts, Beans, Lentils) 8 Did not consume this type of food 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-04. Vegetables

(Green Leaves, Tomatoes, Cabbage, Carrots) 8 Did not consume this type of food 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-05. Meat, Fish, Poultry, Animal Products

(Beef, Pork, Goat, Fish, Chicken, Eggs) 8 Did not consume this type of food 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-06. Milk and Milk Products

(Milk, Yogurt, Cheese) 8 Did not consume this type of food 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-07. Fruits

(Bananas, Apples, Guava, Papaya, Pineapple) 8 Did not consume this type of food 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-08. Fats and Oils

(Cooking Oil, Butter, Margarine, Lard) 8 Did not consume this type of food 1 1 2 2 3 3 4 4 5 5 6 6 7 7 G-09. Sugar Products

(Refined Sugar, Candy, Deserts, Chocolate, Soda) 8 Did not consume this type of food

How often do you eat the following meals? (check one in each category) 0 – 1 time 2 – 3 times 4 – 5 times 6 – 7 time

per week per week per week per week G-10. Breakfast 1 2 3 4

G-11. Lunch 1 2 3 4

G-12. Dinner 1 2 3 4

G-13. In the last 12 months have you been faced with a situation where 1 Yes 2 No you had little food or did not have enough food to feed your household? G-14. If YES to G-13, please indicate the months when it happened: (check all that apply)

1 January 2 February 3 March 4 April 5 May 6 June

7 July 8 August 9 September 10 October 11 November 12 December

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Please indicate if your food shortage situation is caused by any of these factors: G-15. Inadequate household food stocks due to low or no farm inputs 1 Yes 2 No G-16. If YES to G-15, please indicate reasons (check all that apply):

1 Drought 2 Disease 3 Pest 4 Land not fertile enough 99 Other:

G-17. Inadequate household food stocks due animal loss 1 Yes 2 No G-18. If YES to G-17, please indicate reasons (check all that apply):

1 Drought 2 Disease 99 Other:

G-19. Inadequate household food stocks because we don’t have enough 1 Yes 2 No money to buy food every day

G-20. Inadequate household food stocks because food items in the 1 Yes 2 No market are too expensive

G-21. Inadequate household food stocks because the market doesn’t 1 Yes 2 No have enough food to sell

G-22. Unable to reach the market due to high transportation costs 1 Yes 2 No

G-23. Other: 1 Yes 2 No

G-24. Comments on nutrition and food security

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Section H: Environment I-01. In the PAST YEAR, has 1 Drought and/or lack of water the surrounding community 2 Low soil productivity been AFFECTED by any of the 3 Outbreak of a crop or plantation disease: ______following occurrences? 4 Outbreak of an animal disease: ______(check all that apply) 5 Heavy rain

6 Fire

7 No problems affected the surrounding community

99 Other:

How would you RATE the surrounding Poor Fair Average Good Excellent ENVIRONMENT of your home? (circle one) H-02. Air quality 1 2 3 4 5 H-03. Water availability 1 2 3 4 5 H-04. Water quality 1 2 3 4 5 H-05. Food availability 1 2 3 4 5 H-06. Food variety 1 2 3 4 5 H-07. Energy availability (gas, petrol, electricity) 1 2 3 4 5 H-08. Roads quality 1 2 3 4 5 H-09. Temperature 1 2 3 4 5

In comparison to 1 year ago, how Worsened Worsened Stayed Improved Improved would you RATE the following? a lot a bit the same a bit a lot (circle one) H-10. Air quality 1 2 3 4 5 H-11. Water availability 1 2 3 4 5 H-12. Water quality 1 2 3 4 5 H-13. Food availability 1 2 3 4 5 H-14. Food variety 1 2 3 4 5 H-15. Energy availability 1 2 3 4 5 H-16. Roads quality 1 2 3 4 5 H-17. Temperature 1 2 3 4 5

How would you RATE the following… (circle one) None A Little Average A Lot Extreme

H-18. Quantity of humidity in the air 1 2 3 4 5 H-19. Quantity of dust in the air 1 2 3 4 5 H-20. Quantity of wind 1 2 3 4 5 H-21. Quantity of rain 1 2 3 4 5

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In comparison to 1 year ago, how Worsened Worsened Stayed Improved Improved would you RATE the following…? a lot a bit the same a bit a lot H-22. Quantity of humidity in the air 1 2 3 4 5 H-23. Quantity of dust in the air 1 2 3 4 5 H-24. Quantity of wind 1 2 3 4 5 H-25. Quantity of rain 1 2 3 4 5

Please answer the following questions in regards to the weather…

H-26. In regards to 1 This year is HOTTER than last year 4 Don’t know

TEMPERATURE… 2 This year is COOLER than last year (check one) 3 The temperature pattern is similar to last year

H-27. In regards to RAIN… 1 This year it rained MORE than last year 4 Don’t know

(check one) 2 This year it rained LESS than last year

3 The rain pattern is similar to last year

H-28. In regards to AIR 1 This year the air quality is BETTER than last year 4 Don’t know

