University of Florida Thesis Or Dissertation Formatting

THE SOCIOECOLOGY OF DIARRHEAL DISEASE EXPOSURE IN PERI-URBAN COMMUNITIES OF KISUMU, KENYA

JOHN DAVID ANDERSON IV

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2016

To my Grandma, Betty Lou Hetzel and Dr. Hugh Popenoe

ACKNOWLEDGMENTS

First and foremost, erokamano ahinya and asante sana to all of the participants and people in the peri-urban communities of Kisumu who generously gave their time, opened their compounds, walked us through their neighborhoods and told us about their difficulties. Erokamano ahinya and asante sana to all the hard-working Community

Health Volunteers (CHVs) of Nyalenda, Nyawita and Obunga, this research was not possible without them! A special thanks to Zadock Tako, Winnie Alando and Steve Biko for helping us organize research activities, navigate communities and providing the support of their wonderful teams of CHVs.

I thank all the faculty, staff and students at Great Lakes University of Kisumu, for being gracious hosts that welcomed me from the moment I arrived in Kisumu. I thank

Dr. Jane Mumma for not only organizing an amazing team of dedicated and competent researchers but for timely, constant support, laughs and friendship. I thank the leaders of our talented team of researchers, Kevin Achola, Lily Lukorito, Leah Marende, and

Damaris Nalima not only for dedicating long hours and positive spirit to a complex research project but also making me feel at home in Kisumu. A special thanks to all of the research crew, from Jane Agola, Jackson Otieno Anangwe, and Martin Ouma Oyoo of the lab group, to Veronica Odeny, Philip Okello, Lilian Alouch Opiyo, George Otieno,

Kephers Njoga, Carolyne Musula of the household enumeration team to Daisy Kurui,

Josephine Atieno Odhiambo, Ritah Owino, and Anne Sila of the nutrition team, to the environmental team, with whom I worked daily with – Enos Ochieng Migun, Felix

Odhiambo Oketch, Mildred Khaemba, Titus Owour, and Miriam Wanzala. Also and importantly, I thank James and Moses for patiently driving all of us around Kisumu, safely. I thank Dr. Zahid Mahmud and Eteshamul Islam for their help coordinating the

GLUK lab team and bringing their expertise and experience on diarrheal disease lab research. At the Kenya Medical Research Institute and Center for Disease Control

Research Station, I thank Cathrine and Fred Ade for their commitment to their careers and work and to Clayton Onyango for supporting this project. I especially thank Oliver

Cumming for teaching me about coordinating global health research and how to relieve stress by underwater laps. Thanks to KUAP for including me in their CLTS initiatives and partnerships with the health system in peri-urban Kisumu. I thank the SHARE

Consortium for their financial support for this project.

I thank Dunga and Nanga, for days by the lake, at Hill Camp, Abraham’s roof,

Red Gate, and Hippo Point. Many thanks to Carey Francis, Nanshu Tuba, Samora

Ogutu, and Silas “Selassie” Ochieng for controlling scene, solid friendship, and showing me life by Nam Lolwe. Map it! I thank Sir Charles Odongo for his mostly-reliable transportation services and for volunteering your wisdom on a host of worldly matters. I thank my coach, Moses “Jaja” Oduor, for inviting me to play on the Nanga football team and for many safe trips to Kisian. I thank my surrogate parents in Kisumu, Mr. and Mrs.

Pabari and their staff, who, always made me feel at home in their beautiful garden compound and for pleasant Sunday lunches and stimulating discussions. I thank

Charles Ojiambo, Nicodemus Mukabana, Claud Kolongo, Mutua, and Everley for making coming home from long work days and late nights joyful and safe. I thank all the folks around Impala Junction: Dolrose, Douglas Otieno, Bonche, Seth and many others for making the neighborhood a friendly place and for many insightful discussions on the state of Kisumu, Kenya and the world.

5 At the University of Florida in Gainesville, I would like to especially thank my colleagues, Amber Barnes, Poulomy Chakraborty, Karoun Bagamian and Lindsey

Laytner who worked tirelessly alongside our team to complete the WASH Disparities

Study and are pillars of this dissertation. I thank Jacob Atem and Mirna Amaya for their dedication and support for all of the lab’s projects. Over nearly 10 years in Gainesville, there have been many philosophical discussions that have contributed to this work, but the Ethnoeocology Garden was often the setting for these and many other thoughtful idea exchanges. A special thanks to the late Dr. Hugh Popenoe, who mentored and inspired generations of ethnoecologists and a loose confederation of enthusiastic, intellectual gardeners who made the Ethnoecology Garden a beautiful hub of hands-on education. A special thanks to all the past and present members of the Ethnoecology

Society, especially Jay “Mr. President” Bost, Too Blue, Mushu, Nora Rodli, Alvaro Valle,

Matt Palumbo, Wendy Lin Bartels, Erica Van Etten, Nick Kawa, Damion “DEEEEE”

Graves, Jeff Hubbard, Julia Showalter, Asha Bertsch, Dominique Ardura, Brian Tyler,

Sanjiv Jagtap, Dan Stirling, Ethan Kelly and many others.

It was at the Ethnoecology Garden that I met Dr. Richard Rheingans, to whom I am eternally grateful for being a generous and patient mentor who supported me as a research coordinator, student and friend. A second thanks to Nick Kawa, Jeff Hubbard, and later Chris Kawa and the many other denizens of the Estate, especially Rafa

“Krishels” Mendoza and Nikolay Kazakov for providing and maintaining a creative home, with many days and nights of heated and respectful intellectual debate as well as eight years of four lokos, backyard art, settling Catan, and planting an inspiring garden, essential to my final days of dissertation writing. There are so many other friends who

6 have influenced my life during my dissertation. I thank Jessica Jean Casler for providing constant support during my academic pursuits as well as taking time out for the important things in life, such as impromptu afternoon cocktails. I thank Pepe Clavijo for keeping me health and fit with futsal and workouts, hours of advice and good debate over beers and Venezuelan rums, and along with Bryan Tarbox, good excuses to go birding. I thank Joe Feldman and Eric Holgate for high-level intellectual conversations about our research and also boozy conversations about our social circles. I also thank

Drs. Alyson Young and Song Liang for their commitment to my Ph.D. work and for valuable guidance through the final stretch of dissertation writing.

I am blessed to be surrounded by a loving and supportive family who raised me and helped guarantee opportunities to pursue my life goals and interests through higher education. I thank my Mom, Dad, Matthew, Sarah and Emmet for being there for me through my most important life events and for being an endless source of love and support. Finally, I thank my fiancée, Katie Knoll, for always being a loving, patient, steady and supportive friend and partner, who has never wavered, even as we spent over a year an ocean apart and nearly 3 years 1,000 miles apart. I am truly blessed to have her in my life along with many wonderful, supportive friends across the world.

7 TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 10

LIST OF FIGURES ...... 12

LIST OF ABBREVIATIONS ...... 15

ABSTRACT ...... 16

CHAPTER

1 GLOBAL HEALTH AND DISPARITIES IN PERI-URBAN KISUMU ...... 18

Introduction ...... 18 Poverty ...... 19 Peri-urban Ecosystems ...... 21 Informal Settlements ...... 24 Research Objectives ...... 25 Methods ...... 28 Sampling Design ...... 30 Sampling Weights ...... 32 Spatial Analysis ...... 33 Population Density Estimates ...... 34 Poverty Index ...... 35 Results ...... 39 Social Factors and Poverty ...... 39 Water and Sanitation ...... 42 Discussion ...... 44 Limitations ...... 46

2 A HISTORY OF INEQUALITY AND URBANIZATION IN KISUMU ...... 67

Introduction ...... 67 A Brief History of the Nyanza Region ...... 67 The Birth of Kisumu City ...... 70 A Tradition of Urban Migration ...... 72 Public Health in Kisumu ...... 73 Kenyan Independence ...... 75 Modern Kisumu ...... 76 Peri-urban Kisumu ...... 77 Methods ...... 78 Results ...... 79 Discussion ...... 79

8 3 A STUDY OF PERI-URBAN FILTH FLY ECOLOGY IN PERI-URBAN KISUMU KENYA ...... 93

Introduction ...... 93 Methods ...... 95 Data Collection ...... 96 Data Analysis ...... 98 Results ...... 99 Generalized Linear Mixed Models ...... 101 Discussion ...... 102 Limitations ...... 104

4 PREPARING FOR BETTER SANITATION: AN ASSESSMENT OF COLLECTIVE ACTION IN KISUMU ...... 116

Introduction ...... 116 Methods ...... 119 Qualitative Data ...... 119 Quantitative Data ...... 120 Results ...... 122 Landlord-Tenant FGDs ...... 122 Housing ...... 123 Latrines ...... 124 Solid waste ...... 127 Drainage ...... 129 Household Survey ...... 131 Discussion ...... 133 Potential for Collective Action ...... 133 Collective Action in Peri-Urban Kisumu ...... 136

5 CONCLUSION ...... 150

Peri-urban Poverty and WASH ...... 150 Fly-Proof Sanitation ...... 152 Opportunities for Sanitation Solutions in Peri-urban Kisumu ...... 153

LIST OF REFERENCES ...... 155

BIOGRAPHICAL SKETCH ...... 169

9 LIST OF TABLES

Table page

1-1 Description of WASH variables used in analyses ...... 48

1-2 Population density estimates from the 2009 Kenya Census for select areas around Kisumu City (Kenya National Bureau of Statistics 2009) ...... 49

1-3 Confirmatory factor analysis model results from analyses in the lavaan package. Lambda values are the factor loadings for each variable ...... 50

1-4 Summary statistics for social factors across the overall population and terciles of socioeconomic status (SES) as determined by the wealth index. Estimated proportions shown as percentages of the total ...... 51

1-5 Summary statistics for sanitation characteristics and categories of poverty terciles. Estimated proportions shown as percentages of the total and 95% confidence intervals ...... 52

3-1 List of variables used in Generalized Linear Mixed Models as predictors for fly counts ...... 107

3-2 List of sanitation variables used in Generalized Linear Mixed Models as predictors for fly counts ...... 108

3-3 Model fit results from testing all combinations of models for compound counts of Musca domestica, Chrysomya putoria, and Musca sorbens...... 109

4-1 Description of collective action principles in the context of the WASH Disparities Study ...... 138

4-2 Number of times each problem was referenced and frequency of response in tenant and landlord FGDs ...... 139

4-3 Number of times a person was referenced as responsible for a task and acting as conflict moderators for housing and latrines. Frequency of responses in tenant and landlord FGDs ...... 140

4-4 Number of times a person was referenced as responsible for a task and acting as conflict moderators regarding solid waste management. Frequency of responses in tenant and landlord FGDs ...... 141

4-5 Number of times a person was referenced as responsible for a task and acting as conflict moderators regarding water drainages. Frequency of responses in tenant and landlord FGDs ...... 142

10 4-6 Model selection results from models predicting respondent participation in collective action...... 143

11 LIST OF FIGURES

Figure page

1-1 Conceptual diagram of peri-urban diarrheal disease...... 53

1-2 Socio-ecological conceptual model used to guiding the WASH Disparities study...... 54

1-3 Socio-ecological conceptual model of WASH in peri-urban Kisumu. Arrows indicate the directions of effects. Topics in bold are covered in depth throughout chapters of the dissertation ...... 55

1-4 Maps showing estimated improved sanitation coverage across Kenya, based on data from the 2008-9 Kenya DHS. The first panel (All) shows sanitation coverage estimates calculated based on all households in the dataset ...... 56

1-5 Map of Kisumu study area with convex hulls of households sampled for each of the 40 neighborhoods (darker green). The lighter, transparent green buffer surrounding each polygon is ½ the mean distance between points ...... 57

1-6 Maps of the study area (A) and zoomed views of Obunga / Nyawita (B) and Nyalenda (C) showing mean centers (points) and 2 standard deviations from the mean (circles) for each neighborhood ...... 58

1-7 Maps of the study area (A) and closer views of Obunga / Nyawita (B) and Nyalenda (C). The color and size circles represent average household elevation and estimated population density (persons / km2), respectively...... 59

1-8 Poverty measure models showing correlation coefficients for each relationship between measures and the latent variable, poverty ...... 60

1-9 Agreement between wealth and poverty index scores for each household...... 61

1-10 Correlation matrix showing neighborhood proportions of households for variables used in the poverty index along with population density...... 62

1-11 Map of improved sanitation coverage using JMP definitions and mean poverty index for households in each community...... 63

1-12 Scatterplot matrix of neighborhood means and proportions for selected water and sanitation conditions and socio-demographic variables. The diagonal is a density distribution for each variable ...... 64

1-13 Estimates of the proportion of households with improved sanitation, on-plot water and window screens in all windows of a dwelling by indicators for each domain of poverty and by poverty terciles ...... 65

12 1-14 Map of neighborhood percentages of household use of off plot improved drinking water (color) and improved sanitation (size) coverage using JMP definitions...... 66

2-1 Map of Kenya (red) and surrounding countries in East Africa. Kisumu County in western Kenya is highlighted in blue...... 83

2-2 Map of Kisumu County (blue) in western Kenya...... 84

2-3 Map of Bantu Expansion routes in Sub-Saharan Africa from Marchant and Lane (2014)...... 85

2-4 Map of southern migration of pastoralists, including Nilotic ancestors of Luo, Kalenjin, and Masaai peoples from Smith (1992)...... 86

2-5 Map of territories for subtribes of groups along the Winam Gulf of Lake Victoria in western Kenya around the early 1900s, from Lonsdale (1977)...... 87

2-6 Map of blocks designated by British authorities to prevent the spread of disease after a number of outbreaks of the plague ...... 88

2-7 Current map of Kisumu showing the informal settlements (yellow, dashed) and key community features...... 89

2-8 Map of community sanitation including open defecation and solid waste piles, overlaid on community neighborhood population density estimates...... 90

2-9 Map of Kisumu’s urban footprint from a recent report by the Kenya Government (2014)...... 91

2-10 Map of land tenure designations in Kisumu from a recent report by the Kenya Government (2014)...... 92

3-1 Socioecology of fly populations conceptual model...... 110

3-2 Density distribution of fly counts for all species pooled together and counts disaggregated into the 3 major species caught in traps...... 111

3-3 Correlations between pooled species counts, the three top species, and the poverty index...... 112

3-4 Species and indoor counts by social factors, gray water observed in the compound, location of the Quickstrike trap and temperature at the time of sampling. Improved latrines are as defined by JMP guidelines ...... 113

3-5 Species and indoor fly counts by latrine characteristics, the ‘Flies’ category refers to flies being present in the latrine during enumerator’s visit to the household. Lines represent the upper and lower interquartile ranges with the

13 spaces between those lines representing median values, while dots represent outliers...... 114

3-6 Simulated results of best fitting negative binomial mixed models for Musca domestica (panels a and b), Chrysomya putoria (panel c and d) and Musca sorbens (panels e and f) ...... 115

4-1 Map of proportions of reported respondent participation in collective action around sanitation and group membership during the year prior to being surveyed for each study neighborhood...... 144

4-2 Summary statistics for collective action and predictors included in model selection. Error bars represent upper and lower 95% confidence intervals ...... 145

4-3 Proportion of respondents who said they participated in collective action around sanitation once or more in the last 12 months by categories of predictors variables included in model selection...... 146

4-4 Proportion of respondents who said they participated in collective action around sanitation once or more in the last 12 months by categories of predictors variables included in model selection...... 147

4-5 Correlation scatterplot matrix of selected neighborhood proportions of social and sanitation outcomes and mean poverty scores and their association to respondent collective action and group membership...... 148

4-6 Map of percentage of households that reported water entering their homes after rains and fraction of surveyed households that participated in collective action around sanitation at least once in the last year for each community ...... 149

14 LIST OF ABBREVIATIONS

BIC Bayesian Information Criteria

CHEW Community Health Extension worker

CHV Community Health Volunteer

CU Community unit, an admistrative division below the sub-location

GLMM Generalized Linear Mixed Model

GIS Geographic Information System

GPS Global Positioning System

JMP WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation

KES Kenya shilling, the national currency of Kenya

MDG Millennium Development Goals

RMSEA Root Mean Square Error of Approximation

SES Socioeconomic Status

SDG Sustainable Development Goals

WASH Water, sanitation and hygiene

15 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

THE SOCIOECOLOGY OF DIARRHEAL DISEASE EXPOSURE IN PERI-URBAN COMMUNITIES OF KISUMU, KENYA By John David Anderson IV

August 2016

Chair: Richard Rheingans Major: Interdisciplinary Ecology

Currently, high migration from rural areas to cities is outpacing construction of safe, municipal sanitation infrastructure. Migrants take up residence in unplanned, informal settlements in peri-urban communities, where the urban poor reside in substandard housing with poor access to safe water and sanitation. This research implements a socio-ecological systems approach in a cross-sectional study of 1) how urbanization and poverty contribute to high diarrheal disease exposure and 2) how collective action could mediate exposure in peri-urban Kisumu, Kenya.

A history of migration and city planning in Kisumu provides context for qualitative and quantitative data analyses of tenant and landlord Focus Group Discussions (FGDs), household surveys (N = 800) and observations of household compound water, sanitation, and hygiene (WASH) conditions and filth flies. Text analysis of FGDs were used to identify key domains necessary for successful collective action. Generalized

Linear Mixed Models (GLMM) are used to test hypothesized effects of social and WASH conditions on filth fly density estimates and predictors of self-reported collective action around improving sanitation.

In response to plague epidemics in the 1900s, colonial administrators implemented a racially-biased zoning schema to prevent spread of disease, leading to today’s informal settlements. Models of effects of sanitation conditions on fly counts showed negative associations between compound latrines with intact slabs and blowfly

(Chrysomya putoria) counts, while absence of foul latrine smell was negatively associated with housefly (Musca domestica) counts. Housefly counts were positively associated with trapping near refuse piles than latrines, while blowfly counts were positively associated with residing in Nyalenda A. Only 13% of respondents said that they participated in collective action to improve sanitation in the year leading up to the survey. Models of effects of household demographics showed that households who lived in their dwelling for more than 1 year was positively associated with participation in collective action. Improving sanitation facilities and solid waste collection are crucial to reducing exposure from filth fly-transmitted diarrheal disease in historically neglected peri-urban communities. Collective action offers promise to overcome land tenure issues by involving community members in government initiatives, improving low neighborhood sanitation coverage.

17 CHAPTER 1 GLOBAL HEALTH AND DISPARITIES IN PERI-URBAN KISUMU

Introduction

Infectious and preventable diarrheal diseases remain one of the main causes of child mortality globally, taking the lives of an estimated 578,000 children before they reached age 5 in 2013 (9% of all U5 deaths, Liu et al. 2015). The distribution of burden of under-5 deaths from diarrheal disease remains inequitable, with the majority (54%) occurring in Sub-Saharan Africa alone (Liu et al. 2015). Deaths attributed to poor or inadequate water, sanitation, and hygiene (WASH) show a similar pattern of disparities with 44% of an estimated 840,000 deaths among low and middle income countries occurring in Sub-Saharan Africa (Prüss-Üstün et al. 2014). While significant improvements were made from 1990-2015, Sub-Saharan Africa lagged behind nearly every other region and fell short of meeting Millenium Development Goal (MDG) targets for both water and sanitation (United Nations 2015a).

In 2007, for the first time in history, more of the world’s human population lived in urban settings than in rural (UNPD 2008). Global urbanization patterns have resulted in rapid increases in peri-urban populations, outpacing urban infrastructure and municipal planning (UNICEF 2010; WHOUN-Habitat 2016; WHO/UNICEF Joint Monitoring

Programme for Water Supply and Sanitation 2014), trends that are commonplace across continents as populations shift from rural and agrarian livelihood strategies to higher density urban settings. The trend is predicted to continue with the overwhelming majority of future population growth (~86%) projected to occur in cities and urban areas in low-income countries, especially in sub-Saharan Africa (Montgomery 2008). A better understanding of disease transitions in the urban context is becoming more imperative.

18 In general, urban populations benefit from higher concentrations of resources, health services and economic opportunities, than rural populations (WHOUN-Habitat

2016). The urban poor, the majority of an estimated 880 million people living in informal settlements and slums worldwide (United Nations 2015a), are deprived of benefits of urban life, putting them in some of the most overcrowded and unhealthy environments in the world (Save the Children 2015). In Sub-Saharan Africa, more people (62%) live informal settlements and slums than in planned urban neighborhoods (United Nations

2015a). The result is large disparities in disease exposure between the urban rich and poor, complicated by increasing population density without access to improved water and sanitation (Rheingans et al. 2012) or medical treatment facilities.

Eliminating health disparities is a core tenet of global health as a science, a practice and state of the world’s human populations and ecosystems (Koplan et al.

2009). This research focuses on peri-urban diarrheal disease, a problem that sits at the nexus of global health, poverty and urbanization, nested within an ecosystem shaped by a unique historical background (Figure 1-1). Systems thinking (Eisenberg et al. 2012) was used to develop socio-ecological models (Figures 1-2 & 1-3) to conceptualize and generate research questions around understanding dynamic social and ecological interactions between enteric pathogens and rapidly growing and vulnerable populations living in peri-urban ecosystems in Kisumu, Kenya.

