Risk Perceptions As Potential Mediators of Environmental Toxicants Associated with Biomass Fuel Use

RISK PERCEPTIONS AS POTENTIAL MEDIATORS OF ENVIRONMENTAL TOXICANTS ASSOCIATED WITH BIOMASS FUEL USE

DAVID THORNE DILLON

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

2020

To my mother, father, brother, and granny

ACKNOWLEDGMENTS

I would like to thank first my mother, brother, father, and grandmother for their help and support during these years in graduate school and during fieldwork in Zambia.

Without their support, none of this would have been possible. Your visits here to

Gainesville were always such a welcome and fun break from the sometimes repetitive routine of graduate school. My mother and brother’s visit to Zambia halfway through fieldwork was a highlight of my time there and much needed. I can always count on them to lend an ear or offer advice, particularly my brother, on anything data-related.

Fieldwork and the recent pandemic have had the fortunate side effect of all of us talking much more frequently, something that I would not change for the world.

I have been lucky to have the guidance and support of wonderful advisors, Drs.

Alyson Young and Chris McCarty. Alyson had the misfortune to edit draft after draft of the following chapters and made them all the better for doing so. A big thank you is owed to Dr. Joseph Bisesi for introducing me to toxicology and allowing work in his lab.

Joe and Amanda Buerger facilitated the environmental data collection, bringing equipment with them to deliver to be after they attended a conference in South Africa.

Dr. Pete Collings provided great feedback and help on projects in the years preceding and then during this research. You can always count on Pete to have a good backyard fire and to provide some local beer. Special thanks to Dr. Adrienne Strong for assisting in helping me out last minute when she did not have to. Additionally, I need to mention both Drs. William Alexander (UNCW) and Dan Temple (George Mason) for initially inspiring me to pursue graduate school, take me to my first conferences, and making anthropology so interesting.

This work could not have been completed without the dozens of people who helped assist this research in both Lusaka and Chipata. First to Charles and Frida

Bwenge for giving me a home to stay upon my arrival to the country, as well as Paul and Marjatta Psychas for providing a welcoming home at the beginning and end of my time in Zambia. Drs. Alice Ngoma and Nosiku Munyinda were invaluable in helping navigate the University of Zambia and providing feedback and guidance while there.

Similarly, a debt of gratitude is owed to the University of Zambia’s ethics board for approving this work as well as the National Health Research Council for their support of the work in-country. The provincial health environmental officer, Bernard Khoza, introduced me to the faculty of the clinics and the laboratory services of Chipata

General Hospital. Thank you to the microbiology laboratory in the hospital for allowing me to store personal and environmental samples in their -80° freezer during the last portion of this study. The healthcare clinics’ staff and volunteers Samuel Nyrienda,

Eunice Tembo, Julian Kawele, Anna Banda, and Muyangu Chirwa, who aided in data collection and translation, rain or shine, and were the ones that made this research possible. There also cannot be enough thanks specifically for Monica and Emelia Banda for providing a loving home and company during my time living in their house (as well as invaluable advice and delicious food).

The friends I have made during my graduate school years at the University of

Florida need to be acknowledged. Joshua Crosby, Choeeta Chakrabarti, Faith Lambert,

Amanda Buerger, Alexis Wormington, Hailey Duecker, Catrina Cuadra, Christie

Washer, and too many others to name. Your support during the sometimes stressful and fun periods of grad school has truly made this a special experience that will not

ever be forgotten. Joshua and Choeeta provided my home away from home during the interesting time of COVID-19, and I truly do not know what I would do without them. The support of my coach Donald Smith and my teammates at Team Florida Gainesville

Weightlifting cannot be overlooked as he is responsible for making sure my posture did not completely deteriorate during the writing of this dissertation. Remy Tamer, Jack

Whitmarsh, Willy Huang, Eric Johnson, Kurt Kochanski, Joe Nugent, Jordan Eicher, and many friends I’ve had from undergrad and earlier brightened up the sometimes lonely process of fieldwork with a phone call or sometimes just a great meme.

Both Drs. Cecilia Silva-Sanchez and Mohammadzaman Nouridelavar deserve a huge amount of thanks for teaching and assisting with the methods for urinary metabolite extraction. Dr. Marianne Kozuch helped significantly working out a protocol for extraction of the PUF disks used in the passive samplers and in the analysis to look for parent PAH compounds.

It cannot be overlooked that the generous funding for this work was made possible by the National Science Foundation Cultural Anthropology DDRIG and the

Elizabeth Eddy Doctoral Completion Award through the University of Florida,

Department of Anthropology. None of this work happened in isolation, and I am forever thankful to the countless people named and otherwise that assisted throughout the years leading up to this dissertation.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 10

LIST OF FIGURES ...... 12

LIST OF ABBREVIATIONS ...... 14

ABSTRACT ...... 16

CHAPTER

1 INTRODUCTION ...... 18

Global Context of Air Pollution ...... 19 Air Pollution and Sub-Saharan Africa ...... 23 Economic Burden of Environmental Pollution ...... 26

2 BACKGROUND ...... 30

A History of Risk Perception Research ...... 30 Culture, Risk, and Air Pollution ...... 32 Cooking, Household Characteristics, and AP Exposures ...... 37 Sex-Specific Biological Impacts of Air Pollution ...... 39 Polycyclic Aromatic Hydrocarbons and Fine Particulate Matter ...... 40 Pregnancy as a Female Specific Risk ...... 44 Sex and Age-Specific Differences in Infants, Girls, and Boys ...... 46 Zambian History and Current State ...... 49 Post-independence Zambia ...... 50 Impact of Structural Adjustment Policies (SAPs) ...... 52 Current Demographics & Poverty Reduction Efforts ...... 54 Demographic Information on Eastern Province ...... 56 Toxicology, Capacity, and Recent Scholarship in Africa ...... 57

3 RESEARCH DESIGN AND METHODS ...... 66

Study Area ...... 67 Sampling ...... 72 Research Objectives ...... 76 Research Objective One (R1) ...... 76 Research Objective Two (R2) ...... 76 Research Objective Three (R3) ...... 77 Research Objective Four (R4) ...... 77 Research Objective Five (R5) ...... 77

Ethnographic Methods ...... 78 Behavioral Observation ...... 78 Structured Questionnaires ...... 79 Free Lists ...... 79 Pile Sorts ...... 80 Rank Order of Disease Severity ...... 82 Bayesian Cultural Consensus Analysis (BCCA) ...... 83 Environmental and Biological Methods ...... 86 Biometric Measures ...... 86 Active Air Quality Monitoring ...... 87 Urine Analysis ...... 89 Refuse Identification in Chipata Township...... 92 Brief Overview of methods ...... 93

4 RESULTS OF ETHNOGRAPHIC DATA ...... 103

Free Listing Activity ...... 103 Pile Sort of Local Diseases ...... 106 Identification and interpretation of clusters ...... 107 Principle components analysis (PCA) ...... 110 Consensus and similarity...... 112 Ranking Perceived Disease Severity ...... 113 Kunyu (seizures) Prevalence and Interpretation in Eastern Province ...... 114 Free list, Pile Sort, and Ranking Data Integration ...... 116 Formal Consensus Analysis ...... 117 Kalongwezi consensus ...... 117 Mchini consensus ...... 119

5 RESULTS OF ENVIRONMENTAL AND BIOLOGICAL DATA ...... 140

Active Air Quality Monitor (PM10 and PM2.5) ...... 140 Case study: Mchini Participant 1-66 ...... 140 Case study: Kalongwezi Participant 46-151 ...... 142 Relationship between PM10 and PM2.5 ...... 144 Analysis of Urinary Metabolites 1-Hydroxypyrene and 3-Hydroxybenzo[a]pyrene 145 Biometric Data ...... 146 Utilitarian Benefits of the Charcoal Brazier ...... 147 Refuse Identification in Chipata Township ...... 150

6 DISCUSSION ...... 166

Risk Perception and Brazier Use ...... 166 “When the brazier stops smoking, it is safe to bring inside” ...... 168 ‘Finite Pool of Worry’ ...... 173 Exposure to Household Air Pollution ...... 174 Outdoor burning ...... 174 Issues of single-use plastics in developing countries ...... 175

Cardiovascular and Pulmonary Biometric Data ...... 177 The Role of Biomass Fuel Use in Disease Etiology ...... 179 Limitations ...... 181

7 CONCLUSION ...... 185

8 FUTURE DIRECTIONS ...... 190

APPENDIX

A QUESTIONNAIRES ...... 193

B DESCRIPTION REFUSE IMAGES ...... 208

C COMPOUND PILE SORT NMDS, ANTHROPAC PILE SORTS 1.0 ...... 211

D SPIROGRAPH OF MARY PHIRI ...... 213

E TOXICOLOGY SUPPLEMENTARY DATA ...... 214

LIST OF REFERENCES ...... 222

BIOGRAPHICAL SKETCH ...... 249

LIST OF TABLES

Table page

2-1 List of EPA priority PAH pollutants ...... 60

2-2 Demographic Information for Eastern Province ...... 64

3-1 Inclusion Criteria for participation in this study...... 96

3-2 Household Demographics for all participants ...... 97

3-3 Marital Status for all participants ...... 98

3-4 Household Economic Indicators for each household ...... 98

3-5 Model parameters for CCA on proximity matrices of pile sort data for both Mchini and Kalongwezi ...... 99

3-6 Model Parameters for BCCA on domains of local disease knowledge and cooking practices ...... 99

3-7 Declustering potential, collision energy and collision cell exit potential for urinary metabolites of benzo[a]pyrene ...... 102

4-1 Kalongwezi free list frequency, average rank, and salience (Smith’s S) ...... 120

4-2 Mchini free list frequency, average rank, and salience (Smith’s S) ...... 121

4-3 Pile sort clusters identified for Mchini and Kalongwezi compounds ...... 127

4-4 Consensus Eigenvalues for Pile Sort Data and Associated Measures ...... 130

4-5 Results of quadratic assignment procedure comparing Mchini and Kalongwezi item proximity matrices ...... 130

4-6 The rank order of disease severity in Kalongwezi for the 27 most frequently cited local diseases ...... 130

4-7 The rank order of disease severity for participants in Mchini and maids for the 27 most frequently cited local diseases ...... 131

4-8 Results of Bayesian cultural consensus analysis on the domains of cooking and local disease knowledge ...... 131

4-9 Selected answer key for Kalongwezi cooking domain, highlighting answers that influence cooking practices ...... 134

4-10 Selected answer key for Kalongwezi disease knowledge domain for diseases associated with biomass fuel use ...... 135

4-11 Selected answer key for Mchini cooking domain, highlighting answers that influence cooking practices ...... 138

4-12 Selected answer key for Mchini disease knowledge domain for diseases associated with biomass fuel use ...... 139

5-1 PM10 and PM2.5 mean, minimum, and maximum values for personal air quality monitor ...... 153

5-2 Results of Urinary Concentrations of 1-Hydroxypyrene in Biomass and Electricity Using Groups Adjusted for both Creatinine and Specific Gravity ..... 159

5-3 Results of Biometric Measurements among all participants ...... 159

5-4 Summary of baseline spirometry data (all participants) ...... 160

5-5 Summary of baseline spirometry data (passed only) ...... 161

5-6 Results of Mary Phiri spirometry ...... 162

5-7 Total waste split into constituent parts ...... 165

B-1 Description of individual images of refuse piles in Chipata township ...... 208

B-2 Counts of different types of plastics visible in refuse piles for each individual image taken in Chipata township ...... 209

E-1 Final urinary creatinine concentrations for all participants who provided urine for metabolite analysis ...... 214

E-2 Urine samples adjusted for 1-Hydroxypyrene based on levels of urinary creatinine ...... 216

E-3 Urine samples adjusted for 1-Hydroxypyrene based on specific gravity ...... 218

LIST OF FIGURES

Figure page

1-1 Diagram outlining factors that influence both HAP emissions and exposures .... 29

2-1 A typical household brazier in use cooking chips ...... 60

2-2 Metabolic pathways of benzo[a]pyrene ...... 61

2-3 Chemical exposures assessed in environmental epidemiology DOHaD studies ...... 62

2-4 Map of Zambia and surrounding countries ...... 63

2-5 All epidemiologic publications examining ambient air pollution and a specific health outcome in sub-Saharan Africa ...... 65

3-1 Map of Kalongwezi clinic catchment area...... 94

3-2 Mchini compound during a cold, dry season afternoon ...... 95

3-3 Participant Sibongile Mwanza demonstrating how to perform spirometry using the Spirobank II portable monitor ...... 100

3-4 Participant Emily Mwanza in her post-monitoring interview wearing the Aeroqual Series 500 ...... 101

4-1 Frequency chart of diseases listed between compounds ...... 122

4-2 Scatterplot of free list salience scores with Spearman rank correlation with 45° abline ...... 123

4-3 Scatterplot of free list salience above 0.05 (malaria and HIV removed) with 45° abline ...... 124

4-4 The optimal number of clusters for Mchini Pile sort data ...... 125

4-5 The optimal number of clusters for Kalongwezi Pile sort data ...... 126

4-6 Mchini principle component analysis for pile sort data dimensions 1 & 2, 95% confidence ellipses outlining clusters ...... 128

4-7 Kalongwezi principle component analysis for pile sort data dimensions 1 & 2, 95% confidence ellipses outlining cluster ...... 129

4-8 Observed eigenvalue ratio plotted in the posterior distribution for Kalongwezi cooking consensus data ...... 132

4-9 Observed eigenvalue ratio plotted in the posterior distribution for Kalongwezi disease consensus data ...... 133

4-10 Observed eigenvalue ratio plotted in the posterior distribution for Mchini cooking consensus data ...... 136

4-11 Observed eigenvalue ratio plotted in the posterior distribution for Mchini disease consensus data ...... 137

5-1 Woman cooking fritters using wood inside the walls of her home ...... 154

5-2 Daily PM10 Exposure of Participant 1-66 ...... 155

5-3 Concentrations of PM2.5 during a day of brazier use: Participant 46-151 ...... 156

5-4 Concentrations of PM2.5 during a day of electricity use: Participant 46-151 ..... 157

5-5 PM2.5 Plotted against PM10 ...... 158

5-6 Pulse rates (BPM) of participants in Kalongwezi and Mchini ...... 160

5-7 Kalongwezi: average age to begin assisting in cooking duties for adolescent females and males ...... 162

5-8 Mchini: average age to begin assisting in cooking duties for adolescent females and males ...... 163

5-9 Roadside refuse pile seen before burning with 5 x 5 gridlines ...... 164

5-10 Image of Rabecca Miti lighting her brazier with a crisp bag before cooking on the porch of her home ...... 165

6-1 Fire burning in a municipal dumpster on a crowded street corner near the Chipata business district ...... 183

6-2 Solid waste disposal OECD vs. Africa ...... 184

C-1 Visualization of Kalongwezi Pile Sort in nMDS produced using Anthropac 1.0 211

C-2 Visualization of Mchini Pile Sort in nMDS produced using Anthropac 1.0 ...... 212

D-1 Output of the spirograph of Mary Phiri demonstrating mild restriction in her airways ...... 213

E-1 Passive air quality monitor in the kitchen of a participant residing in Kalongwezi...... 221

LIST OF ABBREVIATIONS

AAP Ambient Air Pollution

AHR Aryl Hydrocarbon Receptor

AP Air Pollution

ARI(s) Acute Respiratory Infection(s)

ASE Automated Solvent Extractor

BCCA Bayesian Cultural Consensus Analysis

BMI Body Mass Index

BPDE Benzo[a]pyrene-7, 8-dihydrodiol-9, 10-oxide

CCA Cultural Consensus Analysis

CHWs Community Health Workers

CO Carbon Monoxide

COHb Carboxyhemoglobin

DBP Diastolic Blood Pressure

DCM Dichloromethane

DOHaD Developmental Origins of Health and Disease

FAO Food and Agriculture Organization of the United Nations

FEV1 Forced Expiratory Volume (over one second)

FISP Farmer Input Support Program

FRA Food Reserve Agency

FVC Forced Vital Capacity

GBD Global Burden of Disease Study

GLI Global Lung Initiative

GRZ Government of the Republic of Zambia

HAP Household Air Pollution

HDPE High-Density Polyethylene

HICs High-Income Countries

IDH Isolated Diastolic Hypertension

IMF International Monetary Fund

LDPE Low-Density Polyethylene

LMICs Low and Middle-Income Countries

MCMC Markov Chain Monte Carlo

MMD Movement for Multi-Party Democracy

NCDs Non-communicable Diseases

O3 Ozone

PAHs Polycyclic Aromatic Hydrocarbons

PET Polyethylene Terephthalate

PM2.5 Particulate Matter under 2.5 micrometers

PM10 Particulate Matter under 10 micrometers

PUF Polyurethane Foam

QAP Quadratic Assignment Procedure

SBP Systolic Blood Pressure

SES Socioeconomic Status

SSA Sub-Saharan Africa

TB Tuberculosis

UN United Nations

VC Vital Capacity

WHO World Health Organization

ZMW Zambian Kwacha

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

RISK PERCEPTIONS AS POTENTIAL MEDIATORS OF ENVIRONMENTAL TOXICANTS ASSOCIATED WITH BIOMASS FUEL USE

David Thorne Dillon

December 2020

Chair: Christopher McCarty Major: Anthropology

In this dissertation, I collect and analyze ethnographic, biological, and environmental data to investigate if risk perceptions mediate exposure to airborne toxicants associated with biomass fuel (e.g. charcoal) use in the home. This research took place over a period of twelve months in Chipata, Zambia. This study enrolled participants which were entirely reliant on biomass fuel and a pseudo-control group which had consistent access to electricity. The five main aims of this research were to

(1) determine the prevalence of cardiovascular/pulmonary diseases in these communities, (2) determine environmental and personal airborne toxicant exposure levels for both groups, (3) assess the extent of shared cultural models of disease and cooking practices within and between these two groups, (4) determine if risk perceptions mediate exposure to airborne toxicants, (5) and lastly identify if there is cultural significance of braziers and whether this significantly affects their use. There were no significant differences between these two groups in the prevalence of cardiovascular or pulmonary disease or function. Both groups were exposed to air pollution at levels far higher than safe thresholds outlined by the WHO; however, the biomass group was significantly higher between the two for particulate matter and

urinary metabolites of polycyclic aromatic hydrocarbons. Bayesian cultural consensus analysis showed mixed results between groups. Nearly all participants indicated they routinely performed tasks that increased their risk of exposure to household air pollution such as cooking indoors using charcoal. Relatively little risk is associated with the use of charcoal even inside the home. While most participants expressed a desire to have electric stoves, electricity is still prohibitively expensive. Further complicating these findings are droughts and climactic instability in the region that restricts access to electricity even to those who are able to afford it. In a country reliant on hydroelectric power, droughts result in long periods of electricity rationing and blackouts. The results of this study demonstrate human exposure to chronically high levels of airborne toxicants attributable to biomass fuel use and points to the need for more research on this topic in sub-Saharan Africa.

CHAPTER 1 INTRODUCTION

This study investigates if (1) perceptions of risk and vulnerability and (2) whether knowledge concerning disease and disease causation act as mediators of human exposure to household air pollution (HAP) associated with biomass fuel use in eastern

Zambia. Reliance on biomass fuels (e.g., charcoal, wood) as a primary energy source

puts billions of people at risk of adverse health outcomes from both acute and chronic

exposures (Anenberg et al., 2013). Previous research on biomass fuel use

demonstrates that both risk perception and cultural aspects of traditional cookstoves

influence individual exposure levels (Rhodes et al., 2014). This project empirically tests

hypotheses linking culturally constructed risk perception to specific and measurable

biological outcomes (Beck, 1992; Nichter, 2003; Worthman & Kohrt, 2005). By

combining methods and analyses from anthropology, toxicology, and public health, this

study assesses individual and intergroup exposure to airborne toxicants in multiple

ways. Long-term participant observation illuminates how life in a sometimes resource-

scarce environment can influence individual agency, reducing the ability to avoid a

potential hazard even if individuals perceive a risk(s). Understanding these processes is

critical in sub-Saharan Africa as reliance on biomass fuel is predicted to continue for

decades, along with a significant rise in morbidity and mortality attributable to air

pollution (Lelieveld et al., 2015).

This work builds on anthropological research concerning risk, risk perception,

and how they influence environmental exposures at both the individual and group levels

(Bickerstaff, 2004; Checker, 2007; Douglas, 1992). Grounding this work within

anthropology and the unequal distribution of risk in an African context allows me to

employ methods and analysis from cognitive anthropology to examine local risk and

vulnerability models. Using this grounded technique provides a more fine-grained

assessment of risk and vulnerability at the local level. Incorporating biological and

environmental measures allows for me to investigate how the local environment alters

biological responses to external stimuli in the study population. While basic biological

functions operate in the same manner for all humans, this study acknowledges how

local experiences and exposures may alter certain biological processes and outcomes.

For example, growth restriction in early life may alter lung function throughout the life

course of an individual. Combining and incorporating these paradigms further

contributes to the biocultural anthropology literature on risk perception, public health,

and human-environmental interactions. Utilizing anthropological methods and theories

allows a more detailed look at a topic generally addressed through population-level

epidemiological studies (e.g. NHANES, Sister Study) while helping to explain the

variance in these broader, population-level measures.

Global Context of Air Pollution

Environmental toxicants (e.g., environmental pollution) precipitate the premature

deaths of approximately 9 million people every year, far outstripping mortality caused by

many infectious diseases such as malaria, HIV, and tuberculosis (TB) (Landrigan et al.,

2018). Thus, environmental pollution directly contributes to a staggering 1 in 7 deaths

worldwide (Landrigan et al., 2018). Of these deaths, 94% occur in low and middle-

income countries (LMICs), disproportionately affecting those living in poverty (Landrigan

& Fuller, 2012). Within these countries, the most vulnerable populations are

predominantly affected by diseases of pollution—for example, women are often

exposed to greater HAP levels caused by biomass fuel use.

Monetary investment into curbing pollution-related deaths remains relatively low compared to infectious disease, despite pollution’s massive impact on economies, human, and environmental health (Suk et al., 2016). Illustrative of this is the Global

Fund for HIV, Malaria, and TB. In 2013, the fund totaled more than 28 billion United

States dollars (USD). In comparison, the amount of monetary aid that went toward addressing environmental pollution issues that year amounted to less than 100 million

USD (Suk et al., 2016). The relatively limited spending on pollution occurs despite the evidence that initial cost to return on investment, seen in many pollution reduction efforts in high-income countries (HICs), would be economically advantageous in the long run for LMICs to implement similar strategies (Suk et al., 2016). Many of these measures are well known and involve relatively small changes, such as the United

States removing lead from gasoline in the 1970s. Grosse et al. estimate the economic benefit of this decision alone at approximately 3 trillion USD (primarily due to increased worker productivity accompanying increased IQ points) (Grosse et al., 2002).