QUALITY… 2 This year the air quality is WORSE than last year (check one) 3 The air quality is similar to last year H-29. How often do you get RAIN in Almost All the this region? (circle one) Never Rarely Sometimes Often time 1 2 3 4 5 H-30. How much does the RAIN PATTERN in this Not at Just a Moderate Very region bother you? (circle one) all little amount Much Extremely 1 2 3 4 5 H-31. In general, how much does the Not at Just a Moderate Very SURROUNDING ENVIRONMENT bother you? all little amount Much Extremely (circle one) 1 2 3 4 5 H-32. How much does the TEMPERATURE in this Not at Just a Moderate Very region bother you? (circle one) all little amount Much Extremely 1 2 3 4 5 H-33. How much does the AIR QUALITY in this Not at Just a Moderate Very region bother you? (circle one) all little amount Much Extremely 1 2 3 4 5 H-34. To what extent do you have PROBLEMS Not at Just a Moderate Very with the ROADS OR TRANSPORT to do your daily all little amount Much Extremely activities? (circle one) 1 2 3 4 5 H-35. To what extent do you have PROBLEMS Not at Just a Moderate Very with ACQUIRING FOOD on a daily basis? all little amount Much Extremely (circle one) 1 2 3 4 5 H-36. To what extent do you have PROBLEMS Not at Just a Moderate Very with ACQUIRING SAFE WATER for drinking on a all little amount Much Extremely daily basis? (circle one) 1 2 3 4 5

Survey is finished. Thank you kindly for your time and patience answering these questions!

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APPENDIX A Section F: Children and their Health Individual child information (if more than 4 children, use Appendix A at the end of survey) Child 5 (E) Child 6 (F) Child 7 (G) Child 8 (H)

F-05. Name of child 1 Male 1 Male 1 Male 1 Male F-06. Gender of child 2 Female 2 Female 2 Female 2 Female F-07. Date of Birth F-08. Height (cm)

F-09. Weight (kg)

F-16. In general, how 1 Poor 1 Poor 1 Poor 1 Poor is your child’s health 2 Fair 2 Fair 2 Fair 2 Fair now? (circle one) 3 Average 3 Average 3 Average 3 Average

4 Good 4 Good 4 Good 4 Good

5 Excellent 5 Excellent 5 Excellent 5 Excellent

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Do your children have any of the following conditions? Child 5 (E) Child 6 (F) Child 7 (G) Child 8 (H)

Name of child

F-18. Allergies 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-19. Respiratory disease 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-20. Diabetes 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-21. Heart problems 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-22. Kidney problems 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-23. HIV 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-24. Cancer 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-25. Behavior problems 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-26. Developmental 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No disability (if YES, list disability)

In the past 6 months, did any of your children have…? Child 5 (E) Child 6 (F) Child 7 (G) Child 8 (H)

Name of child

F-27. Diarrhea 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-28. Pneumonia 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-29. Malnutrition 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-30. Tuberculosis 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-31. Hepatitis 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-32. Meningitis 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-33. Malaria 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-34. Chagas 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-35. Typhoid 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

F-36. Leishmaniasis 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No

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F-40. In the PAST 6 MONTHS, how many times were your children SICK? (check one) Child 5 (E) Child 6 (F) Child 7 (G) Child 8 (H) Name: Name: Name: Name:

1 1 4 4 1 1 4 4 1 1 4 4 1 1 4 4

2 2 5 5 or more 2 2 5 5 or more 2 2 5 5 or more 2 2 5 5 or more

3 3 6 Never sick 3 3 6 Never sick 3 3 6 Never sick 3 3 6 Never sick

F-41. Please list any ILLNESSES your children had in THE PAST 6 MONTHS… Child 5 (E) Child 6 (F) Child 7 (G) Child 8 (H) Name: Name: Name: Name:

1. 1. 1. 1. 2. 2. 2. 2. 3. 3. 3. 3. 4. 4. 4. 4. 5. 5. 5. 5.

F-42. Are any of your children taking MEDICATION(S)? Child 5 (E) Child 6 (F) Child 7 (G) Child 8 (H) Name: Name: Name: Name:

1 Yes 2 No 1 Yes 2 No 1 Yes 2 No 1 Yes 2 No If YES, list If YES, list If YES, list If YES, list medication(s): medication(s): medication(s): medication(s):

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A.2 Health Facility Baseline Assessment Questionnaire

Health Facility Baseline Assessment Questionnaire

INTRODUCTION: As part of a multi-lateral study about the factors that contribute to childhood health in the La Guajira Department, the following questionnaire aims to assess the local healthcare system and its delivery. Our study is especially concerned with desertification and its effects on a child’s well being. By answering the following questions you help us gain an in-depth overview of the health sector and identify possible challenges it faces. CONFIDENTIALITY STATEMENT: We guarantee that the information and data herewith collected will be handled with the utmost confidentiality, will be analyzed in an anonymous way and no personal information will be forwarded or shared with other parties without your permission. INSTRUCTIONS: This health facility survey includes 9 sections. Check or circle only one answer per question, unless specified that more than one answer can be checked or circled. If you feel uncomfortable answering a specific question in this survey, you may skip it. Examples:

INTERVIEWER NAME: ______

START TIME OF SURVEY: ______END TIME OF SURVEY: ______

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Section A: General Information

A-01. City/Village: ______A-02. Municipality: ______

A-03. City/Village Setting: 1 Rural 2 Urban

A-04. Desertification Level: 1 High 2 Medium 3 Low

A-05. Name of Health Facility A-06. Interviewee Name A-07. Interviewee Position A-08. Interviewee Email or Telephone number

A-09. TYPE of Health Facility 1 IPS Private 3 Independent Professional

(check one) 2 IPS Public 4 Don’t know

A-10. COMPLEXITY level of 1 High 2 Medium 3 Low 4 Don’t know Health Facility (check one) A-11. Reach of Services 1Number of people served: ______

2Number of cities served: ______A-12. What LANGUAGES are 1 Spanish 2 Wayúunaiki 3 English spoken at the health facility 99 Other: (check all that apply)

Does this facility offer the following health services? A-13. Emergency services 1 Yes 2 No 2 Don’t Know