Poverty

One of the ultimate causes of the inequitable distribution of infectious diseases is poverty. The most biologically basic definition is absolute poverty, where a person or population is deprived of consistent access to food, water, and shelter. Relative poverty typically refers to income or wealth inequality, comparing the income or asset holdings

of the poor against others in society. Being deprived of either of these physically and emotionally wears down individuals, causing disease and psychosocial stress (Sapolsky

2005) that have lasting impacts across generations, consuming all aspects of personal life and interactions with society (Narayan 2000). However, the causes of poverty are more complex and multidimensional than absolute or relative poverty alone, occurring at levels well outside the control of the household sphere.

Poverty manifests itself in the bodies of individuals through chronic malnutrition, disease and violence, reflecting systemic inequalities of society (Krieger 2004). At a societal level, the poor embody institutionalized inequalities or structural violence

(Farmer et al. 2006) driven by discrimination based on race, ethnicity, class, religion, political views or cultural background (Dressler 2004; Dressler et al. 2005; Gravlee

2009; Krieger 2003). Lack of access to basic health services and infrastructure disproportionately concentrates disease into these areas and populations. At the heart of global health problems is understanding how structural violence leads to high infectious disease burdens in vulnerable populations (Farmer 2001).

Spatially, poverty constraints choice of living conditions, leaving many resigned to communities lacking safe and maintained infrastructure. In cities, the divide between communities made up of temporary housing, pit latrines and poor drainage and communities with permanent homes, paved streets and planned drainage is often as small as a highway. However, this boundary is enforced by a large imbalance in social and political power. Moving across the highway may require higher income to afford rent, which may become more expensive as rapid population growth drives up housing demand. Poor access to education may prevent the social mobility needed to get a new

job to afford more education. Additionally, many people may have financial obligations in rural homes ranging from medical bills, celebrations, funerals and maintaining houses and farms.

Peri-urban Ecosystems

Peri-urban areas are landscapes in transition as rural livelihoods give way to higher density urban economies. Transitions are driven by social and economic conditions that operate at global, regional and local scales (McGranahan et al. 2005).

Peri-urban communities develop at the fringe of city boundaries, delineating strictly zoned and organized city centers from an unplanned mosaic of rapid development of rural to peri-urban spaces (Allen 2003). Often, peri-urban spaces are a mix of concurrent development approaches better suited to managing natural resources in a rural landscape than in urban areas, which require city planning approaches, focused on building infrastructure suited for higher population density (Mbiba and Huchzermeyer

2002).

Peri-urban captures a wide range of development around city centers, sharing with some commonalities across communities. The main driver of urbanization in Sub-

Saharan Arica is the perception that migrating to urban spaces leads to economic opportunity and prosperity. The nature of this process is shaped by how institutions mediate the relationship between the landscape, development and population growth

(Lambin et al. 2001). Generally, zoning and planning is typically more relaxed due to higher land availability so that growth in these areas is driven by higher availability of affordable housing and lower land prices than in more expensive, planned urban developments.

Despite these generalities, informal settlements in Sub-Saharan African and

North American suburbs represent opposite ends of the expanding peri-urban spectrum, with the former lagging behind the latter by a few decades. Suburban growth in the

United States is planned around daily commuter traffic designed to vary degrees around public mass transit, but more traditionally around individuals commuting in a personal vehicle. The result is suburban centers or urban satellites that fragment the landscape, on one hand disrupting ecosystem functions, but also providing opportunities to bring peripheral and parkland greenspace under more strict conservation (Allen 2003).

Suburbanites balance their economic opportunities with the perceived security of satellite communities offering closer proximity to more ‘natural’ or romanticized agricultural landscapes. Many cities in the US are now undergoing processes of urban rejuvenation as suburban affordability is limited by the inevitability of rising fuel costs and higher prices of real estate in some suburbs relative to neglected city neighborhoods. Eventually, urbanization expands, subsuming the satellite centers, forming large metroplex anchored by the original city center such as those found in New

York, Los Angeles, Dallas, and Miami.

Peri-urbanization in many Sub-Saharan African cities are following demographic shifts of North American and European cities from mainly rural, agrarian to a majority urban or suburban population, as occurred in the United States during the 1950s and

60s (Schneider 1992). However, this is happening in a much shorter time period and higher rate in Southeast Asian and now Sub-Saharan African cities, presenting challenges that require swift action and deployment of resources for infrastructure.

Megacities like Lagos, Nigeria are experiencing rapid increases in personal vehicles

with an estimated 1 million registered vehicles and a metropolitan population of 16 million. In comparison, there are about 6 million registered vehicles in Los Angeles

County, California which has a much smaller population of about 10 million (US Census

Bureau). In East Africa’s hub city of Nairobi, Kenya population growth and rising middle class is out-pacing the building of roads to keep up with increasing numbers of vehicles

(McGregor and Doya 2014).

Modern differences in criteria used to make decisions about establishing urban- rural boundaries vary widely by country and region. Boundaries are drawn based on population size, density, predominant economic activities, number of civic buildings and street corners (see Tacoli 1998). Boundaries may be re-evaluated and adjusted in response to regional and migration patterns, population pressures (Greiner and

Sakdapolrak 2012; Lambin et al. 2001), shifts in economic markets, geographic features

(Allen 2003; Grimm et al. 2008; UN-Habitat 2013), climate change (Adegun 2015;

Dobson et al. 2015; McGranahan et al. 2007; Scovronick et al. 2015) and disease outbreaks (Letema et al. 2014; McGranahan and Satterthwaite 2014). For many cities there have been several iterations of planning and zoning, revising city plans for transportation, parks, and sanitation infrastructure in response to fluctuations in population and economic growth.

The foundations of many modern cities in Sub-Saharan Africa were established or redesigned under colonial regimes that marginalized indigenous, indentured servants, and slave populations by restricting their movements and access to resources to ‘protect’ European residents from exposure to diseases and the unrest of colonial subjects (Letema et al. 2014; Muronda 2008; O'Keefe et al. 2015). In some cases,

decades of urban development may have consumed and buried the evidence of colonial arrangements, leaving only traces and historic monuments. In most other cases, patterns of colonial development entrenched a trajectory of economically marginalized peri-urban communities that are home to the vast majority of the urban poor, whose presence is often underrepresented in city population estimates (Montgomery 2008).

Informal Settlements

Informal settlements may fall under the jurisdiction of the municipality or larger administrative units such as districts, counties or sub-counties when the lie outside of municipal boundaries. However, when informal settlements are part of the municipality, they are distinguished from formal areas based on their lack of planning, housing and urban infrastructure such as roads, sanitation, and zoning. These areas may also contain ‘slums’. A slum is a community comprised mostly of non-permanent and semi- permanent structures, built of material such as mud, dung, tin roofing sheets or bamboo, largely inhabited by the urban poor.

In Sub-Saharan Africa 62% of urban dwellers live in slum areas of cities projected to grow at rates among the highest in the world of the coming decades. Kenya follows this trend, with 55% of the urban population living in swelling slums primarily around major cities of Nairobi, Mombasa and Kisumu. Of the 500,000 inhabitants of

Kisumu, an estimated 60% live in the informal settlements surrounding Kisumu City

(UN-Habitat 2006). This pattern is the culmination of centuries of increasing rural population densities, disease epidemics and colonial policies shaping development of western Kenya and the Lake Victoria basin region collectively known as Nyanza with

Kisumu at its core since the early 1900s.

As increasing numbers of children live in urban settings, understanding social and ecological drivers of child exposure risk to diarrheal diseases is becoming more critical. Poor housing and sanitation infrastructure increases child vulnerable to household contamination, while also contributing to community contamination levels

(Cairncross et al. 1996), impacting their own heath as well as neighboring children.

Disrupting contamination cycles between public and private domains is the main challenge to reducing childhood diarrheal disease morbidity and mortality in urban settings. Without WASH infrastructure appropriately designed for removing and treating large amounts of liquid and solid waste in poor, high density communities, households and communities are left to deal with their own and their neighbors waste with already limited resources.

Research Objectives

The goal of these dissertation research is to apply a socio-ecological approach to address social and ecological diarrheal disease exposure in peri-urban communities in

Kisumu, Kenya. Socio-ecological systems inherently conceptualize complex human- ecological interactions, such as disease exposure, at multiple scales and is more frequently being used to address ecological and development issues in urban contexts

(Grimm et al. 2008). However, most socio-ecological research has addressed urban issues in the Northern Hemisphere. There is urgent need for applying this type of research methodology and approach to complex disease issues where poverty, land tenure, rapid land-use change and population growth combine to create large health challenges addressed by households, communities, government and non-government aid institutions (McHale et al. 2013).

The city of Kisumu, like many cities in Sub-Saharan Africa, has a large ‘slum belt’ of unplanned informal settlements surrounding a well-planned city center (UN-Habitat

2006). The WASH Disparities Study was designed to better understand how WASH conditions and behaviors contribute to child exposure to enteric pathogens in informal, peri-urban communities in Kisumu. A cross-sectional mixed-methods study, with both formative qualitative and quantitative data collection was designed to address the complex socio-ecology of peri-urban WASH issues (Figure 1-2). The goal of the WASH

Disparities study was to assess the relative importance of multi-level factors in predicting contamination from exposure pathways, and to characterize WASH-related disease burden for the high-risk urban poor.

A subset of data and results from the WASH Disparities Study are presented and discussed in this dissertation to address social and ecological factors leading to enteric pathogen transmission (Figure 1-3). Effects between groups are represented by large gray arrows. Each interrelated major group in Figure 1-3 is described further by a list of key theoretical components. Those highlighted in bold are addressed more directly by quantitative, qualitative or historical analysis than topics in italics, which are discussed tangentially but not included as variables in these analyses.

For example, global health trends such as urbanization influence availability of

WASH infrastructure, which is represented at nested spatial scales (green box). In

Kenya, responsibility for ensuring sanitation for all has recently (since the 2010

Constitution) been assigned to county governments. On a national scale, there are hotspots of low improved sanitation coverage concentrate in certain counties, particularly in Western and Northern Kenya (Figure 1-4). These are areas that need to

be addressed specifically by focused investment of resources for WASH infrastructure.

Improving sanitation around urban communities in Kisumu requires resources and cooperation with city government to keep up with increasing rates of urban migration.

In this chapter, following a discussion of peri-urban communities and methodology of the WASH Disparities Study, poverty is described and quantified by a multi-dimensional index (blue box, Figure 1-3), built based on domains of education, wealth assets, employment and security (boldface). Though not measured in this study, there are other important components to poverty (italics). Poverty influences WASH conditions (Table 1-1) within the household but also influences WASH at larger scales.

An urban household with good WASH conditions is still likely to have high exposure to enteric pathogens if the household’s neighborhood is nested within communities without safe water and sewage infrastructure.

Research objectives of this chapter relate to the nature and direction of the relationship between poverty and WASH conditions and describing how they are distributed across neighborhoods. Poor access to safe water and sanitation concentrates diarrheal disease burden into 1) the most impoverished households and 2) in the poorest neighborhoods. The spatial distribution of poor WASH conditions is likely to be heterogeneous across the landscape clustering around areas of poverty and geographic features such as rivers or low elevation, where flooding and high water tables may cause latrines to fill up or collapse. However, differences between neighborhoods within peri-urban communities may not vary as widely among each other as in comparison to populations living in planned urban communities with sufficient

WASH infrastructure.

Urbanization is the primary global health trend addressed in this research (purple box, Figure 1-3). In Chapter 2, the historical ecology of Kisumu and the surrounding regions in western Kenya is used to explain and contextualize current migration and urbanization that has led to marginalization and current urban disparities in WASH conditions. Chapter 3 explores the relationship between estimate filth fly densities and household WASH conditions to better understand fly-transmitted exposure risk (red box). In Chapter 4, the potential for collective action around improvements in WASH conditions (yellow box) in these communities is discussed as a way to mitigate the effects of poverty, leading to improved WASH conditions.

Methods

Research was conducted in informal settlements located in peri-urban Kisumu, the third largest city in Kenya with a population of about 500,000 (UN-Habitat 2006).

Kisumu is located in Kisumu County, along the shores of the Winam Gulf, a feature of

Lake Victoria (Figure 1-5). The region experiences two rainy seasons per year, one beginning in March and ending in June (short rains) and a second beginning in

November and ending in December (long rains).

The first stage of research was formative, qualitative data collection with groups of community members. Formative research activities started in July and were completed in October 2014. Focus group discussions (FGDs) were held with small groups of mothers to understand household-level WASH challenges, including ability to make WASH decisions, child exposure and diarrheal treatment. Separate FGDs with groups of landlords and tenants were conducted to better understand the expectations, challenges, institutional and individual roles and responsibility around maintaining infrastructure and WASH behaviors. Transect walks guided by groups of community

members provided an opportunity to discuss community WASH challenges, roles and responsibilities for managing common areas and challenges faced by individual households in the community. Results from these research activities were combined with enumerator expertise and existing surveys to develop the 106-question WASH

Disparities household survey instrument.

Household sociodemographic and health data was collected for 800 households, during the dry season (February 23rd to March 24th, 2015), using the WASH Disparities household survey questionnaire (Appendix C, Chakraborty 2016). Surveys assessed household and community demographic information related to characterizing poverty, household composition, social organization and cohesion, animal handling, psychosocial stress and collective action. Additional questionnaire modules surveyed community environmental health by assessing household access to and conditions of

WASH infrastructure, perceptions of disease risk, child diarrheal treatment, drinking water sources and storage, latrines, solid waste, drainages, and flooding. Survey questions were drawn from Kenya Demographic and Health Surveys (Kenya National

Bureau of Statistics; ICF Macro 2010), with many questions on social capital and cohesion and psychosocial stress (Chakraborty 2016) adapted from the Social Capital

Assessment Tool (Krishna and Shrader 1999).

The survey was administered by trained staff from Great Lakes University

Kisumu (GLUK), who were all fluent speakers of Kiswahili, Dholuo and English, using

Qualtrics software (2015) installed on iPads. The study tool was written in English with italicized Dholuo translations in parentheses for survey enumerators. All study participants provided written consent before participating in any phase of the study.

Ethical approval was granted by London School of Hygiene and Tropical Medicine

(LSHTM), University of Florida (UF), GLUK, and the Kenya Medical Research Institute

(KEMRI).

Compounds were defined by discussions with residents, landowner and CHVs. In most cases, fences or roads and building construction patterns made identifying compounds obvious. In very few cases, very large areas with several compounds were considered as one big compound by participants (> 40 households) because the land was owned by a single family. However, because the arrangement of the compounds within the larger area resembled the majority of others in the study and that each shared a common latrine, we treated each compound as distinct for compound level sampling of animal waste and filth fly density and diversity estimates.

Sampling Design

A two-stage sampling strategy was used to select households to participate in a

106-question household survey on the socio-ecology of peri-urban WASH conditions and behaviors. Since the introduction of a community health strategy in Kenya's

National Health Sector Strategic Plan II (NHSSP II), community health is monitored through household visitations by volunteer Community Health Volunteers (CHV) (Olayo et al. 2014; Wangalwa et al. 2012). CHVs monitor membership and key health indicators for neighborhoods of approximately 100 households in Nyalenda A & B sub locations and Obunga and Nyawita communities of Kanyakwar sub-location. However, there is some discrepancy between census boundaries in Geographic Information

System (GIS) shapefiles, where Nyawita is absent and within the borders of Kanyakwar, yet population estimates for Nywaita are listed along with other sub-locations (Table 1-

2). The community health database is managed by Community Health Extension

Workers (CHEW), who collect household registers that are maintained by CHVs. The community based health system in these communities served as the basis for the study sampling strategy.

Random selection of 40 CHV clusters without replacement served as primary sampling units (PSUs) or first stage of sampling stratified by the three locations (Figure

1-4). CHVs were selected randomly (Haahr 1998) in Nyalenda A (N=14), Nylenda B

(N=13), and Obunga/Nyawita (N=13) from lists of active CHVs in each area. In

Nyalenda A and Obunga and Nyawita areas within Kanyakwar sub-locations, this was done from the lists provided by the CHEWs. In Nyalenda B, the CHVs preferred, for the sake of transparency, random selection by pulling names out of a hat during their weekly meeting. Each selected CHV was contacted and asked to provide their household registers. If CHVs were selected and did not respond after 3 attempts to make contact or if CHVs had never registered the households in the area, they were excluded and another was randomly selected from the remaining pool of CHVs.

The second stage of sampling consisted of random selection (Haahr 1998) of 20 individual households from the total number of CHV registered households. CHV registers contain basic demographic information about each household. Selected households were identified by the name of the household head and any identification codes used by the CHV. On the day of data collection, the list of selected households was given to the CHV who guided field enumerators to the location of each household.

Most often, households were identified by a code marked on the doorway by the CHV. If a willing participant was found at that location, they were included in the sample, regardless if the home was occupied by a household with a different head than that

indicated during selection. If the dwelling was empty or no one was home, the enumerators simply went to the neighboring household within the same compound, or if necessary, the neighboring compound.

Sampling Weights

Sampling weights were calculated based on the inverse proportion of likelihood of being sampled, adjusted for resampling in the first stage when CHVs were not available. Each CHV’s household coverage area was considered as a neighborhood in this study. First stage sampling weights were calculated for each stratum of the three locations (Nyalenda A, Nyalenda B, and Obunga / Nyawita), where n is the number of sampled clusters and N is the total number of clusters in the stratum (Eq. 1-1).

푁 (1-1) 퐶푊 𝑖 = 푐푙 푛

Not all CHVs were available to participate due to personal schedule conflicts or incomplete household registers for their area. First stage weights were then adjusted for non-participation using Eq.1-2, where 푛푒푥 is the number of sampled CHVs that should have been excluded, 푛표푝 is the number of originally sampled CHVs that participated and

푛푟푝 is number of replacement CHVs that participated. Final first stage

푛 − 푛푒푥 (1-2) 퐴푐푙 = 푛표푝 + 푛푟푝

weights (FW) were calculated using Equation 1-3.

𝑖 𝑖 퐹푊푐푙 = 퐴푐푙 ∗ 퐶푊푐푙 (1-3)

Second stage household weights (HW) were defined as the inverse proportion of the number of households sampled (ℎ) by the total number of households (퐻) in the CHV register (Eq. 1-4).

퐻 (1-4) 퐻푊𝑖 = ℎℎ ℎ

The final sampling weights were calculated as the product of the two stages of sampling weights (Eq.1-5).

𝑖 𝑖 𝑖 푆푊ℎℎ = 퐹푊푐푙 ∗ 퐻푊ℎℎ (1-5)

Spatial Analysis

The location of each surveyed household was recorded using a handheld Global

Positioning System (GPS) using point averaging to increase location accuracy. Data was imported and analyzed in QGIS (Quantum GIS Development Team). GPS points were projected using World Geodetic System (WGS) 84 and Universal Transverse

Mercator (UTM) Zone 27S. Geographic summary statistics including mean center and standard deviations of household clusters were calculated for each neighborhood.

Neighborhood area was estimated by creating a convex hull envelops around surveyed households using the standard vector analyses tools in QGIS. Envelopes are

the smallest possible polygon that includes all household points in a neighborhood cluster. A buffer, representing ½ the average distance between points in each CHV neighborhood was added to the envelopes to approximate the total CHV coverage area and used in population density estimates (Figure 1-5). There were two CHVs that covered disjointed areas, one because of a wide swath of uninhabited land in the

Kasarani section of Obunga (A1 & A2 Figure 1-5) where there were no households. In the second case, a Nyawita CHV had a coverage area split between an area around

Nyawita Market and land adjacent to Tom Mboya estate, on the south side of the

Bypass highway (B1 & B2 Figure 1-5). In both of these cases, polygons for each sub- area were calculated separately using the same methodology as above and then summed for inclusion in the final area and subsequent density estimate.

A Nyawita CHV had a coverage area located in the Migosi informal area on the east side of Kakamega road (Figure 1-5). The area CHEW explained that future plans include creating a new Nyawita sub-location, combining Nyawita with surrounding areas with parts of Tom Mboya estate and Migosi. While this was generally confirmed with

District health officials, the description of the boundaries of this new sub-location varied across individuals. Regardless, sampling continued with the current groups of CHVs operating as part of the units delineated by the health system.

Population Density Estimates

Neighborhood population density was estimated using household membership, number of households with data in the CHV registers and buffered neighborhood areas

(based on sampling clusters). Respondents were asked for the total number of people that regularly overnight at the home. Population density (푃푑) was then calculated by dividing the product of mean household membership (푀푐푙) for the neighborhood and the

number of registered households (ℎ푚푐푙) for each neighborhood by the area of the buffered polygon area (퐴푐푙, Eq. 1-6).

푀푐푙 ∗ ℎ푚푐푙 (1-6) 푃푑푐푙 = 퐴푐푙

Poverty Index

The household survey was developed from groups of questions or modules, which were adapted from widely used surveys including the Demographic and Health

Surveys (DHS, Rutstein and Rojas 2006) which survey assets for wealth indicators of socioeconomic status (SES), household demographics, assessments of household

WASH and self-reported child diarrhea. Questions on social capital and cohesion were adapted from the Social Capital Assessment Tool (Krishna and Shrader 2000).

Questions on safety and security were adapted from suggested modules published in a series on the missing dimensions of poverty summarized by Alkire (2007) and Diprose

(2007).