Landrigan and Fuller (2012) outline four main reasons that wealthier countries should care and prioritize the issue of environmental pollution in LMICs (Landrigan &

Fuller, 2012). First, the pollution produced in LMICs does not stay confined to these countries— for example, air pollution produced in China appears in measurable levels in

North America. Additionally, exports from countries with high pollution levels may be contaminated and pose harm to individuals who handle them along supply chains to consumers (Landrigan & Fuller, 2012). Second, health-related foreign aid from the US and other international donors focuses on strengthening other countries' health systems. Meeting these goals is essential to the completion of this goal and has been

shown, as mentioned, to be extremely cost-effective. Third, addressing pollution in

LMICs promotes both social cohesion and development. Lastly, a concentrated effort to minimize future activities that create harmful chemicals, and cleaning the remnants of past activities, can directly address the looming issue of climate change faced by all countries regardless of GDP (Landrigan & Fuller, 2012).

Within the umbrella of environmental pollution, air pollution (AP) is the most significant contributor to morbidity and mortality, far outstripping water, soil, and occupational exposures (Landrigan et al., 2018). For decades deaths caused by the particulate matter under 2.5 microns (PM2.5) specifically are on the rise. In 1990, 3.5

million premature deaths, 95% CI [3.0–4.0], were attributable to PM2.5, compared to the

estimated 4.2, 95% CI [3.7–4.8] in 2015. Estimates predict this trend to continue with

6.6 million, 95% CI [3.4–9.3] premature deaths per year by 2050 (approximately a 50%

increase), assuming that governmental inaction to lessen the production of PM2.5.

However, if public health policies place more emphasis on reduction efforts, particularly

in cities, models suggest that up to 23% of these deaths may be avoided (Lelieveld et

al., 2015).

While southeast Asia and the Western Pacific have the highest number of premature mortality attributable to AP, Africa is increasingly feeling the health effects of these toxicants. Each year, there are approximately 237,000 premature deaths per year attributable to AP; however, estimates suggest this figure will almost triple to 660,000 by

2050 (specifically for individuals under five or over 30 years of age) (Lelieveld et al.,

2015). This increase is primarily due to population growth, lacking infrastructure, and

urbanization. AP is generally higher in urban areas, particularly in areas such as Lusaka where high population density co-occurs with biomass fuel use.

Air pollution is categorized into ambient air pollution (AAP) and HAP. Though

often analyzed separately, they contain many of the same compounds and frequently

co-occur. Some of the most well-studied compounds include PM2.5, carbon monoxide

(CO), polycyclic aromatic hydrocarbons (PAHs), sulfates, and ozone (O3). Due to this

sometimes false division, both the World Health Organization (WHO) and the Global

Burden of Disease (GDB) study estimate that the total number of deaths attributable to

AP is less than the sum of AAP and HAP together (Forouzanfar et al., 2016). While

eliminating AAP in high-income countries would yield greater health benefits than HAP,

the reverse is true in LMICs (Landrigan et al., 2018).

Airborne toxicants target many organ systems in the human body, but

cardiovascular and pulmonary health suffer the most substantial impact. AP is strongly

linked to both infectious and non-communicable diseases (NCDs). Non-communicable

diseases make up a significant portion of the estimated burden (Schraufnagel et al.,

2019). These include 500,000 lung cancer deaths, 1.6 million COPD related deaths,

and approximately 20% of all cardiovascular and stroke deaths worldwide (Bhatnagar,

2006; Schraufnagel et al., 2019). In addition to these diseases, air pollution levels

positively correlate with diabetes, cognitive deficits, atopic skin disease, and cataracts

(K. H. Kim et al., 2011; Meo & Suraya, 2015; Power et al., 2016; West, 1992).

Multiple variables mediate both HAP emissions and exposures, some under

individual control and others not. Similarly, emissions may come from a combination of

fuel use in the home and neighborhood characteristics outside individual control. The

distinction between emissions and exposures is important as household members will

not all inhale the same level of airborne toxicants. Exposure profiles change based on

time spent in the house, specific activities completed, etc. It is important to take many of

these into account to explain variance in exposures between individuals living within

and between different communities. This way, it is possible to assess which factors

contribute most significantly to HAP and personal exposure levels. Coker et al. 2020

diagram factors influencing both emissions and exposures at the household level in the

following diagram (Figure 1-1) (Coker et al., 2020).

Air Pollution and Sub-Saharan Africa

Sub-Saharan Africa, particularly the Eastern Province of Zambia, is an ideal area

for research on HAP's impacts due to the high percentage of the population that relies

on biomass fuels. This reliance is an issue, like many concerning environmental

toxicants, that disproportionately affect vulnerable populations within LMICs (Bruce et

al., 2000; Landrigan et al., 2019; Suk et al., 2016). Many areas of the world are moving

away from reliance on biomass fuels and moving to cleaner types of fuels; however,

sub-Saharan Africa (SSA) is expected to rely on these sources for decades to come

(Brew-Hammond, 2010). The lag in the transition from biomass fuels to cleaner energy

in sub-Saharan Africa is due to limited infrastructure and stagnant per-capita income

(broadly speaking). The number of people in SSA that rely on biomass is expected to

increase by over 100 million people by 2030 (Brew-Hammond, 2010; IEA, 2006). The

Eastern Province of Zambia, particularly rural areas, is no exception and almost all households completely reliant on biomass as a main fuel source.

Urbanization and NCDs are strongly correlated, and both are rapidly on the rise in many developing countries, particularly in SSA. Numerous factors drive this rise in

NCDs, including changes in daily activity levels, dietary shifts towards calorically dense

foods, exposures to environmental contaminants at higher levels. (Allender et al., 2011;

Goryakin et al., 2017). In SSA, infrastructure often struggles to keep pace with

population growth and movement (Todes et al., 2010). The United Nations (UN)

estimates that by 2050, the number of people living in urban areas will increase by

approximately 2.5 billion—90% of this increase in Africa and Asia (DESA, 2014). An

increase in population of this magnitude means by 2050, 66% of the world’s population

will reside in urban environments. Africa is currently the least urbanized continent, with

approximately 60% of the population residing in rural areas (the UN does not have a

definition for urban/rural—it uses each country’s definition of rurality) (DESA, 2014).

While there is no clear definition, there is a clear trend towards urbanization in sub-

Saharan Africa. However, despite changing residential patterns, most people who live in

urban areas still do not have consistent access to electricity or may preferentially use

charcoal as a cheaper alternative (Mulenga et al., 2019). As of 2015, charcoal was by

far the most common cooking fuel used in urban Zambia, something that is unlikely to

change without more significant subsidies for electricity (Tembo et al., 2015). Charcoal

is more economical for most households to use in lieu of electricity through government

or private providers (discussed more in the context of Chipata in later chapters).

With this shift towards urbanization across Africa, there comes with it a marked

change in disease patterns. Studies suggest that urbanization may decrease overall

mortality by anywhere from 6%-28% (Eckert & Kohler, 2014; Hay et al., 2011; Keiser et

al., 2004). However, over the next decade, the WHO estimates African populations will

experience some of the largest increase in death rates from cardiovascular disease,

cancer, respiratory disease, and diabetes (World Health Organization, 2020a). In most

African countries, health infrastructure still largely focuses on reducing the rate of infectious disease transmission and prevalence (Omoleke, 2013). As a result, many countries are under-prepared to handle the rise in non-communicable diseases in terms of both policy and infrastructure (Mukanu et al., 2017; Siddharthan et al., 2015). NCDs present unique challenges for control and maintenance in a way that many infectious diseases do not. For example, type 2 diabetes mellitus is expected to double in prevalence in SSA in the next 25 years (with South Africa seeing a fourfold increase in

NCD prevalence) (Stephani et al., 2018). In 2018, a systematic review of 13 studies on type 2 diabetes examined management spanning eight SSA countries. At most, 43% of respondents were able to check their blood glucose levels at home (Nigeria), and at worst, no respondents were able to check these levels at home (Uganda) (Stephani et al., 2018). The limited ability to check daily glucose levels occurs alongside other

constraints to diet and lifestyle changes, making type 2 diabetes management difficult

for many living in African countries.

Cultural norms and expectations surrounding food and cooking often result in

disproportionate exposure to toxicants from incomplete combustion for women (Oluwole

et al., 2013; Sehgal et al., 2014; Young et al., 2019). This variation leads to chronic

exposure to HAP for women tasked with cooking for the family/household. HAP in this

area can precipitate chronic and acute conditions that are some of the largest

contributors to adult and child mortality in Zambia (Mukanu et al., 2017). This inequity is

particularly troubling as life expectancy in this Province falls into the lowest tertile

compared to other provinces in the country, averaging just 49 years of age (Central

Statistical Office, 2012). Note this is a statistic from the 2010 Zambian Census and life expectancy has increased since this measure was taken. However, it is illustrative of

Eastern Province when compared to others in the country (Central Statistical Office,

2012).

Economic Burden of Environmental Pollution

The economic burdens associated with environmental pollution are more difficult

to calculate than the costs associated with pollution control (pollution control is

economically advantageous in the long-term) (Preker et al., 2016). There are four

general ways that environmental pollution negatively impacts both global and local

economics. The first is direct medical expenditures for individuals, consisting of hospital

costs, payments to physicians, rehabilitation among other things. The second consists

of indirect medical costs such as time lost from work or schooling, the costs of investing

more in the health system (e.g., increased infrastructure). The third category of

pollution-related disease costs is the diminished economic output by individuals who

have decreased pulmonary, cardiovascular, or cognitive capacity associated with

exposure to pollution. The fourth category is the loss of economic output resulting from premature deaths of individuals. However, none of these categories include intangible costs such as the individual suffering of those with the disease, disruption of family life, which can be equally devastating to health and well-being. It is difficult to quantify how premature death will disrupt a family, the extent of the grief felt by family members and friends, or the amount of stress associated with raising a child with cognitive impairment due to toxin exposure. These are qualitatively different from the direct and indirect economic costs discussed, but no less real to the people who experience them

(Landrigan et al., 2018). And while it is true that the absolute value of the loss due to

pollution (e.g., loss of income through DALYs, healthcare burden) is highest in high- income countries, the relative percentage of gross income is much higher in LMICs

(Landrigan et al., 2018).

Exposure to pollution increases the prevalence of diseases and the burden placed on medical systems. Like economic systems, these medical systems occur on

multiple levels and impact the budget and functioning of local clinics and national

hospitals. Exposure to air pollution increases both the risk of certain infectious diseases

(acute respiratory infections (ARIs)) as well as susceptibility to non-communicable

disease (COPD, cancer) that must be maintained over time and is more costly to

individuals, hospitals, providers. For example, local clinics are more likely to receive

relatively minor diseases such as ARIs or bronchitis, while large hospitals are more

equipped to treat cancer patients. Chronic diseases specifically often require

specialized personnel and services to manage/maintain, both of which may be prohibitively expensive (Samb et al., 2010). Time spent convalescing from these diseases means less participation in the workforce and decreased earnings for individuals and households.

Early exposure to environmental pollution, either in utero or early childhood, may

permanently alter a child’s neurological system. For example, proximity to roadways

and traffic-related pollution correlates with decreased memory and motor skills in children (Freire et al., 2010). These alterations include cognitive deficits, decreased attention span, and other detrimental impacts that potentially limit lifetime earning potential (Freire et al., 2010; Guxens et al., 2018; Sunyer et al., 2015). Women exposed to high levels of AP may be at an increased risk of stillbirth, preterm birth, and

preeclampsia, as well as other poor health outcomes (Sutapa Agrawal, 2012; Amegah et al., 2014). For example, a pregnant mother may have AP induced eclampsia

(hypertension induced seizures), which not only necessitates an emergency trip to the hospital but places her at greater risk of dying from subsequent cardiovascular events in the years following this acute incident (Amaral et al., 2015). The impact of pollution on health contributes significantly to the cycle of poverty seen in many areas in LMICs. The costs and prevalence associated with pollution-related diseases are particularly troubling as it places enormous strain on already stressed medical systems, particularly if those set up to primarily address infectious diseases.

This study investigates the issues related to biomass fuel reliance in Chipata,

Zambia utilizing methods and research design from anthropology, public health, and toxicology. In the following chapter, I outline how each of these fields contributes to this research first in terms of theoretical outlooks from anthropology followed by specific biological impacts of AP, followed by an overview of Zambia through a historical political-economic perspective. Chapter 3 outlines the research goals for this project, along with the methods chosen to complete them and the rationale behind them.

Chapter 4 provides a detailed summary of the study findings and how specifically the ethnographic results build upon and inform one another to outline cultural domains and inform the collection and interpretation of biological and environmental data. The penultimate chapter integrates the ethnographic and biological/environmental components of this study and discusses their theoretical and practical implications.

Lastly, the conclusion summarizes the study findings and outlines future directions for research on environmental health in Zambia and sub-Saharan Africa at large.

Figure 1-1. Diagram outlining factors that influence both HAP emissions and exposures

CHAPTER 2 BACKGROUND

The study of exposure to biomass fuel emissions in LMICs is an important health issue to which anthropology, and related fields, can significantly contribute.

Understanding what beliefs guide behaviors that increase or decrease exposures is necessary for the study of HAP and, in turn, individual exposures. This study examines whether people view exposure to smoke and biomass fuel emissions as constituting a serious health risk. Chapter 2 outlines the: (1) a theoretical background on risk perception, (2) the biological outcomes associated with specific classes of toxicants emitted from biomass fuel, and (3) background on Zambia, outlining why it is an ideal place to perform this research.

A History of Risk Perception Research

Risk perception is an important component of this work as exposure to household fuel emissions may be mitigated if individuals perceive them as constituting a health risk. One of the more influential writers on risk, Ulrich Beck, argues that the social production of risk accompanies the social production of wealth, and there was a qualitative shift in the production of risks following our entrance into modernity.

According to Beck, modernization is the driving force behind the creation of novel, unforeseen hazards— risk, and its uneven distribution are merely latent side effects of this force (Beck, 1992). This modernization resulted in a qualitative shift in terms of the scale of the hazards societies create. For example, while lead is no longer a component of automobile gasoline, aviation fuel still contains it, and lead levels in those who live near airports often reach concerningly high levels (Beck, 1992; Miranda et al., 2011;

Nevin, 2000). Pollutants like this take a central place in Beck’s argument. He states that

a permanent experiment is underway in which the whole of society is exposed to novel

compounds every day (Beck, 1992).

Deborah Lupton’s work has likewise been extremely influential to the study of

risk, which she roughly breaks up into two broad categories. The first is a risk as a

threat to populations from environmental hazards like pollution, nuclear waste, toxic

chemicals. The second is a risk as a consequence of lifestyle choices made by

individuals. The former is external, and the individual has little control over it, while

individual choice and control characterize the latter—when perceived control over an

action increases, so does the blame associated with it. She argues that governments

may co-opt and use risk definitions to maintain existing power structures (Lupton, 1993).

Building on previous Foucauldian work, Lupton focuses on how the state uses risk and

blame to coerce control from a position of authority and how it uses discourse to exert

power over its citizens.

Third, Mary Douglas is responsible for introducing a cultural/symbolic approach

to the study of risk. The use of the term risk has evolved significantly over the years.

First employed in conjunction with a mathematical calculation for games of chance, it

then evolved to predict the probability of success or failure for any number of different

activities. However, the term itself had no connotation, positive or negative. Today, the

definition has essentially shifted from chance to danger—there are fewer positive

associations with the term risk than positive (Douglas, 1992).

Central to Douglas’ work is the insight that risks and the practices associated

with them are part of shared cultural understandings within discrete groups. According

to Douglas, values and worldviews, embedded within social relations and context,

shape the way individuals perceive and frame risk. The most important factor is not a cognitive process like the psychometric paradigm sought to capture, but cultural predilections and biases (Douglas, 1992; Rippl, 2002; Wildavsky & Dake, 1990). The

research suggesting no correlation between the amount of knowledge an individual has and the level of potential risk and concern they feel supports Douglas’s assertion (Rippl,

2002; Wildavsky, 1993).

Culture, Risk, and Air Pollution

Early research on public perceptions of risk posed by air pollution began during

the mid-twentieth century in England, employing largely attitudinal surveys (Breslow,

1956; Degroot et al., 1966; W. S. Smith et al., 1964). While the public acknowledged air

pollution was an issue, they did not believe themselves to be at risk for adverse events

associated with it. In the United States, researchers focused instead on public

perception of nuclear power (Bickerstaff, 2004). The public viewed nuclear power as a

high-risk phenomenon despite expert assessment deeming it extremely safe. From this,

they determined people view actions and exposures outside their control (involuntary)

as riskier than those in which people voluntarily engage (Bickerstaff, 2004).

Research in the 1970s that focused on individuals' self-expressed views, rather

than observed behavior, identified that risk perception could not be attributed entirely to

public ignorance or irrationality (Fischhoff et al., 1978; Slovic et al., 1980). Rather, risk

experts and the public used different categories to evaluate the relative hazard of a risk.

The scientific community trended towards the probability of a fatality (e.g., micromort- a

unit of risk representing a one in one million chance of death), while the public focused

on characteristics of the hazard itself. Slovic’s seminal work in 1980 showed two

principle factors or attributes of a hazard that influenced risk perception. The first was

dread (of a catastrophic event), and the second was fear of the unknown (Slovic et al.,

1980). These researchers used the psychometric paradigm to explain the results of these studies, and others like it; however, it has met with considerable criticism, largely due to the tools used to gather quantitative data. Namely, an overreliance on questionnaires that force individuals to choose between risks in a manner they would

not if open-ended questions were utilized (Bickerstaff, 2004).

During the 1990s, an increasing number of social scientists began examining the

topic of risk perception, grounding their research in cultural and political frameworks

(Hinchliffe, 1996; Irwin et al., 1999; Kempton, 1991). Using this approach, instead of

psychological models, researchers gained insights into locality/place, individual and

collective agency, and power (Bickerstaff, 2004). Perhaps best stated by Bickerstaff:

So where the psychological studies of risk perception and much of the environmental equity literature have really only been able to point to the socio-political factors underlying the findings and relationships they report, recent socio-cultural analyses have complemented their work by presenting grounded accounts of these processes and how they shape risk perception. (Bickerstaff, 2004).

Locality then becomes an important component of risk research. Individuals who

expressed strong attachment to the area they lived in would distance the problems both

geographically and socially from their area of residence. However, if an individual did

not have a strong positive affect for their area, they would be much more likely to draw

attention to the pollution and place it in much closer proximity (Walker et al., 1998). In

line with this is the importance of local memory, which influences how residents ground

and interpret environmental pollution. In England, residents from Jarrow and Teeside,

particularly older residents, associated industrialization and pollution with economic

prosperity. Similar phenomena persist to this day in other areas of the world. In eastern

Serbia, the city of Bor is considered the most polluted in the country, and while citizens there do not like the omnipresent caustic smoke, they link it with economic prosperity and hope for the future (Jovanović, 2018). These two factors became a recurring theme and were strongly associated with identity and place (Phillimore & Moffat, 1999; Walker et al., 1998). However, people may attach a stigma to a place deemed ‘dirty’ or

‘polluted,’ particularly from outsiders (Stoffle et al., 1991; Stone, 2001). Anthropologists

Stoffle and Stone examined areas considered risky using mapping techniques to plot the bounds of geographic areas (in this case of radioactive waste) (Stoffle et al., 1991;

Stone, 2001). This research also recognized the importance of the senses, particularly sight and smell- soot accumulating in the home, smokestacks billowing out black smoke, the smell of chemicals etc. (Bickerstaff & Walker, 2001; Wakefield et al., 2001).

Demographic characteristics are important predictors of risk perception. In the

United States, women consistently express greater concern over technological risks and

risks to the environment than men (Davidson & Freudenburg, 1996). Those of higher

socioeconomic status (SES) were more likely to underestimate the frequency associated with causes of death (others have documented the inverse as well, where those of lower SES tended to overestimate death rates) (Boholm, 1998). These findings have important implications for air pollution due to biomass fuels, especially when combined with intrahousehold power dynamics and resource allocation. If the head of a household does not view chronic biomass fuel use as risky, it may leave little recourse for others in the home to minimize exposures. Compounding this issue is the issue of time, specifically, how people perceive risks far in the future. The more distant in time, an individual perceives a risk, the less likely they are to act on it in the short term

(Bulkeley, 2000; Hinchliffe, 1997). Future discounting is a phenomenon discussed extensively in behavioral economics pulling from diverse backgrounds ranging from global warming to public health campaigns (Bickel et al., 2016; Weber, 2006). Climate

change literature utilizes future discounting to explain how people disconnect disastrous

events far in the future from the daily activities which predicate them (Weber, 2010).

Similar to climate change, daily AP exposures are often difficult to link to untoward and

intangible future events.

Modifying the perception of environmental risk is how communities view the

trustworthiness of regulatory institutions and bodies. Ineffective communication

campaigns have done little to ease public concerns regarding government responsibility

(Barnes et al., 2014; Earle & Cvetkovich, 1995; Renn & Levine, 1991; van Rooij et al.,

2016; Walker et al., 1998). Few issues rely so heavily on scientific consensus as to the

determination of risk related to environmental toxicants. Chronic exposure may occur for

years without an individual or population knowing there are harmful chemicals in the

environment (Checker, 2007). A community could be contaminated with pollutants but

have little to no recourse until scientific consensus and research verifies their

experiences. A lack of local recognition of the hazard posed by an event can create

distrust and resentment, creating a sharp divide between scientific messaging and

residents' lived experiences. In the United States, hazardous waste facilities are

disproportionately situated in neighborhoods of color. Perhaps most famously in recent

history is the 2014-2015 Flint Michigan water crisis, where large sections of the

population, mostly low SES African Americans, were exposed to high levels of lead in

their drinking water (Butler et al., 2016). Actions such as this that perpetuate

environmental racism engender further mistrust in regulatory agencies and the government for allowing this trend to continue and further compromising the health of socioeconomically disadvantaged populations (Checker, 2007). Mistrust is detrimental

to the government/community relationship, and it informs community messaging about environmental risks.

Perhaps the most prevalent work on risk perception in SSA focuses specifically on sexual behaviors in the context of HIV. The focus is on risk-taking behaviors, condom use, and circumcision to name a few (Muchiri et al., 2017; Pranitha & John,

2005; Tenkorang et al., 2011; Warren et al., 2018; Zungu et al., 2016). Though, there are significant contributions to the study of risk perception in this region focusing on other topics, most often simultaneously related to pressing issues of public health

(Boateng et al., 2017; Crona et al., 2009; Peltzer & Renner, 2003; Willems et al., 2016).

In some of these studies the term ‘risk’ is used in a manner that may not fully capture

the specific cultural context. A systematic review of risk perceptions associated with

cardiovascular disease reported results of a qualitative study of risk perception in South

Africa noting that ‘participants were described as being generally unfamiliar with the

concept of risk..’ (Boateng et al., 2017; Surka et al., 2015). It is unlikely that participants

in the study are unfamiliar with the concept of risk, but more likely that perception and

terminology differed. This highlights the need for investigations into risk and risk

perception that take into account cross-cultural differences in perceptions in a

meaningful manner. This has long been recognized as necessary in work on risk in

anthropology, that culture shapes both the subject and severity of perceived risks

significantly (Douglas, 1992; Rippl, 2002).