A-14. Inpatient Services 1 Yes 2 No 2 Don’t Know

A-15. Outpatient Services 1 Yes 2 No 2 Don’t Know

A-16. Pediatric specialist 1 Yes 2 No 2 Don’t Know

A-17. Maternity Services (i.e., antenatal, delivery, postnatal) 1 Yes 2 No 2 Don’t Know

A-18. Obstetrics/Gynecology 1 Yes 2 No 2 Don’t Know

A-19. Nutrition Program 1 Yes 2 No 2 Don’t Know

A-20. Immunizations 1 Yes 2 No 2 Don’t Know

A-21. Drugstore/Pharmacy Services 1 Yes 2 No 2 Don’t Know

A-22. Diagnostics Laboratory 1 Yes 2 No 2 Don’t Know

A-23. Surgery 1 Yes 2 No 2 Don’t Know

A-24. Physiotherapy / Occupational Therapy 1 Yes 2 No 2 Don’t Know

A-25. Mental Health 1 Yes 2 No 2 Don’t Know

A-26. Dental Care 1 Yes 2 No 2 Don’t Know A-27. Outreach Services (i.e., home visits, health education events, newsletters, advertising) 1 Yes 2 No 2 Don’t Know

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Number of patients…

A-28. On average, HOW MANY patients do this facility 1 Less than 25 patients/day see per day? (check one) 2 26-50 patients/day 3 51-100 patients/day 4 100-200 patients/day 5 More than 200 patients/day 6 Don’t know

Patients… A-29. What TYPE of patients 1 EPS Contributory Plan Patients does this facility serve? 2 EPS Subsidized Plan Patients (check all that apply) 3 Prepaid Medicine (Private Plan) Patients

4 Don’t know

99 Other:

A-30. Are there occasions when patients need to 1 Yes 2 No directly PAY this health facility for services? A-31. If YES to A-30, please explain these occasions and what services are normally charged:

A-32. If a PATIENT comes to this health facility but DOESN’T HAVE HEALTH INSURANCE and/or IS NOT ABLE TO PAY FOR SERVICES, please explain what would you normally do for this patient:

A-33. In your opinion, what are some factors that PREVENT PATIENTS from coming to this health facility?

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Section B: Facility Operations and Infrastructure

If interviewee differs from listed on Section A, please note: Interviewee Name Interviewee Position Interviewee Email or Phone

Hours of Operation B-01. Is this facility opened every day? 1 Yes 2 No B-02. If NO to B-01, please specify when this facility is open:

B-03. Is this facility opened on public holidays? 1 Yes 2 No

Infrastructure

Water… B-04. Where does the WATER for the 1 Public piped water 4 Well or borehole health facility come from? 2 River or stream water 5 Rain water collection (check all that apply) 3 Commercialized bottled water

99 Other: B-05. How would you describe the Very Low Low Sufficient Good Excellent QUANTITY of water available to the health facility? (circle one) 1 2 3 4 5 B-06. Over the past few years, how Worsened Worsened Stayed the Improved Improved did the QUANTITY of water change? a lot a bit same a bit a lot (circle one) 1 2 3 4 5

B-07. Does this facility treat or sterilize water? 1 Yes 2 No B-08. If YES to B-07, check all the treatment options that apply:

1 Filter 2 Chemical treatment 3 Boiling 99 Other:

Electricity… B-09. Where does the ELECTRICITY 1 Public electricity 4 Battery inverter for the health facility come from? 2 Petrol generator 5 No electricity (check all that apply) 3 Solar panels 99 Other:

B-10. If you have electricity, how 1 Every day frequent do you have it? (check one) 2 At least 5 days/week

3 At least 3 days/week

4 Only once/week

5 Less than 5x per month

6 We never know when we get electricity

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Lights… B-11. Where does the LIGHT SOURCE 1 Public electricity 4 Gas lamps for the health facility come from? 2 Petrol generator 5 Firewood (check all that apply) 3 Solar panels 6 Candles

99 Other: B-12. How would you describe the Very Low Low Sufficient Good Excellent AMOUNT OF LIGHT available to the 1 2 3 4 5 health facility? (circle one)

Does this facility have…? (A) Is it functional? (B) B-13. A place to wash hands 1 Yes 2 No 1 Yes 2 No B-14. Flushing toilets 1 Yes 2 No 1 Yes 2 No B-15. Septic tank 1 Yes 2 No 1 Yes 2 No B-16. Pit latrines 1 Yes 2 No 1 Yes 2 No B-17. Incinerator/burning pit 1 Yes 2 No 1 Yes 2 No

Water and Power Shortages… B-18. In the PAST MONTH, has WATER SHORTAGES interfered with Not at all A little Moderately Very much Extremely the ability to perform functions? 1 2 3 4 5 (circle one) B-19. In the PAST MONTH, has POWER SHORTAGES interfered with Not at all A little Moderately Very much Extremely the ability to perform functions? 1 2 3 4 5 (circle one)

Roads description… B-20. What type of ROAD is 1 Paved road 3 Walking path only available to this facility? (circle 2 Dirt road 4 Other: one) B-21. What are the CONDITIONS Poor Fair Average Good Excellent of the roads? (circle one) 1 2 3 4 5

B-22. Is there an AMBULANCE available at this health facility? 1 Yes 2 No If YES to B-22… B-23. Describe the TYPE, QUANTITY and B-24. Is there a limit as to HOW FAR the ambulance CONDITIONS of the ambulances: will go to attend to a patient?