Assets included in this study were selected based on the 2008-9 Kenya DHS surveys (Kenya National Bureau of Statistics and ICF Macro 2010). They included housing characteristics such materials used to construct walls, floors, and rooves, having a separate kitchen, and the ratio of people per sleeping room, as well as the type of drinking water source and latrine used by the household, durable and consumable goods. Durable goods that were included were presence of radio, television, mobile phone, refrigerator, bicycle, motorcycle, car or truck and solar panels. Consumable goods were having electricity and the type of fuel used for cooking.

Assets were included in Principle component factor analysis (PCA) in Stata 14

(2015), which measures associations between variables (Rutstein and Johnson 2004)

across the sample of households. All variables included in PCA were binary, so in assets with multiple categories, such as type of cooking fuel, were transformed into binary variables (e.g. natural gas, charcoal, and kerosene would all be coded as presence/absence variables). Water and sanitation assets were excluded to avoid confounding with water and sanitation predictor variables in statistical analyses

(Rheingans et al. 2013). Individual household scores were assigned based on cumulated values based on PCA factor loadings using the ‘predict’ function in Stata 14.

A categorical variable was created by ranking and evenly dividing the households into thirds or wealth terciles, with the highest 1/3 of household scores assigned as the richest and the lowest 1/3, poorest.

The poverty index was constructed from 4 types of indicators of poverty domains using confirmatory factor analysis (CFA) from the lavaan packages in R (Oberski 2014;

Rosseel 2012) and in Stata 14 (StataCorp 2015). Though CFA and exploratory factor analysis are similar, CFA requires the researcher to specify the nature of the relationships between variables and then tests how much of the covariance is explained by the specified model (Brown 2006). In this case, poverty is considered a latent variable that is difficult to measure directly but is known to exist as an important aspect of many facets of household life and health. Thereby, the index is composed of observed or measured indicators that represent multiple dimensions, but are grounded in existing theory about measuring poverty (Alkire 2007). CFA is a technique commonly implemented as a broader method of Structural Equation Modeling that are also referred to as measurement models (Kline 2015).

Measured indicators were drawn from domains related to security (Diprose

2007), employment (Lugo 2007), education (Ibrahim and Alkire 2007) and wealth based on household assets (O'Donell et al. 2007; Rutstein and Johnson 2004). Security was based on two survey questions related to respondent 1) feelings of being unsafe or 2) having been threatened attacked or assaulted while going for a long call or fetching water, with long call meaning defecation. The highest level of the education attained by the head of household and their current employment status, represented education and employment domains for each household.

Multiple models with different combinations of variables from each domain were assessed to determine best fit in the measurement model. Variables included in CFA were ordered in the same direction, meaning higher values were ascribed to lower levels of education, unemployment, experiencing lack of safety or threats of violence and asset values. This eased confusion in interpretation so that the index describes relative poverty and not wealth. Both the continuous variable from the wealth index and the wealth terciles variable were considered in poverty measurement models. With lower asset values considered as indicators of higher poverty in this treatment.

Three approximate model fit indices were used to assess and compare models:

Root mean square error of approximation (RMSEA), Tucker-Lewis Index (TLI), and

Comparative Fit Index (CFI). RMSEA is a fit index scaled as a badness-of-fit index, where values closer to 0 indicates better fit. TLI and CFI are both relative fit indices comparing the proposed model to fits adjusted for model parsimony (TLI, penalizes for increasing numbers of variables) and null ‘worst’ models (CFI) where measures are uncorrelated (Hooper et al. 2008; Jackson et al. 2009). RMSEA values range from 0-1

with values nearer to 0 considered better fits and acceptable model fits below the 0.06 value. TLI and CFI values also range from 0-1, but values closer to 1 indicate better model fit, with recommended cutoff values of greater than 0.95 for both indices (Hu and

Bentler 1999; Jackson et al. 2009).

Models were identified in the lavaan package for R, however, while the lavaan.survey package can account for complex survey design, it does not yet handle categorical variables (Oberski 2014; Rosseel 2012). The sem function in Stata 14 can account for complex survey design and categorical variables, but lacks the model fit variables of lavaan. Therefore, model selection was completed in lavaan, then the model identified using fit indices was calculated in Stata 14 and predicted values for the latent poverty variable were assigned using the predict command. Although not preferred, it is not uncommon for complex models to be fit without including factors for survey design and then refit the best identified model with survey design weights to ensure accurate measures of variance (Carle 2009).

Key WASH conditions (Table 1-1) were analyzed using means and proportions aggregated by poverty terciles and by neighborhood in STATA 14 (StataCorp 2015) and reported with 95% confidence intervals (CI). Pearson’s bivariate correlations between neighborhood means and proportions were calculated and presented using the ggpairs function in GGally (Schloerke et al. 2016) using R (R Core Team 2016).

Spatial distance measures were calculated from field GPS data take at the time of the WASH Disparities survey and projected in the WGS 84 UTM zone 37S reference coordinate system in QGIS (Quantum GIS Development Team) were combined with neighborhood means and proportions calculated from the survey. Key geographic

features were digitized using sub-location polygons from the Kenya Census (Kenya

National Bureau of Statistics 2009) in combination with hand-drawn maps of areas provide by area CHEWs and overlaid on satellite imagery from Google Maps (Google

2016).

Results

In Nyalenda A, Obunga/Nyawita, and Nyalenda B communities 260, 261, and

279 households were surveyed after selection, respectively. On average, CHVs monitored 106 households (range 30 - 234 HHs, Figure 1-6) over an estimated mean coverage area of 0.07 km2 (range 0.007 – 0.57 km2, Figure 1-5). Distances between houses ranged widely (Figure 1-6), depending on where a community fell on the rural- urban spectrum. Mean household size averaged about 4 members across neighborhoods (range 3 - 5 members).

The average elevation of households was 1,150 meters, with the highest areas in

Migosi (1,172 m) and Nyawita (1,178 m, Figure 1-7). The lowest elevation was 1,139 meters in Nanga, in west Nyalenda B, near the lake. The two neighborhoods in Dago in the eastern most area of Nyalenda A, near Nyamasaria were also low at 1,140 meters.

Mean population density across the study neighborhoods was 12,400 people / km2

(range 586 – 40,000 people / km2, Figure 1-7). The highest density areas were in

Nyalenda A & B along Ring Road and centered by the boundary between the two sub- locations. These seven neighborhoods had an average estimated population density of

25,700 people / km2.

Social Factors and Poverty

Six measurement models for poverty were assessed, maintaining at least one measure in each of four poverty domains, including employment, education, wealth

(SES index), and security (Figure 1-8). Model F showed the best fit across indices (Chi- squared = 192, p < 0.0001, Table 1-3) and was the simplest model, with one measure per domain. RMSEA values were 0.032 (CI 0.0001 – 0.08), less than the threshold of

0.06. TLI (0.99) and CFI values (0.97), were both above the threshold of 0.95 (Table 1-

3). Further tests of RMSEA using estimated bounds resulted in failing to reject the close-fit hypothesis (lower bound: 0.0001 < 0.05), and rejecting the poor-fit hypothesis using upper bounds (0.08 < 0.10), both supporting favorable fits. Household head education level was the most highly correlated with the poverty variable at 0.67, followed by the continuous variable for wealth at 0.62 (Figure 1-8). Predicted values from Model F were survey weighted and assigned to each household as index scores.

A higher percentage of the poorest households by wealth index were concentrated in Obunga/Nyawita (42%) than in Nyalenda A (30%) and Nyalenda B

(28%) communities. Both Nyalenda A and B had greater share of households in the richest tercile (39% and 37%, respectively, Table 1-4). Comparing the distribution of households by wealth index to the poverty index, the overall pattern was similar, but the there were differences in how the two classified households. The same analysis of percentages of each household across the three study areas, 35% (CI 30-41%) of

Obunga/Nyawita households were in the poorest tercile, shifting most of the difference to an increased estimates of the poorest households in Nyalenda A up to 36% (CI 31-

42%), with a slight increase in estimates of the poorest in Nyalenda B (29% CI 24-34%).

Overall, SES and poverty terciles showed good concordance (Figure 1-9,

Pearson’s r = 0.89), however, there were some key differences. Twenty-six percent of the wealthiest households by SES were classified into the middle category of household

poverty (Table 1-4). Similarly, 24% of households classified as middle wealth tercile

(SES) were classified into the lowest poverty tercile. Of the households classifies in the poorest terciles by SES, 87% were in the highest poverty tercile.

Overall household head education levels were low with a combined 53% having none, some or finished primary school (Table 1-4). Only 33% of heads of households were able to finish secondary education. The richest and the middle household heads by wealth quintile both had the highest levels for finishing secondary education (39 and

37%, respectively), while only 22% of heads of the poorest households finished secondary education and nearly 25% did not finish primary education. Most household heads were reported to be employed (92%) with 14% of the heads of the poorest households reported being unemployed (Table 1-4). Many respondents (42%) reported feeling unsafe while going for a long call (defecation), with nearly half respondents in both the poorest and middle households reported feeling unsafe. One tenth of respondents reported being attacked or threatened while going for a long call and again, higher percentages of the poorest and middle respondents reported being attacked (12 and 14%, respectively).

As expected from the CFA, most neighborhood estimates for (neighborhood means and proportions) social factors were highly correlated with neighborhood mean household poverty scores, ranging from a Pearson’s r value of -0.97 with mean SES scores to r = 0.79 for proportion of low employment (Figure 1-10). However, poverty scores were moderately correlated with feeling unsafe fetching water (r = 0.45), which was not included in the selected final poverty model (Model F, Figure 1-8). Communities with higher proportions of household heads with low education in a community was also

positively correlated (r = 0.37) with security fetching water. Neighborhood population density estimates were only weakly correlated with community social factors. The majority of most impoverished communities were located on the edges in lower population density areas (Figure 1-11 B & C). Community mean poverty scores were moderately correlated with proportions of tenants (r = -0.31) and weakly correlated for improved sanitation and off plot water (r = -0.25, Figure 1-10). Two communities located along the Awaya river and two communities in Dago in east Nyalenda A had highest proportions of high household poverty and the lowest improved sanitation coverage

(JMP definitions, Figure 1-11).

Water and Sanitation

Household access to an improved water sources was extremely high at 99% (CI

98.6 – 99.9%). Most households reported getting their water from public taps (67%, CI

60.7 – 73.2%), but only 31% (CI 25.2 – 38.2%) had access to an on plot improved water source. Across the three areas, household on plot water source coverage was the highest in Nyalenda A at 97% (CI 90 - 99%), followed by 76% (CI 63 - 86%) in

Obunga/Nyawita and 57% (CI 47 - 67%).

Household access to basic improved sanitation facilities (not considering sharing), was nearly 90%. Eighty percent (CI 75 - 84%) of the households were using pit latrines with slabs and only 46 households reporting having no latrine in their compound. However, this pattern reverses to 90% unimproved when applying JMP definitions of sanitation (WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation 2014) because nearly every household with access to an improved latrine shared it with an average of 7 (CI 6 – 8) other households, 19% sharing with more than 10 households (Table 1-5).

Overall, latrine conditions were poor as 43% of latrines smelled from outside,

79% had flies present, 32% had slabs that were compromised by being broken, cracked or having holes, 29% had feces present and 91% were often left uncovered (Table 1-5).

The poorest and middle households had higher proportions of poor latrine conditions as compared to the richest households. Twenty-two percent of the richest households reported not sharing with any other household, while 26% of the poorest households shared their latrine with more than 10 other households.

The strongest correlation in the neighborhood analysis was between coverages of community off plot water and improved sanitation (JMP defined, r = -0.65, Figure 1-

12). Community off plot water coverage was also moderately correlated with number of households sharing latrines (r = 0.45) and proportion of respondents feeling unsafe while fetching water (r = 0.36). Community sanitation coverage was below 20% and off plot water coverage was above 80% for all neighborhoods in Nyalenda A. In general, central Obunga had neighborhoods with both low sanitation coverage (<10%) and high off plot water sources (>60%, Figure 1-10 B). Nyalenda B had the lowest off plot water coverage and the 3 highest improved sanitation coverages. Conditions in Nyalenda B improved west of the high density areas (Figures 1-7 C & 1-10 C).

The vast majority (85%, CI 80 – 88%) of surveyed households were tenants, renting their home from another owner. The proportion of tenants in a community was moderately correlated with improved sanitation (r = -0.42), mean households sharing latrines (r = 0.33), elevation (r = 0.39), and mean poverty score (r = -0.31, Figure 1-12).

Only about 9 of 40 neighborhoods had tenant proportions less than 80%.

Discussion

Providing water and sanitation for all and eliminating between and within country inequalities are explicitly addressed as Goals 6 and 10 in recently drafted Sustainable

Development Goals (SDGs) for 2030 (United Nations 2015b). One of the challenges all cities in Sub-Saharan Africa will face is upgrading and extending services to slum dwellers in the face of surging urban population growth (WHOUN-Habitat 2016). SDG

Goal 11 specifically addresses improving housing and infrastructure in urban settlements, such as slums and informal settlements by 2030, a population that may double to nearly 2 billion by 2050 (WHOUN-Habitat 2016).

Poverty models fit data of household indicators for 4 domains, measuring education, employment, SES and feeling safe in 798 households. Three model fit indices were in agreement, meaning that much of the variation in covariance in the data was explained by the model. However, the poverty index approximated the more widely used DHS-style asset-based SES index (Figure 1-9). This is supported by the distributions of household access to improved sanitation and on-plot improved drinking water coverages wealth as compared to poverty terciles (Figure 1-13). Coverage estimates for the middle tercile were lower or equivalent to than estimates for poorer households. Mean neighborhood poverty and wealth index scores were also highly correlated (r = 0.96, Figure 1-10).

Access to JMP MDG definitions of improved sanitation in peri-urban neighborhoods of Kisumu were very low, below 40% for all neighborhoods, which was lower than estimated urban sanitation coverage (40%) across Sub-Saharan Africa

(UNICEFWHO 2015). Obunga/Nyawita and Nyalenda A show little variation in sanitation coverage across neighborhoods and low overall at 6% and 3%, respectively

and only slightly better at 14% in Nyalenda B (Figure 1-11). Variation in sanitation coverage in Nyalenda B was higher, mainly because of higher coverage for neighborhoods in the west, with lower coverage values near the high density border with Nyalenda A (Figure 1-11 C). Overall, differences in neighborhood sanitation conditions are not higher because there are very few who have access to private, non- pit latrine improved sanitation, apparent in the weak to mild negative correlation between poverty and improved sanitation (Figure 1-12).

Recent research in urban and rural Tanzania aimed at influencing future SDG definitions of ‘adequate’, has shown that shared improved sanitation was safer than both improved and unimproved sanitation, using JMP definitions (Exley et al. 2015). If further research validates this finding, estimates for peri-urban Kisumu will be much more optimistic (see ‘Latrine facility” vs. “MDG sanitation”, Table 1-5). However, in these communities, indicators of latrine hygiene and cleanliness appear to put many at risk of exposure to disease during use, particularly for the middle and poorest households

(Table 1-5). Additional data on latrine contamination would need to be collected to address the question of the safety of shared sanitation.

Moderate negative correlations between neighborhood coverage of off-plot water and improved sanitation coverage (-0.65, Figure 1-12) indicate that diarrheal disease risk are likely concentrating in specific neighborhoods. Nyalenda A neighborhoods all had over 80% of households using off-plot improved drinking water sources and less than 20% using improved sanitation (Figure 1-14). Differences in the distribution of public standpipes installed and supplied with treated water by the Kisumu Water and

Sewerage Company (KIWASCO) could explain these patterns and may have to do with

the challenges of providing water in the highest density neighborhoods of Nyalenda A &

B (Figure 1-7 C).

All but 7 of the surveyed neighborhoods had less than 80% of sampled households as renting tenants. This is important because improvements in sanitation coverage will have to be addressed primarily by landlords and landowners, who will be key in mobilizing solutions on their properties. The issue of landlord and tenant interactions around sanitation are addressed at length in Chapter 4, nonetheless, it is important for understanding the sanitation landscape and for implementing future interventions.

Limitations

Future analyses of these data should include other estimates of domains of poverty such as gender and psychosocial stress (Chakraborty 2016). Future research should also draw on medical anthropology to consider the impacts of structural violence

(Farmer 2001) on the nature and distribution of poverty. This includes collecting ethnographic and survey data on the urban poor’s experiences with corruption, political violence and discrimination by the health system or law enforcement (Narayan 2000) to generate a clearer picture of poverty and environmental health disparities.

Mean population density was estimated at 12,400 people / km2 (range 586 –

40,000 people / km2). Density estimates are within the range of the most recent census estimates for the area, falling between the densest area in 2009, Manyatta A, and were equivalent to estimates for Nyawita (Table 1-2). Considering that populations have risen significantly over the last 7 years, it is likely that higher estimates, better reflect recent population growth. Additionally, available 2009 Kenya Census sub-location-wide estimates mask heterogeneity in urban and rural population densities found throughout

the areas. Neighborhood population density estimates will need to be validated against other measures of population density estimates with higher population density resolution such as the Global Rural-Urban Mapping Project (GRUMP) or AfriPop databases (Dorélien et al. 2013; Tatem et al. 2013).

Table 1-1. Description of WASH variables used in analyses.

Variable / Levels Type Description Drinking water source Binary Drinking water source according to JMP definitions Unimproved Cart with small tank or surface water Improved Piped into dwelling, compound or plot Public tap or standpipe, tube well or borehole Water location Binary Location of drinking water source On plot Improved water source located on the plot or in the dwelling Off plot Improved or unimproved water source located somewhere outside of the plot JMP Sanitation Categorical Latrine classification according to JMP definitions

No facilities No latrine Unimproved Pit latrine without slab or any facility shared by more than one household Improved Flush latrine, pit latrine with slab, ventilated pit latrine, septic tank, Flush/pour to pit latrine or elsewhere that is not shared with any other households Latrine Smell Categorical Enumerator senses odor from latrine Outside Detected when outside latrine Inside Detected only when inside latrine No Smell No smell inside or outside latrine Latrine Flies Binary Presence or absence of flies Latrine Slab Binary Condition of latrine slab Good Condition Solid and continous Poor Condition Cracked, has holes and/or is broken Feces On Slab Binary Presence of feces on slab Latrine Door Categorical Door to latrine No Door Absent Does Not Close Present but does not close completely Closes Closes completely Latrine Lid Binary Reported as often covered or uncovered with lid

Table 1-2. Population density estimates from the 2009 Kenya Census for select areas around Kisumu City (Kenya National Bureau of Statistics 2009).

Area in Sq. Population Location Sublocation Male Female Total Households Km. Density Town Bandari 3878 3745 7623 1921 7.23 1055.07 Town Kaloleni 6933 7873 14806 3658 2.1 7036.07 Kisumu East Kanyakwar 6447 6107 12554 3553 6.56 1913.25 Kondele Manyatta 'A' 23503 24501 48004 12525 2.36 20333.79 Kolwa West Manyatta 'B' 14219 13733 27952 7808 2.54 10998.23 Kondele Migosi 9182 10644 19826 4795 1.93 10291.2 Town Northern 4804 4935 9739 2107 1.31 7439.46 Kolwa West Nyalenda 'A' 14829 13440 28269 8070 3.16 8952.69 Kolwa West Nyalenda 'B' 16189 16241 32430 8561 4.71 6886.37 Kondele Nyawita 7526 7221 14747 4099 1.31 11281.36 Town Southern 4729 4434 9163 2476 5.21 1759.81

Table 1-3. Confirmatory factor analysis model results from analyses in the lavaan package. Lambda values are the factor loadings for each variable. † denotes acceptable model fit values that are above or below recommended thresholds for each criteria: Root Squared Mean Error of Approximation (RSMEA) values < 0.06, Tucker-Lewis index values > 0.95 and Comparative fit index values > 0.90.

Lambda estimates (standard error) Poverty model A B C D E F Education 1.00 1.00 1.00 1.00 1.00 1.00 Unemployment 1.02 (0.28) 0.87 (0.22) 1.02 (0.30) 0.87 (0.23) 0.82 (0.18) 0.67 (0.13) Unsafe fetching water 2.04 (0.36) 1.64 (0.25) 2.09 (0.38) 1.62 (0.25) - - Violence/threats fetching water 1.60 (0.41) 1.29 (0.31) - - - - Unsafe long call 3.45 (0.66) 2.70 (0.41) 4.19 (0.91) 2.96 (0.48) 1.47 (0.21) 0.41 (0.10) Violence/threats long call 2.16 (0.38) 1.82 (0.27) 2.23 (0.41) 1.81 (0.27) 1.78 (0.26) - Wealth 1.14 (0.20) - 1.16 (0.21) - 0.92 (0.14) 0.93 (0.17) Wealth terciles - 1.18 (0.18) - 1.19 (0.18) - - Model fit indices RMSEA 0.122 0.13 0.147 0.158 0.149 .032† Tucker-Lewis index 0.663 0.638 0.625 0.594 0.571 .974† Comparative fit index 0.775 0.758 0.775 0.756 0.786 .991†

Table 1-4. Summary statistics for social factors across the overall population and terciles of socioeconomic status (SES) as determined by the wealth index. Estimated proportions shown as percentages of the total and 95% confidence intervals (in parentheses) are given for the three levels of terciles. The last group are levels of poverty, with ‘Highest’ indicating households in the top third of poverty index scores.