Cooking, Household Characteristics, and AP Exposures

It is not just the cultural construction of risk that is important when considering the health impacts of biomass fuel use but also cultural conceptions, practices, and the physical space associated with cooking. Rhodes et al. investigated cooking behaviors and preferences in Kenya, Nepal, and Peru (Rhodes et al., 2014). Stove design varied by site and based on the needs of individuals and families. The Peruvian stove, a tullpa, consisted of 2-3 mud walls surrounding a fire with metal bars set on top. The Nepalese constructed the maat chulho from rice husks and clay and varies in design based on family size. Finally, the Kenyans designed the chepkube similarly to the maat chulha, but with an adjacent chamber to warm food. While designs differed based on locale, all used biomass fuel. Participants in each country stressed their stoves' cultural importance and explained that they perform both practical and symbolic functions. One

Peruvian participant summarized their feelings succinctly, stating, “our grandparents cooked with this tullpa.” Those in the study that had previously used another fuel source, such as liquefied petroleum gas (LPG), claimed that the food lacked flavor and that it cooked too quickly (Rhodes et al., 2014).

Women from all three sites in the Rhodes et al. study used ash produced in the stove for various purposes such as cleaning utensils, fertilizer, or treating wounds/cure an illness. How participants used the ash and how much they valued it depended on the type of fuel burned. For example, women used ash from dung for fertilizer, used wood ash for cleaning, and used ash accumulated on the ceiling for medicinal purposes

(Rhodes et al., 2014). In Zambia, people occasionally collect ash to mix with water; this mixture is then cooked with delele (okra leaves) to add a unique flavor.

Participants in Peru, Nepal and Kenya generally expressed that they were

pleased with their current stove as it performed all the functions they desired and,

importantly, allowed them to leave the cooking unattended while they performed other

tasks. However, they had complaints as well. A primary complaint was the amount of

smoke the stoves emitted, though the women in the study did not always link this to

long-term health issues. In both Nepal and Peru, informants only linked exposure to the

smoke with immediate discomfort, nothing else. In Kenya, some participants expressed

concern for long-term health impacts, although some simultaneously viewed it as

something that you became accustomed to with time. Rhodes et al. noted that the stoves needed to be constantly maintained and fixed, though people could use local materials for all repairs and modifications, making the supplies for repair inexpensive and readily available. While the stoves were not perfect in any of the field sites in the study, the stoves were culturally valuable across all sites, indicating a need not only to examine cultural domains of risk perception but also cooking (see figure 2-1 for a typical

Chipata brazier) (Rhodes et al., 2014).

Prior work in SSA does not reflect the importance of culture as a factor in

cookstove use do the extent it needs to in the future. However, there is growing interest

in African research to document the prevalence of traditional vs. improved cookstove

use. These studies focus efforts on characterizing the emissions from traditional

cookstoves, and mapping the distribution of diseases associated with them (Barnes et

al., 2009; Kadir et al., 2010; Kilabuko et al., 2007; Kshirsagar & Kalamkar, 2014). This

work trends towards population-level epidemiology or cross-sectional studies examining

exposure and specific health outcomes. For example, research of Kilabuko et al. typifies

much of the work on biomass fuels and health in the African context. Researchers collected health data using questionnaires that focused on ARIs. Simultaneously, they measured particulate matter under ten microns (PM10), CO, and nitrogen dioxide.

Children under five years of age in homes with biomass stoves had an OR of 5.5 [95%

CI 3.6 to 8.5], compared to an unexposed group (Kilabuko et al., 2007). Work like this is important and points to two main conclusions in this context. First, there is a significant and robust association between biomass fuel use and health outcomes, and second, work on the cultural context of this phenomenon needs to be investigated in much further depth to complement existing epidemiological research.

Sex-Specific Biological Impacts of Air Pollution

There is growing recognition that the health effects caused by exposure to toxicants emitted when using biomass fuels affect women, men, and adolescents differently. Research consistently shows that exposure to air pollution has stronger deleterious effects on females and adolescent females than males and adolescent males. These differences may be attributable, at least in part, to biological differences between sexes and differences at developmental stages and critical periods of growth.

Sex-linked traits and stage of development can influence how susceptible an individual is when exposed to airborne toxicants (Clougherty, 2010). The two airborne toxicants measured in this study are PAHs and PM2.5/PM10, both of which have these sex-specific

responses and health outcomes. Therefore, it is important to examine how and at what

stages of development these chemicals may impact health to properly situate these

biological alterations in an individual’s lifespan (Heindel et al., 2015; Swanson et al.,

2009).

Polycyclic Aromatic Hydrocarbons and Fine Particulate Matter

PAHs are a class of chemicals defined by having two or more fused benzene

rings with no heteroatoms. The US Environmental Protection Agency identifies 16 PAHs

that are priority chemicals (Table 2-1).

PAH compounds themselves are not carcinogenic—rather, they act as pro-

carcinogens with chemicals produced during their metabolism act as the final cancer

causing agent (Alexandrov et al., 2010). The specific carcinogenicity of these

compounds varies, with the most well-studied benzo[a]pyrene (BaP) often acting as a

benchmark. There are three pathways of metabolism for PAHs (1) CYP1A1/1B1 &

epoxide hydrolase pathway, (2) CYP peroxidase pathway and (3) the aldo-keto

reductases pathway (Moorthy et al., 2015; Shiizaki et al., 2017). Most PAHs are

metabolized using the CYP1A1/1B1 and other Phase II enzymes in the liver (UDP

glucuronyl transferases and glutathione S-transferase Mu 1 (GSTM1)) to create, among

other chemicals, diol-epoxides, which can react with DNA to produce adducts. During

metabolism, PAHs bind to the aryl hydrocarbon receptor (AhR), which leads to the

downstream creation of adducts. For example, BaP transforms after several steps into

BP-7,8-dihydrodiol-9,10-epoxide (BPDE), which acts as the final carcinogen (Figure 2-

2) (Moorthy et al., 2015; Shiizaki et al., 2017). BPDE preferentially binds to the p53

tumor suppressor gene (specifically on codons 157, 248, and 273) and KRAS oncogene

(Denissenko et al., 2016). AhRs are present in high levels in epithelial cells lining the

lungs, which can cause numerous issues in addition to the formation of DNA adducts in

lung cells such as cytokine production, cell-cell adhesion interaction, mucin production.

(Moorthy et al., 2015).

PAHs metabolism differs between men and women in important ways as they are potential causative agents to numerous cancers (and subclinical toxic effects) ( Smith et al., 2000; Elmets et al., 2001; Ramesh et al., 2001; Wu et al., 2003). PAH metabolism studied between smokers and non-smokers documented a 3.9 fold higher median level of CYP1A1 activation in female smokers compared to female non-smokers. There were no significant differences between the two male groups (there was no difference in

CYP1B1 levels) (Mollerup et al., 2006). The mutational spectra of men and women differed as well concerning the P53 gene. Compared to female never-smokers, female smokers show a significant increase in C: G to T: A transversions. Men, on the other hand, showed no major differences in mutational spectra between smokers and non- smokers. This data suggests that women may be predisposed to certain genetic mutations compared to men exposed to similar toxicants, which may influence cancer risk (Mollerup et al., 2006).

PM is categorized based on diameter in micrometers (<1, 2.5, and 10 µm). Of these, PM2.5 is the most widely studied as it can penetrate deeply into pulmonary tissue, altering structure and function. These alterations are associated with various infectious diseases and metabolic disorders (Burnett et al., 2014; Dominici et al., 2006; Huang et al., 2015). The specific molecular mechanisms through which PM alters lung, immune, and metabolic functions are still emerging research. After penetrating the lungs, PM can cause the formation of radical oxygen species that damage DNA and mitochondria & inflammatory responses in the lungs (Dagher et al., 2005; N. Li et al., 2003). Recent work in proteomics and metabolomics further suggests that PM exposure may alter carbohydrate/energy metabolism and protein synthesis and degradation (Huang et al.,

2014). The specific metabolic pathways disrupted appear to concentrate on the citrate cycle, amino acid biosynthesis/metabolism, and oxidative stress (Huang et al., 2015).

Compared to other emissions produced during the combustion of biomass fuel,

PM may have some of the most significant effects on human health. Studied extensively over the past decades, particulate matter is associated with a host of acute and chronic diseases. These range from ARIs to cancer (Dominici et al., 2006; Ezzati & Kammen,

2002; Pope III et al., 2002). There is evidence that there are differences between men and women concerning the amount of particulate deposited in the lungs. Larger particles tend to be deposited in more proximal airways, while smaller particles can be deposited deeper in the lungs (particularly with slower breathing rates) (Kim & Hu, 1998;

Yu & Diu, 1983). Female lungs, on average, are smaller than lungs in males, particularly in the upper airways and large conducting airways (Eckel et al., 1994; T. R. Martin et al.,

1987). Women tend to deposit greater amounts of particulate matter than men do, particularly in the proximal airway (Pritchard et al., 1986). These differences may be attributable to structural differences between male and female airways. Relatively more deposition in the upper respiratory tract may have a mild protective effect as deeper penetration in the lungs causes more adverse health effects.

PM also appears to alter the lung blood-gas barrier slightly differently in men and women (Braüner et al., 2009). This barrier is the exchange site of gases between the alveolar surface and blood through diffusion, while at the same time restricting the passage of antigens such as PM. However, if the barrier is injured, it may alter the barrier’s ability to transport gas and become more susceptible to the effects of PM.

Animal models and human studies both support the conjecture than exposure to

relatively high levels of particulate matter and ozone alters the integrity of the lung blood-gas barrier (Foster & Freed, 1999; Kehrl et al., 1987). Bräuner et al. found that

background levels of ambient air pollution did not alter barrier integrity (Braüner et al.,

2009). However, these concentrations are based on European cities and do not

represent Zambian women's routine exposure concentrations (Braüner et al., 2009).

Additionally, a second study exposed individuals to three-hour chamber exposure to

woodsmoke and found indications that this did compromise the lung blood-gas barrier

(Barregard et al., 2008).

Gases emitted during the incomplete combustion of organic materials as by-

products in addition to PAHs and PM. Existing literature suggests that gas absorption

differs between sexes, an important gas being CO. Normal levels of carboxyhemoglobin

(COHb) in the blood fall between 0.4% and 0.7% (Jones & Lam, 2006; Stewart, 1975).

High levels of circulated COHb in the blood can be dangerous and accumulate quickly

as CO has an affinity for hemoglobin approximately 200 times higher than oxygen

(Levy, 2015). Further, there is mounting evidence demonstrating the ability of CO to

precipitate acute cardiac events that may result in death (Wickramatillake et al., 1998).

Exhaled CO is now used as a biomarker to test for certain pulmonary diseases, and,

while it is non-specific, it may give an initial sign that there is an underlying pulmonary

issue (Biernacki et al., 2001). Men consistently exhaled higher levels of CO than

women. Exhaled CO fluctuates consistently with air pollutants and may be indicative of

background levels of air pollution. Increased levels of exhaled CO trended towards

reduced forced vital capacity (FVC) and forced expiratory volume in one second

(FEV1). Whether these results indicate chronic lung deterioration or a symptom of short- exposure to AP is currently under investigation (Jones & Lam, 2006).

Pregnancy as a Female Specific Risk

Pregnancy is a time of rapid biological change for women that increases the risk of various diseases. During pregnancy, exposure to elevated air pollution levels may increase the risk of detrimental health outcomes for both the mother and the developing fetus. The elevated risk during pregnancy is of particular importance in populations with relatively high parity and poor air quality such as Zambia. Maternal exposure to PM10 has a strong association with respiratory diseases, and male fetuses are slightly more susceptible to intrauterine infections due to their slower immunological development compared to female fetuses (Perni et al., 2005). PM10 increases blood viscosity in

adults, which has the potential to reduce placental perfusion. Placental dysfunction is

more frequent in male fetuses, which may have a more severe impact on the

developing male than the developing female (Ghidini & Salafia, 2005). While PAH-

adducts do not show sex-specific differences, males have slower lung maturation during

development, and this oxidative stress may increase the permeability of male lung

epithelium to a greater degree than female (Carter et al., 1997; Catlin et al., 1990;

Donaldson et al., 2001). Mechanisms by which many other airborne toxicants alter the

developing fetus based on biological sex is still being researched; however, this work

points to plausible associations to be investigated (Ghosh et al., 2007).

Exposure to biomass smoke during pregnancy is associated with an increased

risk of stillbirth, low birth weight infants, and a mean decrease in birth weight (Amegah

et al., 2014). Female infants had an increased risk of low birth weight (adjusted OR 1.07

to 1.62) (Glinianaia et al., 2004). On the other hand, males had a higher risk of preterm

birth (adjusted OR 1.11 to 1.20) (Glinianaia et al., 2004). Increased maternal exposure

to CO could be the driving mechanism behind this phenomenon. CO can cross the

placenta via passive diffusion, reducing the amount of oxygen available for the fetus

(Longo, 1977). Fetal hemoglobin (hemoglobin F) has a higher affinity for CO than adult

hemoglobin, so levels of COHb are higher in fetal blood than that of the mother (Aubard

& Magne, 2000). Male fetuses have higher energetic demands than female fetuses;

this, combined with the relatively immature status of male fetuses compared to females

at similar gestational ages, could lead to sex-specific impacts. Studies examining

women who smoke during pregnancy support this hypothesis (Spinillo et al., 1994;

Zarén et al., 2000).

While there is a clear and robust association between toxicant exposure and

adverse birth outcomes, there is an additional risk to the mother during this time.

Increases in maternal blood pressure and preeclampsia correlate with biomass fuel use

(Agrawal & Yamamoto, 2015; Nobles et al., 2019; Wylie et al., 2015). The risk of

anemia also increases with exposure to AP, due to increases in systemic inflammation levels that alter red blood cell formation and longevity (Honda et al., 2017; Stanković et al., 2006). One study documented a relative risk-ratio for mild anemia 1.38, [95% CI:

1.19-1.61], and moderate to severe anemia at 1.79, 95% [CI: 1.53-2.09] (Page et al.,

2015).

While not a focus of this study, it is important to mention the potential implications

of PM, PAHs, and other constituents of AP for the developmental origins of health and

disease (DOHaD). DOHaD research focuses on how environmental conditions during

critical periods of growth and development (prenatal and early life) can have lifelong

effects on health and wellbeing (Gluckman et al., 2016). Epigenetic mechanisms such

as methylation, histone modifications, alteration to mRNA. regulate these phenotypical

changes (Gluckman et al., 2016). With information from ‘natural experiments’ like the

Dutch Winter Famine and now large cohort datasets, researchers can link

environmental conditions to diabetes mellitus, cancers, neurological disorders, and

immune function (Heindel et al., 2017; Jedrychowski et al., 2013; Lakind et al., 2014;

Newbold, 2004; F. Perera et al., 2012; Schulz, 2010). For example, Heindel et al.

recently reviewed all available environmental epidemiological literature examining how

in-utero and early-life exposure to environmental pollutants may be harmful over the life-

course (Heindel et al., 2017). Of the 425 published studies, 78 specifically addressed

AP (see figure 2-3) for more detail). The majority of the outcomes associated with these

exposures focus on respiratory outcomes such as wheezing, asthma, respiratory tract

infections, and decreased lung capacity (though a minority examined neurological

function and disorders) (Heindel et al., 2017). Evidence is building that early life

exposures to these compounds may significantly alter health and productivity for those

exposed throughout the life course.

Sex and Age-Specific Differences in Infants, Girls, and Boys

While many of the biological mechanisms underpinning the differences

contributing to the differential effects between boys and girls are still under investigation,

most research examining this topic document girls at higher risk of adverse health

outcomes (Clougherty, 2010). A systematic review found 23 articles reporting stronger

effects among girls compared to boys while finding only two that reported stronger

effects on boys (and three with null or mixed effect modifications).

Infants have a higher resting metabolic rate and an increased oxygen consumption rate compared to adults due to increased growth rates and high surface to mass ratios. At approximately one year of age, a resting infant’s oxygen consumption is

7 mL/kg/min, while an adult’s consumption is between 3 and 5 mL/kg/min (World Health

Organization, 1986). This increased oxygen consumption and respiratory rate may increase inhaled levels of airborne toxicants. For example, children seven to fourteen years of age exhibit 35% greater PM deposition in their lungs than adults after

controlling for total surface area (Bennett & Zeman, 1998; Ginsberg et al., 2004). Young

children frequently spend more time engaged in vigorous activity than adults, further

increasing oxygen consumption rates and potential exposure to airborne toxicants

(Reigart et al., 1993).

There are differences between adults and children at different developmental

stages that influence the metabolism of toxicants. Newborns have a relatively greater

water volume and reduced body lipids than adults (Clewell et al., 2004). These body

lipid levels rise steadily until 4-7 years of age, then drop again—corresponding to ages

of increased growth rates (Hattis et al., 2003). Infants have a larger liver to body mass

ratio than adults. While this allows for increased metabolic clearance of toxicants, it also

holds greater potential for activating toxic metabolites. PAHs are processed primarily in

the liver and result in diol epoxides capable of forming DNA adducts. In terms of

pharmacokinetic response to toxicants, children are likely to be much more variable in

their responses than adults are (Hattis et al., 2003). Further, children consistently take

much longer to clear xenobiotic compounds than adults— as much as two orders of

magnitude at the youngest ages compared to individual adults.

Hepatic immaturity has been documented using in vivo measures of enzyme activity and protein levels and studies examining drugs that involve mostly hepatic extraction (Cresteil, 1998; Dorne et al., 2001). Variations in hepatic processes are of importance in this context (Gibbs et al., 1997). Some CYP levels are very low in infants immediately after birth, but they generally reach adult levels by the time a child is one year of age. However, CYP1A1 and CYP1B1 levels appear to be high immediately following birth (Alcorn & McNamara, 2002). Previous studies examining the CYP1A family in infants have documented high metabolic activation of BPDE. Activation

occurred at a rate five to seven times greater than those in the adult liver (Shimada et

al., 1996). This increased activation may be because alpha-Naphthoflavone does not

inhibit activation of BPDE— despite being a known inhibitor of CYP1A1 (Shimada et al.,

1996).

Males and females mature at different rates and reach stages of biological

maturation at distinct times. These differences in growth and development may modify

the impacts of airborne toxicant exposure during the life course. Females reach

maximum lung function between 16 and 18 years of age, while male lungs continue

developing for several years after (Gold et al., 1996). Research on adolescent smoking

behavior showed that between the ages of 10 and 18, girls that smoked at least five

cigarettes a day experienced growth in FVC 0.76% slower than girls who did not smoke,

and an even slower rate of FEV1- a reduction of 1.06% per year (Gauderman et al.,

2004). There was no significant relationship between smoking and either measure of

lung function for boys in the group that smoked more than five cigarettes per day.

However, girls experienced significantly reduced lung function compared to their male counterparts (Gauderman et al., 2004).

Two important differences between adolescent girls and boys may involve (1) the length of chronic exposure to biomass smoke and (2) the age that exposure occurred.

As mentioned, female lungs reach maturity before male lungs (Gauderman et al., 2004).

It is unlikely that growth deficits during formative years will be repaired once maturation

is complete; however, removing someone from chronic exposure before growth is

complete may reverse the effects. When boys and girls begin taking on traditional

gender roles and tasks, it may be that boys experience reduced levels of HAP

attributable to biomass smoke, while girls continue to be exposed chronically to

relatively high levels. If this is the case, boys' lung impairment may be reversed while

female lung function remains stunted.

Zambian History and Current State

Zambia is a landlocked southern African country surrounded by the Democratic

Republic of the Congo, Tanzania, Malawi, Mozambique, Zimbabwe, Angola, Botswana,

and Namibia. As of 2017, the total population was 16,405,229, with 1,910,782 citizens

residing in Eastern Province. Eastern Province sits on the far eastern border, adjacent

to Malawi and is one of ten provinces that comprise Zambia. Eastern Province consists

of nine districts with Chipata district situated along the border (Figure 2-4).

The current energy, poverty, and infrastructure issues currently facing Zambia

cannot be understood without studying the country's economic history since its

independence. Scaling and maintaining a stable electric grid, particularly during the dry

season, is a continual problem faced by the country. Kariba Dam produces most of the

electricity in the country. The dam is on the Zambezi River on the border of Zambia and

Zimbabwe. However, in years with low rainfall, the dam cannot meet the population demands, and load shedding is quite common. Electricity for the home is prohibitively

expensive for many in the country, even with government subsidies (100 Zambian

Kwacha, ZMW, for approximately 230 units/kWh). This combination of factors forces

millions of Zambians to rely on biomass as their main source for fuel (firewood in the

rural areas and predominantly charcoal in urban/peri-urban).

Post-independence Zambia

The Government of the Republic of Zambia (GRZ) gained independence from

colonial rule on October 24th, 1964, backed by the fourth-largest economy on the

continent. The country had productive copper mines, approximately 39 million hectares

of arable land suitable for agriculture, and Victoria Falls/national parks to generate

tourism. The transition of power at the time of independence was peaceful, with

optimism for Zambia’s future. However, by the 50th anniversary of independence,

Zambia had some of Africa's worst social and poverty indicators despite gross domestic

product (GDP) growth (Whitworth, 2015).

At independence, the Zambian economy was a blend of public and private

enterprises. The public sector focused on infrastructure, agriculture, financing the

massive rural sector. At the same time, the private sector dominated the mining

industry. By the end of the 1960s, the GRZ concluded that private and ex-patriot

businesses focused more on quick returns than investing in the country and the people

themselves. In reaction to this, President Kenneth Kaunda announced the Mulungushi

Reforms in 1968 to nationalize more areas of the economy. The leading 25 non-mining

companies were ‘asked’ to offer the GRZ a minimum of 51% of their shares. The mining

industry was impacted even more severely by four main measures: (a) Mineral rights

would return to the GRZ. Following this, a new system of granting licenses would be introduced (b) the state would own 51% of shares in new mines (c) a single mineral tax would replace royalties and export taxes (d) like the businesses, previously operating mines were encouraged to give the majority of their shares to the government (Martin,

1972; Whitworth, 2015).

During the 1970s, Zambia adopted the economic policies of import substitution.

Protecting locally made goods from foreign products and manufacturing through tariffs, encouraging the production of Zambian goods to foster self-sufficiency. The country

went from having a mostly privatized economy to a state-dominated economy. This

growth took three distinct forms: (1) investment to reduce dependence on Rhodesia

(Zimbabwe) following the Unilateral Declaration of Independence (UDI), (2) direct

investment in large-scale manufacturing where the private sector was unwilling to

invest, (3) and continued nationalization of private enterprises.

While copper accounted for almost half of the country’s GDP during the first five

years after independence, mining was an industry that had relatively weak links with

many other areas of the economy. Further, it is capital intensive and creates relatively

few jobs compared to other sectors (direct employment by the mines has never

exceeded more than 10% of the total workforce) (Whitworth, 2015). Copper prices are

unstable in the global market, sometimes rising as high as $5690 per ton and falling as

low as USD 1176 (Hill & McPherson, 2004; Wen et al., 2019).