1 Yes 2 No

If YES, please describe the limitations:

B-25. How would you rate this Poor Fair Average Good Excellent system for picking up patients? 1 2 3 4 5 (circle one)

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Section C: Human Resources If interviewee differs from listed on Section A, please note: Interviewee Name Interviewee Position Interviewee Email or Phone

Health Facility Staff… Full-Time Staff Part-Time Staff (A) (B) C-01. Medical Doctors C-02. Physicians Assistant C-03. Nurse Practitioners C-04. Registered Nurses C-05. Nursing Assistants C-06. Midwives C-07. Pharmacists C-08. Pharmacy Assistants C-09. Laboratory Technicians C-10. Laboratory Assistants C-11. Dentists C-12. Nutritionists C-13. Social Workers C-14. Administration Staff C-15. Security Staff C-16. Food Service Staff C-17. Cleaning Staff C-18. Drivers

C-19. Do you feel that there is ENOUGH STAFF working at this facility to attend 1 Yes 2 No to all the community needs? C-20. If NO to C-19, please list the staff that are missing or are needed in higher numbers:

C-21. How DIFFICULT is it to hire qualified staff to work at this clinic? Not at all A little Moderately Very much Extremely (circle one) 1 2 3 4 5 C-22. If there are difficulties in hiring qualified staff, please explain why:

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Section D: Outpatient Services

If interviewee differs from listed on Section A, please note: Interviewee Name Interviewee Position Interviewee Email or Phone

Triage… D-01. Is there reception to welcome and guide patients upon facility arrival? 1 Yes 2 No

D-02. Are the patients screened for emergency danger signs? 1 Yes 2 No

D-03. Is there a system for patient flow? (i.e.: numerical or queue) 1 Yes 2 No

Does a health worker… Always Sometimes Never

D-04. Take the patient’s temperature? 1 2 3 D-05. Measure the patient’s height? 1 2 3 D-06. Measure the patient’s weight? 1 2 3 D-07. Take the patient’s blood pressure? 1 2 3 D-08. Take the patient’s heart rate? 1 2 3 D-09. Take the patient’s respiratory rate? 1 2 3 D-10. Performs a full physical examination? 1 2 3 D-11. Record notes about patient history? 1 2 3

D-12. If a health worker SOMETIMES or NEVER perform a certain Physical Exam or Vital Signs measurements, what are some reasons preventing them from doing it so?

D-13. Are the medical equipment sterilized between Always Sometimes Never patients? (circle one) 1 2 3 D-14. If ALWAYS or SOMETIMES to D-13, please explain how:

D-15. Does the clinic ever reuse disposable equipment? Always Sometimes Never 1 2 3 D-16. If ALWAYS or SOMETIMES to D-15, please explain why:

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Section E: Equipment and Supplies

If interviewee differs from listed on Section A, please note: Interviewee Name Interviewee Position Interviewee Email or Phone

Equipment Is at least one available? (A) Is it functioning? (B)

E-01. Blood pressure cuff/machine 1 Yes 2 No 1 Yes 2 No

E-02. Thermometer 1 Yes 2 No 1 Yes 2 No

E-03. Stethoscope 1 Yes 2 No 1 Yes 2 No

E-04. Otoscope 1 Yes 2 No 1 Yes 2 No

E-05. Height measurement device 1 Yes 2 No 1 Yes 2 No

E-06. Weighing scale for adults 1 Yes 2 No 1 Yes 2 No

E-07. Weighing scale for infants 1 Yes 2 No 1 Yes 2 No

How USUAL is for you to have the following supplies at Always Sometimes Never this health facility… (circle one in each category) Have Have Have E-08. Soap for hand washing 1 2 3 E-09. Alcohol or antiseptic 1 2 3 E-10. Gauze or Cotton 1 2 3 E-11. Medical tape 1 2 3 E-12. Sterile tongue depressor 1 2 3 E-13. Disposable rubber gloves 1 2 3 E-14. Disposable syringes and needles 1 2 3

Waste disposal… E-15. Are there rubbish bins for regular waste? 1 Yes 2 No E-16. Are there rubbish bins for infectious waste? 1 Yes 2 No

E-17. Are there sharps containers? 1 Yes 2 No

B-18. Comments on equipment and supplies

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Section F: Pharmacy If interviewee differs from listed on Section A, please note: Interviewee Name Interviewee Position Interviewee Email or Phone

F-01. Does this facility have a pharmacy? 1 Yes 2 No F-02. If NO to F-01, explain how patients can get medications they need:

Pharmacy Management F-03. Explain the procedure and challenges you have when ORDERING AND RECEIVING MEDICATIONS:

F-04. Once you order medications, usually HOW LONG does it take for you to receive them?

Essential Drugs (circle one answer for each medication) Essential Drugs Available Do Not Out of If Out of Stock, Stock Item Stock how long? F-05. Antibiotics 1 2 3 (ex: Penicillin, Ciprofloxacillin, Metronidazole) F-06. Antimalarials (ex: Artmether/Lumefantrine, Quinine, 1 2 3 Chloroquine) F-07. Antifungals 1 2 3 (ex: Clotrimazole, Fluconazole, Ketoconazole) F-08. Antiprotozoals 1 2 3 (ex: Pentamidine, Pyrmethamine) F-09. Antituberculous 1 2 3 (ex: Ethambutol, Isoniazid, Pyrazinamide) F-10. Antivirals 1 2 3 (ex: Acyclovir) F-11. Antiretrovirals 1 2 3 (ex: Abacavir, Zidovudine, Stavudine, Saquinavir) F-12. Anticonvulsants (ex: Carbamazepine, Diazepam, Magnesium 1 2 3 Sulfate) F-13. Antiacids / Antiulcers 1 2 3 (ex: Magnesium trisilicate, Calcium carbonate) F-14. Antidepressants / Antipsychotics 1 2 3 (ex: Amitriptyline, Chlorpromazine, Haloperidol)