SES terciles (wealth index) Richest Middle Poorest Social factors Overall N = 266 N = 266 N = 266 SES (N = 798) - 34 (28, 40) 33 (28, 38) 33 (27, 40) Location weighted Obunga/Nyawita 20 (17, 23) 16 (11, 22) 19 (15, 25) 25 (19, 33) Nyalenda A 19 (16, 23) 10 (7, 16) 26 (20, 33) 22 (16, 30) Nyalenda B 61 (56, 65) 74 (65, 81) 55 (46, 63) 53 (43, 62) Location unweighted Obunga/Nyawita 33 (29, 36) 24 (19, 30) 20 (15, 25) 42 (36, 48) Nyalenda A 33 (29, 36) 39 (33, 45) 43 (37, 49) 30 (25, 36) Nyalenda B 35 (32, 38) 37 (31, 43) 38 (32, 44) 28 (23, 34) Household head education None or some primary 12 (9, 15) 4 (2, 8) 8 (5, 13) 23 (17, 31) Finished primary 41 (35, 48) 24 (16, 33) 49 (39, 60) 52 (45, 59) Finished secondary 33 (29, 38) 39 (33, 45) 37 (29, 46) 22 (16, 29) Post-secondary 14 (9, 20) 33 (24, 44) 6 (2, 13) 2 (1, 5) Household head employment Employed 92 (89, 94) 94 (89, 97) 96 (93, 97) 86 (81, 90) Unemployed 8 (6, 11) 6 (3, 11) 4 (3, 7) 14 (10, 19) Respondent long call safety Felt safe 58 (53, 63) 73 (67, 78) 54 (47, 61) 48 (38, 59) Felt unsafe 42 (37, 47) 27 (22, 33) 46 (39, 53) 52 (41, 62) Respondent long call violence No threat 90 (87, 92) 95 (91, 97) 86 (79, 91) 88 (83, 92) Attacked/threatened 10 (8, 13) 5 (3, 9) 14 (9, 21) 12 (8, 17) Poverty terciles Richest 34 (27, 42) 74 (64, 82) 24 (19, 30) 1 (0, 5) Middle 34 (29, 40) 26 (18, 36) 61 (55, 66) 12 (8, 16) Poorest 32 (27, 38) 0 (0, 0) 15 (11, 22) 87 (83, 91)

Table 1-5. Summary statistics for sanitation characteristics and categories of poverty terciles. Estimated proportions shown as percentages of the total and 95% confidence intervals (in parentheses) are given for the three levels of terciles, with ‘Highest’ indicating households with the highest poverty indicated by high poverty index scores.

Richest Middle Poorest Sanitation characteristics Overall N = 266 N = 266 N = 266 Latrine Facility (no sharing) No Facility 4 (3, 6) 1 (0, 5) 5 (3, 9) 7 (4, 11) Unimproved 6 (4, 9) 6 (3, 9) 3 (1, 6) 9 (5, 17) Improved 90 (87, 92) 93 (90, 95) 92 (88, 95) 84 (77, 89) JMP Sanitation Unimproved 90 (86, 92) 80 (72, 86) 96 (92, 98) 93 (89, 96) Improved 10 (8, 14) 20 (14, 28) 4 (2, 8) 7 (4, 11) Sharing No sharing 12 (9, 15) 22 (16, 30) 5 (2, 9) 8 (4, 14) 1-5 HHs share 40 (35, 46) 44 (34, 55) 41 (34, 48) 35 (28, 42) 6-10 HHs share 29 (25, 33) 20 (14, 28) 36 (31, 42) 31 (25, 39) > 10 HHs share 19 (15, 24) 13 (9, 20) 19 (14, 25) 26 (20, 34) Latrine Smell Outside 43 (36, 50) 28 (20, 36) 46 (39, 53) 58 (47, 68) Inside 39 (33, 45) 40 (28, 53) 42 (37, 47) 33 (24, 44) No Smell 18 (14, 23) 32 (24, 41) 12 (8, 19) 9 (5, 15) Latrine Flies Present 79 (73, 83) 62 (55, 69) 85 (77, 91) 89 (82, 94) Absent 21 (17, 27) 38 (31, 45) 15 (9, 23) 11 (6, 18) Latrine Slab Broken, Cracks or Holes 32 (27, 38) 24 (18, 32) 28 (22, 34) 45 (36, 55) Intact 68 (62, 73) 76 (68, 82) 72 (66, 78) 55 (45, 64) Feces (On Slab) Present 29 (24, 35) 22 (16, 31) 30 (24, 38) 37 (26, 49) Absent 71 (65, 76) 78 (69, 84) 70 (62, 76) 63 (51, 74) Latrine Door No Door 3 (2, 7) 2 (1, 5) 1 (0, 5) 8 (4, 17) Does Not Close 28 (23, 33) 17 (12, 24) 28 (21, 36) 41 (33, 49) Closes Completely 69 (63, 74) 81 (75, 86) 71 (63, 79) 51 (43, 59) Latrine Lid Often Uncovered 91 (88, 94) 84 (78, 89) 95 (91, 98) 95 (91, 98) Often Covered 9 (6, 12) 16 (11, 22) 5 (2, 9) 5 (2, 9)

Figure 1-1. Conceptual diagram of peri-urban diarrheal disease.

Figure 1 - Social-ecology of sanitation-related health and disparities: conceptual framework

Social and Political Disparities Waste Land tenure Political Solid waste removal and and Housing participation management treatment settlement

Socio- economic Community Environment Status Community Open Excreta Population Drainage sanitation defecation management density coverage

Water Housing Flood supply quality propensity Household

Housing Cooking and Gender condition refrigeration

Handwashing Household Child stool Food hygiene with soap sanitation management

Water treatment Flies Fomites Fingers Fluids Food Child

Place of Water Residence storage Nutritional Preventive Treatment status behaviors access

Figure 1-2. Socio-ecological conceptual model used to guiding the WASH Disparities study.

Figure 1-3. Socio-ecological conceptual model of WASH in peri-urban Kisumu. Arrows indicate the directions of effects. Topics in bold are covered in depth throughout chapters of the dissertation. Topics in italics are important to understanding relationships but that are not covered or only discussed in the context of relationships

Figure 1-4. Maps showing estimated improved sanitation coverage across Kenya, based on data from the 2008-9 Kenya DHS. The first panel (All) shows sanitation coverage estimates calculated based on all households in the dataset, while the remaining panels each display estimated improved sanitation for households classified into each of 5 quintiles based on wealth as defined by asset index scores. ‘Quintile 1’ represents estimates calculated based on the poorest 20% of households, while ‘Quintile 5’ displays coverage estimates calculated based on the richest 20% of households. Improved sanitation was defined based on JMP definitions. Maps were adapted from Jia et al. (2016).

Figure 1-5. Map of Kisumu study area with convex hulls of households sampled for each of the 40 neighborhoods (darker green). The lighter, transparent green buffer surrounding each polygon is ½ the mean distance between points for each neighborhood added to convex hulls for total area estimates. The location of Kisumu County (red) in Kenya is displayed in the top left panel. Two neighborhoods were disjointed because of sections with no population (A1 & A2) or sections with unassigned households (B1 & B2).

Figure 1-6. Maps of the study area (A) and zoomed views of Obunga / Nyawita (B) and Nyalenda (C) showing mean centers (points) and 2 standard deviations from the mean (circles) for each neighborhood. The color of points and circles indicate the total number of households monitored by each CHV as reported in their household registers.

Figure 1-7. Maps of the study area (A) and closer views of Obunga / Nyawita (B) and Nyalenda (C). The color and size circles represent average household elevation and estimated population density (persons / km2), respectively.

Figure 1-8. Poverty measure models showing correlation coefficients for each relationship between measures and the latent variable, poverty (“edc” = education, “wtrn” = unemployed, “wtrn” = unsafe fetching water, “wtrv” = violence/threat fetching water, “lclln” = unsafe during long call, “lcllv” = violence/threate during long call, “sst” = socioeconomic status: wealth terciles, and “ssc” = socioeconomic stata: continuous wealth. Dashed lines indicate the reference variable and the width and darkest of lines indicates the strength of correlations between latent and measure variables.

Figure 1-9. Agreement between wealth and poverty index scores for each household.

Figure 1-10. Correlation matrix showing neighborhood proportions of households for variables used in the poverty index along with population density.

Figure 1-11. Map of improved sanitation coverage using JMP definitions and mean poverty index for households in each community. Community neighborhoods highlighted in purple indicates neighborhoods where over 80% of households are tenants.

1.5

r 1.0 Corr: Corr: Corr: Corr: Corr: Corr: Corr:

P 0.5 −0.305 0.245 −0.251 0.142 0.446 0.249 −0.0669

0.0

0.9

n Corr: Corr: Corr: Corr: Corr: Corr:

n 0.7

e T 0.267 −0.422 0.33 0.134 −0.231 0.386 0.5

r 0.9

w Corr: Corr: Corr: Corr: Corr:

t 0.7

f −0.652 0.452 0.361 −0.233 0.0588 f 0.5

0.3

i t 0.3

a 0.2 Corr: Corr: Corr: Corr:

e 0.1 v −0.5 −0.32 0.0929 −0.121

r p 0.0

I 16

g 12

i Corr: Corr: Corr:

h 8

S 0.26 −0.326 0.366 4

a 0.6

Corr: Corr:

u 0.4

c 0.216 0.0246

S 0.2

n 0.6

e 0.4 f Corr:

i 0.2

r −0.233

S 0.0

−0.3 0.0 0.3 0.6 0.4 0.6 0.8 1.0 0.4 0.6 0.8 1.0 0.0 0.1 0.2 0.3 4 8 12 16 0.3 0.4 0.5 0.6 0.7 0.0 0.2 0.4 0.6 Poverty Tenant Offplot water Improved sanitation Sharing Security water Security defecation Elevation

Figure 1-12. Scatterplot matrix of neighborhood means and proportions for selected water and sanitation conditions and socio-demographic variables. The diagonal is a density distribution for each variable. Panels above the diagonal show Pearson’s correlation values. Panels below show points representing bivariate correlations for neighborhood means with a line of best fit and shading representing 95% confidence intervals for the estimated linear relationship.

Figure 1-13. Estimates of the proportion of households with improved sanitation, on-plot water and window screens in all windows of a dwelling by indicators for each domain of poverty and by poverty terciles. Lines represent estimates of 95% confidence intervals.

Figure 1-14. Map of neighborhood percentages of household use of off plot improved drinking water (color) and improved sanitation (size) coverage using JMP definitions.

CHAPTER 2 A HISTORY OF INEQUALITY AND URBANIZATION IN KISUMU

Introduction

The purpose of this chapter is provide historic depth and context for interpreting and understanding the current WASH and poverty landscape in peri-urban Kisumu.

Community development is contextualized within the historical migration patterns of the

Nyanza region, which encompasses the lake basin and adjacent highlands surrounding the Winam Gulf. The majority of this history focuses on the Luo, the largest ethnic group of the pre-colonial landscape in the lake basin region during the founding of Kisumu

City, at the beginning of the 20th century. This is followed by a discussion of the origins of peri-urban communities during the era of the British control and how the allocation of resources for city planning combined with poor anticipation of population growth, laid the foundations for modern WASH disparities in diarrheal disease exposure and urban poverty in Kisumu.

In the historic global health context this account of peri-urban Kisumu spans multiple periods of complex shifts in disease burden in response to demographic shifts in population known as epidemiological transitions (Omran 2005). The discussion begins midway through the 1st epidemiological transition, which began about 10,000 years ago, during the Bantu and Nilotic Expansions, to current 2nd and 3rd epidemiological transitions generally ongoing in Western Kenya as well as most other regions in Sub-Saharan Africa.

A Brief History of the Nyanza Region

Kisumu is situated on the eastern tip of the Winam Gulf, a small portion of Lake

Victoria that falls within the modern Kenyan border with Uganda and Tanzania (Figure

2-1). In Kenya, the lands surrounding Winam Gulf are referred to as Nyanza, which also served as the official provincial name before being recently devolved into Siaya,

Kisumu, Homa Bay, Migori, Kisii and Nyamira counties by a new Constitution in 2010 and implemented beginning with 2013 elections (Figure 2-2). The Nyanza region stretches from the Winam Gulf basin lowlands north to the to the edge of the Nandi

Hills, east to the agriculturally productive Central Highlands, south and southeast to the

Mau Escarpment, the border of the northern corner of the Mara-Serengeti ecosystem, and west by greater Lake Victoria. The Gulf basin and surrounding lands are colloquially referred to as ‘Luoland’ in reference to the Luo, the largest ethnic group of the Nyanza region. Though there is a wide range of meanings and origins for Kisumu, it is generally thought to have derived from the Dholuo word ‘kisumo’, meaning a place to look for food.

Prior to the arrival of the Luo, the rich Great Lakes region already supported dense populations of Bantu peoples, who cleared vast swaths of forest for cultivation starting around 3000 years ago. Though the historical order of events are contested,

Bantu expansion from West Africa to Southern and East Africa (Figure 2-3) was facilitated by cultivating bananas, yams, and cereals using tools made from ironworks

(Oliver 1966). Bantu groups added animal husbandry, adopted from encounters with neighboring Nilotic and Cushitic peoples, quickly forming mixed agricultural systems

(Schoenbrun 1993). It is unclear how many Bantu were in the lake basin area, but the area around Mt. Elgon is thought to be a historically important population center that gave rise to the modern Luhya or Abaluhya. They are thought to have inhabited the lake

basin area, but largely withdrew to the wetter and cooler highlands, perhaps before Luo clans began migrating into the region (Ogot 1967).

Modern Luo are descendants of Nilotic pastoralists who moved south as part of a larger complex of southern migrations from what is now Sudan and South Sudan in response to shifts to drier climates and socio-religious change (Ogot 1967; Smith 1992).

Around 1400 CE, after a series of complex splits and migration events, Luo groups eventually began settling lake basin area along the shores of ‘Nam Lolwe,’ the Dholuo name for Lake Victoria. They became one multi-clan group known as Jo-Luo, bringing settlement patterns traditional to many Nilotic groups originating from that region of

Africa. Permanent and semi-permanent settlements were built on higher ground overlooking vast lowland grasslands, providing pasture, forage, and water for cattle herds and fish from rivers and wetlands (Ogot 1967). A preferences for this settlement pattern made the lake and surrounding rivers attractive to Luo clans.

Gradually, through ongoing displacements, assimilation and fighting with neighboring Bantu groups, the waves of migrating Luo adopted a more sedentary lifestyle in Nyanza. They first established settlements in lowland areas around the lake including Alego, Asembo and Uyoma (Figure 2-5). However, subsequent waves of migrations established settlements in more upland areas of North Ugenya, Gem, and

Kisumu and continued up until 1900 (Ogot 1967). One Luo wave of migrants between

1590 and 1670, was described as a “conquest of the lakeshore region”, bringing warfare between clans and the Kakwar into what is now peri-urban Kisumu (Ogot 1967). Later, after a series of battles with Jo-Gem, a group that became known as Jo-Kisumu arrived in the area of modern day Kisumu around the early 1800s. During this period, the

system of largely patrilineal clans was replaced by Luo chiefdoms of mixed family lineages.

This process often included internal displacements and fighting between Luo clans as well as conflicts with Luhya, Nandi, and Maasai groups, likely driven by land constraints from increasing populations of people and cattle, a reoccurring theme throughout the cultural history of the area. Throughout their history, Luo inter-married with their neighbors, especially Luhya, evidence that pre-colonial identities were somewhat flexible (Lonsdale 1977). Today, ‘Luoland’ is bordered by the Luyha to the

North, Masaai to the south, Kalenjin and Nandi to the northeast, and Kisii or Abagusii to the southwest. The Luyha and Kisii are Bantu in origin, while the Masaai, Kalenjin and

Nandi, like the Luo, are descendants of Nilotic peoples.

The Birth of Kisumu City

In the mid-1800s Arab and Kiswahili ivory traders first arrived in the lake region, the end of a caravan trail originating along the well-established coastal cities of Malindi and Mombasa (Ogot 1967). In 1895, the British took over much of what is modern-day

Kenya declaring the area as the East African Protectorate or British East Africa, extending from the coast to Lake Naivasha, further extended into the Uganda territory in

1902. Nyanza was at the far reaches of colonial activities as well as the edge of activities of Arab ivory traders and slavers.

In the early 1900s, the region was home to less than 1 million people, with the highest densities along the ‘border’ of Luhya and Luo settlements (Figure 2-5). Lonsdale estimates that there were roughly about 20 Luo, 15 Luhya, and 6 Kisii settlements or tribes averaging less than 20,000 people. Luo tribes were organized by a unilineal family ideology, linked by common familial lines, marriage and clan associations. Justice

and community decisions were served at the tribal level, with no larger governmental oversight (1977). British colonialization met little resistance from Luo tribes, with more violent encounters with the Kisii and Nandi groups. Lonsdale recounts that there were

“thoughts of military action” on about 40 occasions, resulting in “coercive steps” being taken on 30 occasions against about 18 tribes (1977). The main interest of the British was establishing transportation routes through the area to the Uganda territory, with no plans for occupying or extracting resources on lands occupied by Luo and Luhya tribes.

Partly due to this historic trade route and lakeside location, Kisumu, then called

Port Florence, was chosen as the terminus for the Uganda Railway line that was to stretch from Mombasa to the interior of British East Africa. The railway was completed in 1901, with a bulk of the construction performed by indentured servants brought primarily from Goa, Gujarat and Punjab areas of modern India. This secured the territory for the British, who could quickly bring goods and settlers to the area on rail lines.

The British Colonial Township Board designed Kisumu with its center in the well- drained, upland areas rising from the lake shore. In response to outbreaks of bubonic plague (Yersinia pestis), the Township Board zoned residential areas of the city into

Blocks A-C in 1908 (Letema et al. 2014). European and Asian residences were zoned

Block A, including what is now Milimani, Kibuye and Tom Mboya estates, the port and city commercial center (Figure 2-6). Zone B was designated as an undeveloped buffer zone, between Blocks A & C, encompassing what is now Nyalenda and Manyatta communities. Block C was ‘officially’ designated as the African residential area by

British authorities. This area included what is now Obunga, Nyawita and Migosi as well the Nanga and Dunga areas of Nyalenda B and Nyamasaria (Figure 2-7).

African settlements that developed in and around Block C were semi-rural with plots demarcated as compounds (Figure 2-6). Local Native Councils were charged with providing sanitation services in these areas, but lacked organization and resources to fulfill sanitation mandates. Africans were paid low or no wages and received few services from resource limited councils, deepening the divide between town and peripheral communities (Letema et al. 2014). British authorities classified African settlements as temporary, assuming that Africans would return to their rural home after opportunities for labor became scarce (Oucho 1979).

A Tradition of Urban Migration

The historical literature on Kenyan is rich in addressing rural-urban migration

(see Greiner and Sakdapolrak 2012). Some of the more important features of this work highlight the emergence of urban capitalist centers for labor migration, unique to Kenya as compared to surround East African nations. In the colonial period, the Luo were not approached with significant British plans for agricultural projects, after a series of failed attempts, and were instead viewed as a source for manual labor (Atieno-Odhiambo

2002). These labor opportunities were more attractive to many Luo men then waiting to inherit increasingly smaller tracts of land for agriculture.

Agricultural productivity in Western Kenya has been limited by decreasing land availability due to increasing rural populations (among the highest in rural Africa), creating more intensive agricultural systems with shortened or no fallow periods for restoring soil fertility (Conelly and Chaiken 2000). Traditionally, land tenure was determined traditions of linear familial and clan controlled inheritance systems that

subdivide lands among male offspring. As rural populations increased, subdivisions of land inheritance resulted in smaller and smaller plots for agriculture. A complex system of customary laws were developed to address inter-clan conflict over land (Odenyo

1973). During the colonial period, Luo customary land rights became more complicated by the rise of a parallel system of private land ownership and titled lands (Hebinck and

Mango 2008; Olima and Obala 1998). As a result of increasingly limited land availability, many began seeking opportunities for paid labor by migrating into Kisumu.

Some of the most popular settlements for rural immigrants were Nyalenda and

Manyatta, established in 1914 (Macoloo 1984). Kaloleni settlement was established in

1924 for largely Muslim railway workers. Similarly, Manyatta Arab was established as a settlement for Arab laborers. Both of these areas are located within the Old Municipal boundaries (Figure 2-1). Bandani settlement was created in 1929 to house displaced largely Muslim populations of Nubian descent for the construction of a new Aerodrome

(Macoloo 1984).

Public Health in Kisumu

Kisumu was officially proclaimed a municipality in 1941 by the Kisumu Municipal

Council and was clearly divided by ethnic and racial lines as a result of colonial development plans (Macoloo 1984). Through the next two decades, initiatives were made to build housing developments inside of the Municipal boundary for African laborers. In the 1940s, the British authorities established the Division of Vector Borne

Diseases (DVBD) to help monitor and control diseases in Kisumu and the surrounding areas. Eventually, this would be overtaken by the Kenyan Ministry of Health after

Independence in 1963.

Geissler gives an extensive overview of the DVBD and how it predated modern neoliberal development strategies that led to the establishment of partnerships between

Kenyan national institutions and international organizations that would combat Malaria and the devastating HIV epidemic. In this treatment, Geissler documents his interviews with former DVBD employees, many of whom were proud employees and inhabitants of government settlements established within the municipal boundaries (2013). Many of the employees retired to their rural home areas, a common pattern and ideology held by the Luo and many other citizens of Kisumu. While employed in Kisumu, resources and remittances were sent to the home area to build and maintain the house (‘simba’ or

‘dala’) and garden plots (‘puodho’ or ‘shamba’) on family lands. In this sense, Kisumu was not viewed as a permanent home by many of its inhabitants with ties to the surrounding rural areas.