The economy was shocked in 1975, which began a sharp economic decline for

the country. The price of copper on the global market fell 51%, leading to a massive

decline in Zambian exports. Simultaneously, a major increase in world oil prices led to

rising transport costs and the sealing of the Rhodesian border in 1973, severely disrupting transport (Whitworth, 2015). At that time, the account deficit for Zambia was

K439million or 85% of exports. Increases in government expenditure from 30.8% of

GDP in 1974 to 50.1% in 1975 compounded this deficit. The drop in prices ended up lasting decades, devastating the economy, despite estimates from the World Bank anticipating rebounds as soon as 1980 (Whitworth, 2015). This drop led to one of the steepest declines in income ever seen in peacetime, during which Zambia had the

highest level of debt per capita in the world. Increased debt and economic downturns

led to a collapse of basic social services, and in 1991, 61% of the population was

classified as ‘extremely poor’ by the GRZ (Central Statistical Office, 1993). The

privatization of mines in the late 1990s and early 2000s led to a rebound in production,

coinciding with a price peak in 2004 (continuing to 2010).

Impact of Structural Adjustment Policies (SAPs)

Zambia first adopted structural adjustment policies in 1983 as part of a

requirement to receive external assistance from international financial institutions

(international monetary fund, IMF, and World Bank being the most significant). The

stipulations attached to the agreement included (1) a devaluation of the Kwacha, (2) a

limit to wage increases, (3) removal of government control over central commodities, (4)

and removal of subsidies on mealie meal and fertilizers. Importantly, this policy created

a foreign exchange auction system, meant to efficiently allocate foreign exchange and

end the foreign licensing system (Simutanyi, 1996). The effects of this led to a devalued

currency and deteriorating the living conditions. These conditions, combined with the

removal of mealie meal (maize flour) subsidies, led to a riot that killed fifteen protesters.

The public protest caused Kaunda to cancel the agreement with the IMF in 1987 and

implement the Interim New Economic Recovery Program (INERP). INERP adopted previous measures and recorded impressive real GDP growth of 6.2%, but it was not sustainable as the next year, international donors denied aid. The situation in Zambia became worse following this action, forcing the government to return to the IMF.

Following the implementation of new IMF policies, the prices of staple foods increased

by over 100%, which led to riots that again killed protesters; however, despite protests,

this time, the SAPs were not removed (Simutanyi, 1996).

During the early 1990s, there was widespread support for a new political party,

the Movement for Multi-party Democracy (MMD)— the main opposition party to United

National Independence Party (UNIP). Six weeks before the election, the IMF and World

Bank suspended their latest program and major donors withdrew support (totaling

approximately 200 million USD). The driving reason behind this was Zambia’s failure to

meet another arrears payment and implement austerity measures (Loxley, 1990). In

September of 1991, the budget deficit was so severe that the Food and Agriculture

Organization (FAO) considered Zambia eligible for emergency food supplies (Geisler,

1992). This rapid decline in agriculture during the late 1980s and early 90s is a direct

result of agricultural mismanagement and ill-conceived policies pushed under the yoke of SAPs.

Following the election, MMD agreed to continue with the implementation of the reform package. The MMD inherited an economy riddled with excessive deficits, inflation, erosion of infrastructure, and a decline in employment in the formal economy

(Van de Walle & Chiwele, 1994). The party counted on its widespread popularity to successfully implement these austerity measures. After implementing these measures,

the government withdrew all subsidies on mealie meal and fertilizer (1993), and the price of maize increased from 250K to 4000K (Simutanyi, 1996). Perhaps due to widespread support, the public did not protest to the same degree seen under the

Kaunda administration.

Current Demographics & Poverty Reduction Efforts

Despite consistent increases in many health and economic indices in recent years, Zambia continues to struggle with many of the issues associated with widespread poverty. The projected population growth rate for Zambia is quite high, estimated at 2.9% annually. On average, the projected total fertility rate for Zambia is relatively high, with the country-wide estimate of 4.7 (total fertility rate is the total number of anticipated live births for a woman between the ages of 15-49, weighted by years in each age group, and divided by 1,000) (Zambia Statistics Agency, 2019). The current life expectancy in Zambia is 63.5 years, with females averaging higher life expectancy than males at 66.4 and 60.5 years, respectively (World Bank, 2020a). This

increased life expectancy for women compared to men may be driven by a number of

factors such as (1) Zambian men are more than twice as likely to die from

accident/suicide (2) malnourished male children are at increased risk of dying (3) men

are less likely to adhere to recommended anti-retroviral medication therapies and (4)

women on average live longer than men (Central Statistical Office, 2012; Denison et al.,

2018; Munthali et al., 2015).

Child nutrition and stunting is a constant issue for the country despite having a

large agricultural base to their economy. Between 15-45 months of age, the prevalence

of stunting peaks at 45%, underweight at 19%, and lastly, wasting at 11%. Specifically,

stunting is a measure of chronic malnutrition, while wasting is more indicative of severe

acute bouts of malnutrition. The current under-five mortality rate is 75 per 1,000 live births, a decline of approximately 61% in the past two decades (Zambia Statistics

Agency, 2019).

Zambia’s current recommendations are exclusive breastfeeding during the first six months after birth and a combination of breastfeeding and supplementary food until two years of age. These recommendations hold for HIV-positive mothers in resource- limited settings as the rate of mother to child transmission via breastmilk is extremely low with appropriate use of antiretroviral medication (Kilewo et al., 2009; Peltier et al.,

2009; Thomas et al., 2011). The prevalence of HIV in the country is estimated to be

13.3% of people between the ages of 15-49, based on the 2013/14 Demographic and

Health Survey (Central Statistical Office, 2014).

The GDP of Zambia was 20.96 billion USD as of 2016, with a per capita GDP of

1,315.80 USD. The country’s two most important trading partners are Switzerland and

China, with China increasingly playing a prominent role in the economy (World Bank,

2018). In Zambia, 58% of the population is living in poverty (World Bank, 2020b).

Prevalence is slightly higher in Eastern Province, where 70% of the population lives in poverty as defined by international poverty indicators (i.e., less than USD 1.90). A slight

majority of the population, 53.8%, lives in rural areas with the rest residing in urban

environments. Approximately 61.4% of the urban population is unemployed and 38.6%

of the rural.

Today, many poverty reduction efforts focus on reducing staple food prices by

introducing subsidies and boosting agricultural productivity. More than 90% of GRZ

funding aimed at poverty reduction is allocated towards the Food Reserve Agency

(FRA) and the Farmer Input Support Program (FISP) (Mason & Myers, 2013). These two programs are responsible for purchasing maize at above-market prices and

subsidized fertilizer distribution, respectively (Mason & Myers, 2013). Despite these

efforts, there was no significant rural poverty reduction from 2004 to 2011. While maize

production increased 92% on average per household, the net value added only

increased by 20%. However, in 2011 favorable weather patterns may have driven this

shift. Mason & Myers (2013) estimate that weather patterns increased the area harvest

ratio by 58%-75%. Fertilizers may contribute as much as 36% of increased yield, but

only 5% of the total area under cultivation. Together this explains only 15% of growth as

of 2011.

Further, it appears that larger farms (> 2 hectares) disproportionately benefit from

these programs. Those households cultivated less than one hectare of land only saw

7% of the increase in maize production. Efforts by both FRA and FISP may have

contributed to an increase in market prices of maize by up to 20%, negatively affecting

urban populations and rural populations that most supplement smallholder production

with purchased maize (Mason & Myers, 2013).

Finally, Zambia is an excellent country to study how environmental pollution

impacts population health as the Global Burden of Disease (GBD) study places Zambia

in the highest tertial (comparing all countries with available data) of deaths attributable

to pollution. This high prevalence of death places it in a similar tertial with India and

ahead of China in terms of deaths per 100,000 people (Forouzanfar et al., 2016).

Demographic Information on Eastern Province

The most common languages spoken in Eastern Province are Chewa, Nsenga,

Nyanja, Tumbuka. Eastern Province is one of the most rural (only 13.8% of people in

Eastern province live in an urban area) and relies significantly on agriculture for home consumption and to sell for liquid assets (Central Statistical Office, 2012). Despite the reliance on agriculture, stunting is relatively high in Eastern Province at 34%, making it the fourth highest of ten provinces. The median age at first birth is 18.7, and infant mortality rate is above the national average, sitting at 97 deaths per 1,000 live births

(Central Statistical Office, 2012). The most common cause of premature death is disease/illness at 75.7%. The rates of premature deaths due to suicide and accidents were twice as high in males as opposed to females. Male children were at increased risk of dying in a group of under-five children with severe acute malnutrition (Central

Statistical Office, 2012; Munthali et al., 2015). The largest proportion of men with no

education is in Eastern Province at 13%, and similarly the literacy rate is lowest at

54.4%. The percentage of the population attending school at the age of five or older is

27.5% (Zambia Statistics Agency, 2019). When only primary school is included the

percentage increases to 63.2%. Women and men in this Province are least likely to

report knowing methods for prevention of HIV, this lack of education additionally results

in this region having the highest reporting rate for STIs (12%) (Zambia Statistics

Agency, 2019). Additional demographic information is outlined in Table 2-2.

Toxicology, Capacity, and Recent Scholarship in Africa

Noémi Tousignant describes the issues of toxicant exposure in Africa, focusing

on Senegal, in her recent work Edges of Exposure: toxicology and the problem of

capacity in post-colonial Senegal (Tousignant, 2018). She outlines three main topics of

growing interest among toxicologists working in Africa broadly. These general groups

are heavy metals (e.g., lead, mercury), pesticides (e.g., organophosphates,

organochlorines), and aflatoxins (largely focused on Aflatoxin B1) (Tousignant, 2018).

The first two are largely driven by economic activities that lead to exposures, such as spraying a field with pesticides in the absence of proper protective gear. Aflatoxins are a naturally occurring toxin produced by the Aspergillus flavus fungus that contaminates

and grows on stored crops such as maize and groundnuts. Air pollution as a central

focus still lags behind these other sources of exposure despite compelling data that it

precipitates huge numbers of premature deaths annually. As an example, Coker and

Kizito completed a systematic review investigating epidemiologic studies of AAP in SSA

and found only twelve studies in peer-reviewed journals, all published in 2011 or later

(Figure 2-6) (Coker & Kizito, 2018).

Tousignant then distinguishes between the terms ‘waste’ and ‘toxicity’ as they relate to Nixon’s idea of ‘predicaments of apprehension.’ Waste is generally an obvious phenomenon (e.g., leaking waste barrels, piles of electronic equipment waiting for their metals to be repurposed). Waste an individual can see, smell, and taste when there are high levels of waste in the environment around them. Conversely, many environmental toxicants are encountered in sublethal levels whose effects accumulate over time in a

‘slow-motion toxicity’ (Nixon, 2011). This difference is particularly relevant to many components of AP as they may be completely undetectable by either smell or taste in harmful concentrations—and related to issues of risk perception previously outlined.

Secondly, Tousignant focuses on framing these exposures as inevitable, given the lack of protection instead of a missed opportunity for protection. Thus, reframing the issue in a way that does not naturalize the absence of protection (Tousignant, 2018).

Another issue concerning toxicological research, or research that includes elements of toxicology, is that of surviving as a publicly funded endeavor on the

continent. While national investment in the sciences saw a boom in post-colonial Africa

through the 1970s, many countries experienced severe economic declines in the 1980s,

during which public investment in the sciences stagnated and has yet to fully recover in

many areas (Gaillard et al., 2005; Ghai & Alcantara, 1990). While international and transnational funding for health research and public health interventions, toxicological work receives relatively little funding (Tousignant, 2018). The relative difference in funding may be due in part to the focus on infectious disease prevalence, from the

1980s through today, and the prohibitive costs of some toxicological equipment.

Tousignant informally talks with and interviews scientists in Dakar who express a desire to have and frustration with access to laboratory equipment, to publish in more impactful journals, have more interesting projects for their students. (Tousignant, 2018). However, there may be positive news in the future concerning African scholarship. Recent bibliometric analyses show that since 2004 African scholarship, measured by publication numbers and impact, is increasing faster than has been seen yet (Confraria

& Godinho, 2014). While the main thematic areas of focus are agricultural sciences,

environment/ecology, and some specialized areas of health science, the trend is

positive and will hopefully encourage further interdisciplinary collaboration (Confraria &

Godinho, 2014).

Figure 2-1. A typical household brazier in use cooking chips, July 27, 2019. Chipata. Courtesy of the author.

Table 2-1. List of EPA priority PAH pollutants (Andersson & Achten, 2015) PAH Carcinogenic (c); PAH Carcinogenic (c); mutagenic (m); mutagenic (m); genotoxic (g) genotoxic (g) Naphthalene C, M, G Chrysene C, M, G Acenaphthylene M, G Benzo[a]anthracene Unknown Acenaphthene Unknown Benzo[b]fluoranthene Unknown Fluorene M, G Benzo[j+k]fluoranthene Unknown Phenanthrene M, G Benzo[a]pyrene C, M, G Anthracene M, G Indeno[1,2,3-cd]pyrene Unknown Fluoranthene C, M, G Dibenzo[a,h]anthracene Unknown

Table 2-1. Continued PAH Carcinogenic (c); PAH Carcinogenic (c); mutagenic (m); mutagenic (m); genotoxic (g) genotoxic (g) Pyrene M, G Benzo[ghi]perylene Unknown

Figure 2-2. Metabolic pathways of benzo[a]pyrene (Harvey et al., 2005)

Figure 2-3. Chemical exposures assessed in environmental epidemiology DOHaD studies (Heindel et al., 2017)

Figure 2-4. Map of Zambia and surrounding countries (World Atlas, 2020)

Table 2-2. Demographic Information for Eastern Province Population Males 784,680 Sanitation Females 807,981 Improved 39.2% Total Population 1,592,661 Unimproved 44.5% Median Age 16.1 Open Defecation 16.3% Growth Rate 2.6% Education (Women) Media Access None 13.2% Newspaper 16.6% Some Primary 51.0% Television 19.0% Completed Primary 9.9% Radio 33.7% Some Secondary 19.5% All Three 3.6% Completed 3.5% None 51.6% Secondary Higher 2.9%

Figure 2-5. All epidemiologic publications examining ambient air pollution and a specific health outcome in sub-Saharan Africa (Coker & Kizito, 2018)

CHAPTER 3 RESEARCH DESIGN AND METHODS

This research project is a mixed-methods study, utilizing both qualitative and quantitative data to address the specific research objectives and answer the associated hypotheses. The first half of the study focuses on traditional anthropological methods and those stemming from cognitive anthropology. Personal interviews and participant observation, in combination with techniques such as consensus analysis, allowed for the measurement of individual personality and idiosyncrasies while testing which beliefs hold over the larger cultural group(s). The second half of the study uses biometric measures and personal/environmental samples to assess airborne toxicants' presence and levels. The study specifically focuses on biometric measures of cardiovascular and pulmonary health as these are the most impacted directly by inhaling airborne toxicants

(Franklin et al., 2015; Lee et al., 2014; Sun et al., 2010). Three separate methods were used for environmental and personal samples to triangulate exposures to airborne toxicants.

The combination of qualitative and quantitative data is necessary to determine what beliefs and behaviors govern exposure to these chemicals. By tying exposures and perceptions together, this study builds on existing literature in several ways. First, it provides information that helps account for unexplained variance in epidemiologic studies between groups that do and do not use biomass fuel as their primary energy source. Additionally, the use of mixed-methods helps assess the degree to which perceived risk influences behavior in resource-scarce environments, commonly seen in many areas of Zambia and sub-Saharan Africa.

Study Area

Chipata, the capital of Eastern Province, lies at the end of the Great East Road- the main highway that snakes through the country beginning in Lusaka and ending almost 600 kilometers to the east where Zambia meets Malawi. This is the only road that connects the country’s capital to Chipata and the Eastern Province. It is a two-lane tarmac that, through capital provided by other countries, is reasonably well maintained.

Potholes are infrequent and the tarmac has been recently repaved in many sections of the road by funding provided by the Italian government. At the eastern end of this highway, just a few kilometers shy of the border with Malawi is the township of Chipata.

Chipata is the largest city in Eastern Province and one of the largest in the country, hosting a population of roughly 450,000. It sits in a valley surrounded by hills that are heavily wooded, which is harvested frequently to produce charcoal. The official language of Zambia is English, but there are seven main local languages and a total of

72 recognized in the country. In Chipata, Chinyanja (interchangeable with Chichewa) is the primary language spoken, similarly the dominant ethnic group are the Chewas.

Other significant ethnic groups in this area are the Nsenga, Ngoni, and Tumbuka. While

Bemba is the most dominant language in the country outside of English, it is not commonly spoken in this area, it is much more common to hear Bemba spoken in the

Copperbelt and Northern Provinces. The population here relies primarily on agriculture, with maize and groundnuts being the primary cash crops.

The main business/economic area of town lies along either side of this road. The largest buildings are three grocery stores, scattered around them are numerous other chains and small businesses. Just to the south/southeast of this main shopping area lie the golf course and hospital, oriented along the mostly paved Kalingalinga road which

runs parallel to the Great East Rd. This area is a middle-class area of Chipata that is popular for its convenient location in town with an easy walk to the grocery and shopping areas.

The two compounds (the term compound is interchangeable with neighborhood)

in which this study took place are Kalongwezi and Mchini. Kalongwezi is much closer to

the main area of town. When walking to Kalongwezi from town, you see the ‘golf course’

on the right-hand side of the road. The course is a large, open field of grass, dotted with

trees with footpaths that crisscross its area. On the weekend you can occasionally see

some older men hitting golf balls in the field and putting in several gravel greens. To the

left-hand side are dead-end roads that branch off perpendicularly to Kalingalinga, but do

not reach the main road. Houses line these roads, separated from each other and the

areas outside by walls lined with barbed wire or broken glass. More affluent houses in

the area might have electrified wire running the course on top of their cinderblock walls,

while less affluent community members have walls and gates constructed from wood

and woven grasses (Figure 3-1 and Figure 3-2).

Houses in this neighborhood are typically well constructed. Made of either brick

or cinder block and often covered with a smooth cement. Frequently they are painted

bright colors and stand out starkly from the brown dust and dry grass that dominates the

landscape from April until the rains begin again in November. Most of the homes in this

compound have yards large enough to have gardens and free space for clotheslines,

and outdoor areas for guests to sit. All the households here have access to electricity

and the vast majority connect to the province’s main water/sewage company.

The home of Elizabeth Banda (a pseudonym—all participant names are pseudonyms to protect individual identities) is an example of a typical Kalongwezi household. The main sitting area has couches and love seats organized around a coffee table and the television off to the side along a wall when you enter the household. If you continue through to the next room, you find yourself in the kitchen.

Inside is a refrigerator, an electric stove, and a cabinet with various dry goods, pots/pans. Another doorway in the kitchen connects to a small hallway with two bedrooms, a bathroom, and a shower. Walking through the third and last doorway in the kitchen leads you to an enclosed patio area with a kitchen table and a sink for washing dishes. While it is walled and has a roof, the windows do not have panes, only iron bars to stop anyone from entering in the middle of the night. Outside the house is a large garden where the family grows vegetables for household consumption and gifts for

friends. This year they are growing maize, sweet potatoes, onions, tomatoes, and

various other vegetables and herbs. The rest of the yard contains various flowers and

fruit trees, including red and white guava, granadilla (passion fruit), and mango.

People living in this area use primarily electricity for cooking, with some charcoal

use for foods that take longer to cook (e.g. beans, dried fish). Liquefied petroleum gas is

more expensive in this area than electricity, so it is rarely used in homes. Homes here

have indoor showers, with water heaters on the outside of homes. On the occasions

there are power outages people here may resort to heating water in buckets over the

charcoal brazier to warm them for bathing.

Residents of Kalongwezi work in town in a largely professional setting such as

banking or a municipal occupation. The reliable paychecks and higher wages let this

community live comfortably with more economic stability than some of the other areas in

town. Many of the women enrolled in this study were similarly educated to their

husbands, having either advanced certificates or college degrees from one of the

country’s universities. Despite holding college degrees, it is difficult to find employment

for many people in Zambia, and many participants who lived in Kalongwezi were

actively looking for employment during the 12-month data collection period. However,

one income (usually the husbands or a relative) was enough to support a family

comfortably. The most common professions for women in this sample were teaching or

running side businesses out of the home. The most lucrative of these side businesses is

raising broiler chickens for sale. Several hundred chicken can be raised in a small

space, and they mature very quickly, reaching maturity in just six to eight weeks. The

households in this area are generally nuclear families consisting of a husband and wife

along with their children. On occasion grandchildren would live in the home as well if the

son or daughter were unable to support them alone.

If you continue down the main road east towards Malawi, approximately two

kilometers, you will find the compound of Mchini. Located farther outside town, the

houses here are much smaller than those in Kalongwezi, usually made from mud-brick,

and spaced much more closely together. Unlike the relatively flat ground that the main

area of town and Kalongwezi rests on, the terrain in Mchini is much hillier, with most

houses built on foundations fitted into the sides of hills. Apart from one road cutting

through this compound, all of the paths to walk or drive are unpaved and pockmarked

by rocks and gullies from water cutting through during the rainy season. Walking areas

are lined with trash and picked over by the ubiquitous chickens and dogs in the area.

Homes in this compound generally consist of two rooms, a sitting room, and a bedroom shared by all household members. However, some households are large enough to have more than one bedroom, and occasionally an indoor cooking area. Most of the houses connect to electricity through Zesco, the national electric company.

Electricity in this compound is used primarily for lights, though some of the more affluent families have television sets and most have a radio of some variety. The roofing consists of thatching and metal sheets to keep out the rain, though leaks are a consistent issue in the rainy season (November to April).

Occupations in this area differ significantly from those typical to Kalongwezi as well. Here most households engage in a variety of ‘piece work,’ which consists of finding odd jobs to sustain themselves. These jobs could be cleaning, washing, or other similar work. There are many small stalls in which women display vegetables grown in their gardens such as tomatoes, onions, and (English) rape, alongside small plastic bags of charcoal, sweets, and bottled of the locally brewed spirit kachasu. The husbands of

participants here work more manual, unskilled labor around town than those husbands

in Kalongwezi. These occupations ranged from construction to selling goods such as

belts, keychains, and the like in the parking lots of the grocery stores in town. These

parking lots are the most likely place for tourists who are driving through to stop, which

are their primary clientele. There used to be a bicycle manufacturing facility in Chipata

that, even though it is closed, inspired a culture of biking in town. Many men, beginning

in their teenage years, will purchase a bike with a seat on the back. They will sit on busy

street corners around town and give rides to people for a small fee (usually 2-5 kwacha

depending on the distance traveled). In the same areas you find children selling fritters

in plastic tubs, prepared by their parents earlier that morning. These bicycle taxis are frequent customers for these fritter sellers, along with other people walking to and from town for either work or to run errands.