154

Essential Drugs Available Do Not Out of If Out of Stock, Stock Item Stock how long? F-15. Antihistamines 1 2 3 (ex: Diphenhydramine, Chlorpheniramine) F-16. Cardiovascular Medications 1 2 3 (ex: Atenolol, Hydralazine, Furosemide, Digoxin) F-17. Oxytocics / Prostaglandins 1 2 3 (ex: Ergometrine, Methylergometrine) F-18. Emergency Drugs 1 2 3 (ex: Adrenaline, Aminophylline, Atropine, Insulin) F-19. Anesthetics 1 2 3 (ex: Halothane, Ketamine, Lidocaine, Lignocaine) F-20. NSAID / Analgesics 1 2 3 (ex: Paracetamol, Codeine, Diclofenac, Ibuprofen) F-21. Laxatives 1 2 3 (ex: Bisacodyl) F-22: Steroids 1 2 3 (ex: Beclometasone, Betametasone, Prednisone) F-23: Antihelminthics 1 2 3 (ex: Albendazole, Ivermectin) F-24: Contraceptives 1 2 3 (ex: Oral contraceptives pills, Implants, Depo) F-25: Vitamins and Minerals 1 2 3 (ex: Vitamins A, B and C, Folic Acid, Calcium) F-26: Solutions to correct water, electrolyte and acid-base disturbances 1 2 3 (ex: oral rehydration salts, water for injection)

Vaccines Available Do Not Out of If Out of Stock, Stock Item Stock how long? F-27. BCG (tuberculosis) 1 2 3 F-28. DPT (diphtheria, pertussis, tetanus) 1 2 3 F-29. Varicella (VAR – chickenpox) 1 2 3 F-30. Hepatitis A and B 1 2 3 F-31. Pneumococcal 1 2 3 F-32. Hib (Haemophilus influenzae type b) 1 2 3 F-33. MMR (measles, mumps, rubella) 1 2 3 F-34. Meningococcal 1 2 3 F-35. Polio (IPV) 1 2 3 F-36. Rabies 1 2 3 F-37. Tetanus (Td or Tdap) 1 2 3

F-38. Comments on Pharmacy

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Section G: Laboratory If interviewee differs from listed on Section A, please note: Interviewee Name Interviewee Position Interviewee Email or Phone

G-01. Does this facility have a laboratory? 1 Yes 2 No

G-02. If NO to G-01, does this facility refer laboratory tests to another facility? 1 Yes 2 No

G-03 If YES to G-02, name the facility: ______

Does the laboratory offer the following tests… (circle one answer for each test) Do not offer TEST Offer test test G-04. Bacteriology cultures (blood, urine, throat, CSF) 1 2 G-05. Full blood analysis (complete blood count, kidney, liver 1 2 and thyroid function, lipid and glucose panels) G-06. Urine analysis (kidney, liver and glucose panels) 1 2 G-07. Stool analysis (parasites) 1 2 G-08. HIV 1 2 G-09. Malaria 1 2 G-10. Chagas 1 2 G-11. Dengue, Yellow Fever, Zika, Chikungunya, West Nile 1 2 G-12. Hepatitis 1 2 G-13. Tuberculosis 1 2 G-14. Typhoid 1 2

Laboratory equipment Is at least one available? (A) Is it functional? (B) G-15. Microscope 1 Yes 2 No 1 Yes 2 No G-16. Centrifuge 1 Yes 2 No 1 Yes 2 No G-17. Mechanical Shaker or vortex 1 Yes 2 No 1 Yes 2 No G-18. Heating equipment (incubator, 1 Yes 2 No 1 Yes 2 No Bunsen Burner or hot plate) G-19. Balances 1 Yes 2 No 1 Yes 2 No G-20. Refrigerator 1 Yes 2 No 1 Yes 2 No G-21. Autoclave/sterilizer 1 Yes 2 No 1 Yes 2 No G-22. Other: 1 Yes 2 No 1 Yes 2 No G-23. Other: 1 Yes 2 No 1 Yes 2 No G-24. Other: 1 Yes 2 No 1 Yes 2 No G-25. Other: 1 Yes 2 No 1 Yes 2 No G-26. Other: 1 Yes 2 No 1 Yes 2 No

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Always Sometimes Never How USUAL is for you to have the following supplies at Have Have Have this laboratory… (circle one in each category) G-27. Soap for hand washing 1 2 3 G-28. Alcohol or antiseptic 1 2 3 G-29. Gauze or cotton 1 2 3 G-30. Disposable rubber gloves 1 2 3 G-31. Disposable syringes and needles 1 2 3 G-32. Microscope slides and reagents 1 2 3 G-33. Test tubes, petri dishes and pipettes 1 2 3 G-34. Laboratory reagents (stains, buffers and chemicals) 1 2 3 G-35. Glucose analysis sticks 1 2 3 G-36. Urine analysis sticks 1 2 3

G-37. In general, do you believe the necessary Always Sometimes Never LABORATORY EQUIPMENT AND SUPPLIES needed to Available Available Available perform laboratory duties are available to the clinic? 1 2 3 (circle one) G-38. If SOMETIMES or NEVER to G-37, please list items and explain possible reasons why they are lacking:

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Section H: Maternity and Delivery Services If interviewee differs from listed on Section A, please note: Interviewee Name Interviewee Position Interviewee Email or Phone

H-01. Does this facility offer maternity and delivery services? 1 Yes 2 No

H-02. If NO to H-01, does this facility refer maternity patients to another facility? 1 Yes 2 No

H-03 If YES to H-02, name the facility: ______

H-04. In general, do you think that the materials Always Sometimes Never necessary to perform MATERNITY duties are available to Available Available the clinic? (circle one) Available 1 2 3 H-05. If the answer is SOMETIMES or NEVER AVAILABLE to H-04, list the materials and explain the possible reasons why they are missing:

H-06. How often do mothers in All the the community come to this clinic Never Rarely Sometimes Normally time for prenatal consultations and 1 2 3 4 5 deliveries? (circle one) H-07. In your opinion, what are some factors that prevent mothers from coming to this health center for prenatal consultations and deliveries?