Geissler continues to describe the erosion of the DVBD in Kisumu in favor of larger international partnerships, the largest example in the region is the US Center for

Disease Control (CDC) and the Kenya Medical Research Institute (KEMRI). This partnership highlights the rise of the aid sector that is still a major part of the landscape of Kisumu. The region is notorious for poor health outcomes due to heavy disease burdens syndemic with poverty in the area, such as malaria, HIV-AIDS, diarrheal disease and many other neglected tropical diseases (Singer and Bulled 2013).

Syndemics have attracted international aid from North American and European donors and private non-governmental organizations (NGOs) to combat poor performance in the health sector through partnerships with local and national government. The same is true

for agricultural sector and increasing participation in infrastructure development contracts from German, Chinese and Japanese firms.

Kenyan Independence

The rich agricultural prospects in the Central Highlands were some of the areas where tensions between the British and local populations throughout the regions solidified behind the Mau – Mau Rebellion, where struggles over land led to episodes of brutal violence that was sensationalized as merciless, gruesome and “unprovoked” acts against peaceful European settlers by the British government. The Mau – Mau

Rebellion is the most popularized, misrepresented and stigmatized event that sparked

Kenyan Independence (Berman 1991; Lonsdale 1990). Recent retributions and court settlements and other records British colonial records reveal a history of violent conflict and unjust imprisonments of activists involved in protests against colonial rules from across the British Protectorate (York 2015).

However, this overshadows the combined efforts of resistance from contributions of tribal groups united towards control of their ancestral lands and traditions. In Western

Kenya, this included nationally revered leaders such as Tom Mboya (Goldsworthy 1982) and Jaramogi Oginga Odinga (Anyang' Nyong'o 2007). Eventually, Jomo Kenyatta would be elected as President with Oginga Odinga as Vice President, both then members of the Kenya African National Union (KANU). Later differences in political ideologies between Oginga Odinga, who was more left and communist-leaning and

Mboya and Kenyatta, who were more conservative and aligned pro-West capitalism.

In 1966, Oginga Odinga would leave KANU to form the Kenya People’s Union

(KPU) that would be politically popular in the Nyanza region (Anyang' Nyong'o 2007). In

1969, the Soviet-funded New Nyanza General Hospital was opened, a project courted

by Oginga Odinga. President Kenyatta would reluctantly lead the opening ceremonies.

After the ceremony, riots broke out, fueled by suspicions over the recent murder of

Mboya, attacking Kenyatta’s group. Ten people were killed by Kenyatta’s security. KPU would be dissolved in 1969, transforming Kenya into a one party state, deepening a political divide between Nyanza and the Central Highlands that persists today (Atieno-

Odhiambo 2002). The hospital would later be renamed the Jaramogie Oginga Odinga

Teaching and Referral Hospital (JOOTRH) and is commonly referred to as “Russia.” It is currently closest public health facility to Obunga and Nyawita communities.

Modern Kisumu

Over time, the western sections of the Uganda railway fell into disrepair until most of the railway was closed by the Kenya Railways Corporation in 2012. Despite the decline of the railway, Kisumu is firmly established as a hub for regional trade and commerce and international development, now the third largest city in Kenya. After independence, the new Kenyan government took over control of all urban lands, allowing some lease holds of 99-year terms (Olima and Obala 1998). However, in

Kisumu and many other cities, populations have outgrown original government- controlled urban land holdings, limiting government involvement in urban development.

Recent estimates of land use for modern Kisumu are 42 km2 of planned urban space,

53 km2 of unplanned informal and peri-urban settlements with and 202 km2 of mixed used suburban and unplanned rural development (Letema et al. 2014).

With the adoption of a new constitution in 2010, Kenya devolved its 8 provinces into 47 counties, who are now responsible for meeting national guidelines for public services, including meeting targets for water and sanitation coverage (Whimp 2013).

Kisumu continues its role as the administrative capital for the region, now of the newly

defined Kisumu County (Figure 2-2), with similar boundaries to the former Kisumu

District once within the old Nyanza Province. The County has an estimated population of about 1.2 million (Government of Kenya Ministry of Health 2015) living at an average density of 464 persons / km2 (Kenya Ministry of HealthWater and Sanitation Program

2014) . About 62% of the county population is considered urban. Sanitation coverage for the county is low, with only 30% of people having access to unshared, improved sanitation (Jia et al. 2016) and 12% practicing open defecation (Kenya National Bureau of Statistics 2009). Malnutrition is an ongoing issue among the 200,000 children living in the county, 18% are moderately to severely stunted while nearly 7% are underweight

(Kenya National Bureau of StatisticsICF Macro 2015).

Peri-urban Kisumu

An estimated 60% of the population of the Kisumu (UN-Habitat 2006) area live in about 53 km2 of unplanned peri-urban, informal settlements (Letema et al. 2014). In

1971, the municipal boundaries were extended to include the growing peri-urban communities in Manyatta and Nyalenda. However, no formal planning or zoning schema has been widely implemented as evidenced by the web of dirt roads weaving around and between freeholder plots. Manyatta A and B are often considered the oldest of the informal communities, located along the eastern edge of the Kibuye market, the largest in Kisumu. A paved road takes traffic from the city center through Manyatta and out to the Kibos sugar cane fields and processing plants.

Nyalenda A & B are two sub-locations within with populations of 37,000 and

40,000 people, respectively, located on the western edge of Kisumu City (Maoulidi

2010, 2015 projection). Nyalenda A is the eastern portion of the now subdivided

Nyalenda estate and has a higher population density but is smaller in area (2.8 km2)

than Nyalenda B (6.1 km2). A straight unpaved road lined by one of the only government-constructed stone drainages near the KAWATA water management office on Ring Road forms the common border between Nyalenda A & B (Figure 2-7).

Obunga and Nyawita are two adjacent communities located within the larger

Kanyakwar sub-location, along the northern boundary of Kisumu City (Figure 2-7).

Kanyakwar is home to an estimated 13,400 residents living within the 8.5 km2 area of the sub-location (Maoulidi 2010, 2015 projection) Obunga and Nyawita are located in the southeastern corner of Kanyakwar and are the most densely populated areas of the sub-location. These two areas were sampled as one study area though residents acknowledge them as two separate areas. The National Census lists Nyawita as a sub- location in statistical reports, but geographically as part of Kanyakwar sub-location.

Health officials indicated that Nyawita will be re-designated as its own sub-location that will include areas adjacent to Tom Mboya estate across Busia road to the south and

Kondele and Migosi on the other side of Kakamega road to the east.

Methods

The WASH landscape in peri-urban Kisumu was characterized using survey data and household and community geospatial information (Chapter 1). Community mapping of key landmarks, open defecation sites, and solid waste piles were recorded for 40

CHV coverage areas using a Garmin hand-held GPS device. CHVs were randomly selected from lists of active volunteers provided by area CHEWs. However, this random sample of 40 CHVs were selected separately during the formative research phase, while the 40 CHVs that participated in the survey were selected during a second sampling exercise (Chapter 1, Methods).

During mapping exercises CHVs walked researchers around their communities, documenting important community landmarks and areas that were being used for open defecation and large refuse piles of solid waste deposited by residents. At each site, a visual survey for number of refuse piles and the presences or absence of diapers and bags of human feces (“flying toilets”) were recorded as well as any general information about users of the sites. If a site was an open defecation site with refuse piles, it was considered only as an open defecation site. The goal was to map the CHV areas, but often CHVs enthusiastically insisted on documenting sites that were outside of their coverage areas and are also included in these results.

Results

During community walks with 40 CHVs 142 refuse and 121 open defecation sites were identified by CHV throughout the study area (Figure 2-8). Open defecation sites were most often located in empty lots (50%, CI 41 – 59) or open fields (11%, CI 6 –

18%), but also along roads (11%, CI 6 – 18%) and inside of compounds (10%, CI 6 –

17%). Human feces was present at 98% (CI 95 – 100%) of identified open defecation sites, along with diapers (74%, CI 73 – 87%), refuse (80%, CI 66 – 82%), and flying toilets (58%, CI 49 – 67%). Children were often mentioned as users, especially in more conspicuous location than sites designated primarily for adult users.

Discussion

Labor migration has been the driving force for urbanization in the Great Lakes region for the last 120 years. Despite this long-standing population trend, colonial and independence governments have failed to address needs for appropriate housing and infrastructure. Since 1971, most of Kisumu residents live inside of the municipality, but the same racially-biased borders established to separate the ‘unsanitary’ African

laborers living in Block C from affluent Block A residences (Figure 2-6). Today, the majority of Kisumu’s population faces the legacy of the city’s first ‘unsanitary’ citizens

(Briggs 2003), many living in the same types of housing of those early urban pioneers of

Block C.

In peri-urban Kismum, community-level distribution of refuse and open defecation sites, pose a constant threat of exposure particularly in areas with the highest population densities (Figure 2-8). In the absence of public sanitation infrastructure, risk is mediated by the ability of the home owner or tenant to protect or afford protection of compounds insulated from community contamination. This places more disease burden on the urban poor in these areas, who have little resources to mobilize to improve both private and public sanitation conditions.

In the 2010 Constitution, Kenya adopted policy that declares water and sanitation as a basic human right, passing the realization of the right on to the county governments (WSP 2013). Kisumu County and municipal governments must now find a way to bring sanitation solutions to the heterogeneous peri-urban communities. The

Kenyan Ministry of Health has adopted the Community-led Total Sanitation (CLTS) intervention program as its main policy for improving access to sanitation, implemented by each county government (Anderson and Mills 2015). While showing success and promise in the rural African context, including rural Kenya, Public Health Officers

(PHOs) and CHEWs implementing this in Nyalenda B, Nyawita and Obunga communities are cautious about its success in the peri-urban context (Kar and Milward

2011; McGranahan 2013).

In the few communities that CLTS has been piloted in peri-urban Kisumu, the most frequent infrastructure solution to reducing open defecation has been increasing access by hand-dug pit latrines that often extend directly into the high water table. With population densities in the tens of thousands, are cheaply constructed pit latrines the sustainable solution or will this result in continued large scale contamination of ground water? The answer is likely to be no, unless income levels rise enough for every landlord to prioritize and install septic systems large enough to accommodate an average of 7-10 households, which would still be limited in high density areas.

It is also unlikely that tenants will invest in their resources in sanitation infrastructure on someone else’s land (McGranahan 2015; O'Keefe et al. 2015; Scott et al. 2015) especially with financial obligations to family in both peri-urban and rural residences (Agesa 2004; Greiner and Sakdapolrak 2012). A recent study of tenants in peri-urban Kisumu reports that not only were tenants were unlikely to invest in the sanitation infrastructure of rentals, they also were not willing to pay fees for communal sanitation (Simiyu 2015). Landlords interviewed in this study reported dramatic increases in rent prices if tenants had access to private sanitation facilities.

Today, the footprint of Kisumu has expanded to include a much wider area than the original colonial blocks, encompassing a much larger area of rural and peri-urban land (Figure 2-9). The ability of a large scale government-led sanitation solution and a comprehensive urban plan for Kisumu is limited by land tenure. Laws established during the 1950s that recognized private ownership, allowing land to be bought or sold, blending state and customary land management (Hebinck and Mango 2008). In peri- urban Kisumu the loosely coordinated mosaic of individual landlords provide housing

using rural settlement patterns (UN-Habitat 2006) instead of building infrastructure necessary supporting urban population densities numbering in the tens of thousands.

While moving boundaries to include expanding peri-urban populations, the city did not maintain control of land in anticipation of persistent and increasing migration and future housing needs, quickly converting all available land to residential spaces (Olima and

Obala 1998). As a result, sanitation solutions must now coordinate a heterogeneous multi-actor landscape of small free-held plots (Figure 2-10).

Figure 2-1. Map of Kenya (red) and surrounding countries in East Africa. Kisumu County in western Kenya is highlighted in blue.

Figure 2-2. Map of Kisumu County (blue) in western Kenya.

Figure 2-3. Map of Bantu Expansion routes in Sub-Saharan Africa from Marchant and Lane (2014).

Figure 2-4. Map of southern migration of pastoralists, including Nilotic ancestors of Luo, Kalenjin, and Masaai peoples from Smith (1992).

Figure 2-5. Map of territories for subtribes of groups along the Winam Gulf of Lake Victoria in western Kenya around the early 1900s, from Lonsdale (1977).

Figure 2-6. Map of blocks designated by British authorities to prevent the spread of disease after a number of outbreaks of the plague. Block A were largely European and Indian residences, Block B was an undeveloped buffer and Block C was the home for ‘local Africans’, Adapted from UN-Habitat (2006)

Figure 2-7. Current map of Kisumu showing the informal settlements (yellow, dashed) and key community features.

Figure 2-8. Map of community sanitation including open defecation and solid waste piles, overlaid on community neighborhood population density estimates.

Figure 2-9. Map of Kisumu’s urban footprint from a recent report by the Kenya Government (2014).

Figure 2-10. Map of land tenure designations in Kisumu from a recent report by the Kenya Government (2014).

CHAPTER 3 A STUDY OF PERI-URBAN FILTH FLY ECOLOGY IN PERI-URBAN KISUMU KENYA

Introduction

One challenge to understanding the dynamics of diarrheal disease is identifying which of the five pathways (Fingers, Fluids, Fomites, Food and Flies: redbox, Figure 1-

2) is contributing to disease burden in children (Briscoe 1984) and how pathways covary over seasons, years, and across the peri-urban landscape. Despite being considered one of the 5 transmission pathways of diarrheal diseases (Kawata 1978; Wagner and

Lanoix 1958), little is known about the dynamics between and synthanthropic filth fly populations thriving in modern peri-urban settings.

Informal settlements often lack in both access to sanitation and effective solid waste management, creating ideal conditions for frequent contact with enteric pathogens through fecal-oral pathways. Contact is accelerated by the lack of sanitation infrastructure combined with increasingly high population densities. Continued unplanned growth and poor community sanitation continue to accelerate fecal-oral transmission pathways (McGranahan et al. 2005; McGranahan 2015), supporting larger populations of filth flies, concentrating disease risk into the poorest populations

(Rheingans et al. 2012).

High rural population densities throughout the history of the region have led to rapid rural-urban migration that has been underestimated by the British and Kenya governments since Kisumu become a city at the turn of the 20th century (Chapter 2).

Lack of planning for appropriate sanitation infrastructure and the urban-rural transition has led to the widespread use of pit latrines, open defecation, poor or no solid waste management, and high populations of domesticated animals (UN-Habitat 2006).

The abundance of human and animal waste and decaying organic matter is ideal for supporting large populations of coprophagous and polyphagous filth flies of families

Sarcophagidae, Muscidae, and Calliphoridea (Emerson et al. 2005; Irish et al. 2013;

Lindsay et al. 2012; Schoof et al. 1954; Schoof and Siverly 1954). Members of these families are mechanical vectors for a wide range of enteropathogens including entero- hemorrhagic Escherichia coli, Shigella spp., Campylobacter spp., Salmonella spp.,

Aremonas spp., Cryptosporidium spp., Vibrio cholera, Giardia spp., and enterohelminths (Fetene and Worku 2009; Fotedar 2001; Graczyk et al. 2001; 2005; Gupta et al. 2012). High densities of humans and domesticated animals in an urban settings is likely to increases risk of zoonotic enteric transmission, which has been shown in agricultural settings (Conn et al. 2007; Fegan and Gobius 2013; Graczyk et al. 2001).

Population level studies of fly transmitted diarrheal diseases have shown strong associations between filth fly densities and diarrheal disease prevalence, most notably for shigellosis (Farag et al. 2013; Levine and Levine 1991). Successful interventions targeting fly control have shown reduced incidences of diarrheal disease, especially with use of insecticides (Chavasse et al. 1999; Watt and Lindsay 1948), but also with increased latrine coverage (Cohen et al. 1991; McCabe and Haines 1957). High human population densities with poor access to water for hygiene or sanitation facilities are favorable conditions for transmission of trachoma (Chlamydia trachomatis) infections by

M. sorbens (Stocks et al. 2014).

Few studies in peri-urban settings in sub-Saharan Africa have sought to understand how human management of environmental conditions related to water, sanitation and hygiene (WASH) at community and household levels impacts filth fly

populations, resulting in child exposure to diarrheal disease. This research project applies a socio-ecological systems framework to urban fly ecology to improve our understanding these relationships between humans and companion fly species. The outputs from this study contribute to a better understanding of the complexities of addressing sanitation-related health risks from rapid urbanization resource-poor urban settings.

Quantitative data on social and environmental conditions and density estimates were collected following the fly socioecological conceptual model (Figure 3-1). The main social driver for this model is poverty (light blue), measured in four dimensions (Chapter

1). Poverty is the determining factor for the ability of the household to protect itself or control environmental conditions (yellow rectangles) that could sustain fly populations in the compounds and individual households (red circles). Sampling conditions such as temperature and trap location also impact fly density estimates (dark blue rectangles).

Methods

The aim of this research is to identify and test hypotheses about relationships between key social and ecological drivers of filth fly density. Hypotheses are tested using empirical data collected using a household survey instrument (Chakraborty 2016) combined with observations of environmental sanitation conditions in the compound and estimates of filth fly density. Filth fly density is expected to be positively associated with proximate environmental sanitation drivers such density of households, poor latrine infrastructure and condition, open defecation, and presence of animals and animal waste. However, these environmental conditions will have different effects on densities of specific taxa of importance for diarrheal transmission.

Data Collection

Fly populations were estimated in each compound using both the Scudder grill

(Scudder 1947) and Starbar Quickstrike fly abatement strips (QS traps). Compounds were sampled if they contained at least one selected household with a child aged 6-36 months. The child age restriction was a result of budget restrictions and goals of the larger project, which included multiplex PCR analysis of collected fly samples.

A Scudder grill was used to survey fly populations at both the latrines and solid waste piles within the compound. The Scudder grill has been used widely as a tool for estimating density that relies fly curiosity around novel objects in their environment.

Grills are placed in areas were flies are expected to congregate. In this environment, flies congregate around large solid waste piles, containing organic and fecal matter including food waste, used diapers and bags of human waste known as ‘flying toilets.’

Flies, especially Calliphorids, can also be seen congregating around latrines.

Once the compound boundaries were identified, the area was visually scanned for solid waste piles greater than 2 m in diameter and the latrine used by residents. This diameter was chosen as a way of ensuring that there was a significant amount of solid waste to attract flies. Solid waste is often scattered throughout the compound, so setting this criterion provided some objective way of selecting significant waste piles.

Confirmation of latrines used by the compound were obtained from the residents before sampling. If there were multiple latrines or eligible solid waste piles, a table of random numbers was used to select the pile or latrine (Haahr 1998). If there was no solid waste pile or latrine, then we recorded that type as missing for that compound.

After selecting the latrine and solid waste pile to be sampled, the scudder grill was placed adjacent to the location for about 30 seconds, allowing any flies that were

displaced to resettle. An observer then records each fly that lands on the grill for during a one-minute period using a timer and a hand counter. This process was replicated two additional times at the same location. The observer then moved to the second location and repeated this protocol.

QS traps were used to capture filth flies at the selected site within the compound.

The traps consist of a red plastic frame grid that have yellow cells containing a sugar matrix embedded with Nithiazine, a fast-acting insecticide designed to kill flies in a matter of seconds. A tube of liquid chemical bait was mounted at the end of the grid and when opened, emitted a foul odor designed to attract flies. Two of these traps were mounted on a white plastic foam board parallel to one another and placed on the ground next to the desired trapping location. Sunlight degrades the insecticide, so when trapping areas were not shaded, a metal stand with an umbrella was used to minimize sun exposure. Manufacturer recommendations are that traps will last up to 8 weeks before replacement. However, to ensure little degradation of baits or insecticide, traps were discarded after 10 days of use and replaced with 2 fresh traps.

The QS trapping location (solid waste pile or latrine) within the compound was determined using the highest scudder grill count across both locations. After site selection, the traps were placed within one meter of the refuse pile or latrine doorway for 30 minutes. At the end of the trapping period, all flies were collected from the board and placed in a sterile sampling tube for identification. This time period proved to both attract a sufficient amount of flies for microbial analysis, while balancing sampling of other environmental compartments within the compound. All flies captured were

counted and identified to species using published keys for medically important filth flies

(Crosskey and Lane 1993) and for African Calliphorids (Irish et al. 2014).

Data Analysis

Explanatory variables were fit to fly count data using generalized linear mixed models (GLMM) using the glmer.nb command from the lme4 package (Bates et al.

2016; Bolker et al. 2009) in R version 3.3.0. These packages were used because negative binomial distribution was the best fit for over dispersed fly count data (Figure 3-

2). Model selection was performed using the glmulti R package (Calcagno and de

Mazancourt 2010), which automates the selection of a best fit model from all possible models based on specified response and explanatory variables and probability distribution. Models of best fit were selected based on the lowest Bayesian Information

Criterion (BIC) score.

Fixed effects of explanatory variables including sampling temperature, trap location (Table 3-1) and latrine type and characteristics (Table 3-2) were included in model selection. Sampling clusters (neighborhoods) and poverty terciles were included as random effects, accounting for differences between cluster and poverty tercile estimates by fitting intercepts and slopes for each neighborhood and tercile grouping.