Chipata sits on the border with Malawi, which is much smaller and less developed than Zambia. The relative strength of the Zambian kwacha compared to the

Malawian kwacha led to frequent border crossings from Malawian citizens. It is common to find men walking over the border and spending several days in Chipata, selling wares

(shoes, clothing) on street corners before heading back to Malawi. Frequently Zambian farmers will sell crops, particularly maize and groundnuts, that are of too low a quality to sell in town to Malawian traders. These Malawian businessmen are colloquially known as ‘suitcase’ traders and come over the border in droves at the end of the harvest. A

50kg bag of unshelled groundnuts may sell in town for 7 Zambian kwacha, but to fetch this price the crops must be undamaged and healthy. Suitcase traders offer far less money, 2-3 kwacha per bag, but the farmers are able to offload otherwise unsellable crops and recoup some investment. Money traders are common in this area as well.

These money traders are men who group together in the main business area of town and flash thick rolls of Malawian kwachas, claiming to offer a better exchange rate than the banks of either country.

Sampling

Based on the advice of the Provincial Environmental Health Officer, Mr. Bernard

Khoza, Kalongwezi and Mchini compounds were chosen due to the high prevalence of electricity use in the former and almost exclusive charcoal/wood use in the latter.

Sampling was performed at random, pulling from areas within the Mchini and

Kalongwezi clinics' catchment areas that were reachable during all seasons as

predefined sampling frames. A random sample was chosen from these frames to estimate the population parameters of those living in these two compounds (Bernard &

Gravlee, 2014). However, this technique does not always yield experts in specific cultural knowledge, and experts' cultural data was vital to this study. To account for this,

key informants were recruited during participant observation to augment the information

from others in the random sample. Following random sampling, 25 households in each

compound were chosen for a total of 50 households in the entire study. This sample

size was chosen to ensure validity and reliability when designing the formal consensus

analysis questionnaires in the latter half of the study (Weller, 2007).

In Kalongwezi, a random number generator was used to select households from

a census recorded by the 2017 Ministry of Health National Malaria Elimination Center

(random number generation created using RStudio 3.5.2) (R Core Team, 2018). In

Mchini, there was no available registration due to the peri-urban and rapidly expanding

nature of the compound. In lieu of this, the section chief, Mr. Frank Kosi Mseteka,

conducted a census of a subset of Mchini C that occupied an area south of the road that

divided this sub-compound from Mchini B. From this census, households were randomly selected in the same manner as Kalongwezi, using a random number generator to select homes for participation in the study. In each compound, a greater number of

households were randomly sampled and identified for contact to compensate for

households that did not meet inclusion criteria or who were not interested in

participating in the study (Table 3-1).

A total of 55 participants were enrolled in the study, 30 and 25 in Kalongwezi and

Mchini, respectively. Participants' characteristics are summarized in Table 3-2, Table 3-

3, and Table 3-4. The two compounds represent two distinctly different SES groups. On

average, the households in Kalongwezi have fewer children, higher education levels,

and relatively larger homes. They are more likely to have access to improved water and

sanitation facilities and have a bank account. Other metrics such as participant age and

marital status do not vary significantly between the groups (significance determined

using t-test, chi-square, or other non-parametric methods when appropriate).

Households in Kalongwezi, being of a slightly higher SES, frequently employed a

maid. Male heads of house in this compound frequently held jobs in the local

government or worked in businesses in town in one capacity or another that provided

consistent wages. In some cases the husband, or female head of house, would be

retired and their children would help support them, providing money for maids and

household goods. As a result, 5 of the homes sampled relied on maids to primarily cook

meals throughout the day. In these cases, both the maid and the woman in the house

were enrolled in the study. This approach was used to assess how/if cooking practices

change in the presence of a maid (e.g., differences in types of meals prepared or

shared cooking responsibilities) and to measure whether employing a maid meant the

woman of the house was exposed to less AP when a maid fills the role as the primary

cook throughout the day. The addition of the maids brought the total number of

individuals enrolled in the study to 55. However, data collected from maids were not

analyzed alongside the data gathered from those who lived in the compound as they

come from a distinctly different SES background. One participant became pregnant

during the latter half of the study, but this did not impact her ability or willingness to

continue participating.

The Co-PI worked with four community health workers (CHWs), two from each compound’s clinic. Mr. Samuel Nyirenda & Mrs. Eunice Tembo in Mchini, and Ms. Anna

Banda & Mr. Julien Kawele in Kalongwezi. Households, specifically the main cook for the home, were initially contacted via door-to-door visits, explained what this study entailed, and asked if they would be interested in participating. Those who agreed we returned to for a follow-up visit to sign consent forms— fingerprints were taken for those

unable to sign their names (all forms with identifying information were kept in a secure

location on-site and will be destroyed following final reporting). These households were

included in the remainder of the study and used to inform the methods used in the

second half of the study (R3).

Each household in this sample was subject to at least seven visits, during which

time the interview, biological, and/or environmental data were gathered. All visits took

place between the hours of 8:00 and 17:00. Interviews took place in either English or

Chinyanja, depending on which language the participant was most comfortable and

could most accurately answer questions. Interviews that took place primarily in

Chinyanja were conducted by the Co-PI for the study (Dillon) with CHWs acting as translators. All interviews took place in the participants' homes or in the areas immediately surrounding them based on the participant's activity when the research team arrived. Interviews were scheduled in advance, when possible, to ease any time

burden felt by the respondents. Similarly, no interview lasted more than one hour.

The consensus analyses, conducted in August 2020, utilized a convenience

sample out of the Kalongwezi and Mchini clinics (see research objectives). The Co-PI

worked with two research assistants (Ms. Monica Banda and Ms. Emelia Banda) to

recruit the first 50 individuals who arrived at each clinic who met the same inclusion criteria as the participants who participated in the larger part of this study. This approach yielded 100 participants who answered two consensus analysis questionnaires focusing on local knowledge of disease/illness and cooking practices.

Research Objectives

The overall objective of this research is to investigate if the perceived risk/vulnerability, in a socially and structurally constrained environment, mediates exposure to environmental toxicants associated with biomass fuel use. This research project contains five specific research objectives with separate sets of methods,

analyses, and hypotheses.

Research Objective One (R1)

Determine chronic cardiopulmonary disease prevalence within the populations of

interest using biological measures and interviews to elicit recent/current illnesses.

1. Hypothesis 1 (H1): There is a higher prevalence of cardiopulmonary disease in the population that uses traditional cookstoves. 2. Hypothesis 2 (H2): There are higher incidence rates of respiratory/cardiovascular diseases in the population that uses traditional cookstoves.

Research Objective Two (R2)

Determine environmental and personal toxicant exposure levels in biomass and

electricity using populations, by deploying passive air samplers to monitor ambient

toxicant levels, personal air quality monitors to estimate personal exposure levels, and

urine collection for metabolite analysis.

3. Hypothesis 3 (H3): Ambient levels of airborne toxicants are higher in the population that uses traditional cookstoves. 4. Hypothesis 4 (H4): Personal exposure levels to airborne toxicants are higher in the population that uses traditional cookstoves.

5. Hypothesis 5 (H5): Urinary metabolite levels associated with airborne toxicants are higher in the population that uses traditional cookstoves.

Research Objective Three (R3)

Determine if there are shared models of risk and disease/illness between/within

communities by utilizing traditional ethnographic field methods, in-depth interviews, and

cognitive anthropology methods to create and test a formal cultural consensus model.

6. Hypothesis 6 (H6): There are shared cultural models of risk between the two study populations. 7. Hypothesis 7 (H7): There are shared cultural models of disease and disease etiology between the two study populations.

Research Objective Four (R4)

Determine if risk perceptions associated with exposure to biomass fuel emissions correlate with increased or decreased personal levels of exposure to toxicants by comparing self-reported perceptions of risk with the results of the toxicological analyses.

8. Hypothesis 8 (H8): Increased perceived risk of biomass fuel emissions results in lower personal levels of exposure as measured by urinary metabolites in the exposed population.

Research Objective Five (R5)

Determine what utilitarian and cultural aspects of cookstoves are important in the study populations using traditional ethnographic field methods and semi-structured in- depth interviews with informants to elicit narratives.

9. Hypothesis 9 (H9): Traditional cookstoves in the exposed population perform culturally important functions beyond their utilitarian uses. 10. Hypothesis 10 (H10): Improved cookstoves have different cultural meanings than traditional cookstoves.

Ethnographic Methods

This study combines anthropology methods with methods from both toxicology and epidemiology to meet study research goals. By incorporating techniques from anthropology, specifically cognitive anthropology, with techniques to assess AP exposure levels, it is possible to get fine-grained detail of what factors mediate exposures. Aggregating this data to the group level, then, makes it possible to see if there are consistent trends that may represent a significant threat to health in these populations. These methods build and integrate onto one another throughout this study and provide personal and group-level data on AP exposure in the Eastern Province of

Zambia.

Behavioral Observation

The Co-PI lived in one of the research compounds (Kalongwezi), renting a home

from a family he met during fieldwork in Chipata in 2016. I resided there the entirety of

my stay in Chipata (over nine months). Residing in the community allowed the Co-PI to

observe daily life and the routines that influenced air quality in the compound. Long-

term residence in the compound was important for allowing the Co-PI to witness

biomass fuel burning in various contexts outside of cooking such as garbage burning,

and burning yard waste. There were relatively few incidents of public cooking as a

cholera outbreak two years prior resulted in a ban on cooking and selling food in the

street. People wishing to sell baked goods such as fritters prepared them at home early

in the morning and packaged them in large plastic bins to carry and sell in and around

town.

Structured Questionnaires

Each participant answered questions from a structured questionnaire designed to collect household demographics, socioeconomics, cooking practices, and health. The questionnaire was written in both English and Chinyanja, and the questions were asked

in the language each participant felt most comfortable using. Direct questions about

household income were not used to avoid making participants feel uncomfortable.

Instead, proxy questions were used to assess general household wealth (e.g., number

and type of appliances, access to electricity, automobile ownership). Quantitative results

were compared between groups to note any statistically significant differences between

the groups using parametric and non-parametric tests when appropriate. The qualitative

information from interviews informed later unstructured and semi-structured

questionnaires on these topics. For each visit to the home, the study participants were

paid for their time with 20 ZMW to compensate them for the time spent away from work

or daily activities. When possible, these visits were scheduled in advance to ease the

burden on participants.

Free Lists

Participants answered an unconstrained free list prompt to elicit all the diseases

they thought were important or problematic in the community/area. Though written free

lists are recommended when possible, the relatively low frequency of literacy among the

study population made this prohibitive (Quinlan, 2005). As a result, all participant lists

were collected verbally to minimize bias of multiple collection methods. Participants

were systematically probed to elicit more diseases until they could provide no additional

diseases that affected their area of residence significantly. Follow-up questions were

then asked for the first five diseases mentioned in each list to assess disease attributes

such as perceived prevalence, severity, general knowledge concerning the disease, and where they get most of their information on diseases. This free list activity served several purposes. First, it helped assess whether the diseases associated with biomass

fuel use were perceived as locally common or important diseases. Second, it allowed

participants to identify smoke inhalation as a potential cause of disease. Third, it allowed the elicitation of responses for later use in the pile sort activity.

Responses were aggregated and analyzed for frequency, rank, and salience

(Smith’s S) to help delineate the local knowledge of disease/illness using Visual

Anthropac 1.0 (Analytic Technologies, 2003a; J. J. Smith & Borgatti, 1997). Spearman

rank correlation was completed on the salience scores (extracted from Anthropac) to

estimate cultural significance differences between groups. Salience scores were then

plotted together on different axes with a 45° abline to visualize how diseases pulled

more towards one community or another. Spearmen rank correlation, and figures were

produced using RStudio 3.5.2. (R Core Team, 2018).

Pile Sorts

Pile sort cards were created from the results of the free lists from both

Kalongwezi and Mchini. Any item that appeared with ≥15% frequency was included in

the pile sort. To directly compare piles between the two groups, the items were

compiled into a single set. This approach resulted in 27 diseases/illnesses per group.

Laminated cards were made with the name of the disease and a picture associated with

that disease for participants who were unable to read. The piles were presented to each

participant laid out in a grid. The participants then asked to complete an unconstrained

pile sort and cluster the cards into groups of diseases that made intuitive sense to them

(they were advised to limit the number of singletons left at the end of the exercise). After

a participant finished sorting the cards, they were asked to explain for each pile why they put the diseases together and what the diseases had in common with one another.

Additionally, the participants were asked to explain the criteria they used to place

diseases together (e.g., symptoms, area of the body affected, contagion). Initially, the

pile sorts were compiled and analyzed using Visual Anthropac 1.0 (nMDS visualizations

available in the appendix) (Analytic Technologies, 2003b).

Clusters were identified from the aggregated pile sort data using three different k-

means clustering metrics: elbow, silhouette, and gap-statistic. The "elbow" method uses

the sum of squared distances between centroids and between individual data points.

The result is a scree plot where a sharp turn, or elbow, is a good indication of the ideal

number of clusters; however, many times, there is no such obvious point. Silhouette

clustering measures (1) the average distance between all points in a single cluster, (2)

the average distance to the next closest cluster of which data point i is not a member,

(3) and computes a coefficient ranging from -1 to 1. Many items with a score close to ‘1’

indicate good cluster separation (Rousseeuw, 1987). Lastly, the gap statistic method

examines intra-cluster variation to a null distribution. The difference between observed

intra-cluster variation and null is then calculated (along with standard deviation). The

goal is then to select the first time the gap statistic falls within one SD of the gap at

Gap(k) ≥ Gap(k + 1)−sk + 1 (Tibshirani et al., 2001). I used all three together to triangulate

the optimal cluster solution because (1) a readily apparent ‘elbow’ was not clear and (2)

to make sure centroids were not unduly pulled by outliers using the silhouette or gap

statistic method alone. I then used ethnographic data to decide on the final cluster

assignments. These clusters were visualized in PCA space with clusters mapped to

examine if modes of categorization emerged that were not readily apparent from initial pile sort data (and to examine how much variance may be attributed to these modes).

Quadratic assignment procedure (QAP) (Pearson correlation) was then used to examine the pairwise similarity between the two matrices in UCINET 6.665 (Borgatti et al., 2002). Five thousand permutations were run on the Mchini C and Kalongwezi item aggregate proximity matrices. Additionally, consensus analysis was performed on the compounds separately and on data aggregated together for all participants to determine the extent of an underlying cultural domain governing the categorization schema for local diseases. While this violates one of the underlying assumptions of consensus analysis for pile sorts, that all participants divide into the same number of piles, it is still useful for generally estimating if there is an agreement within and between groups.

Consensus analysis was performed using the CCTpack package in RStudio version

3.5.2. Model parameters are included in Table 3-5 (Anders, 2017; R Core Team, 2018):

These parameters were used to run the Mchini C and Kalongwezi groups separately

and together, to compare levels of consensus and whether both groups drew from the same underlying cultural knowledge.

Rank Order of Disease Severity

Participants were asked to rank all the diseases presented in the pile sort based on how dangerous/severe they perceived the illness to be. A score of 1 would correspond to the most dangerous disease, while 27 the least dangerous. These results were aggregated within each group to examine which diseases were consistently seen as particularly dangerous and which diseases were thought of as relatively minor. Like the free list activity, this helped assess if any of the diseases related specifically to

exposure to biomass smoke were seen as dangerous (comparatively) and something that the participants actively worried about developing/contracting.

Bayesian Cultural Consensus Analysis (BCCA)

A cognitive theory of culture posits that culture comprises a collective understanding and meaning between individuals within a discrete group. Culture is the information passed extra-somatically between people and generations, allowing members to navigate within their cultural group. This insider knowledge is organized into working schema and models. These schema and models act as stripped-down representations of a cultural domain (e.g., cooking, disease, religion). These domains are uniquely cultural as the information passed from others within the cultural group

(Batchelder & Romney, 1988; Dressler, 2005; Dressler et al., 2012). In theory, no one person can have perfectly authoritative knowledge of any domain. Instead, knowledge is distributed among individuals that together comprise the cultural knowledge (Batchelder

& Romney, 1988; Roberts, 1964). Within any domain, there are members of the cultural group that will be more or less competent based on their specific pool of knowledge

(and in the case of cultural consonance, to the degree their agency is constrained to act on the knowledge). Relying on culture as consensus is useful because sampling from dozens of participants adjusts for some of the noise related to individual personality, and idiosyncrasies.

Cultural consensus analysis (CCA) and a suite of techniques associated with cultural consensus theory have been used and refined in cultural anthropology, specifically by those focusing on this cognitive theory of culture (Batchelder & Romney,

1988; Weller, 2007). To create a questionnaire to perform formal cultural consensus analysis first requires in-depth ethnographic experience and cultural knowledge of a

specific cultural domain. This research is informed by participant observation, free lists, pile sorts, semi-structured and unstructured interview data. Responses to these questionnaires are then used to estimate the ‘culturally correct’ answer to a question

(Weller, 2007).

Three basic assumptions must be met to perform CCA (1) informants should provide their information independent of others (2) all questions should be on a singular topic (3), and there should be a high underlying level of agreement between informants.

If these three assumptions are not met, then CCA is not an appropriate tool to use. The formal model accommodates open-ended questions, multiple-choice, and dichotomous

data. While the informal version is suited for ordinal, interval, and scaled responses.

Though they use different data, both methods aim to estimate the culturally correct

answer to each question (Weller, 2007).

This analysis builds from classic CCA and uses BCCA for dichotomous data to

analyze interview data collected on the domains of cooking and local disease

knowledge. BCCA is based on the standard CCA model, also known as the General

Condorcet Model, derived from signal detection theory (Batchelder & Romney, 1988;

Macmillan & Creelman, 2004). Using BCCA allows for not only the point estimate for

each participants’ competence but a probability distribution to estimate standard

deviation, guessing bias, etc. (Oravecz et al., 2014). Using the BCCA Toolbox allows for

estimating distributions and parameters not available in previous software such as

UCINET or ANTHROPAC (Borgatti et al., 2002).

The general rule to determine if there is a single underlying cultural domain in

classic CCA is a first to second eigenvalue ratio of 3:1. However, this is dependent on

the competence of informants. BCCA uses a posterior predictive check instead with simulated datasets based on the model parameters. Because the posterior predictive data is difficult to predict directly, the BCCA Toolbox utilizes Markov Chain Monte Carlo

(MCMC) to develop a sampler to predict these values. Due to the random nature of the starting values used by the MCMC sampler, the first values (usually 1000) are discarded during a burn-in period in which the model adjusts. These initial values do not accurately represent the data and may skew the results. Using this simulated posterior distribution, the observed eigenvalue ratio is then plotted to see if it falls within the 95% posterior predictive distribution of simulated eigenvalue ratios. This approach is particularly useful when the number of respondents is relatively small (Oravecz et al.,

2014). The current BCCA Toolbox incorporates informants' ability/competence, guessing bias, and item difficulty in each model (Oravecz et al., 2013).

Information from the free list, pile sorts, interviews, and participant observation were used to create two questionnaires for formal consensus analysis. The two cultural domains assessed were (1) disease/illness beliefs, and (2) cooking. The questionnaire used to examine the local disease/illness domain contained 51 questions (49 true/false, and two open-ended). The questionnaire used to examine cooking beliefs and practices contained 46 questions (40 true/false and six open-ended). Participants were recruited to participate in this portion of the research using convenience samples from two clinics, one in which Kalongwezi fell in the catchment area and the second in which Mchini C fell in the catchment area. The participants chosen for this component of the study met the same inclusion criteria as participants in the rest of the study to ensure the same population was sampled and tested. To ensure sufficient numbers to assess validity and

reliability in each location, 100 questionnaires (50 for each consensus analysis) were completed. The analysis used the Bayesian Cultural Consensus Toolbox using General

Condorcet Models (parameters in Table 3-6).

Data from the two compounds were analyzed both separately and together,

using these model parameters. BCCA has shown promise in recent years, allowing not

just for the reliance on the rule of thumb 3:1 eigenvalue ratio. Instead, the posterior

predictive distribution of that eigenvalue ratio is assessed using simulated data based

on the empirical data.

Environmental and Biological Methods

Biometric Measures

Biometric measures and self-report health data were collected on every

participant in the study. Participant weight was measured in kilograms using a portable

scale. The same scale was used to measure every participant. A TSEC TS-H1 portable wireless height meter was used to measure the height of participants. From these two measures, a body mass index (BMI) score for each person was calculated. Blood pressure (BP) was taken according to set standards (e.g., taken after sitting for a minimum of ten minutes, measured in triplicate). Participants sat for ten minutes before the readings were taken to ensure their blood pressure was not falsely elevated from activity. BP was measured using an Omron 7 Series Wireless Upper Arm Blood

Pressure Monitor (oscillometric method). This Omron model was chosen to minimize operator bias and Omron’s consistency in passing validation recommendations (El

Assaad et al., 2003; O’Brien et al., 1996). The results of the three readings were then averaged together. At the same time, pulse rates were taken and averaged in the same manner as blood pressure.

After blood pressure measurement, each participant performed spirometry tests to assess predicted vs. observed lung function. A Spirobank II spirometer was used to measure FVC, FEV1, Vital Capacity (VC), and other associated measures. FVC refers to the total amount of air an individual can exhale during a forced breath. FEV1 is the maximum volume an individual can forcibly exhale over one second. And VC refers to the total volume of air an individual can exhale following a full inhalation. These measurements were taken using the Occupational Lung and Disease Unit guidelines,

Department of Respiratory Medicine, Birmingham Heartlands Hospital (Moore, 2012).

Spirometry data were recorded using winspiroPro 8.1, which provided tables of

pulmonary measurements and quality assessment measures of reproducibility.

The SpiroBank II measurements were compared against predicted values drawn

from the Global Lung Initiative (GLI). GLI estimates were chosen as the reference

database because it has been used in the past, specifically on SSA populations that

may have experienced growth restriction during development (Arigliani et al., 2017).

This approach is particularly useful, both in measure and interpretation, given the

widespread prevalence of stunting in the Zambian context. Participants completed each

of these tests several times to get as many repeatable tests as possible to ensure

reliable results. Participants were seated upright, feet flat on the ground, and head in a

neutral position (Figure 3-3). Pre-calibrated disposable turbines were used for everyone

to decrease the likelihood of pathogen transfer between participants. Additionally, all

equipment was sanitized between each household visit.

Active Air Quality Monitoring

Each participant in the study wore an active air quality monitor for approximately

8 hours over the course of a typical day to assess the amount of airborne particulate

matter (PM10 and PM2.5) exposure. An apron was made, so the device sat on the upper

chest, near the mouth and nose. The Aeroqual S500 active monitor was the specific

model used, pre-calibrated to ensure accurate measurements (calibration is

recommended once per year, so there was no need to re-calibrate in the field). This

monitor uses a light scattering method to count the number and size of PM present in

the air and has been used extensively in other studies focused on AP monitoring (Aini

Jasmin et al., 2012; Haque et al., 2018; Yasmeen et al., 2019). Measurements were

made every minute during the day the device was worn, resulting in approximately 480

measurements per person. Each participant wore the device from approximately 8:00-

16:00 hours during the day from June-September. At 16:00 hours, the daily data was

downloaded and visualized. The participant then outlined their day, explaining specific

activities they engaged in, the times any activities took place, and the location. This

information was used to match specific activities and places with an increase in the

overall airborne PM.