H-08. How often do you have All the problems with children and Never Rarely Sometimes Normally time mothers during or after delivery 1 2 3 4 5 (circle one) H-09. What are some of the common problems you face during or after delivery?

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Section I: Child Nutrition If interviewee differs from listed on Section A, please note: Interviewee Name Interviewee Position Interviewee Email or Phone

I-01. On average, how many CHILDREN BETWEEN 0 – 5 1 Less than 5 children/day

YEARS OF AGE come to this clinic because of malnutrition 2 5-20 children/day issues? (check one) 3 21-50 children/day

4 51-75 children/day

5 More than 75 children/day

6 Don’t know

Does a health worker … Always Sometimes Never

I-02. Measure a child’s height? 1 2 3 I-03. Measure a child’s weight? 1 2 3 I-04. Calculates a child’s BMI? 1 2 3 I-05. Measure a child’s head circumference (0-3 years)? 1 2 3 I-06. Measure a child’s mid upper arm circumference? 1 2 3 I-07. If you answered SOMETIMES or NEVER, please explain reasons why:

Does a health worker teach parents or guardians… Always Sometimes Never

I-08. How to identify malnutrition signs? 1 2 3 I-09. How to make an oral rehydration solution? 1 2 3 I-10. What to do if they believe a child is malnourished? 1 2 3

I-11. If a health worker assesses that child is malnourished, what do they suggest parents or guardians to do?

I-12. Does this facility use trained staff to identify malnutrition in the community? 1 Yes 2 No

Survey is finished. Thank you kindly for your time and patience answering these questions!

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A.3 Health Worker Questionnaire

Health Worker Questionnaire INTRODUCTION: As part of a multi-lateral study about the factors that contribute to childhood health in the La Guajira Department, the following questionnaire aims to assess the local healthcare system and its delivery. Our study is especially concerned with desertification and its effects on a child’s well being. By answering the following questions you help us gain an in-depth overview of the health sector and identify possible challenges it faces. CONFIDENTIALITY STATEMENT: We guarantee that the information and data herewith collected will be handled with the utmost confidentiality, will be analyzed in an anonymous way and no personal information will be forwarded or shared with other parties without your permission. INSTRUCTIONS: This health worker survey includes 8 sections. Check or circle only one answer per question, unless specified that more than one answer can be checked or circled. If you feel uncomfortable answering a specific question in this survey, you may skip it. Examples:

INTERVIEWER NAME: ______

START TIME OF SURVEY: ______END TIME OF SURVEY: ______

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Section A: Personal Information

A-01. City/Village: ______A-02. Municipality: ______

A-03. City/Village Setting: 1 Rural 2 Urban

A-04. Desertification Level: 1 High 2 Medium 3 Low

A-05. What is your full name? A-06. What is your email or phone? A-07. Date of birth (DD/MM/YYYY) A-08. Gender 1 Male 2 Female

A-09. What is your highest 1 No Formal Education 5 Vocational School level of education? 2 Primary School (1-5 grade) 6 University – Undergraduate (check one) 3 Elementary School (6-9 grade) 7 University – Graduate

4 Secondary School (10-12 grade)

A-10. What is your current 1 Single 4 Divorced marital status? (check 2 Married 5 Widowed one) 3 Separated 6 Other:

A-11. What is your ethnic 1 Indigenous (tribe: ______) background? (check one) 2 European descendent 5 Mix European + African

3 Mix European + Indigenous 6 Mix African + Indigenous

4 African descendent 99 Other:

A-12. What languages can 1 Spanish 2 Wayúunaiki 3 English you speak? 99 Other: (check all that apply)

Section B: Personal Health B-01. Have you ever had any of the following conditions? (check all that apply) 1 Allergies 5 High Blood Pressure 9 Sexually Transmitted Disease

2 Asthma or Bronchitis 6 Heart Problems 10 Tuberculosis

3 Anxiety or Depression 7 Hepatitis 11 Cancer

4 Diabetes 8 Kidney disease 99 Other:

B-02. In the past year, how many times 1 1 4 4 were you sick? (check one) 2 2 5 5 or more

3 3 6 I was never sick in the past year

B-03. In the past year, how many days did 1 1 4 4 you miss from work because you were 2 2 5 5 or more sick? (check one) 3 3 6 I did not miss work due to illness in the past year

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B-04. Please list any illnesses you had in the past year… 9 I was never sick in the past year 1. 5. 2. 6. 3. 7. 4. 8.

B-05. What kind of health 1 EPS contributive 3 Private medical insurance insurance do you have? (check 2 EPS subsidized 4 Do not have a health insurance one) 99 Other:

Section C: Work General Information C-01. What is the name of health facility you work for? C-02. What is your job at this health facility?