Temperature was a fixed effect forced into every model because of the impact it has on fly activity (Goulson et al. 2005) and volatility of bait odors (Pickens and Miller 1987).

Poverty terciles were calculated from the poverty index generated in the Confirmatory

Factor Analysis in Chapter 1. Coefficients and confidence intervals for fixed effects were transformed into Incidence Rate Ratios (IRR) for ease of interpretation. IRRs represent the difference in rate of fly counts between categories of an explanatory variable relative to a reference level of that same variable. For the continuous temperature variable, IRR

can be interpreted as a percent positive or negative change in counts for an increase in one degree in temperature. Random effects are reported as the median effects of neighborhood slope parameter estimates on logged counts. Both fixed and random effects from each model were calculated from 1000 simulations of each model of best fit for each fly species count using feSim and reSim functions in the merTools package

(Knowles et al. 2016).

Compound fly counts were analyzed by disaggregating counts for the three most common species captured in QS traps: Musca domestica (Muscidae), Chrysomya putoria (Calliphoridae) and Musca sorbens (Muscidae). Species were analyzed separately to account for species differences in fly ecology and behavior. William’s means are used to summarize average fly counts across explanatory variables as they are more appropriate for count data with zeros (Alexander 2012).

Results

Fly density estimates were taken in 475 compounds associated with 539 households with children under 5 years of age. QS traps were placed at latrines in

50.5% of compounds and refuse sites in 49.5%. Average regional sampling temperature was 31.6C, ranging from 24 – 37C. The majority of the 5,844 files that were collected from traps were M. domestica (91%), followed by C. putoria (4%) and M. sorbens (4%).

Other species counts included 5 Sarcophaga spp. (Sarcophagadae), 1 Trigonospila spp. (Tachinidae) and 7 unknown Muscidae spp.

William’s mean pooled catch across all samples was 7.4, ranging from 0 – 65 files per compound. M. domestica was the most represented in catches with an average of 6.3, ranging from 0 – 63 flies, followed distantly by C. putoria and M. sorbens with

means of 0.3 (0 – 16) and 0.3 (0 – 3) flies per compound, respectively. Fly strips were collected and counted from cooking areas of 665 out of 800 surveyed households. The

William’s mean was 0.25, ranging from 0 – 50 of flies per strip. Strips hung for an average of 30.5 hours, with a mean catch rate of 0.61 flies / day for flies caught in household cooking areas.

Density distributions of fly counts show the highest probabilities of zero fly counts occur in C. putoria, and M. sorbens counts (Figure 3-2). M. domestica and counts pooled for all species had less but still high occurrences of zero catches. In analyses of all fly counts, negative binomial distributions were the most appropriate models to fit to the data. Not surprisingly, M. domestica was the most often caught and thus the most highly correlated with overall counts (Figure 3-3). Correlations between M. domestica and M. sorbens were moderately correlated (0.35). All other correlations between counts and poverty were weak (< 0.20). Interestingly, weak correlations with pooled, M. domestica and M. sorbens counts were negative. Only weaker correlations between poverty and C. putoria were positive.

Trends in count data for each of the social factors, sanitation characteristics, and sampling conditions are difficult to interpret for C. putoria and M. sorbens counts since their medians are all zero even with attempts to transform the data. M. domestica counts showed trends in higher refuse trap counts as well as higher counts in Nyalenda

A (Figure 3-4). More M. domestica were caught as temperature increased up to the 28

32C category but do not appear significantly different than the 32-23C temperature category. Unimproved latrines and the poorest categories show a trend in lower M. domestica counts. Although compounds were household reported no latrine use

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showed higher counts for M. domestica and larger interquartile ranges for C. putoria.

Examination of counts across latrine conditions trend toward increased M. domestica counts when survey enumerators observed flies in or around the latrine, when smell was absent and when the pit was sealed (Figure 3-5). Again trends for C. putoria, M. sorbens are difficult to interpret.

Generalized Linear Mixed Models

Effects of sanitation conditions on fly density estimates differed across models of different fly taxa and included household poverty terciles and sampling neighborhood as random effects, temperature as a fixed effect in all models and then all sanitation condition and trap location variables as possible fixed effects (Table 3-1 & 3-2). The top five models from selection are shown in Table 3-3 for each taxa, ordered from lowest to highest BIC value.

The most important fixed effects predicting M. domestica counts were trapping near a refuse site (IRR = 1.3, CI 1.16 – 1.56) within the household’s compound, regional temperature (IRR = 1.08, CI 1.04 – 1. 12), and when a foul smell was not detected by the enumerator inside or outside the latrine (IRR = 0.72, CI 0.58 – 0.89, Figure 3-6a).

Simulations of random effects showed significant effects for about 15 neighborhoods

(Figure 3-6b). In 7 of those neighborhoods, effects were positive and negative for 8 neighborhoods. Terciles did not show significant effects for any levels of poverty.

Important fixed effects in the best fit model for C. putoria were slab conditions

(IRR = 0.60, CI 0.41 – 0.87), households residing in Nyalenda A (IRR = 4.3, CI 1.6 –

11.3) and temperature (IRR = 1.10, CI 0.99 – 1.22, Figure 3-6c), although the lower CI for temperature just slightly overlaps with 1. Random effects for about 6 neighborhoods are significant while poverty terciles did not have a significant effect on C. putoria counts

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(Figure 3-6d). The best model fit for M. sorbens counts did not include fixed effects other than temperature, which was held constant across all models (Figure 3-6e). The effect of temperature was positive and marginally significant, because the lower confidence interval just overlaps with 1 (IRR = 1.06, CI 0.98 – 1.13). Random effects from 9 neighborhoods are likely significant, but again poverty terciles did not have significant effects and trends towards higher counts in less impoverished households

(Figure 3-6f).

Discussion

Compound filth fly counts were analyzed across a range of socioecological variables to better understand diarrheal disease transmission in children living in a peri- urban setting in western Kenya. Fitting models to different species counts allow for a richer interpretation of how socioecological conditions drive synanthropic fly populations. Associations were found between socioecological and sanitation conditions and filth fly populations, but the dynamics between resources and flies varied across taxa.

M. domestica were the most frequently trapped taxa (Figure 3-. Trap counts were

30% higher when traps were set near refuse piles and 28% lower when a foul smell was not detected inside or outside of the latrine (Figure 3-6a). In studies of filth fly emergence from municipal solid waste and landfill sites, M. domestica emerged and were captured at the highest rates, as compared to other filth fly taxa (Goulson et al.

2005; Howard 2001; Lole 2005; Nurita and Abu Hassan 2013). In a study set in peri- urban Nigeria, M. domestica was the most captured fly in low-income settings with abundant refuse (Nmorsi et al. 2007). Chrysomya spp were also detected in high numbers in these studies. Finding higher counts in Nyalenda A than other communities

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could be partially explained by its proximity to a large municipal landfill near the northwest corner and waste water treatment facility along the eastern boundary of the sub-location. Improvements in solid waste management must be implemented to reduce fly vectors but also decrease human contact with refuse, which often contains flying toilets and diapers (Chakraborty 2016) and has severe consequences for drainage systems (Njeru 2006).

Median C. putoria counts were 40% lower when latrines had intact slabs indicates the importance of maintaining latrine infrastructure, particularly for Chrysomya spp, who commonly breed in the contents of pit latrines (Emerson et al. 2005; Irish et al.

2013; Kilpatrick and Schoof 1957; Lindsay et al. 2012; 2013; Schoof et al. 1954; Schoof and Siverly 1954). There is also strong evidence that having roofs and solid superstructure on latrines are important for reducing breeding in filth flies in general and

C. putoria specifically (Irish et al. 2013). In peri-urban Kisumu, where 90% of toilet facilities are considered ‘improved’ by infrastructure alone (ignoring household sharing), our findings indicate that current pit latrines common throughout the study area are not safe in reducing filth flies, a finding supported in a recent review by Nakagiri (2016).

Counts for C. putoria were 4.3 times higher if households were residing in

Nyalenda A than in Obunga/Nyawita communities. Confidence intervals on the estimated effect of locations are large but significant, indicating wide variation in the effects of location. However, of the 3 study areas, only 3% of households in Nyalenda A had access to unshared, improved sanitation and estimated coverages were at 0% in 8 out of the 13 neighborhoods (Chapter 1, Figure 1-11). Conditions in Obunga/Nyawita were not that much better with access estimated at only 6%. Additionally, Nyalenda A is

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the closest to the municipal dump and is the location for waste treatment settling ponds

(Figure 2-8). It is unclear how proximity to these two features may contribute to C. putoria without similar strong effects on M. domestica and M. sorbens, but suggests high attractiveness to concentrations of waste to filth flies. Future research is needed to investigate how latrine characteristics and concentrations of solid waste influence breeding and larval growth as well as attractiveness of larger landscape features to different species of filth flies.

Surface feces are ideal for breeding and feeding M. sorbens, but a significant pattern was not observed in selected models for households that reported no latrine facilities. This is likely because members of these households may use the latrines of neighbors or practice open defecation, which likely occurs away from the compound.

M. sorbens transmission of trachoma should be of concern in other peri-urban areas, but is not currently endemic to Kisumu, perhaps because of the more humid wet conditions. However, trachoma is endemic and shows moderate to high prevalence in drier regions to the north and south (International Trachoma Initiative).

Limitations

The main limitation of all cross-sectional studies is not having longitudinal data that can be used to measure changes over time and across seasons important for understanding diarrheal disease exposure risk. Filth fly populations, like most other insects, are heavily influenced by climate, though patterns vary and are heterogeneous within and across species (Goulson et al. 2005; Nurita and Abu Hassan 2013; Ramesh et al. 2016; Richards et al. 2009). A recent study linked seasonal increases in air temperature to increased filth fly populations that were followed by subsequent peaks in reported incidence of Shigellosis in one year old children in Mirzapur, Bangladesh

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(Farag et al. 2013). Seasonality of filth fly density may also be influenced by competition dynamics with other coprophagus insects such as dung beetles (Ramesh et al. 2016).

In Kisumu, latrine overflow and flooding during the wet season are also likely to effect the breeding cycle of flies breeding in toilets and those breeding on surface feces.

Though many of the latrine conditions where not selected in the best fit models, trends are nonetheless useful for improving our understanding of fly ecology and evaluating sampling methods. Higher median M. domestica counts when feces were absent, slabs were intact or pits were sealed are difficult to interpret. Effort was not made more specifically grade the overall cleanliness of latrines, which likely plays an important role specific to fly vector ecology but also other fecal-oral pathways and risks of household sharing (Heijnen et al. 2014).

While in many ways favorable for collecting or controlling flies, a survey method that relies on attracting flies to assess compound density is dependent on bait attractiveness relative to nearby resources and trapping effort. A study using QS as a sampling method were evaluating them monitoring and control in Florida dairies as compared to other methods (Geden 2005). The two dairies had about 250 and 400 milking animals, and they reported catches of 6-8,000 flies / day / trap. The cumulative sampling effort in this study was about 10 days, approximately 584 flies / day using two traps in 30-minute increments. Overall, since fly densities are typically very high in dairy settings, the QS performed well for monitoring flies, especially for M. domestica. Study of filth fly populations in peri-urban area would benefit from increased sampling effort with prolonged trapping times.

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Another limitation of using the QS traps for estimating densities for all species is evident in the overwhelming fraction of M. domestica in the catches. Fly species composition of sweep net catches in a study of filth flies in a different setting, markets areas of northwest Ethiopia, captured nearly equal numbers of M. domestica (33%) and

Chrysomya rufifacies (32%) and a much higher proportion of M. sorbens (23% , Fetene and Worku 2009). Field observations of large mixed species congregations of filth flies during this study corroborate results from research in Ethiopia. Compositions of flies around small fish harvested from Lake Victoria used in a favorite local dish, omena, were composed mostly or equally of blowflies, likely C. putoria, as compared to M. domestica.

Between species density comparisons from these results should be taken with caution because of possible biases QS baits (Starbar 2012) and the possibility of pheromones in the baits (Durham 2007). Future filth fly research projects would benefit from optimizing effective monitoring, perhaps employing other species specific traps such as funnel (Bunchu et al. 2012; Chaiwong et al. 2012; Hanski 1987), pit latrine emergence traps (Emerson et al. 2005; Irish et al. 2013; Lindsay et al. 2012; 2013), sweep net techniques (Adenusi and Adewoga 2013; Srinivasan et al. 2009; Sukontason et al. 2007) to better understand the role of filth fly diversity in diarrheal disease transmission.

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Table 3-1. List of variables used in Generalized Linear Mixed Models as predictors for fly counts. Variable / Levels Type Description Social Socioeconomic status Continuous Asset index based on Kenya Demographic and Health Surveys 2008-9 Wealth terciles Categorical Ranked household asset score grouped into terciles Poorest Bottom third of households Middle Middle third of households Richest Top third of households Household head education Categorical Highest education level completed None No education to some primary Primary Finished primary to some secondary Secondary Finished secondary Post-secondary All levels of education after completing secondary Household head employed Binary Any type of employment Unsafe during long call Binary Felt unsafe Location Categorical Location of household in Nyalenda A & B sub-locations or in Nyawita and Obunga communities within Kanyakwar sub-location Field conditions Temperature Continous Temperature data from Kisumu International Airport at sampling time, from NOAA weather database Trap location Categorical Waste pile or latrine as determined by grill counts

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Table 3-2. List of sanitation variables used in Generalized Linear Mixed Models as predictors for fly counts. Variable / Levels Type Description Latrine Facility Binary Facility type classified using MDG definitions excluding criteria for sharing Unimproved No latrine, pit latrine without slab Improved Flush latrine, pit latrine with slab, ventilated pit latrine, septic tank, Flush/pour to pit latrine or elsewhere MDG Sanitation Categorical Latrine classification according to MDG definitions No facilities No latrine Unimproved Pit latrine without slab or any facility shared by more than one household Improved Flush latrine, pit latrine with slab, ventilated pit latrine, septic tank, Flush/pour to pit latrine or elsewhere that is not shared with any other households Latrine Smell Categorical Enumerator senses odor from latrine Outside Detected when outside latrine Inside Detected only when inside latrine No Smell No smell inside or outside latrine Latrine Flies Binary Presence or absence of flies Latrine Slab Binary Condition of latrine slab Good Condition Solid and continous Poor Condition Cracked, has holes and/or is broken Feces On Slab Binary Presence of feces on slab Latrine Door Categorical Door to latrine No Door Absent Does Not Close Present but does not close completely Closes Closes completely Latrine Lid Binary Reported as often covered or uncovered with lid

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Table 3-3. Model fit results from testing all combinations of models for compound counts of Musca domestica, Chrysomya putoria, and Musca sorbens. Model BIC Weights M. domestica count ~ 1 + temp + smell + trloc 3466.6 0.355 count ~ 1 + temp + trloc 3467.8 0.200 count ~ 1 + temp + smell + feces + trloc 3469.1 0.105 count ~ 1 + temp + smell + slab + trloc 3470.5 0.051 count ~ 1 + temp + feces + trloc 3471.3 0.034 C. putoria count ~ 1 + temp + loc + slab 882.0 0.118 count ~ 1 + temp + slab 882.4 0.097 count ~ 1 + temp + loc 882.5 0.093 count ~ 1 + temp + loc + slab + lid 882.6 0.090 count ~ 1 + temp + slab + lid 882.9 0.078 M. sorbens count ~ 1 + temp 909.3 0.298 count ~ 1 + temp + trloc 910.5 0.169 count ~ 1 + temp + gray 911.3 0.112 count ~ 1 + temp + gray + trloc 912.8 0.053 count ~ 1 + temp + smell 913.5 0.038

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Figure 3-1. Socioecology of fly populations conceptual model.

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Figure 3-2. Density distribution of fly counts for all species pooled together and counts disaggregated into the 3 major species caught in traps.

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Figure 3-3. Correlations between pooled species counts, the three top species, and the poverty index.

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Figure 3-4. Species and indoor counts by social factors, gray water observed in the compound, location of the Quickstrike trap and temperature at the time of sampling. Improved latrines are as defined by JMP guidelines, but without considering criteria for sharing. Lines represent the upper and lower interquartile ranges with the spaces between those lines representing median values, while dots represent outliers.

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Figure 3-5. Species and indoor fly counts by latrine characteristics, the ‘Flies’ category refers to flies being present in the latrine during enumerator’s visit to the household. Lines represent the upper and lower interquartile ranges with the spaces between those lines representing median values, while dots represent outliers.

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Figure 3-6. Simulated results of best fitting negative binomial mixed models for Musca domestica (panels a and b), Chrysomya putoria (panel c and d) and Musca sorbens (panels e and f). Fixed effects are shown as Incidence Rate Ratios (IRRs) in panels a, c, e and random effects are displayed for panels b, d, f.

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CHAPTER 4 PREPARING FOR BETTER SANITATION: AN ASSESSMENT OF COLLECTIVE ACTION IN KISUMU

Introduction

Benefits of safe sanitation in the private domain cannot be fully realized as long as community sanitation coverage remains low in the public domain (Cairncross et al.

1996). In increasingly dense peri-urban environments, safe, sustainable, collective management of human waste at the household level does not guarantee desired health outcomes unless accompanied by improvements in overall community sanitation coverage. Studies of collective action around sanitation have shown promise for providing sanitation solutions to resource limited and complex peri-urban communities across the globe, ranging from communities in Pakistan (Sinnatamby et al. 1986) and several cities in Brazil (Barreto et al. 2007; Genser et al. 2008; Mara et al. 2010;

Satterthwaite and McGranahan 2006). Improvements in sanitation coverage in these settings were achieved by building low-cost condominial sewerage systems.

Theory behind collective action around management of common pool resources is built largely on the work of Elinor Ostrom and her colleagues (Ostrom 2009; 2012).

Collective action arises and is sustained when a groups of community members are able to organize and manage to achieve a common goal, in this case, improving sanitation conditions. Depending on the resource being managed, this may require the oversight of a governing body, or institutions that may supply resource and support.

Studies of collection management of cover a variety of common pool resources including fisheries, forests, pastures, and irrigation systems (Ruttan 2008).

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Eight design principles that lead to successful collective action emerge from

Ostrom’s work (1990) and are summarized in Table 4-1. Sanitation solutions where collective action has been the most successful is when community efforts are supported by larger private or public institutions including organizations and levels of government.

Co-production occurs when a synergistic relationship between community members and larger societal institutions, typically government, produces sustainable sanitation solutions (Ostrom 1996). Co-production is most successful when there are high levels of trust and clear communication of expectations across involved groups (Ibid.). The imposition of unrealistic national planning standards and regulation without community consultation, often combined with endemic corruption, can undermine residents’ faith in government capability and competence (McGranahan 2015).

Larger social capital elements such as social cohesion and trust between individuals, groups and institutions play key roles in collective action (Bisung et al.

2014). Sociocultural and economic diversity are thought to negatively influence the success of collective action, however the relationships are not clear and dependent on the resource being managed (Ruttan 2008; 2006). In systems with large and diverse users, nested subgroups are necessary to effectively manage resources that may be distributed across a heterogeneous landscape or involve groups with different cultural backgrounds, sanitation norms or WASH-related disease ethnotheories (Brewis et al.

2013).

In Salvador, Brazil, successful city-wide, public programs dramatically improved sanitation coverage, clearly showing how elimination of household sewage streams to open street drains results in reduced incidence of helminth infections (Barreto et al.

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2007). Before city-wide improvements, access to indoor toilet facilities was high overall, at nearly 80%, but nearly 60% lacked connections to sewage or safe treatment. Large numbers of latrines were flushing domestic waste onto the public streets, ultimately into the surrounding surface water. Increases in coverage after the city-wide intervention were also realized by the poorest communities, where disease burdens were highest

(Genser et al. 2008).

Pit latrines are the most widely used form of sanitation in Kisumu’s peri-urban communities, the facilities used by 90% of households in this study. However, nearly all of those facilities are shared and considered unimproved sanitation by current JMP classifications, with community sanitation coverages ranging from less than 10% in some communities to 40% in communities with the highest coverage (Chapter 1, Figure

1-11). Seasonal rains cause lake levels to rise, recharging already high water tables, causing latrines to overflow, flooding fecal contaminants into a disorganized network of open compound drainage systems. Eventually, drainage systems dump contaminants into rivers that empty into an already eutropic lake, a crucial source of income and food for many people in the area.

The absence of city plans and enforcement of development regulations, rapid migration and urbanization outpace the allocation of resources for upgrading and expanding safe and sustainable sanitation services in peri-urban areas. Keeping pace requires high levels of coordination across diverse sectors including water and sanitation, health, transportation, city developmental with local communities (Ostrom

2000). Incentives for tenants to improve sanitation infrastructure in rental properties is

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low, so poor tenant-landlord relationships are paramount to household sanitation improvements (McGranahan 2015).

The aim of this research was to evaluate the potential for collective action on sanitation in three informal settlements in peri-urban Kisumu, Kenya by addressing three objectives: 1) apply collective action design principles to sanitation in peri-urban communities; 2) describe barriers to collective action in this setting; and 3) identify exogenous factors which inhibit collective action on sanitation. This work is guided by a socio-ecological modelling framework, based largely on theory (Ostrom 2012) combining documented community characteristics contributing to the likelihood of overcoming specific barriers (McGranahan 2015) to collective action around sanitation

(Figure 4-1).