At the end of the days that participants wore active air quality monitors, they

participated in a semi-structured interview that collected qualitative and quantitative

information about their daily activities (Figure 3-4). In addition to the daily recall of

activities, this interview focused primarily on cooking practices, risk perceptions

associated with cooking, and any events in their past that might influence or alter their

current cooking practices. These interviews did not last longer than one hour for any

participant to limit respondent burden, particularly after wearing the active air quality

monitor during the daily activities. The results of select individual air quality monitors

were then visualized and explained using case studies with visualizations completed

using the ggplot2 package in RStudio 3.5.2 (R Core Team, 2018; Wickham, 2016). Data manipulation and filtering were performed using the ‘dplyr’ package 1.0.2. in RStudio

3.5.2 (R Core Team, 2018; Wickham, 2016).

Urine Analysis

Urine samples were collected from each participant at approximately 17:00 hours the same day participants wore the active air quality monitors. 20 mL vials were used to collect and store each urine specimen. These samples were immediately taken and stored at -80°C in the Microbiology Lab at Chipata General Hospital. Samples were transported from Zambia on dry ice to the Center for Human and Environmental

Toxicology at the University of Florida.

Urine extraction was performed using methods modified from Luo, Gao, and Hu

2015 (Luo et al., 2015). The specific metabolites of interest, in this case, are 1- hydroxypyrene and 3-hydroxyBenzo[a]pyrene, metabolites of benzo[a]pyrene. These two compounds were chosen specifically due to their use in previous studies as reliable biomarkers of smoke inhalation (Förster et al., 2008; Å. M. Hansen et al., 2008).This

procedure was performed in a dark room to ensure that UV light did not degrade any of

the metabolites present in the urine samples. 1 mL of urine was diluted with 3 mL of 250

mM sodium acetate buffer (pH 4.5) and allowed five minutes to reach equilibrium after

mixing. 20 µL β-glucuronidase/arylsulfatase was then added, and the samples were

incubated in a mechanical shaker for approximately 16-20 hours at 37°C in the dark.

Sep-Pak C18 (6 cm3, 500 mg) cartridges were used to clean the urine specimens and

isolate the 1-hydroxypyrene. The cartridges were conditioned successively with 8 mL

methanol, 8 mL tetrahydrofuran, and 8 mL Milli-Q water at a flow rate of approximately 1

mL per minute. The urine specimens were then loaded onto the cartridge and washed

with 8 mL of 20% methanol. Cartridges were then dried under a nitrogen stream and eluted with 3 mL of methanol.

The methanol was then evaporated under a nitrogen stream in a water bath at

40°C. The samples were then reconstituted in 200 µL 100 mM sodium bicarbonate buffer (pH 11) and 200 µL 0.3 mg/mL dansyl chloride in acetone. This reconstituted mixture was shaken vigorously for 1 minute and set to incubate at 65°C for 60 minutes.

1 mL of milli-Q water and 3 mL of hexanes were then added to the mixture and vortexed for one minute. The organic portion of this mixture (hexanes) was removed in a liquid/liquid extraction and saved. Three additional mL of hexanes were added to the

remaining milli-Q water, and the process of centrifuging and liquid/liquid extraction repeated two more times for a total yield of 9 mL hexanes. The samples were then passed through silica cartridges (1g) previously conditioned with 8 mL DCM and 8 mL hexanes. The samples were then eluted with 5 mL 1:1 hexanes: DCM (v/v). The eluted samples were again dried under a nitrogen stream and reconstituted with 100 µL of acetonitrile and transferred to LC vials for analysis. Two urine specimens (one from each group) were sacrificed to establish a range and standard curve for analysis.

Samples were analyzed using ultra-high-performance liquid chromatography

(UHPLC, Shimadzu Co., Kyoto, Japan) coupled to a Triple Quadrupole Linear Ion Trap

(QTrap 6500, AB Sciex, CA). The identification method utilized a Zorbax eclipse plus

C18 narrow bore RR with a binary gradient. Mobile phase A was 0.1% formic acid in water, and mobile phase B was 100% methanol. The gradient began at 20% phase B and increased to 80% within one minute and to 100% within four minutes. Then remained at these levels for two minutes. Mobile phase B was then reduced to 20%

within one minute. The flow rate was 0.3 mL/min, with an injection volume of 10 µL into

the LC and as it eluted, transferred to the mass spectrometer via electrospray

ionization- for a total running time of eleven minutes. The LCMS was run under

scheduled multiple reaction monitoring (MRM) method and three transitions under

which the exact retention times of each compound were considered for quantification

and qualification. Declustering potential, collision energy and collision cell exit potential

were optimized and introduced in Table 3-7. Low-quality controls and high-quality

controls were prepared and analyzed along with the samples. For the standard curve,

synthetic urine was used and spiked with concentrations (after extraction) ranging from

0.005 to 20 ng/mL. Mass spectrometry analysis was performed by the analytical

toxicology core lab at the University of Florida.

Urine was then standardized by measuring creatinine content in each of the

samples to control for concentration levels of analytes between participants (Barr et al.

2005). Samples of urine were thawed, 1 mL aliquots taken, and refrozen at -80°C.

Following this, 50 µl were diluted in 950 µl of milli-Q water (five samples were diluted in

water in a 1:40 ratio as preliminary analyses suggested they were extremely high in

creatinine). Creatinine levels were then measured using the Cayman Chemical

Creatinine (urinary) Colorimetric Assay Kit, item no. 5007 (Cayman Chemical, Ann

Arbor, MI, USA). After standardization with creatinine the levels of 1-hydroxypyrene can

then be directly compared between participants in a meaningful way.

Specific gravity was used to standardize urine samples as a secondary method

that ignores creatinine content. Specific gravity may be a more reliable measure in this

case for several reasons. Specific gravity ignores issues of interference with other

molecules in the urine that disrupt the colorimetric assay. Freeze and thaw cycles of the urine may influence the levels of creatinine which does not influence specific gravity, which relies on relative salt content in urine samples. Creatinine excretion rate is not constant throughout the day and age, muscle mass, as well as diet significantly influence urinary creatinine levels (Boeniger et al., 1993). Given the numerous factors that influence urinary creatinine, studies suggest that standard gravity is a more consistent metric to standardize with as it is less significantly influenced by physiological parameters (Boeniger et al., 1993; Duty et al., 2003; Sauvé et al., 2015). Corrections for

standard gravity were made using methods from Duty et al. and the analog

refractometer used was… Prior to the measurement of standard gravity urine samples

were brought to 25° C to ensure differences in temperature did not influence readings.

Refuse Identification in Chipata Township

During August, the Co-PI took photographs of refuse piles in Chipata township to

catalog the amount and types of plastics and organic waste. present in areas that were likely to be burned. The types of plastics/refuse examined in the photographs were high-

density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene,

polyethylene terephthalate (PET), polystyrene, and organic waste. The relative

abundance of each category of refuse for each image was recorded to determine

potential exposures to airborne toxicants this project did not measure. Different types of

plastics and organic matter produce unique chemical profiles when combusted and

while technically illegal in Chipata, it was common in both research areas to see burning

refuse.

A total of 31 pictures were taken around Chipata for the identification of plastics and organic waste. Each image was divided into a 5x5 grid and examined at approximately 300% original size to aid in systematic identification of refuse. Plastics were identified based on the products present in the refuse piles. For example, disposable grocery bags are most commonly made of LDPE. The Co-PI and an

undergraduate assistant, Yui Fujii, examined the images separately and then compared

results using a third party to decide any discrepancies between them.

Brief Overview of methods

The first phase of this research focuses largely on ethnographic and cognitive

anthropological methods to build a foundation for this study. The latter half is then more

focused on gathering quantitative biological, personal exposure, and environmental

data. The ethnographic data informed the second half of the study to a great extent by

giving the co-PI information concerning local cooking and burning practices that directly

influenced exposure sources. Additionally, it made it possible to tailor aspects of the

second half of the study to specific participants (e.g., days of the week available to wear

a monitor, likely sources of AP for each household). Combining these methods and the

benefits that accompany long-term residence in a research site makes it possible to

answer research questions that would not be possible using each discipline in isolation.

It also highlights the importance of interdisciplinary research and the value that

anthropology methods have to offer complex problems concerning human health going

forward.

Figure 3-1. Map of Kalongwezi clinic catchment area. February 20, 2019. Chipata. Courtesy of the author.

Figure 3-2. Mchini compound during a cold, dry season afternoon. August 23, 2019. Chipata. Courtesy of the author.

Table 3-1. Inclusion Criteria for participation in this study 1. Female 2. Between 18-60 years of age 3. Identify as the main cook in the 4. Primarily use electricity (malaiti) if home residing in Kalongwezi OR primarily use biomass sources (charcoal = malaisha or wood = nkhuni) in residing in Mchini 5. Not be a former or current tobacco 6. Not currently pregnant user 7. Not have a chronic condition that would alter normal cardiovascular or pulmonary function

Table 3-2. Household Demographics for all participants Demographics Mchini Kalongwezi Maids (n = 25) (n = 25) (n = 5)

Mean SD Mean SD Mean SD Age (years) 38.04 9.68 37.28 13.74 32.17 6.34 Children** 4.2 1.98 2.48 1.9 2.17 1.17 Household 6.04 2.7 5.16 1.62 3.67 1.51 Members Size of House 3.88 1.45 7.6 2.78 3 1.67 (rooms)*** Number working 1.36 0.64 1.71 0.69 1.5 0.55 in household Head of House Male Female Male Female Male Female 20 5 21 4 3 2 (80%) (20%) (84%) (16%) (60%) (40%) Education Level*** None 4 0 2 (16%) (0%) (40%)

Primary 12 2 2 (48%) (8%) (40%)

Secondary 9 14 1 (36%) (56%) (20%)

Certificate 0 1 0 (0%) (4%) (0%)

College or 0 8 0 University (0%) (32%) (0%) Note: These p-values reflect only differences between Mchini and Kalongwezi groups. * p-value ≤ 0.05; ** p-value ≤ 0.005; *** p-value ≤ 0.0005

Table 3-3. Marital Status for all participants Demographics Mchini Kalongwezi Maids (n = 25) (n = 25) (n = 5) Marital Status

Married 20 (80%) 15 (60%) 2 (40%)

Divorced 0 (0%) 2 (8%) 1 (20%)

Single 1 (4%) 5 (20%) 2 (40%)

Widow 4 (16%) 2 (8%) 0 (0%)

Other 0 (0%) 1 (4%) 0 (0%) Note: These p-values reflect only differences between Mchini and Kalongwezi groups. * p-value ≤ 0.05; ** p-value ≤ 0.005; *** p-value ≤ 0.0005

Table 3-4. Household Economic Indicators for each household included in this study Mchini Kalongwezi Maids (n = 25) (n = 25) (n = 5) Yes No Yes No Yes No

Water 2 (8%) 23 (92%) 24 (96%) 1 (4%) 2 (40%) 3 (60%) Access*** Improved 1 (4%) 24 (96%) 23 (92%) 2 (8%) 2 (40%) 3 (60%) Sanitation*** Rainwater 17 (68%) 8 (32%) 8 (32%) 17 (68%) 2 (40%) 3 (60%) Use* Bank 2 (8%) 23 (92%) 22 (88%) 3 (12%) 1 (20%) 4 (80%) Account*** Automobile 0 (0%) 25 (100%) 14 (56%) 11 (44%) 0 (0%) 5 (100%)

Farm 6 (24%) 19 (77%) 9 (37%) 16 (64%) 0 (0%) 5 (100%)

Animals 6 (24%) 19 (77%) 13 (52%) 12 (48%) 1 (20%) 4 (80%)

Economic 3 (12%) 22 (88%) 8 (32%) 17 (68%) 1 (20%) 4 (80%) Assistance Note: These p-values reflect only differences between Mchini and Kalongwezi groups. * p-value ≤ 0.05; ** p-value ≤ 0.005; *** p-value ≤ 0.0005. Economic assistance is defined as receiving any financial assistance from someone living outside the home

Table 3-5. Model parameters for CCA on proximity matrices of pile sort data for both Mchini and Kalongwezi Model Parameters Clusters: 1 Iterations: 10,000 Burn-in: 2,000 Guessing Bias: True Chains: 3 Thin: 1 Item Difficulty: False

Table 3-6. Model Parameters for BCCA on domains of local disease knowledge and cooking practices Model Parameters Informant Abilities: heterogenous Iterations: 1,000 Adaptation: 1,000 Guessing bias: heterogenous Chains: 10 Thin: 10 Item Difficulty: neutral

Figure 3-3. Participant Sibongile Mwanza demonstrating how to perform spirometry using the Spirobank II portable monitor. June 10, 2019. Chipata. Courtesy of the author.

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Figure 3-4. Participant Emily Mwanza in her post-monitoring interview wearing the Aeroqual Series 500 to monitor her exposure to both PM10 and PM2.5. July 4, 2019. Chipata. Courtesy of the author.

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Table 3-7. Declustering potential, collision energy and collision cell exit potential for urinary metabolites of benzo[a]pyrene Q1 mass Q1 mass Q3 mass DP CE CXP Identification (Da) (Da) (Da) (Volts) (Volts) (Volts)

3-OH BaP_1 502 502 268 35 35 12 3-OH-BaP_2 502 502 171 35 48 12 3-OH-BaP_3 502 502 156 35 73 12 1-OH-PYR_1 452 452 218 15 30 12 1-OH-PYR_2 452 452 171 15 35 12 1-OH-PYR_3 452 452 156 15 70 12 1-OH-PYR-d9_1 461 461 227 70 38 12 1-OH-PYR-d9_2 461 461 171 70 35 12 1-OH-PYR-d9_3 461 461 156 70 77 12 *3-OH BaP - 3-hydroxybenzo[a]pyrene; 1-OH-PYR – 1-hydroxypyrene; 1-OH-PYR-d9 – 1-hydroxypyrene d9

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CHAPTER 4 RESULTS OF ETHNOGRAPHIC DATA

This chapter outlines the results of the free list, pile sort, and rank order analyses based on interviews completed during data collection in which participants provide information on diseases in their community. The first section of this chapter outlines the results of free lists, which provide information on what diseases participants believe are important or particularly prevalent in their community. Further information on disease etiology, symptoms, and treatments. were collected on the most salient diseases to assess how/if any illnesses are perceived as related to biomass smoke exposure by participants. The second section of the chapter analyzes the results of unconstrained pile sorts participants completed using diseases that appeared in free lists with >15% frequency. The analysis compares aggregated pile sorts between Mchini and

Kalongwezi to determine if both groups are using similar classification schema for local diseases and the relationship between diseases. To determine if these classification schemas represented a single underlying domain, I performed consensus analysis within and between the two groups. The next section of the chapter focuses on the participants ranked these diseases from most to least severe to determine if diseases commonly associated with biomass fuel use are considered dangerous or relatively minor. A detailed discussion of the implications of these findings is located in the chapter discussing how these perceptions and beliefs may alter exposure to HAP.

Free Listing Activity

Free listing is a useful technique for determining what items are important within a cultural domain, and which are the most salient among them. Salience, in this context, refers to Smith’s S, a combination of an item's frequency and place within the list, so the

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more an item is mentioned and the earlier it is mentioned, the higher its salience (Smith

& Borgatti, 1997). When discussing diseases, salience is a useful technique for

identifying common diseases and important diseases in a community (Gravlee, 2011).

Elicited diseases will further inform the ethnographic and statistical tests for the next two

sections analyzing pile sort and rank-order data.

I collected 59 free lists from participants, 28 from Kalongwezi residents and 31 from Mchini residents and maids (maids in this group worked in Kalongwezi but resided in Mchini or a similar compound). The average difference in the number of items listed was not statistically significant between the two groups (Kalongwezi = 12.69, Mchini =

12.26, t-test results p = 0.62). Kalongwezi residents listed a wider variety of diseases than those in Mchini (70 diseases compared to 50, respectively), though many appear only once. Of the items listed in Kalongwezi, 31 of 70 items (44.3%) were mentioned by only one participant, whereas in Mchini, only 15 of 50 items (30.0%) were listed by only one individual. Certain diseases were only mentioned once and were unique to individuals with (1) personal experience with a specific disease or (2) disease awareness because of news coverage. There was more variability in the total number of diseases listed by Kalongwezi respondents (SD = 3.87) compared to respondents in

Mchini (SD =2.42). Kalongwezi residents listed a greater number of total diseases with

70 unique diseases/illnesses compared to 50 items listed by the Mchini residents and maids working in Kalongwezi. Education level does not appear to correlate to the number of listed items by individuals within each group or over the entire sample

(Kruskal-Wallace: all participants p = 0.39; Kalongwezi p = 0.67; Mchini/Maids p = 0.08).

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Tables 4-1 and 4-2 outline the 20 most frequently mentioned items between the

two compounds along with their average rank and salience (additionally see Figure 4-1).

The scatterplot of salience scores between the two compounds illustrates the divide in

relative concern about different types of diseases in the communities. I performed a

Spearman rank correlation on salience scores, which resulted in a ρ = 0.734. Figure 4-2

displays all shared diseases between the two groups plotted together. Diseases that

have relatively higher salience in Mchini trend above the 45° abline, while those that are

more salient to Kalongwezi residents fall under this line (those that are equally salient

fall on this line). Figure 4-3 displays the same data as figure 4-2 but omits HIV/malaria and any disease whose salience falls below 0.05 to allow for easier visual interpretation of data. Matukumwa, which appears in the first scatterplot, is a disease characterized by a swelling of the tonsils, but not discussed further due to its frequency being lower than the inclusion threshold for pile sort and rank order.

Diseases that have been the subject of public health campaigns in the area, such as malaria, HIV, tuberculosis etc. tend to fall relatively close to this line and appear in greater frequencies near the beginning of participant free lists. HIV and malaria are most consistently mentioned by participants, which is not unexpected given that respondents are frequently provided aid and information (national and international) about malaria and HIV.

While the composition of the most commonly mentioned items is similar, the salience of items changes in significant ways between the two, reflecting each group's lived reality. There is a trend towards a separation between infectious and non- communicable diseases in these two communities. Residents in Kalongwezi listed more

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diseases associated with western lifestyle factors such as hypertension, diabetes, and heart problems (I use the term 'western lifestyle' here in a broad sense encompassing such factors as high sugar/fat diet, high processed foods, and low physical activity levels) (Carrera-Bastos et al., 2011). In contrast, residents of Mchini were more concerned with diarrhea, cough, measles, and epilepsy (kunyu).

Hypertension and sugar (the local term for diabetes mellitus) are of increasing concern to those living in Kalongwezi, and many people report family members and friends who suffer from one or both. There is a general fear surrounding these two diseases, specifically, with some participants stating that they do not get checked for these because they are afraid of the results they will get back from the hospital. This area is more economically stable on average than Mchini; the homes were larger, had more consistent access to electricity, more luxury items (e.g., television, computers), and owned automobiles. The average BMI was higher (31.89 compared to 26.91), almost two points into the 'obesity' category as defined by the National Institute of

Health (NIH). Sugary drinks are common in this community, including sodas, fruit juice concentrates, and tea/coffee with sugar added by the individual.

Pile Sort of Local Diseases

Participants then performed unconstrained pile sorts on items that appeared in both compound's free lists with >15% frequency (Bernard, 2017). An unconstrained pile sort allowed participants to place the final 27 most salient diseases into clusters; however, they thought fit best with the prompt "place those diseases together which are most similar." It is important to note that these diseases are not all associated with use of biomass fuel, rather they reflect what participants identified as the most important to their community. Using this technique allows us to understand how participants

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understand the relationships between diseases in their community and their underlying classification system. For clarity, in this section, the term 'cluster' refers to those identified from aggregate participant pile sorts. When the term 'group' is used, it describes the action and results from a single participant.

Identification and interpretation of clusters

Eight clusters were identified for both compounds using triangulation of k-means

"silhouette," "elbow," and "gap-statistic" tests along with ethnographic data collected during fieldwork to characterize each cluster. For both compounds, the optimal numbers of clusters ranged from 7-10 in Mchini, and 7-9 in Kalongwezi. Using experience and ethnographic data collected while observing the pile sort activity, eight clusters fit the data best for Mchini and Kalongwezi. Below, figures 4-4 and 4-5 depict the optimal number of clusters using the three different methods.

These clusters are explained by participants similarly between participants in both compounds and follow similar patterns with some exceptions. Table 4-3 outlines the theme of each cluster and the diseases included in each, respectively. I designated the labels for each of the clusters, characterizing them as best as possible by (1) the diseases they contained and (2) the terminology participants used to describe the clusters they created (acknowledging that clusters were not always uniform).

Kalongwezi and Mchini participants relied on two main factors when grouping the list of diseases, (1) the area of the body affected by the disease and (2) the perceived similarity of symptoms between diseases. However, there were some exceptions, notably the STI category and the malaria category. HIV, syphilis, and gonorrhea were almost always identified as sexually transmitted infections and clustered together. For example, respondents grouped syphilis with gonorrhea in 100% of pile sorts (in both

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Mchini and Kalongwezi), and as a result, they appear in the same coordinate location

'stacked' on top of one another. The ‘malaria’ cluster is the second exception to these general rules. In this cluster, participants would use malaria as a ‘seed’ disease and place other diseases with it that were either (1) symptoms of malaria or (2) diseases caused by untreated malaria. For example, because severe malaria causes high fevers that may induce seizures, it was common for people to place kunyu in the same cluster as malaria. The same underlying logic places back pain, meningitis, and anemia with malaria; they are viewed as a direct symptom of the disease or something that untreated malaria can turn into (in the case of meningitis).

The airborne cluster contains the diseases that would most commonly be associated with the effects of biomass fuel exposure, which includes chifua, asthma, bronchitis, flu, pneumonia (Kalongwezi only), and tuberculosis (association, in this case, refers to scientific literature, most cases participants did not link biomass fuel use with the development of these diseases). These diseases primarily affect the lungs, and participants identified their main symptom as coughing. Chifua is a broad term for a cough, it can be both a disease and a symptom of other diseases. Most of the time the chifua is not thought of as a serious disease, more a dry cough that will go away after a few days. However, if it is not treated at the clinic, people say that it can develop into more serious illnesses such as bronchitis or pneumonia.

Participants grouped arthritis, elephantiasis, and cancer based on swelling as a symptom. Participants named both cervical and breast cancer (vaccinations for HPV being common along with messaging from local clinics) as present in their communities.

Participants focused primarily on swelling as a symptom of breast cancer when placing

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it in this cluster. Elephantiasis is not common in the region anymore, outside of very

rural areas, with the increased treatments available, but participants still remember the

effects and severe swelling the disease can cause to limbs.

High blood pressure, sugar, and heart problems are known diseases of obesity to

the participants and community. However, high blood pressure is the only disease in

this cluster that can also be caused by worrying or 'thinking' too much. People in

Kalongwezi seem to dread a diagnosis of high blood pressure as everyone has an

anecdote of someone they know or have heard about falling suddenly victim to a stroke

caused by high blood pressure. As one participant worded it, "People are afraid of

stroke because if you have it, then you become a burden on your family… my father-in-

law had a stroke and now complains of paralysis on one side of his body" (Misozi

Banda.