C-03. For how long have you Years: ______Months: ______worked at this health facility? C-04. What is your current 1 Full-Time 2 Part-Time work status? C-05. Routinely, how do you 1 Car 2 Bicycle 3 Bus reach the health facility? 4 Motorcycle 5 Walk 99 Other: (check all that apply) C-06. How long does it take 1Hours: ______Minutes: ______for you to reach the health 2How many kilometers (if known)? ______facility from your home? C-07. If you work in more than one health center, please specify the days and times you work at others

C-08. What types of ROADS 1 Paved road 3 Walking path only are available to this facility? 2 Unpaved dirt road 99 Other: (check all that apply) C-09. How do you describe Poor Fair Average Good Excellent the ROAD CONDITIONS to 1 2 3 4 5 reach work? (circle one) C-10. Over the past few years, Worsened a Worsened Stayed the Improved a Improved a how have the road conditions lot a bit same bit lot change? (circle one) 1 2 3 4 5

Work Security and Incentives C-11. Do you have a signed contract? 1 Yes 2 No

C-12. Do you usually receive your salary on time? 1 Yes 2 No

C-13. Do you get yearly raises or a bonus? 1 Yes 2 No

C-14. Do you receive any transportation allowance? 1 Yes 2 No

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C-15. Do you receive drinks or food during work hours? 1 Yes 2 No

C-16. Does your employer offer appreciation incentives throughout the 1 Yes 2 No year? (such as: family events, birthday celebrations, holiday parties)

C-17. Does your employer provide you educational training opportunities? 1 Yes 2 No (such as: paid conferences, workshops, certifications, mentorships)

Section D: Work Satisfaction D-01. How much do you enjoy the type of work Not at Just a Moderate Very you do? (circle one) all little amount Much Extremely 1 2 3 4 5 D-02. Do you like working at this health facility? Not at Just a Moderate Very (circle one) all little amount Much Extremely 1 2 3 4 5 D-03. Do you get along with your co-workers? Not at Just a Moderate Very (circle one) all little amount Much Extremely 1 2 3 4 5 D-04. Do you feel appreciated by your Not at Just a Moderate Very superiors? (circle one) all little amount Much Extremely 1 2 3 4 5 D-05. Do you feel safe at work? Not at Just a Moderate Very (circle one) all little amount Much Extremely 1 2 3 4 5 D-06. How secure do you feel that you will have Not at Just a Moderate Very this job for at least the next 5 years? all little amount Much Extremely (circle one) 1 2 3 4 5 D-07. Do you ever feel stress at work? Not at Just a Moderate Very (circle one) all little amount Much Extremely 1 2 3 4 5 D-08. Do you ever feel that you’re being Not at Just a Moderate Very prevented from doing your job at work? all little amount Much Extremely (circle one) 1 2 3 4 5 D-09. Do you ever feel that there is more you Not at Just a Moderate Very could do for your patients? all little amount Much Extremely (circle one) 1 2 3 4 5

D-10. General comments on work satisfaction:

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Section E: Work Equipment E-01. Are the MEDICAL EQUIPMENT needed to perform Always Sometimes Never your job available to you? (example: stethoscope, Available Available Available otoscope, thermometer, blood pressure monitor) 1 2 3 E-02. If SOMETIMES or NEVER to E-01, please list what items are lacking and why:

E-03. Are the MEDICAL SUPPLIES needed to perform Always Sometimes Never your job available to you? (example: gloves, mask, Available Available Available tongue depressor, gauze, antiseptic, syringes, needles) 1 2 3 E-04. If SOMETIMES or NEVER to E-03, please list what items are lacking and why:

E-05. Are the MEDICATIONS needed to prescribe to Always Sometimes Never patients available? (example: antibiotics, antivirals, Available Available Available steroids, analgesics, non-steroidal anti-inflammatories) 1 2 3 E-06. If SOMETIMES or NEVER to E-05, please list what items are lacking and why:

Section F: Patient Care F-01. Do you routinely take VITAL SIGNS from your patients? 1 YES 2 NO 3 N/A Circle how often you perform VITAL SIGNS components Always Sometimes Never on your patients… (circle one in each category) Perform Perform Perform F-02. Temperature 1 2 3 F-03. Heart Rate 1 2 3 F-04. Respiratory Rate 1 2 3 F-05. Blood Pressure 1 2 3 F-06. Pulse Oximetry 1 2 3 F-07. Weight 1 2 3 F-08. Height 1 2 3

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F-09. Do you routinely perform a PHYSICAL EXAM on your patients? 1 YES 2 NO 3 N/A Circle how often you perform PHYSICAL EXAM Always Sometimes Never components on your patients… (circle one in each Perform Perform Perform category) F-10. General Assessment 1 2 3 F-11. Ears (with otoscope) 1 2 3 F-12. Eyes (with light) 1 2 3 F-13. Nose (with otoscope) 1 2 3 F-14. Mouth/Throat (with otoscope/tongue depressor) 1 2 3 F-15. Neck glands (touch) 1 2 3 F-16. Heart (with stethoscope) 1 2 3 F-17. Pulmonary (with stethoscope) 1 2 3 F-18. Gastro-intestinal (touch and stethoscope) 1 2 3 F-19. Genitals (inspection) 1 2 3 F-20. Extremities (inspection) 1 2 3 F-21. Neurologic test 1 2 3

F-22. If you SOMETIMES or NEVER perform a certain Physical Exam or Vital Signs measurements, what are some reasons preventing you from doing it so?

F-23. Please list the major health problems from ALL PATIENTS that you encounter in your daily work (List them starting from most common to the least common)

1. 6. 2. 7. 3. 8. 4. 9. 5. 10.