Methods

A total of 12 landlord and tenant FGDs and 800 household surveys were completed addressing housing and sanitation conditions in each community. Qualitative data on tenant and landlord relationships in peri-urban Kisumu adds depth to better understanding barriers to collective action around 8 domains including housing, water, sanitation, and household and community solid waste and drainage management.

Quantitative data from household surveys was used to assess distribution of collective action and group memberships along with household WASH conditions aggregated into community neighborhoods based on CHV coverage areas (Methods, Chapter 1).

Qualitative Data

Six landlord and six tenant FGDs addressed housing and environmental sanitation conditions were conducted in meeting places throughout each study area

Nyalenda A & B and Obunga/Nyawita) in July – September 2014. Two tenant FGDS

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and at least one landlord FGD was conducted in each study area with the exception of

Obunga/Nyawita, where two landlord FGDs were held. Discussions were led by a moderator with a note taker and recorded using iPads. The moderator used written prompts for each of the study domains to guide conversation related to collective action and WASH conditions. Landlords FGDs were separated into two categories, one FGD for landlords who reside within the compound with tenants and a second FGD for landlords who do not live in the compounds with their tenants.

Text from translated transcripts of audio and video files was analyzed using directed content analysis performed in MAXQDA software. A hierarchical coding structure based on collective action design principles (Table 4-1) was developed for each domain. Codes for descriptions of problems, roles, responsibilities, conflict and conflict resolution and sanctions were coded openly and added if they were new examples of actions or conditions. Costs of rent, construction, provision of sanitation facilities and services were also coded when mentioned by participants. All codes were applied independently and allowed to overlap within sentences and paragraphs.

Frequencies of coded text and exemplary quotes are presented to indicate priority and salience of topics with each domain. Overlap between categories provided insight on the frequency that codes occur together in the same sentences or paragraphs.

Interpretation of exemplary statements are presented to provide depth of issues around community conditions and collective action problems.

Quantitative Data

Household sociodemographic and health data was collected using the WASH

Disparities household survey questionnaire (Appendix C, Chakraborty 2016), The tool included questions about household assets, respondent characteristics and assessment

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of WASH conditions adapted from recent Kenya Demographic and Health Surveys

(Kenya National Bureau of StatisticsICF Macro 2010). Questions on social capital and cohesion were adapted from the Social Capital Assessment Tool (Krishna and Shrader

1999). Participation in collective action around sanitation was assessed by respondent answers to a question framed by Bisung et al. in a 2014 study conducted in Usoma, a village south of Bandani and the Kisumu International Airport across the Winam Gulf from Kisumu town (2014): “How many times in the past year have you joined together with others to address a common issue related to access to latrines, solid waste management, or drainage in the community?.” Respondents were also asked about their membership in other groups such as Community-Based Organizations (CBOs), savings groups, Community Health Organizations (CHOs), Resident Associations, or faith-based organizations.

Generalized linear mixed models (GLMM) including explanatory variables expected to effect the likelihood of the respondent participating in collective action over the last year, were fit using the glmer command from the lme4 package (Bates et al.

2016; Bolker et al. 2009) in R version 3.3.0 (Figure 3-2). The binomial distribution was specified because the response variable was coded as 0 for never participating and 1 for having participated in one or more collective action events. Model selection was performed using the glmulti R package (Calcagno and de Mazancourt 2010), which automates the analysis of all possible models based on specified response and explanatory variables and probability distribution. Models of best fit were selected based on Bayesian Information Criterion (BIC).

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Fixed effects of explanatory variables including access to water and sanitation, poverty, group membership, community cohesion, household tenure, respondent gender and education level were included in model selection. Sampling clusters

(neighborhoods) were included as random effects, accounting for between- neighborhood effects by modelling intercepts and slopes for each neighborhood.

Coefficients and confidence intervals for fixed and random effects from each model were calculated from simulations of the model of best fit for each fly species count using feSim and reSim functions in the merTools package (Knowles et al. 2016).

Poverty terciles were calculated from the poverty index generated in the

Confirmatory Factor Analysis in Chapter 1. Results for household questionnaires were aggregated to weighted proportions and means for each neighborhood in STATA 14

(StataCorp 2015), then mapped in QGIS (Quantum GIS Development Team) and presented as Pearson’s correlations in scatter matrices created using ggpairs in R (R

Core Team 2016; Schloerke et al. 2016).

Results

Landlord-Tenant FGDs

A total of 919 text segments were coded for domains related to WASH problems and collective action across 6 tenant (442 segments) and 6 resident landlord (469 segments) FGDs. Groups averaged around 7-8 participants, all except one all male landlord FGD had more female participants than men. Landlords described themselves as freeholders who obtained land titles through inheritance or purchase. Several female landlords specified that they were co-owners representing their husbands and others mentioned they inherited their land after the death of their husbands.

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Housing

In FGDs, monthly rent quotes ranged from 1,000 to 3,000 KES ($10 – 30 2016

US) for houses without access to a self-contained latrine. Houses with private access or an indoor latrine causes monthly rent to at least double, ranging from 4,000 to 10,000

KES ($40 – 100 2016 US) and one participant claimed 20,000 KES per month. One participant from Obunga describes housing and rent prices their community:

Do not rate us so high at the level of the rich neighborhoods where one has the ability to part with 3,000kshs for a mud house. The type of houses we build these ends are mud semi-permanent houses and if there owners peg there rental amount at 2,000kshs, then surely the house must be an architectural marvel.

Mud semi-permanent housing refers to the fortifying of mud walls with an external layer of concrete, a common upgrade to prolong the life of the walls of houses and rental properties. Rent was collected by a landlord or an agent assigned by the landlord.

Housing problems most commonly listed by tenants were exposure to diseases, including malaria, measles and cholera followed by collapsing and sinking houses

(Table 4-2). Landlords and repairs were mainly listed as responsibility of the landlord though 42% of tenants said they have some responsibility for maintaining plots, especially regarding refuse and when landlords were slow to respond to disrepair and tenant needs.

In Nyalenda ‘A’ those doing repairs are either landlords or tenants. It depends. Because most of these landlords don’t have the capacity to. I don’t know how they came to build these houses. Then they have gone to the village. They only come for rents at the end of every month. They are not bothered. Some even tell you that if you see any ting wrong, you repair. We have such problems. The plot belongs to them but tenants do the repair.

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Latrines

The latrine domain had the highest number of segment codes, with 219 references to latrines. Much of this text was generated during discussion that produced a lengthy list of problems associated with household use of latrines. The most frequently discussed problems were pit latrines filling and spilling over during the rainy season or when landlords neglected emptying pits (Table 4-2). The second most discussed problem was restricting latrine use to rent paying tenants (Table 4-2). Since provision of toilets was included in the rent price, landlords talked about preventing people outside of the compound from using latrines intended only for tenants living inside the compound. This was accomplished by adding padlocks to latrine doors and distributing the keys to approved users. Landlords referenced unintended use by people passing by, tenants sharing keys to their friends living outside of the compound, and people sneaking in or breaking padlocks to use latrines because their landlords had not provided latrines, as recounted by this landlord:

Toilets are available where I come from. You can build yourself a toilet. The tricky part is that a neighbor landlord without a toilet would not mind crossing over with His or Her tenants to share your facilities. Raising such concerns with him or her; neighbor land lord on the need to construct their own toilets, encourages a heated exchange/ quarrels and conflicts, somewhat the rich verses the poor struggle. Personally speaking, I have rental houses and a complete household. I have also constructed toilets. Therefore, those who do not have toilets are the ones who annoy me.

Both tenants and landlords mentioned the problem of environmental contamination from poor latrine management (Table 4-2), leading to spreading of disease as one tenant participant describes a cholera outbreak:

You know as it has been said that the toilet waste are lifted up, they mix with drainage and can bring meiosis, cholera, and malaria. Because where it goes, it stagnates there. So that brings very many infections. And that is why outbreak of cholera is difficult to stop. It spreads very fast.

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Overall, landlords were referenced as being responsible for providing and constructing latrines as well as repairing them (Table 4-3). The costs of installing a latrine ranged depending type with basic, unlined pit latrines, consisting between 10,000

– 15,000 KES ($100 – 150 2016 US). One landlord quoted 40,000 KES ($400 2016 US) for constructing a two door latrine and a bathroom. More substantial construction often including deep-hole brick or concrete lined “lasting” latrines ranging from 40,000 –

60,000 KES ($400 – 600 2016 US). The construction process was outlined by one landlord:

Construction of a modern Toilet is a very expensive undertaking. The construction of a modern lasting toilet requires more emphasis on standards from the pit-hole below; besides, you also need materials such as wire mesh, cedar poles and concrete; gravel and sand. Once you have these materials and the hole is completed, bricks will be needed for building of the walls, assuming your choice is bricks. Bricks are costly, plastering of the walls and the floor follows. Therefore, the amount is bound to go higher than 50,000 earlier mentioned.

The most expensive private pit latrines were “in-built”, or built within the house were estimated to 150,000 KES ($1500 2016 US). Costs of toilet repairs were much higher than costs of housing repair with estimates ranging from 30,000 – 40,000 KES ($300 –

400 2016 US). Emptying latrines by private exhauster was also costly, ranging from

7,000 – 10,000 KES ($70-100 2016 US), though one landlord mentioned the municipal exhausters which cost 1,500 KES ($15 2016 US). There were other mentions of more dangerous methods of digging new pits adjacent to the filled and manually emptying or draining the contents into the new pit when exhausters could not access latrines:

There are also some places where the compound is so squeezed such that when the pit latrine is full, there is no space even for digging another pit for emptying it. So it is left to collapse and dry up before a digging the same spot for a new one.

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Landlords also quoted the price of building a new latrine, since that was often the most viable option instead of paying for dangerous work.

Maintaining toilet cleanliness was most often the responsibility of tenants (Table

4-3). Tenants were often also those who established usage rules, although this could also be the landlord or agent as well. The most common way of establishing rules was by creating a duty roster amongst tenants that assigned and scheduled latrine cleanings, as described by this participant:

We can do a duty roster but if you do not cooperate during your turn then we prohibit you from using the toilet.

This was also one of the only mentions of sanctions as a penalty for those who did not fulfill latrine maintenance responsibilities. Eviction and forced payment for emptying were other more often mentioned sanctions for latrine misuse. Application of sanctions was often done by tenants and landlords. Conflicts were resolved at the compound level often by discussions between tenants, moderated by landlords (Table

4-3). When disagreements could not be resolved at the compound level, the village elder and chief or assistant chief would be called upon to moderate disputes. One resident landlord described the hierarchy of conflict resolution:

Tenant conflicts can at times prove unmanageable, especially when it involves other plots/neighbors. One of the aggrieved parties can proceed to the village elder. He may opt to call the quarrelling parties as an administrative mediation effort and as such cascade up the line of authority to the Assistant Chief and eventually to the Chief depending on the magnitude. They are the established agents of peace we have around.

Monitoring latrine provision was expected from the government, but there no mentions of schedules or identified government officials responsible for surveillance of latrine provision or inspection of facilities. One tenant referred vaguely of an administration that advised tenants:

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The plot belongs to them but tenants do the repair. We had also been warned by the administration not to rent a house in a plot without toilet. So the tenant on fearing such orders makes a temporary pit latrine and promises to build a permanent one. Now it is one door and the plot has over fifteen families and at times it is the same toilet being used as bathroom. So you go in shifts.

Solid waste

The domain of solid waste was referenced in 141 text segments across all FGDs.

The main problems revolved around not having specified and acceptable locations for solid waste collection, which led to littering throughout the communities and feelings of disgust, as expressed by one tenant:

What I can say is that let the community be clean, Dunga as a whole needs to be clean. Not like now that there’re heaps of garbage which is very filthy. When you pass by the road you can even vomit.

The most accepted and regularly used disposal method was burning of solid waste.

Landlords more often assigned this duty to themselves, though tenants were usually expected to do their part, mostly by collecting and keeping the plots clear of waste

(Table 4-4). Among tenants, the responsibility was divided between landlords and tenants. A good example of this was outlined by a landlord:

On the same subject, like I mentioned earlier, I live in my home and have leased part of it to the tenants. I have given them instructions to only pour water into the drainages, while other physical rubbish like polythene is poured on open ground, where it’s spread to dry under the sun. Using a stick and a wreck, I help them in drying and later on burning.

Other services were mentioned as options in certain communities, including references to community or youth groups that provide bags and then came around for regular pick up. Costs of this service ranged from 20-50 KES ($0.20 – 0.50 2016 US), depending on if it was a plastic bag or a larger container. One tenant referenced a plastic collection and recycling program:

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There’s a project for plastic. Like my sister who has started, she is the one who knows what she gains in it. Now, we need to find a way of utilizing garbage. Thing like tissue papers can be burnt then we are given somewhere to put plastic wears.

Participants reasoned that the government was ultimately responsible for removal of refuse in the communities. One landlord summed up the issue as problems interact across sanitation domains:

It is a problem because I may not have a place to dispose. I throw at so and so place. For those who do not have toilets, you can see a cellophane bag somewhere, only to find human defecation in it. So I request this way, the government to assist Nyalenda. We have a problem there can be cholera outbreak anytime.

Human waste was regularly described as part of solid waste streams, mainly due to disposal of diapers (pampers) and flying toilets in refuse piles. Flying toilets are a quick solution to not having access to a latrine, and involves defecation into a bag that is deposited at the user’s convenience, many ending up in refuse piles or clogging latrines and drainage. They are part of a broader problem of environmental contamination, which was referred to as the most common solid waste problem, followed by non-participation in collection or site designation efforts and disease transmission (Table 4-2).

Conflicts over solid waste management are often over the placement of personal refuse from the private sphere into the public sphere. One tenant describes this violation of boundaries:

At times his compound is very clean and even has a warning at the gate written MBWA KALI but he still doesn’t have an appropriate way of disposing waste. While on the other hand we strive to make our community clean to evade cholera outbreak. Now when it rains children run on bare foot, they’ll end up stepping on polythene bags with garbage.

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Conflicts were resolved much in the same way as latrine disputes. First solutions are sought at the compound level between tenants or landlord and tenants (Figure 4-4). If these efforts do not resolve the issue, or if disputes are between landlords, then village elders maybe consulted followed by the chief or assistant chief. There was one reference a piece for involving police or government administration.

There were two references to sanctions for not disposing of refuse properly. One was the eviction of non-complying tenants. The second was one tenant’s plea for better enforcement of laws by local authorities:

I concur with what my sister has said. My request is directed to administration. Particularly the Asst. chief that there should be laws dealing with those who just drop their trash anywhere. That will make them afraid of fines or arrest. That can help to a certain extent.

Drainage

A total of 190 text segments were coded as a reference to drainage. The most often talked about problem for drainage was flooding (Table 4-2). At 58 coded segments, it was the most discussed topic across all FGDs and domains. Flooding becomes a problem during rainy seasons when absence and disorganization of the drainage system is filled with rainfall. Wetlands fill with water, backing up water into the lower elevation areas. Flooding was caused by both swelling of rivers and to accidental diversion of water into a compound, causing displacement of tenants and destruction of property, with no compensation for damages. Sometimes lack of solid waste disposal was listed as a cause for clogged drainages that lead to flooded households. This was one landlord’s description of a problem at their compound:

Concerning drainage, it is challenging because these neighbors behind me are used to scattering their garbage and any time I ask them over it, they start quarreling when it is clear to them that my compound get flooded because of their act of blocking drainage systems.

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Participants most often responded to questions about drainage simply because often there is no recognizable system of drainage at all:

We do not have drainages. When it rains, the flowing waters find their natural pathways. The drainage system is so poor. If we had proper drainages, our situation would really improve.

The drainages that are present are part of a large network of drainages constructed by landlords or tenants for their own individual dwellings. Tenants often took the installation upon themselves:

Actually we dig drainage by ourselves. Even as we are talking, I have assigned someone to dig a trench in the compound in which I stay. If you don’t do that, the landlord is not bothered. Some people are still removing water from their houses as we are talking, and it rained three days ago. You only see them with jembes [hoes] when it rains. There are no proper drainage systems.

A resident landlord explained how extensive flood damage can be to temporary housing:

Currently, the rains have destroyed our houses. Once the walls are damaged, water quickly gushes in. Flooding rain water is now a real challenge. Our very own houses are at risk, tenant houses are therefore not exceptional. One retires to bed, only to be surprisingly woken up to the harsh reality of a collapsed house.

There are differences in who was perceived as responsible for drainages in

FGDs. Tenants considered themselves responsible, mostly because there was no clear expectation for landlords to do it for them. However, landlords held themselves responsible for providing and constructing drainage (Table 4-5). In this domain, more than others, government was considered responsible for channeling waterways and maintaining roadside drainages as explained by one Obunga resident, who compared the situation with one river to one of the rivers in Nyalenda:

Once the [Awaya] river is full, the water overflows to the houses. The river is small; therefore if the government would consider its expansion,

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especially to the level of River Auji, it would be a great relief to us; less problems.

Despite references to government responsibilities the only reference to sanctions were to declare them absent, as these two tenants expressed:

Yes, then throw in the drainage meant for water only. When it rains, the water then is blocked. Hygiene is not good because there is no law guiding that. So people are at will to do what they feel is good for them.

Here people do not respect each other’s’ property. This is because we have no laws guiding us. So if you want peace, you keep silent.

Conflict resolution was dealt with by landlords and then involved the chiefs and village elders, if disputes could not be settled at the compound level (Table 4-5). Often participants expressed that there was no one to got to resolve drainage issues.

Household Survey

The majority of households surveyed during this study, were tenants in 82% (CI

76 – 86%) of households in Nyalenda B, 86% (CI 74 – 93%) in Nyalenda A up to 90%

(CI 83-94%) in Nyawita and Obunga. Only 13% (CI 10-15%) of respondents said they participated in collective action around sanitation at least once during the 12-month period preceding the survey. Respondents that said they came together to address sanitation did so an average of 4.4 (CI 3.1 – 5.7) times during the previous year. The highest proportion of collective action was in neighborhoods located near the Awaya

River in Obunga and Nyawita with three neighborhoods ranging between 30-40% participation (Figure 4-1).

Group membership was much higher with 59% (CI 54 – 64%) of respondents participating in at least one group (Figure 4-2). Of these respondents, membership was most often in Women’s (25%, CI 21 – 30%), savings (25%, CI 22 – 29%), and church groups (20%, CI 14 – 28%). Membership in community sanitation (2%, CI 1 – 3%),

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resident associations (2%, CI 1 – 4%), and health committees (< 1%) were much lower.

Proportions of group membership were higher in communities across Nyalenda, particularly in Nyalenda B (Figure 4-1).

The overwhelming majority (92%) of respondents were female (Figure 4-2). Over

50% of all respondents had lived in their homes between 1-5 years, followed by those with tenures of 5 years or more (27%) and less than 1 year (20%). The majority of respondents did not feel like bonds between members of the community were particularly weak or strong (Neutral, Figure 4-2). However, more felt that their personal ties to community members were close or not particularly close or not close (Average,

Figure 4-2) than not close. Proportions of respondents who participated in collective action around sanitation at least once during the previous year did not differ significantly by social cohesion, education level, group membership, poverty, sanitation or water source (Figure 4-3)

Two models had best fit scores within 2 BIC scores of one another (Table 4-6). In the model with the best fit tenure of stays longer from 1 – 5 year and more than 5 years were showed higher effects for predicting respondent collective action than those who had stayed in their home for less than 1 year (BIC = 558.6, 1.4  0.4 SE, p < 0.01 and

1.4  0.5 SE, p < 0.01, respectively, Figure 4-4). The second model of best fit included gender in addition to tenure as mixed effect predictors (BIC = 559.7, Table 4-6).

Coefficients for gender predicting collective action in the previous year were lower for females than for males (-0.84  0.3 SE, p < 0.05). In this model coefficient estimates were slightly lower but significant for people with tenures greater than 5 years (1.3  0.5

SE, p < 0.01) and were the same for tenures between 1-5 years (Figure 4-4). In both

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cases, random effects from one neighborhood in Obunga/Nyawita were significant and located near the Awaya River.

Community proportions of collective action and group membership were only weakly correlated (r = 0.12, Figure 4-5). Both community proportions of participation in sanitation collective action and group membership were moderately correlated with community proportions of household flooding (r = 0.34 and r = 0.40, respectively). This relationship was particular strong in communities along the Awaya river in Obunga and

Nyawita, many of which also had at least 50% of households in the highest poverty tercile (Figure 4-5). Community proportions of participation in sanitation collective action and group membership were also moderately correlated with means of households sharing latrines, but in opposite directions (r = 0.28, r = -0.47, respectively, Figure 4-5).

Community group membership was also negatively and moderately correlated with community proportions of tenants (r = -0.39)

Discussion

Potential for Collective Action

The current sanitation realities of Kisumu of landlords and tenants reflect the chaotic mosaic of development that has plagued peri-urban Kisumu for over 100 years

(Chapter 2). The provision, management and regulation of household and community sanitation takes place at the compound level, where most FGD participants say that issues around housing, latrines, drainages, solid waste take place. When disputes cannot be settled within the compound, village elders and chiefs are asked to help resolve conflict. In a very few instants, police were mentioned as the conflict moderators if chiefs and elders could not resolve issues. Expectations for government involvement were not mentioned often during FGD discussions, revealing a more autonomous

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system of monitoring and conflict resolution around sanitation issues in peri-urban communities. Though there are many individual efforts to deal with the lack of environmental sanitation in communities, success will be determined by the formation of functional community-government partnerships to address all aspects of sanitation needed for a healthy urban environment.