Participants often group chickenpox and measles because they manifest similarly

on an individual (i.e., distinct rashes on the skin). Both categories cause spots that are

highly visible and uncomfortable to appear all over the body. Measles is not a common

problem in town anymore, though many of the older participants in this study remember

family members contracting it years ago and the lasting effects on their bodies. One

participant tells of how her younger brother contracted measles as a child and how it

caused him to become permanently blind (Emily Phiri).

While the composition of seven of the clusters is similar between both compounds, the last cluster varies between compounds. In Mchini, this cluster contains pneumonia, back pain, and Ebola. In this region of Zambia, participants cited a sharp pain in the side as a defining symptom of pneumonia. This sharp pain led some

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participants to cluster it with back pain, instead of placing it in the cluster containing the

majority of the diseases of the lungs. Ebola was the outlier in many participant pile sorts

because while most heard news surrounding Ebola on the radio (specifically

surrounding the 2014-15 pandemic in West Africa), they had no specific knowledge of

the disease other than its high mortality rate. The lack of specific knowledge led many to

place it in a cluster by itself or guess where it belonged relative to the other diseases.

Participants in Kalongwezi had a more consistent underlying logic associated with this,

their cluster containing Ebola, cholera, and diarrhea. Participants viewed Ebola and

cholera as the two most dangerous diseases due to their mortality rate, and the

perceived severity of these diseases led to participants placing them in the same

cluster. Like participants in Mchini, most people in this compound did not have specific

knowledge concerning Ebola, only citing that it was very deadly. Diarrhea is in this

cluster because it is the main symptom of cholera. Cholera is a significant public health

issue in Zambia, with seasonal outbreaks occurring in major cities. Lusaka experienced

many outbreaks of cholera in the past decade with tens of thousands of cases (Mwaba

et al., 2020). In Chipata, the sale of street food is no longer legal as people suspected it

played a factor in an outbreak of cholera in years prior (exact years unclear).

Principle components analysis (PCA)

PCA provides dimension reduction that allows an examination of higher dimension data, in this case, 27 dimensions, compressed down to two or three. The benefits of this are twofold for this pile sort data: (1) it is a useful technique to visualize clusters identified by participants in both groups, and (2) it allows investigation into the

main categorization modes underlying this data that might not be immediately apparent

from examining these clusters with ethnographic data alone (Alcántara-Salinas et al.,

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2013). I performed principle components analysis on the distance matrix produced by

Anthropac Pile Sorts 1.0 to produce the results discussed in the rest of this section

(Analytic Technologies, 2003b).

Figures 4-6 and 4-7 show the results of PCA performed on the pile sorts from both compounds. Both charts show the first two principle components with the variance explained in Mchini and Kalongwezi being 47.4% and 44.3%, respectively. The addition of the third principle component explains approximately 60% of the variance in both compounds. The identified clusters are outlined in each PCA visualization by 95% confidence ellipses and identified with unique color and shape combinations.

Clusters in the Mchini compound appear with less overlap in the first and second principle components than those in Kalongwezi. Though there is approximately the same amount of variation explained in the first two principle components for Mchini and

Kalongwezi. Likewise, similar clusters emerge from the pile sort data between the two compounds (colors and shapes correspond to similar clusters between compound PCA graphs, e.g., blue triangles identify diseases of the lungs that one catches from the air).

Mchini participants show more clearly defined and separated clusters in the first two principle components.

The first dimension is the same between compounds and follows the airborne diseases that specifically affect the lungs. This pattern is the clearest in Mchini PC1, where bronchitis, flu, chifua, asthma, and tuberculosis are pushed to the far left of the visualization and away from diseases that primarily affect other parts of the body.

Pneumonia appears in a midway position between these five diseases as it does primarily affect the lungs, but participants identify it as being a cause of 'pain in the

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sides of the ribs.' Chickenpox and measles trend even more slightly toward this cluster

as a few participants cited that they are airborne diseases that a person ‘breathes’

(inhales) into the body through the lungs. At the other end of this component are

diseases identified as coming from other sources that do not affect the lungs or

breathing in any significant manner, such as cholera, diarrhea, and stomach pain.

The PC2 axis contains a gradient that, on one end, contains diseases caused by intimate personal contact (e.g., sexually transmitted infections) and, on the opposite end, those that have an environmental origin. Healthcare campaigns and messages in the area have promoted the HPV vaccine for women, linking cervical cancer to sexual contact and virus transmission between individuals. Malaria is the primary disease driving the other end of this component as being entirely environmental, contracted through bites from infected mosquitoes. The diseases in the 'malaria' cluster are all potential disease symptoms in more or less severe forms. The first two principle

components from Kalongwezi follow these same patterns, though the visualization is

less clear because there was less consensus than in Mchini.

Consensus and similarity

Some differences emerged between the two study populations during the pile

sort activity. Kalongwezi participants had more education compared to Mchini

participants (the majority of people in Kalongwezi had at least a secondary education, a

third of whom also had some college education or an advanced certificate) and were

able to draw on a wider variety of categories in which to use during the activity. This

difference in education led to greater variation in the Kalongwezi participants and, in

some cases, clusters that did not appear as clearly in either PCA or nMDS (results of

nMDS in appendix). For example, one participant, EB-46-151, used slightly different

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criteria that placed malaria and elephantiasis in a group together with no other diseases.

Instead of using malaria as a seed illness as most other participants in the study did, she identified these two as being transmitted specifically by mosquitoes. 'Seed illness' in this case refers to a cluster existing solely built off one main disease—in this case, malaria. Focusing instead on how individuals contracted the disease rather than on symptoms or related diseases. She was also the only individual to break apart the single 'airborne' group into two separate groups, one containing asthma, bronchitis, and pneumonia, while the second contained tuberculosis, chifua, flu, Ebola, and chickenpox.

While both of these groups affect the lungs and are airborne, the first group are diseases that specifically make breathing very difficult. The latter group's diseases do not have this is a primary symptom of any of their diseases.

Participants in Mchini had a more similar educational background compared to

Kalongwezi residents and tended to use a more consistent classification scheme to create their unconstrained pile sorts. While some individuals arranged their pile sort groups in unique ways, it was not as common for participants in this compound to deviate from the norm as often as those in Kalongwezi. However, both compounds exhibit strong consensus when analyzed individually and together, suggesting a single cultural group underlying this domain (Table 4-4). Similarly, when I compared the two similarity matrices between the two compounds using QAP, their Pearson correlation is extremely high, r = 0.934 (Table 4-5).

Ranking Perceived Disease Severity

Perceived disease severity differed slightly between the groups and reflected diseases that were more or less present in their daily lived environment, e.g., diseases they have personal experience with, or a neighbor/family member has experienced.

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Ebola was named the most dangerous disease among the most salient 27 items derived from the free list and used in the pile sorts (Tables 4-6 and 4-7). However, as mentioned, participants in both groups consistently indicated they had little to no knowledge about this disease and had just heard about it on the radio or television. Of more interest is the difference in non-communicable vs. communicable diseases between the groups.

Participants in Kalongwezi were much more concerned with non-communicable diseases associated with lifestyle factors such as hypertension (BP), cancer, heart problems, and diabetes mellitus (sugar). The participants in Kalongwezi were significantly heavier and, on average, more sedentary than their counterparts in Mchini, which may, in part, explain their greater concern for non-communicable diseases.

Hypertension and diabetes were two that people discussed as being particularly dangerous as most have seen the effects firsthand. Relatives or friends who have had feet or toes amputated due to complications of diabetes. Similarly, those who suffered strokes related to hypertension and were permanently disabled afterward. Many stated that they worried about becoming a burden to their families, for example, if the family had to care for them after a stroke. The colloquial term for type II diabetes mellitus, sugar, shows the disease etiology is well known by participants— consuming too much sugar is a risk factor for developing diabetes.

Kunyu (seizures) Prevalence and Interpretation in Eastern Province

Seizures, or kunyu, was a commonly reported disease and relatively surprising.

The etiology given to this disease fell into three general categories: a person is born with kunyu, they have epilepsy, or are bewitched by someone (usually from some perceived jealousy). If a person goes for treatment at the hospital, but the care they

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receive was unsuccessful, the next step is to consult a traditional healer as the

disease— which failed to be treated by western medicine— now had a supernatural

origin. And, as such, needed traditional medicines only available through a witch doctor

or traditional healer.

After discussing this phenomenon with one of the laboratory managers at

Chipata Central Hospital, they informed me that the primary driver for the high rates of

seizures seen in Eastern Province was related to the high levels of tapeworm infection,

specifically Taenia solium. Upwards of eighty percent of those who experience epileptic

seizures live in low-income countries where tapeworm infection is more common and

treatment less likely to be available. Of the three species of tapeworm that infect

humans, T. solium is the only one that causes major health problems (the other two

being Taenia saginata and Taenia asiatica) (World Health Organization, 2020b). This

species of tapeworm is endemic to Zambia and is a public health concern. Infectious by

T. solium can lead to neurocysticercosis, a condition in which the tapeworm larvae

infect the brain, and other tissues, leading to the onset of seizures. Estimates suggest

that 30% of reported epilepsy cases result from tapeworm infections (World Health

Organization, 2020b). Prior studies have shown that Eastern and Southern Provinces in

Zambia have some of the highest prevalence of porcine cysticercosis in the world (Phiri

et al., 2002). Similarly, a recent cross-sectional study completed in Eastern Province found that of a group of patients diagnosed with active epilepsy, 57.1% were either confirmed positive or probable for neurocysticercosis (Mwape et al., 2015). This population may likely be experiencing a high prevalence of tapeworm infection, which manifests in epileptic seizures.

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Free list, Pile Sort, and Ranking Data Integration

Several related conclusions can be drawn from the results in this chapter. The first of which is that diseases associated with exposure to biomass fuel feature prominently in free lists (associations in this case refer to those documented in scientific literature, not associations made by participants). During the month that these data were collected, the clinics in Kalongwezi and Mchini reported 440 and 520 respiratory infections (non-pneumonia), respectively. Second, free list composition is similar between the two compounds but differs in composition and salience based on those diseases that most affect each area. Third, diseases in these free lists are not consciously associated with fuel use, but more commonly attributed to changing seasons or infectious disease. Fourth, classification schemes are similar between clusters, slightly more consistent with Mchini, which may be attributable to greater education similarity. Fifth, many of the diseases associated with biomass fuel burning are considered relatively minor and largely are ranked in the bottom half of the rank order of perceived severity. Chifua, pneumonia, asthma, and bronchitis are all mentioned as common in the community but generally thought of as diseases that do not pose a serious risk. Chifua and flu appear at the end of both lists, despite both areas having a relatively high prevalence of respiratory infections. It is clear that diseases associated with biomass fuel use are present in the community, in terms of both prevalence and in the minds of the people who reside there, but (1) they are generally not perceived as serious and (2) are not commonly thought to be associated with the use of wood/charcoal in and outside the home. This finding is interesting as the most commonly recorded illnesses at the Mchini and Kalongwezi clinics during the month- long period that these data were collected were non-pneumonia acute respiratory

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infections. Lastly, this lack of association between potential cause and effect may increase exposure for many people and does not curb practices such as cooking inside versus outside (when weather allows).

Formal Consensus Analysis

Formal cultural consensus analysis was performed from the clinics in Mchini and

Kalongwezi to test if there is a single underlying cultural understanding of (1) local diseases and (2) cooking knowledge and practices. These two domains are important to understand if there are consistent understandings and practices present in these two communities that increase or decrease both levels of HAP and personal exposures. The formal questionnaire uses ethnographic data gathered during the eight months of prior research to ensure questions are relevant to the populations and of equivalent difficulty.

The cooking and disease questionnaires consist of 40 and 49 true/false statements, respectively. Fifty individuals from Mchini and fifty from Kalongwezi were recruited at the clinics using a convenience sample and completed these questionnaires. Data were analyzed using Bayesian cultural consensus analysis using the Bayesian Cultural

Consensus Toolbox version 2.0 (Oravecz et al., 2013). Eigenvalue ratios were calculated for each of the four datasets and compared to the estimated posterior predictive model (Table 4-8).

Kalongwezi consensus

The results of the Kalongwezi consensus demonstrate that there is a strong underlying cultural model for cooking practices (Figure 4-8). Disease knowledge was more heterogeneous, and while there was a relatively large eigenvalue ratio (6.66), the results indicate that there is significant heterogeneity in the data, suggesting more than one underlying cultural model (Figure 4-9). The two tables outline the results that

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(Tables 4-9 and 4-10) highlight areas of agreement and disagreement in the two domains. In the aggregated responses concerning cooking practices, it is very common to bring the brazier into the home during the rainy season and cold season

(approximately November to August). However, participants acknowledged that breathing in smoke from the brazier may lead to becoming sick or even fainting and dying. During the hot season it is much more common to cook outside the home, unless participants were making food that cooks quickly, such as rice, eggs, or tea. The general belief in the area is that when the brazier has stopped actively producing visible smoke, it is safe to bring inside the house. This approach is the general practice in both the rainy and cold seasons unless rain forces them to bring the brazier indoors prematurely. This belief is problematic as the brazier continues to produce toxicants even if it is not actively smoking.

Without visible smoke, most participants do not associate charcoal burning in the home with developing respiratory or cardiovascular diseases. It is much more common for participants, and those who lived in Chipata in general, to cite the changing weather as the cause of their chifua or flu, rather than from environmental exposure or something they contract from another person. The cause of asthma is less well agreed upon than diseases like chifua and flu as the etiology and potentially infectious nature is not as clear. For example, almost one-quarter of participants thought it was possible to catch asthma from being in proximity to someone else who has it, and 74% thought you were born with it and it does not develop later in life. There is strong agreement that both the prevalence of sugar and hypertension are higher than ten years prior to these interviews (August 2019). While biomass fuel use is associated with both diabetes and

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hypertension, it is impossible to know from this data if its use contributes along with a shift in diet and activity patterns.

Mchini consensus

The results from Mchini largely echo those of the participants from Kalongwezi

(Figures 4-10 and 4-11). Most acknowledged that the smoke from a brazier could cause disease, with 88% of participants stating that once the visible smoke burned off the brazier, it was safe to bring indoors (Table 4-11). This acknowledgment comes despite

70% of those participants reporting that they had become sick from breathing in smoke from the brazier. Cooking patterns indoors follow the same pattern where two out of three seasons, participants may cook indoors more than outdoors. In Mchini, it is relatively less common to have an outdoor covered area appropriate for cooking on in the rain. When the brazier is inside, the much more immediate concern is (1) that the brazier will catch something on fire by mistake or (2) a child will accidentally burn themselves running too close by. There is more heterogeneity concerning asthma in

Mchini, with 48% of participants stating that it is possible to catch the disease from someone by sleeping in the same room as them (Table 4-12).

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Table 4-1. Kalongwezi free list frequency, average rank, and salience (Smith’s S) Item Frequency (%) Average Rank Salience Malaria 100.00 2.82 0.85 HIV 85.70 3.79 0.65 BP (hypertension) 82.10 5.43 0.53 Sugar (diabetes) 71.40 6.15 0.43 Tuberculosis 71.40 4.60 0.52 Cholera 53.60 8.40 0.26 Chifua (cough) 53.60 5.00 0.36 Syphilis 53.60 8.27 0.28 Diarrhea 50.00 6.93 0.29 Asthma 50.00 9.00 0.22 Gonorrhea 46.40 9.85 0.20 Cancer 42.90 5.08 0.31 Flu 25.00 4.57 0.17 Pneumonia 25.00 7.43 0.12 Kunyu (seizures) 25.00 12.14 0.08 Anemia 25.00 8.86 0.11 Measles 25.00 9.29 0.11 Heart Problems 21.40 9.83 0.08 Stroke 17.90 8.60 0.05 Dysentery 17.90 11.00 0.05 *For brevity, this table shows only the 20 most frequently mentioned items. Heart problems is a general term for cardiovascular issues

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Table 4-2. Mchini free list frequency, average rank, and salience (Smith’s S) Item Frequency (%) Average Rank Salience Malaria 100.00 3.23 0.82 HIV 100.00 4.26 0.73 Diarrhea 93.50 5.83 0.56 Tuberculosis 83.90 5.50 0.53 Syphilis 71.00 6.68 0.40 Cancer 71.00 8.59 0.28 Chifua (cough) 67.70 5.67 0.44 Cholera 64.50 7.70 0.31 BP (hypertension) 64.50 7.30 0.32 Asthma 48.40 8.93 0.18 Sugar (diabetes) 41.90 7.69 0.20 Kunyu (seizures) 41.90 7.92 0.19 Measles 38.70 7.42 0.20 Gonorrhea 35.50 8.82 0.14 Elephantitis 32.30 8.00 0.15 Ebola 32.30 9.20 0.12 Back Pain 19.40 5.83 0.12 Heart Problems 16.10 11.60 0.03 Ulcers 16.10 8.40 0.07 Chickenpox 16.10 7.40 0.09 *For brevity, this table shows only the 20 most frequently mentioned items. Heart problems is a general term for cardiovascular issues

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Figure 4-1. Frequency chart of diseases listed between compounds

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Figure 4-2. Scatterplot of free list salience scores with Spearman rank correlation with 45° abline

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Figure 4-3. Scatterplot of free list salience above 0.05 (malaria and HIV removed) with 45° abline

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Figure 4-4. The optimal number of clusters for Mchini Pile sort data using (a) silhouette (b) elbow and (c) gap statistic methods

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Figure 4-5. The optimal number of clusters for Kalongwezi Pile sort data using (a) silhouette (b) elbow and (c) gap statistic methods

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Table 4-3. Pile sort clusters identified for Mchini and Kalongwezi compounds Cluster Mchini + Maids Kalongwezi

Asthma, Bronchitis, Chifua, Asthma, Bronchitis, Chifua, Airborne Cluster Flu, Tuberculosis Flu, Pneumonia, Tuberculosis Anemia, Kunyu, Malaria, Anemia, Back Pain, Kunyu, Malaria Cluster Meningitis Malaria, Meningitis

STI Cluster Gonorrhea, HIV, Syphilis Gonorrhea, HIV, Syphilis

Arthritis, Elephantiasis, Arthritis, Elephantiasis, Swelling Cluster Cancer Cancer

Obesity Cluster BP, Heart Problems, Sugar BP, Heart Problems, Sugar

Cholera, Diarrhea, Stomach Stomach Pain, Ulcers Stomach Cluster Pain, Ulcers

Skin Disease Cluster Chickenpox, Measles Chickenpox, Measles

Ebola, Back Pain, Cluster 8 Cholera, Diarrhea, Ebola Pneumonia

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Figure 4-6. Mchini principle component analysis for pile sort data dimensions 1 & 2, 95% confidence ellipses outlining clusters

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Figure 4-7. Kalongwezi principle component analysis for pile sort data dimensions 1 & 2, 95% confidence ellipses outlining cluster

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Table 4-4. Consensus Eigenvalues for Pile Sort Data and Associated Measures Compound Eigenvalue EigenRatio

Mchini + Maids 10.44 10.54

Kalongwezi 10.69 10.18

All Participants 21.02 10.78

Table 4-5. Results of quadratic assignment procedure comparing Mchini and Kalongwezi item proximity matrices Observed Significance Standard Permutations Value Deviation (n) Pearson 0.9342 0.0002 0.0538 5000 Correlation

Table 4-6. The rank order of disease severity in Kalongwezi for the 27 most frequently cited local diseases Kalongwezi Disease Average (SD) Disease Average (SD) Ebola 4.80 (5.05) measles 14.19 (5.85) cholera 5.04 (4.49) diarrhea 14.62 (7.34) cancer 7.08 (4.36) pneumonia 15.27 (4.41) BP (hypertension) 7.31 (5.50) elephantiasis 15.68 (7.31) HIV 7.50 (7.18) bronchitis 16.27 (5.37) heart problems 8.08 (5.22) kunyu (seizures) 17.96 (6.14) sugar (diabetes) 9.85 (4.56) arthritis 18.31 (5.07) syphilis 10.38 (6.48) chickenpox 18.38 (5.33) tuberculosis 10.46 (4.39) ulcers 18.96 (4.69) meningitis 10.62 (6.06) stomach pain 21.69 (4.94) gonorrhea 11.81 (6.66) back pain 22.54 (5.15) anemia 12.85 (5.24) chifua (cough) 23.50 (3.89) malaria 13.50 (6.92) flu 25.62 (1.94) asthma 14.15 (5.72) *the average associated with each disease is the average rank they received from participants with '1' being the most severe/dangerous and 27 the least severe/dangerous

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Table 4-7. The rank order of disease severity for participants in Mchini and maids for the 27 most frequently cited local diseases Mchini & Maids Disease Average (SD) Disease Average (SD) Ebola 2.09 (1.90) sugar (diabetes) 13.14 (6.63) cholera 5.38 (4.75) cancer 13.28 (6.65) anemia 6.28 (4.80) gonorrhea 13.45 (6.56) meningitis 7.72 (5.85) asthma 13.62 (5.80) measles 8.90 (5.78) tuberculosis 13.66 (4.42) diarrhea 9.17 (5.89) stomach pain 18.00 (4.54) BP (hypertension) 9.76 (6.13) ulcers 18.28 (3.55) malaria 9.86 (5.55) chickenpox 18.72 (5.43) HIV 10.90 (6.55) back pain 21.00 (3.51) pneumonia 12.10 (4.83) elephantiasis 22.15 (4.56) kunyu (seizures) 12.21 (5.29) chifua (cough) 23.34 (3.38) heart problems 12.34 (4.76) arthritis 24.59 (2.81) bronchitis 12.55 (6.09) flu 25.31 (2.11) syphilis 13.00 (6.30) *the average associated with each disease is the average rank they received from participants with 1 being the most severe/dangerous and 27 the least severe/dangerous

Table 4-8. Results of Bayesian cultural consensus analysis on the domains of cooking and local disease knowledge Compound Eigenvalue Ratio PPC Global Bayesian P-value Kalongwezi 6.66 Failed 0.34 Disease Kalongwezi 4.91 Passed 0.22 Cooking

Mchini Disease 8.51 Passed 0.42

Mchini Cooking 2.76 Failed 0.26

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Figure 4-8. Observed eigenvalue ratio plotted in the posterior distribution for Kalongwezi cooking consensus data

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Figure 4-9. Observed eigenvalue ratio plotted in the posterior distribution for Kalongwezi disease consensus data

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Table 4-9. Selected answer key for Kalongwezi cooking domain, highlighting answers that influence cooking practices Questions True N (%) False N (%)

Breathing the smoke emitted from charcoal or 50 (100%) 0 (0%) wood cookstoves is harmful to your health Drinking milk after cooking with charcoal or wood, will counteract the effects of the smoke and help 47 (94%) 3 (6%) heal your lungs/stomach Charcoal braziers are safe to bring inside after they 44 (88%) 6 (12%) have finished smoking