Patient care satisfaction… F-24. Are you HAPPY with the services that you Not at Just a Moderate Very are able to provide to your patients? (circle one) all little amount Much Extremely 1 2 3 4 5 F-25. Do you feel that you go ABOVE AND Not at Just a Moderate Very BEYOND your job description to help your all little amount Much Extremely patients? (circle one) 1 2 3 4 5

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F-26. Do believe that PATIENTS ARE HAPPY with Not at Just a Moderate Very the services they receive from this health all little amount Much Extremely facility? (circle one) 1 2 3 4 5

F-27. How would you rate the OVERALL CARE this Poor Fair Average Good Excellent health facility provides to the patients? (circle one) 1 2 3 4 5

F-28. Comments in regards to patient care:

Section G: Childhood Health When considering CHILDREN BETWEEN 0 AND 5 YEARS, how COMMON are the Not Fairly Very Extremely Average following conditions… common Common Common Common (circle one answer for each condition) G-01. Pneumonia 1 2 3 4 5 G-02. Diarrhea 1 2 3 4 5 G-03. Malnutrition 1 2 3 4 5 G-04. Ear infections 1 2 3 4 5 G-05. Throat infections 1 2 3 4 5 G-06. Urinary infections 1 2 3 4 5 G-07. Tuberculosis 1 2 3 4 5 G-08. Hepatitis 1 2 3 4 5 G-09. Meningitis 1 2 3 4 5 G-10. Malaria 1 2 3 4 5 G-11. Chagas 1 2 3 4 5 G-12. Typhoid 1 2 3 4 5 G-13. Leishmaniasis 1 2 3 4 5 G-14. Tape worms or round worms 1 2 3 4 5 G-15. Dengue, Yellow Fever, Zika 1 2 3 4 5 G-16. Allergies 1 2 3 4 5 G-17. Asthma or respiratory diseases 1 2 3 4 5 G-18. Diabetes 1 2 3 4 5 G-19. Heart problems 1 2 3 4 5 G-20. Kidney problems 1 2 3 4 5 G-21. HIV (AIDS) 1 2 3 4 5 G-22. Cancer 1 2 3 4 5 G-23. Disability (mental or physical) 1 2 3 4 5 G-24. How would you rate the overall PHYSICAL Poor Fair Average Good Excellent HEALTH OF YOUNG CHILDREN (AGES 0-5) that you 1 2 3 4 5 see on a daily basis? (circle one)

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G-25. How would you rate the overall Poor Fair Average Good Excellent NUTRITIONAL HEALTH OF THE YOUNG CHILDREN 1 2 3 4 5 (AGES 0-5) that you see on a daily basis? (circle one)

G-26. List the main health problems of your young patients between 0 - 5 years of age (List them starting from the most common to the least common) 1. 5. 2. 6. 3. 7. 4. 8.

G-27. What are some of the SUGGESTIONS you give to parents or guardians in regards to preventing childhood diseases and/or to improve their child’s overall health?

How often do you… Always Sometimes Never

G-28. Asks and notes down a child’s age? 1 2 3 G-29. Measure a child’s height? 1 2 3 G-30. Measure a child’s weight? 1 2 3 G-31. Calculates a child’s BMI? 1 2 3 G-32. Measure a child’s head circumference (0-3 years)? 1 2 3 G-33. Measure a child’s upper arm circumference? 1 2 3 G-34. If you answered SOMETIMES or NEVER, please explain reasons why:

How often do you teach parents/guardians… Always Sometimes Never

G-35. How to identify malnutrition signs? 1 2 3 G-36. How to make an oral rehydration solution? 1 2 3 G-37. What to do if they believe a child is malnourished? 1 2 3

G-38. If you assesses that child is malnourished, what do they suggest parents or guardians to do?

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Section H: Environment

I-01. In the PAST YEAR, has 1 Drought and/or lack of water the surrounding community 2 Low soil productivity been AFFECTED by any of the 3 Outbreak of a crop or plantation disease: ______following occurrences? 4 Outbreak of an animal disease: ______(check all that apply) 5 Heavy rain

6 Fire

7 No problems affected the surrounding community

99 Other:

How would you RATE the surrounding Poor Fair Average Good Excellent ENVIRONMENT of the health facility? (circle one) H-02. Air quality 1 2 3 4 5 H-03. Water availability 1 2 3 4 5 H-04. Water quality 1 2 3 4 5 H-05. Food availability 1 2 3 4 5 H-06. Food variety 1 2 3 4 5 H-07. Energy availability (gas, petrol, electricity) 1 2 3 4 5 H-08. Roads quality 1 2 3 4 5 H-09. Temperature 1 2 3 4 5

How would you RATE the following… None A Little Average A Lot Extreme (circle one) H-18. Quantity of humidity in the air 1 2 3 4 5 H-19. Quantity of dust in the air 1 2 3 4 5 H-20. Quantity of wind 1 2 3 4 5 H-21. Quantity of rain 1 2 3 4 5

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Please answer the following questions in regards to the weather…

H-26. In regards to 1 This year is HOTTER than last year 4 Don’t know

TEMPERATURE… 2 This year is COOLER than last year (check one) 3 The temperature pattern is similar to last year

H-27. In regards to RAIN… 1 This year it rained MORE than last year 4 Don’t know

(check one) 2 This year it rained LESS than last year

3 The rain pattern is similar to last year

H-28. In regards to AIR 1 This year the air quality is BETTER than last year 4 Don’t know

Survey is finished. Thank you kindly for your time and patience answering these questions!

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About the Author

Douglas Fernandes Gomes Da Silva is a scientist, writer and avid traveler. Born in São Paulo, Brazil, he received his education and training at University of Wisconsin – Madison, U.S.A. and University of Zürich, Switzerland. Throughout his life, he has lived in 7 countries and visited a total of 52. He has done longitudinal health capacity work in Uganda, Ethiopia, Thailand and most recently in Colombia, and currently supports children’s education in an orphanage in Nepal. He is passionate about development work and how humans are affected by and interact with their environment.

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