Both landlords and tenants agreed that access to private sanitation facilities would likely double or triple the price of rent for access to private household latrines.

Reports of rent prices from a study of latrine construction in peri-urban Kampala ($125 –

1045 US , Bwaise III ) and Dar es Salaam ($200 – 855 US, Temeke), were very similar to prices participants mentioned peri-urban Kisumu ($100 – 1,500 US, this study), with the same magnitude of price increase determined by levels of pit latrine infrastructure upgrades and private usage (Isunju et al. 2013).

There were no mentions of eco-san composting toilets or usage of communal latrines as alternatives to locked or filled toilets. In the SImiyu study, people were not willing to travel to one of the few communal toilets dispersed across the study communities to pay to defecate (2015). Composting toilets were not viewed by residents as a viable long-term solution for rapidly urbanized areas with minimal agricultural.

Simiyu also found that revenues from communal latrines were largely generated from more highly used bathing rooms, which were far more popular, indicating differences in how people view communal sanitation.

Even though residents in Simiyu’s study would not overcome distance and usage fees for sanitation, residents throughout peri-urban Kisumu regularly pay for water at public standpipes installed and supplied with treated water by the Kisumu Water and

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Sewerage Company (KIWASCO). KIWASCO is an independent water utility that provides water infrastructure and standpipe connections to many locations throughout peri-urban Kisumu. Operation and distribution of water from standpipes are the job of contracted local vendors who may have purchased the new connection or must fulfill obligations to expand water pipes (Ayalew et al. 2013). The KIWASCO – vendor relationship is a good example of a collectively managed system that has well defined roles and responsibilities as outlined in a contract between providers and vendors and fixed, flat-rate tariff that customers pay per 20L of water. Vendors have autonomy in when and how they decide to sell water and this seems to work well enough to bring access levels up near universal coverage levels across these communities.

The high frequency of mentions of flooding and high tenant and landlord involvement in creating and managing the drainage system, across all FGDs, could indicate high motivation for co-produced government-community partnerships that produce solutions for the construction and maintenance of drainage systems in the area

(Ostrom 1996). Thirty percent of respondents said that water entered their home sometimes or often. High proportions of households that said their homes flooded (>

40%) are located near rivers and by the lake in all 3 study areas (Figure 4-6). Landlords and tenants living in these neighborhoods would benefit most from a relationship with local and county government that would provide resources and expertise in exchange for labor and community oversight of construction and maintenance of a well-designed and maintained drainage system, safely channels water and contaminants from higher elevations into rivers and Lake Victoria and out of household domains.

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Collective Action in Peri-Urban Kisumu

Selected GLMM models demonstrate a positive relationship between living in a dwelling and community for more than one year and participating in collective action around sanitation (Figures 4-3 & 4-4). However, relationships between indicators for social cohesion and group membership and collective action did not account for significant variance in the data and were not included in any of the top 3 models during model selection (Table 4-6). Recent research in Usoma, a rural village near Kisumu on

Lake Victoria explored the distribution of collective action and found components of social capital, such as cohesion were positively associated with collective action around water and sanitation (Bisung et al. 2014). Group membership and trust were found to be significant in the likelihood of reporting involvement in collective action.

In this study only weak correlations between neighborhood group membership and social cohesion and collective action were found (Figure 4-5). Group membership in

Kisumu peri-urban communities were tied closely to financial support groups, which may have very different goals than groups that address community sanitation.

Nonetheless, duration of time living in the community are likely correlated with accumulation of social capital and higher feelings of closeness and trust with community members. Future research should include a more comprehensive and detailed description of social capital to better understand how they relate to sustained collective action.

A second GLMM with BIC values just slightly greater than the best fitting model suggests that participation in collective action may be influenced by gender, with higher participation mentioned by male respondents than female respondents. While the gender of respondents was highly skewed in favor of females, of the 8% of respondents

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who were male, nearly 30% of them said they participated in collective action around sanitation. Though this estimate has wide confidence intervals indicating high variation, this a result that should be considered in future study of collective action by including a more balanced sample of men and women. The fact that gender influences collective action is not surprising given the gendered WASH landscape of these communities, where high levels of insecurity, violence and psychosocial stress while accessing water are felt disproportionately by women than by men (Chakraborty 2016). However, there was no mention of gender roles regarding collective action around sanitation in the

FGDs, which also lacked probes on gender roles in relation to collective action and group membership.

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Table 4-1. Description of collective action principles in the context of the WASH Disparities Study.

Collective Action Design Principle (Ostrom Description Examples 2012) Latrine placement and privacy, refuse Spatial delineation of the specified place where WASH Physical boundaries collection sites, public and private activities are allowed to take place. sanitation. Defined responsibilities of actors in usage activities of Tenant cleaning schedule, landlord Roles and Responsibilities WASH infrastructure including specified roles for housing repairs, and government participating in management and maintenance. drainage upkeep. Benefits of investing resources in management of Benefits from collective management WASH to mediate risk of exposure to contamination Reduced disease burden and flooding, system during daily life outweigh perceived and actual costs in and improved community aesthetics. time and resources necessary for safe collective use. Formulation of management rules, established by Community feedback and discussion, cooperative by governing institutions, strongly Collective-choice arrangements government forums, and community influenced by participation and experiences of WASH resource mapping. users in the community. A systems of monitoring and frequent assessment of Sanitation facility inspections, WASH Monitoring adherence to rules and environmental health needs assessments, or household conditions, changes in land-use and demographics. visits by CHVs. Opportunities to resolve conflicts by an agreed upon Meeting with the village elder, police Conflict resolution process that can resolve issues by agreements or involvement, or legal action. applying sanctions for violators. A clear system of graduated sanctions that applied to Eviction, redefined roles, restricted Sanction deter violation of maintenance and provision rules. use, or a fine. Community-Based Organizations Decision making and monitoring accomplished by Larger systems with nested subgroups reporting to CHVs, organized by the nested groups across levels of management. Ministry of Health. Acknowledging and ensuring the user has a right to Right to safe water and sanitation or Recognition user rights use a resource and have a voice in its collective health equality. management.

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Table 4-2. Number of times each problem was referenced and frequency of response in tenant and landlord FGDs.

Problems Tenants Landlords Total Housing Disrepair 14% - 1 Collapse 29% - 2 Diseases 57% - 4 Total 100% - 7 Latrines Filled toilets 13% 19% 22 Unintended users 3% 14% 12 Environmental contamination 10% 4% 10 No access 12% 1% 9 Sinking or collapse 1% 3% 3 Child exposure 1% 3% 3 Sharing 4% 0% 3 Affordability 0% 3% 2 Other 2% 4% 5 Total 47% 52% 69 Solid Waste Environmental contamination 27% 5% 7 Non participation 14% 5% 4 Diseases 9% 5% 3 Child exposure 5% 5% 2 Diapers 9% 0% 2 Other 0% 18% 4 Total 64% 36% 22 Drainage Flooding 31% 24% 58 Blocked by refuse 4% 8% 12 Individual disputes 4% 6% 10 Other diseases/infections 6% 0% 6 Child exposure 5% 0% 5 Road drainage 1% 3% 4 Diverted improperly 2% 1% 3 Cholera outbreak 2% 1% 3 Environmental contamination 2% 2% 5 Total 56% 44% 106

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Table 4-3. Number of times a person was referenced as responsible for a task and acting as conflict moderators for housing and latrines. Frequency of responses in tenant and landlord FGDs.

Collective Action Principle Tenants Landlords Total Housing Responsibilities 54 Plot maintenance Tenants 32% 11% 8 Landlords 32% 26% 11 Total 63% 37% 19 Repairs Tenant 14% 0% 5 Agent 6% 3% 3 Landlord 43% 34% 27 Total 63% 37% 35 Latrines Responsibilities 59 Cleanliness Tenants 36% 39% 21 Landlords 7% 18% 7 Total 43% 57% 28 Repairs Landlords 44% 33% 14 Tenants 17% 0% 3 Agents 0% 6% 1 Total 61% 39% 17 Provision Landlords 43% 36% 11 Tenants 7% 7% 2 Government 0% 7% 1 Total 50% 50% 14 Conflict moderators Landlord 9% 31% 13 Tenants 16% 0% 5 Village elder 0% 13% 4 Chief 3% 9% 4 Compound meeting 6% 3% 3 Police 3% 3% 2 Agent 0% 3% 1 Total 38% 63% 32

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Table 4-4. Number of times a person was referenced as responsible for a task and acting as conflict moderators regarding solid waste management. Frequency of responses in tenant and landlord FGDs.

Collective Action Principle Tenants Landlords Total Roles and responsibilities Collection Landlords 8% 21% 7 Tenants 13% 17% 7 Government 4% 8% 3 Agents 4% 4% 2 Others 17% 4% 5 Total 46% 54% 14 Burning Tenants 27% 40% 10 Landlords 7% 27% 5 Total 33% 67% 15 Cleanliness Tenants 67% - 8 Landlords 17% - 2 Others 17% - 2 Total 100% - 12 Conflict moderators Landlord 25% 8% 4 Village elder 0% 17% 2 Chief 17% 0% 2 No one 8% 0% 1 Tenants 8% 0% 1 Police 0% 8% 1 Government 0% 8% 1 Total 58% 42% 12

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Table 4-5. Number of times a person was referenced as responsible for a task and acting as conflict moderators regarding water drainages. Frequency of responses in tenant and landlord FGDs.

Collective Action Principle Tenants Landlords Total Roles and responsibilities Provision / construction Tenants 50% 0% 9 Landlords 0% 28% 5 Government 11% 11% 4 Total 61% 39% 18 Maintenance Tenants 38% 8% 6 Landlords 8% 15% 3 No one 15% 0% 2 Residents association 8% 0% 1 Government 8% 0% 1 Total 77% 23% 13 Conflict moderators Landlord 11% 22% 6 Chief 6% 17% 4 No one 0% 17% 3 Village elder 6% 11% 3 Tenants 6% 6% 2 Total 28% 72% 18

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Table 4-6. Model selection results from models predicting respondent participation in collective action.

Fixed Effects Model BIC Weights collective action ~ 1 + tenure + 1 | neighborhood 558.6 0.57 collective action ~ 1 + tenure + gender + 1 | neighborhood 559.7 0.32 collective action ~ 1 + tenure + on-plot water + 1 | neighborhood 564.4 0.03 collective action ~ 1 + tenure + group + 1 | neighborhood 565.0 0.02

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Figure 4-1. Map of proportions of reported respondent participation in collective action around sanitation and group membership during the year prior to being surveyed for each study neighborhood.

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Figure 4-2. Summary statistics for collective action and predictors included in model selection. Error bars represent upper and lower 95% confidence intervals. Respondent sample sizes were N = 795 for all predictors except N = 756 for tenure and N = 793 for poverty terciles. This discrepancy is due to missing values in the data.

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Figure 4-3. Proportion of respondents who said they participated in collective action around sanitation once or more in the last 12 months by categories of predictors variables included in model selection. Error bars represent upper and lower 95% confidence intervals. Respondent sample sizes were N = 795 for all predictors except for tenure (N = 756) and poverty terciles (N = 793). This discrepancy is due to missing values.

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Figure 4-4. Proportion of respondents who said they participated in collective action around sanitation once or more in the last 12 months by categories of predictors variables included in model selection. Error bars represent upper and lower 95% confidence intervals. Respondent sample sizes were 795 for all predictors except for tenure (N = 756) and poverty terciles (N = 793). This discrepancy is due to missing values.

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Figure 4-5. Correlation scatterplot matrix of selected neighborhood proportions of social and sanitation outcomes and mean poverty scores and their association to respondent collective action and group membership.

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Figure 4-6. Map of percentage of households that reported water entering their homes after rains and fraction of surveyed households that participated in collective action around sanitation at least once in the last year for each community neighborhood. Green circles around a community indicate that more than 50% of households in that community were in the bottom third poverty tercile.

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CHAPTER 5 CONCLUSION

A history of structural violence, persistent poverty, and environmental change has resulted in unequitable distributions of social, financial, and ecological resources underlying current disparities in worldwide burden of preventable, diarrheal diseases.

Ensuring water and sanitation for the world’s population by eliminating between and within country inequalities are explicitly addressed as Goals 6 and 10 in recently drafted

Sustainable Development Goals (SDGs) for 2030 (United Nations 2015b). Today, Sub-

Saharan Africa faces the largest share of both estimated child diarrheal mortality and sanitation-related disease (Liu et al. 2015; Prüss-Üstün et al. 2014). High rates of urbanization and population are a challenge to global health, leading shifts in the nature of and location of poverty and disease burden that are difficult to anticipate without a detailed understanding of the broader historical and socio-ecological context.

Peri-urban Poverty and WASH

Over the last 4000 years, growing human population density and access to arable land has led to a long history of migration and urbanization in the Great Lakes region for the last 120 years. Despite this long-standing population trend, colonial and independence governments have failed to address needs for appropriate housing and infrastructure. Since 1971, all peri-urban communities are now located within official municipality boundaries, however these communities are still lack urban planning and infrastructure, reflecting origins as communities defined racially-biased borders meant to separate the ‘unsanitary’ African laborers from affluent Block A residences (Figure 2-6).

Today, the majority of Kisumu’s population faces the legacy of the city’s first ‘unsanitary’

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citizens (Briggs 2003), many living in the same types of housing of those early urban of

Block C pioneers.

Overcoming this history means massively expanding and upgrading sanitation services and urban infrastructure to the networks of dirt roads and paths twisting through a bright and vibrant, but congested landscape of plots, housing compounds, shops, and meeting places. In Kisumu, the few permanent houses and apartment buildings dot a landscape of largely U and L shaped temporary and semi-permanent mud and pole or corrugated steel structures divided into 1 or 2 room apartments that range anywhere from a few up to 20 or more households. Compounds with 10 or more households often share 1-2 latrines and a shower, with long waiting lines, compromising the morning routine for school children and adults who must keep coveted employment.

The drainage system is a mosaic of independent compound schemes managed by individual landlords or ambitious tenants. In the absence of solid waste services, garbage piles occupy empty spaces between compounds, empty lots and line streets and drainages.

Major differences by poverty were not as apparent within populations of peri- urban communities as what might be expected in a city-wide sample of WASH conditions, which would include the wealthiest households in Kisumu. This was particularly noticeable in minimal differences between poverty estimates of WASH conditions, filth fly density and collective action indicators for the poor, middle and rich households in summary statistics and model results. It is likely that dividing a sample of

800 households living in generally similar social and ecological environments into 3 even terciles of poverty skews the fact that everyone is living in neighborhoods with

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improved sanitation coverages that are less than 40% with a dismal average of 8% across all study neighborhoods.

This means that even the ‘richest’ in these populations, who can afford to build walls and install private septic systems, are still likely exposed to wide spread fecal contamination by daily interactions with a landscape riddled with liquid and solid waste, open defecation and poor drainage. During the rainy season, already high water tables rise, causing pit latrines to overflow. As a result, contaminated surface waters flood compounds during seasonal rainfall events, creating conflict between community members as well as moving pathogens down elevation gradients.

Independent of household wealth or income, institutional support and governmental resolve could greatly improve the quality of the lives for the urban poor, especially in Kisumu and throughout Sub-Saharan Africa. Types of government actions that could help develop these communities include enforcing existing housing and sanitation laws, eliminating corruption (Lindner 2014), increasing fair policing, and further developing and implementing urban plans that build or improves public infrastructure, such as affordable schools and health facilities. Healthy urban environments relieve burdens of stress and disease bringing us steps closer to enabling productive and fulfilling lives for all.

Fly-Proof Sanitation

As the world moves forward with a new development agenda through recently formulated Sustainable Development Goals (United Nations 2015b), there is emphasis on going beyond basic sanitation by moving people up the ‘sanitation ladder’. The ultimate and most effective goal in high density peri-urban settings in Kisumu and even more so in mega cities, is linking everyone piped water and sewage. Until that is

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possible, emphasis should be placed on improvements in latrine infra- and superstructure such as building VIP latrines to reduce smell and fly populations (Levine and Levine 1991) especially in peri-urban settings as found in Kampala, Uganda

(Nakagiri et al. 2015). Improving recommendations for latrine superstructure and ventilation are critical as only 2.5% of households had access to a VIP latrine in this study. Equally important is ensuring behavioral components, such as keeping doors closed (Dumpert et al. 2012) and latrine cleanliness (Nakagiri et al. 2015) are in place to maximize effectiveness of improved sanitation.

Opportunities for Sanitation Solutions in Peri-urban Kisumu

The Kisumu Municipal Council is ultimately responsible for city planning and provision of water, empting of pit latrines and sewage systems as well as environmental sanitation services such as solid waste collection and disposal (Maoulidi 2010; Simiyu

2015; UN-Habitat 2006). However, provision and maintenance of drainage in Kisumu is now the responsibility of the Lake Victoria South Water Services Board and KIWASCO.

Monitoring sanitation provision in Kisumu is a problem stemming from failure to enforce existing laws and in excluding basic, unlined pit latrines as an acceptable form of sanitation in Kisumu due to their poor suitability and stability in black cotton soils with high water tables (Maoulidi 2010).

In 2014, the Kisumu Integrated Strategic Urban Development Plan (ISUD) was released a plan to expand water and sanitation infrastructure in the greater Kisumu area through a collaboration between international aid organizations and the Kenyan government (Kenya Government et al. 2014). With support from the French government, large sewerage trunk lines are to be installed through Nyalenda and

Obunga and Nyawita communities. It is unclear how connections will be made to the

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main sewerage trunk lines, but lessons can be learned from successful implementation of condominial sewerage systems in Karachi (Sinnatamby et al. 1986) and a few

Brazilian cities (Barreto et al. 2007; Mara et al. 2010). In these examples, users participate in the planning, implementation and investment of simplified networks of shallow-dug sewage lines that are partly maintained by communities but are connected to larger sewerage trunk lines maintained by the municipality.

If similar condominial sewerage or collective solutions to solid waste and drainage management are to be implemented in Kisumu, it must involve freeholding landowners, landlords and tenants and involve the village elders, traditionally involved in community decisions and conflict resolution. Regulations and sanctions should be clear and enforced by municipal oversight that includes local chiefs. Momentum from efforts of local solid waste removal groups and community group such as the Nyawita

Residents Association (RA) should be supported and welcomed into the design of future sanitation plans. Changes to tenure laws should be altered so that the Municipality can extend some control over city planning necessary for extending sewers and environmental services to peri-urban communities (2006).

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BIOGRAPHICAL SKETCH

John joined the University of Florida during the summer of 2006 as a graduate student in the School of Natural Resources and the Environment (SNRE). John was born in Elizabeth City, North Carolina while his parents, John and Marcia, were vacationing in the Outer Banks. He grew up exploring the hills around Hurricane Creek in Teays Valley, West Virginia, where he developed a keen interest in natural history and ecology.

After graduating from Winfield High School in 1997, John attended Virginia

Polytechnic Institute and State University studying biochemistry for one year before transferring to Shepherd College in Shepherdstown WV to study biology and environmental science. In 2002, John graduated Cum Laude with a degree in Biology and minor in Environmental Studies. Anxious to explore the world and continue studying ecology, he became a research assistant for a study on the effects of forest fragmentation and feeding habits of critically endangered Diademed sifakas

(Propithecus diadema) in Tsinjoarivo, Madagascar.

He returned to the United States in August of 2003 and began a position as lab manager for the Psychology of Voice and Sound Research Lab, led by the late Dr.

Michael Owren and his graduate student, Dr. Erik Patel, where he worked on the acoustics of human and non-human primate vocalizations. Dr. Owren developed a collaboration with Dr. Sue Boinski, a professor of Anthropology at the University of

Florida on a project that looked at percussive foraging behaviors in brown capuchin monkeys (Cebus apella apella) in Raleighvallen, Suriname. John was selected to execute a research project on the acoustic characteristics of sites percussed by capuchins and the role this may play a signal for sexual selection.

169 After completing a year of data collection, John enrolled as a Master’s student in

August 2006 in SNRE, under the advisement of Drs. Sue Boinski, Micheal Owren, and

Steve Phelps. He completed his masters these entitled “Hearing Monkeys through the

Trees an Acoustic Evaluation of Branches as a Medium for Capuchin Communication”, graduating in 2008 with a Master’s of Science. Following interests in ethnoecology and agriculture, he shifted research programs from primate to human ecology, John enrolled as a Ph.D. student in SNRE under the advisement of the late Dr. Hugh Popenoe, and

Drs. Francis “Jack” Putz and Peter Frederick, and began a study of grazing ecology on

Kanapaha Prairie in Gainesville, Florida.

After 2 years of Ph.D. coursework, John was hired as a Research Coordinator for

Dr. Richard Rheingans’ lab studying global health disparities in diarrhea diseases. He immediately enjoyed applying his background in ecology and anthropology to global health. In 2011, he re-enrolled in his Ph.D. program in SNRE as a full time employee for the Department of Environmental Global Health. In July of 2014, John was sent to

Kisumu, Kenya to manage the WASH Disparities Study that has now, in addition to his own, led to two other Ph.D. dissertations.

170