In the cold season I cook inside more than outside 47 (94%) 3 (6%)

In the rainy season I cook inside more than outside 50 (100%) 0 (0%)

During the hot season I prefer to cook outside more 45 (90%) 5 (10%) than inside If the brazier is inside, I am more concerned about burns or catching something on fire than what 33 (66%) 17 (34%) impact it might have on my health

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Table 4-10. Selected answer key for Kalongwezi disease knowledge domain for diseases associated with biomass fuel use Questions True N (%) False N (%)

Breathing in smoke can cause tuberculosis 40 (80%) 10 (20%)

Breathing in smoke can cause chifua 46 (92%) 4 (8%)

Breathing in smoke can cause the flu 39 (78%) 11 (22%)

Diseases such as flu, chifua, pneumonia etc. are 47 (94%) 3 (6%) primarily caused by the changing weather I have gotten sick from breathing in smoke from the 35 (70%) 15 (30%) brazier before BP (hypertension) is more common than it was 10 50 (100%) 0 (0%) years ago Sugar (diabetes) is more common than it was 10 50 (100%) 0 (0%) years ago I can catch asthma if I sleep next to someone who 11 (22%) 39 (78%) has it Asthma is a disease that you are born with, it 37 (74%) 13 (26%) cannot develop

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Figure 4-10. Observed eigenvalue ratio plotted in the posterior distribution for Mchini cooking consensus data

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Figure 4-11. Observed eigenvalue ratio plotted in the posterior distribution for Mchini disease consensus data

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Table 4-11. Selected answer key for Mchini cooking domain, highlighting answers that influence cooking practices Questions True N (%) False N (%)

Breathing the smoke emitted from charcoal or 48 (96%) 2 (4%) wood cookstoves is harmful to your health Drinking milk after cooking with charcoal or wood, will counteract the effects of the smoke and help 46 (92%) 4 (8%) heal your lungs/stomach Charcoal braziers are safe to bring inside after they 44 (88%) 6 (12%) have finished smoking

In the cold season I cook inside more than outside 43 (86%) 7 (14%)

In the rainy season I cook inside more than outside 47 (94%) 3 (6%)

During the hot season I prefer to cook outside more 49 (98%) 1 (2%) than inside If the brazier is inside, I am more concerned about burns or catching something on fire than what 35 (70%) 15 (30%) impact it might have on my health

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Table 4-12. Selected answer key for Mchini disease knowledge domain for diseases associated with biomass fuel use Questions True N (%) False N (%)

Breathing in smoke can cause tuberculosis 46 (92%) 4 (8%)

Breathing in smoke can cause chifua 49 (98%) 1 (2%)

Breathing in smoke can cause the flu 49 (98%) 1 (2%)

Diseases such as flu, chifua, pneumonia etc. are 46 (92%) 4 (8%) primarily caused by the changing weather BP (hypertension) is more common than it was 10 44 (88%) 6 (12%) years ago Sugar (diabetes) is more common than it was 10 45 (90%) 5 (10%) years ago I have gotten sick from breathing in smoke from the 35 (70%) 15 (30%) brazier before I can catch asthma if I sleep next to someone who 24 (48%) 26 (52) has it Asthma is a disease that you are born with, it 18 (36%) 32 (64%) cannot develop

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CHAPTER 5 RESULTS OF ENVIRONMENTAL AND BIOLOGICAL DATA

Active Air Quality Monitor (PM10 and PM2.5)

Each participant wore the active air quality monitor for only one day. Collectively,

they represent over 50 days and more than 400 hours of personal exposure to both

PM10 and PM2.5. The active air quality monitor results indicate that participants residing

in both compounds, Kalongwezi and Mchini, are exposed to levels of both PM10 and

PM2.5 much higher than recommended by the WHO. For reference, over a 24-hour

period, the WHO recommends that levels of airborne PM10 and PM2.5 not exceed 50

µg/m3 and 25 µg/m3, respectively. Safe levels of annual exposures are even lower, set

at 20 µg/m3 and 10 µg/m3. Though both groups were exposed to elevated PM levels, the participants residing in Mchini were exposed to significantly higher levels than

Kalongwezi (Kruskal-Wallis Test). As shown in table 5-1, the airborne particulate matter

levels in Mchini are drastically higher than safe 24-hour or annual levels with PM10 and

3 PM2.5 averaging 183.68 (SD: 353.85) and 39.68 (SD: 80.74) µg/m during the hours that

participants wore the active monitor. Standard deviations for these measures are

inflated because the underlying data for the measurements is significantly right skewed.

The positive skew results from extremely high acute exposure due to specific, daily

activities performed by the women in this study (in some cases upward of 2,000 µg/m3).

Case study: Mchini Participant 1-66

Participant 1-66, Mary Phiri, is typical of many of the women who live in this compound. She is 48 years of age and is responsible for a household containing 11 children, 7 of which are hers and the rest she has taken responsibility for from relatives who could not provide for their children. Her husband has a job at one of the local

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bakeries, but she must supplement their income by making fritters to sell several days of the week (her husband spends a significant portion of his income on alcohol). They live close by the southern side of the main road that cuts through Mchini, separating

Mchini B from Mchini C. Their home is small for the number of household members, consisting of a modest sitting room and a bedroom separated by a curtain of fabric. It is constructed of the mudbrick ubiquitous to homes in this community, made locally and held together by cement. Inside, there is a bare lightbulb that hangs in the sitting room, though this is rarely used during the day, instead reserved for nighttime as to limit the units used. The ground outside their home is bare, and it is common for her or one of the children, to sweep the area twice a day to keep it neat and orderly looking. She does most of the cooking for the household in the walled area around the front of their home or in the doorway to the house, with the help of the children. In the rainy season inclement weather may necessitate bringing the brazier inside and frequently in the cold season. After cooking is completed, the brazier is brought inside to use the residual heat of the charcoal to warm the home.

Making fritters necessitates waking up at approximately 3:00 am to begin preparation. The fritters are a mix of flour, sugar, water, and a few other ingredients to form a dough then portioned off and fried. Due to the early hour that preparation begins for the fritters, Mary begins preparing them inside the home—for fear of being robbed if she is outside the home while it is still dark. The frying pan is placed on three rocks, underneath several wooden sticks that come together to act as the fuel (as seen in figure 5-1). The cooking oil needs to reach a certain temperature to fry the fritters correctly and necessitates using wood as the fuel because charcoal will not reach and

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sustain the desired temperatures. When it becomes sufficiently light, she moves outside to cook within their yard, a level bare area outside the front of the home surrounded by an open mud-brick wall. Once she has fried all the fritters, she places them into plastic bins to carry and sell individually at the local market or along the roadside. If there is firewood left, she will use it to prepare breakfast to avoid wasting fuel.

The use of wood in Mary’s case elevates her exposure to airborne toxicants, particularly PM. When wood burns, it produces significantly higher levels of PM than charcoal, particularly if it is not thoroughly dried beforehand. Accordingly, in her case, the elevation in PM occurs primarily around cooking times. There are three significant peaks of PM exposure during the day Mary wore the active monitor that all correspond to breakfast, lunch, and a small afternoon meal (Figure 5-2). The walls surrounding the cooking area may contribute to these increases as they block any wind that might blow fuel emissions out of the immediate area. Frequently the female children assist in the cooking process, particularly the older ones, potentially exposing them to similar levels of these airborne toxicants.

Case study: Kalongwezi Participant 46-151

Participant 46-151, Margaret Zulu, resides in the more middle-class residential area of Kalongwezi. She lives with her older sister, both of whom consult work on various projects throughout the country (though primarily in Eastern Province where their translation skills are most applicable). In contrast to the homes in Mchini, their land parcel is surrounded by fencing covered in vines to reduce visibility inside. The wall facing the road running by their house is covered with these vines that reduce visibility inside, while the fence surrounding the other sides is more a demarcation of where their property ends. Inside they have a large garden where they cultivate various local and

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exotic produce, ranging from sweet potatoes, onions, maize, Chinese cabbage etc. Fruit trees grow around their buildings and, in the rainy season, produce mangos, granadillas, and a host of other fruits for household consumption. Four small dogs run and play in the yard, eagerly eating in the chicken wing tips, and nshima Margaret prepares them for their dinner.

Inside the home, there is a large sitting area attached to the kitchen and bedrooms. In the kitchen, there is a refrigerator, freezer, and a four-burner electric stove. It is on the electric stove that they cook most of their food, saving the charcoal brazier for grilling meat or use in the dry season when daily power outages coincide with mealtimes. Their diet is varied, often using the vegetables that they grew in their garden and what is seasonally available in the market. This year they invested more time and energy in their garden as a fun activity to do when they are not working (this year, their garden was so productive they frequently would give away vegetables to friends and family).

In contrast to many in the area, they try and cook nshima only once or twice throughout the week as Margaret and her sister are trying to cut down on eating as many carbohydrates. Instead they cook rice, pasta, and potatoes on the stove. These foods are more well suited to cooking on the electric stove as they take relatively less time to cook and use fewer units of power. As previously mentioned, power units from

ZESCO (currently the state-owned power company- though many Zambians fear a

Chinese takeover of the company should the country default on its debts) are prohibitively expensive for many Zambians. Those with stoves frequently express that they reserve the stove for cooking ‘quick foods’ such as rice, eggs, or water for tea.

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When Margaret and her sister use the brazier it is outside, or if it is particularly windy or cold, inside a patio area that is walled, but has no glass windows to completely enclose the space. This allows for some air flow through this space, albeit interrupted.

Additionally, Margaret states that they rarely bring the brazier inside for warmth, only once or twice a year, if it is particularly cold or if there is a family gathering.

Margaret agreed to wear the active air quality monitor twice for the purposes of this study. This was done to compare for a single person how PM levels would change when the only variable changed (under our control) was fuel use. The first day she cooked a meal using the charcoal brazier, and the second she went about her day as she normally would. The levels altered relatively drastically between the two days, though the majority of the difference comes from the approximately hour and fifteen minutes in the late afternoon Margaret spent cooking dinner using the brazier. The day

3 she used the brazier, levels of PM2.5 reached almost 1,500 µg/m at some points. In contrast, during the day she went about her normal routine, at the most levels of PM2.5 failed to even reach 250 µg/m3 (Figures 5-3 and 5-4). In this case the relatively high levels in the morning we see are a result of neighbors burning yard waste outside their home and the wind blowing the smoke through their residence. Outside of brazier use and the neighbors burning yard waste, the levels for each day were relatively similar.

Relationship between PM10 and PM2.5

Distinct exposure sources result in increased PM10 and/or PM2.5 production. The scatterplot of PM10 on the x-axis and PM2.5 on the y-axis shows two distinct ‘arms’ that appear when the two PM types appear together. Each dot represents both PM10 and

PM2.5 at the same discrete point in time. In some instances, there are large increases of

PM10 with relatively little change in PM2.5. In other circumstances, both appear to

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increase at the same rate. From the daily activity recalls given by participants in the

study, it is possible to build a general idea of activities and circumstances that result in

these different kinds of exposures. Sweeping in and around the home is a daily chore

for most women in this study, a task that tends to produce a significant amount of

airborne PM10, but relatively little PM2.5. Exposure to smoke, in particular, wood smoke,

increases both types of PM at approximately the same rate. However, there appears to

be a threshold of around 3000 µg/m3 for PM10. At this concentration, PM10 levels remain

relatively static, while PM2.5 will increase to their highest points (approximately half those

3 of PM10 at 1500 µg/m ) (Figure 5-5).

Analysis of Urinary Metabolites 1-Hydroxypyrene and 3-Hydroxybenzo[a]pyrene

Analysis of urinary metabolites show detectable levels of 1-Hydroxypyrene in

samples from both populations (Table 5-2). Levels of 1-Hydroxypyrene are significantly higher when analyzed as ng/mL; however, results are not significant after adjusting for creatinine (creatinine levels were not significantly different between the two groups).

This could be a result of the small sample sizes used in this case. Seven participants had creatinine levels outside of the standard curve and could not be included in this portion of the analysis. It is possible with their inclusion the results could be altered.

While there are not enough measurements to run a test for statistical significance, 3- hydroxybenzo[a]pyrene was detected in only one participant from Kalongwezi and thirteen participants from Mchini. This further supports the claim that those in this study reliant on biomass fuel inhale and metabolize more PAHs in their environment than those who have access to electricity.

Due to interference in the colorimetric assay to estimate urinary creatinine

concentrations, specific gravity was used as a secondary method for standardizing

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urinary concentration. When adjusted for specific gravity the results show significant

differences between these two populations (p = 0.022) (Table 5-2).

Biometric Data

The results of the biometric data show several distinct differences between the

Kalongwezi and Mchini participants. Importantly, age and height did not significantly vary between the two groups. On average, the participants living in Kalongwezi were significantly heavier than those living in Mchini and had a significantly higher average

BMI. Systolic hypertension (> 130 mmHg) affected 24% of participants in both Mchini and Kalongwezi; however, diastolic hypertension was much more prevalent. 64% and

52% of participants in Mchini and Kalongwezi respectively were hypertensive for this measure (> 80mmHg) (Table 5-2). When comparing the continuous blood pressure data there were no significant differences between the two groups in either systolic or diastolic blood pressure (DBP and SBP). The Kalongwezi households were relatively

wealthier than their Mchini counterparts and had more consistent access to calorically

dense and processed foods. These include prepackaged snacks such as crisps and

candy, sugary drinks, and relatively more meat. However, despite this they had a lower

resting pulse rate (Figure 5-6).

To reduce the burden on participants in the study, spirometry was performed until

(1) repeatable measures were obtained between tests or (2) the participant decided

they were too tired to continue. As a result, not all participants provided tests which

passed quality control for repeatable trials. There were 27 acceptable trials, three for the maids, 16 for Mchini participants, and eight Kalongwezi from Kalongwezi (all participant

trials reported in Table 5-3 and all passed reported in Table 5-4). These scores followed

similar patterns seen by Arigliani et al. comparing predicted GLI scores in three SSA

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countries, particularly in areas where there is likely malnutrition/undernutrition early in

life leading to stunting (Arigliani et al., 2017). Both FVC and FEV scores are lower than

predicted, but FEV1/FVC(L) are in an expected normal range (see Table 5-3).

Utilitarian Benefits of the Charcoal Brazier

The brazier used by most participants were more practical than something of

important cultural significance. There were attributes of the brazier that participants

appreciated for certain dishes more than cooking over wood or electricity. Charcoal

burns at a lower temperature than wood and does not produce the same amount of

smoke. This method allows food to cook more slowly and develop flavors in a way that

neither of the other two sources can. Wood introduces a strong smoky flavor that most

people cite as unpleasant, and electricity cooks things too quickly without adding any

flavor to the dish. Food cooked on the brazier can be left for extended periods while it

cooks, giving the person cooking time to complete other chores in and around the

household.

Charcoal is reasonably priced in this area; a person can buy a 50kg bag of

charcoal for approximately 60 ZMW in the hot season and 75 ZMW in the cold season

(prices vary by season). While it is cheaper to buy directly from the rural areas it is

produced in (prices as low as 35 ZMW per bag), the time and expense of traveling is

prohibitive to most people. In Kalongwezi participants usually bought charcoal in these

larger quantities. In Mchini, it was common to see entrepreneurs with stalls selling fresh

vegetables, crisps, and smaller bags of charcoal. They will buy a bulk purchase of 50kg

and then break these into smaller, grocery-sized bags. These would then be sold by vendors for 2-3 ZMW each depending on the season. While more expensive in the long term, it was often what participants in Mchini could afford to buy at any given time

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(saving enough for a 50kg was not possible for many participants). The amount of

charcoal a household would use in a month largely depended on the number of

individuals residing there and how carefully they managed charcoal consumption. At

most a house would use four large charcoal bags per month and a minimum of ½ of one bag (based on self-report data).

Participants often cite wood as cheaper than charcoal, but it is not used often

(with one or two exceptions for specific purposes like frying fritters) for two reasons. The first is that it smokes to a much greater degree than charcoal during the cooking process, adding a strong smoky flavor to food. This flavor was disliked by almost everyone interviewed, both formally and informally. The second reason is that wood is not common in the area. To find a suitable area to cut down the wood a person needs to hike into the hills surrounding Chipata and spend hours chopping and processing wood before breaking it into appropriate sizes for use. The amount of time and energy required to obtain wood in this way is not something many people do, though there is a small minority who will spend the time and energy for free fuel. While all participants cited their desire to have an electric stove, most acknowledged that the cost of units sold by Zesco (national electric company) were the most expensive. Despite efforts to lower the cost of electricity, charcoal is still cheaper to use as a daily fuel than electricity. Even households in Kalongwezi will not cook certain meals on the stove because they take a long time to prepare and will use too many units. Dishes like dried fish, beans, and nshima are almost universally prepared on the brazier due to the length of time they take. When people express a desire to have an electric stove it is mostly for the convenience it provides for cooking what are known as ‘fast’ foods. Fast foods are

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dishes like tea, eggs, or rice. that cook very quickly and do not use many units of

electricity. Having an electric stove for these dishes saves considerable time since the

cook does not have to wait to light the brazier and for it to finish smoking before putting the food on to cook. Further, participants stated a positive trait of electric stoves was

that they added no smoky flavor to the food cooked on them (though it is important to

note that some participants in Mchini stated they had never had food cooked on an

electric stove and could only guess at the flavor).

There did not appear to be significant cultural meaning attached to these

charcoal braziers and participants discarded them after a couple of years use and

replaced them with a newer model (though the length of time varies depending on how

well the brazier is maintained). Participants varied considerably on how they perceived

daily cooking tasks. Some participants stated they did not particularly enjoy cooking and

wished that they did not have to do it every day, while others talked about how much

they enjoy cooking and feeding their families. Other participants fell somewhere in the

middle, and stated cooking was just another part of daily life and did not swing

significantly towards one extreme or the other. This lack of significant cultural meaning

falls counter to some Rhodes et al.’s findings in previous studies; though, this lack of

cultural significance may be because the brazier is not strictly a traditional cookstove

(Rhodes et al., 2014). Braziers are made from a thin cylinder of metal with holes

punched in the side and the bottom to allow oxygen to flow. They are not made of

strictly local materials, rather mass-produced and for sale in local markets. The ease of

replacement and uniform nature may contribute to braziers being viewed as just another

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implement of cooking, not particularly unique or eliciting strong responses from those using them.

The typical age that children begin to assist with cooking-related tasks is slightly earlier for adolescent girls than boys. It is common for younger girls (six to eight years of age) to receive a small pot and practice cooking on their own, lighting small fires using sticks and wood and imitating what they witness their mother or older siblings do daily.

The first dish almost any child makes is nshima, mixing the maize flour with boiling water and learning to stir until it is cooked and the correct consistency. When they reach approximately ten years old, they will start to help out with meals by chopping up vegetables and slowly watching and learning from their household's older members. It is not as common for adolescent boys to learn to cook at a young age. Boys usually begin assisting with cooking duties two years later than girls, around 12 years of age (Figures

5-7 and 5-8). However, it should be noted that there is a significant spread in report data, particularly for adolescent boys.

Refuse Identification in Chipata Township

Over one month (August), the Co-PI took pictures of refuse piles in Chipata township located in or near Kalongwezi and Mchini, including 31 images of refuse currently being burned or piled up for burning later. While there are dumpsters located in the main area of town, there is no infrastructure for household trash pick-up, so it is common for people to either burn or bury trash. The choice largely depended on the land available to a household and any time constraints that would make burning the preferable option.

These refuse piles would almost always contain some level of organic matter, the most common of which would be peeled sugar cane leftovers people would chew as a

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snack throughout the day. The climate in this region is suitable for growing sugar cane,

and households often have a small copse growing in their lot. The remainder of the

trash piles are composed largely of different kinds of plastic from goods and foods

commonly eaten in the compounds. Plastic shopping bags like those used in markets,

made of LDPE, made up a significant percentage. Crisp (potato chip) bags made of

polypropylene and water bottles made of PET are ubiquitous. Figure 5-9 shows the

typical roadside waste that is periodically gathered and burned:

The Co-PI counted and cataloged all visible, identifiable refuse types with an undergraduate assistant's help, from the 31 images taken. The team was conservative in identification, ignoring specific types and composition of waste that was not readily apparent to avoid falsely inflating the totals. The totals are summarized in Table 5-6 (the

appendix contains a complete table showing information on individual images):

These refuse piles may cause exposures to various airborne toxicants different

from those produced from just biomass fuel. Burning polyethylene produces a host of

chemicals that include methane, acetaldehyde ethylene, formaldehyde, methanol,

acetone, benzene, terephthalic acid, styrene (ethynylbenzene) ethanol, toluene

(methylbenzene), xylene (dimethylbenzene) ethylbenzene, naphthalene, biphenyl and

phenol (Sovová et al., 2008). Mixed household wastes such as different types of

plastics can produce endocrine-disrupting compounds, volatile organic compounds,

chlorobenzenes, Polybrominated diphenyl ethers, polybrominated dibenzo-p-dioxins,

and furans (Estrellan & Iino, 2010; Lemieux, 1998). While the open nature of these fires

diffuses these compounds relatively quickly, they often occur next to businesses, and

children will frequently warm themselves by these fires on cool mornings. It was outside

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the scope of this study to directly measure these compounds, but the ubiquitous refuse burning in these areas implies exposures to a variety of harmful compounds that may have a deleterious effect on the health of those exposed.

While many of these burns occur outdoors along streets and in yards, often refuse is used to start braziers in the home. Crisp bags light very quickly and burn long enough that when placed on charcoal, they will ensure it ignites (Figure 5-10).

Alternatively, people may use LDPE shopping bags (left over from a trip to town or picked up from the street).

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Table 5-1. PM10 and PM2.5 mean, minimum, and maximum values for personal air quality monitor (approximately 400 hours of monitoring) Fine Particulate Mchini Kalongwezi Significance Matter (n = 25) (n = 26) Mean SD Mean SD

PM10 Minimum 10.44 9.96

PM10 Mean 183.68 353.85 84.19 146.57 p = <0.0001

PM10 Maximum 2200.28 1491.19

PM2.5 Minimum 4.6 5.85

PM2.5 Mean 39.68 80.74 27.54 47.48 p = <0.0001

PM2.5 Maximum 647.96 443.15

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Figure 5-1. Woman cooking fritters using wood inside the walls of her home, July 12, 2019. Chipata. Courtesy of the author.

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Figure 5-2. Daily PM10 Exposure of Participant 1-66

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Figure 5-3. Concentrations of PM2.5 during a day of brazier use: Participant 46-151

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Figure 5-4. Concentrations of PM2.5 during a day of electricity use: Participant 46-151

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Figure 5-5. PM2.5 Plotted against PM10

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Table 5-2. Results of Urinary Concentrations of 1-Hydroxypyrene in Biomass and Electricity Using Groups Adjusted for both Creatinine and Specific Gravity Urinary Metabolites Mchini Kalongwezi Significance

Mean SD Mean SD

Creatinine (mg/dL) 243.378 221.578 198.354 146.744 0.380

Specific Gravity 1.026 0.011 1.027 0.012 0.768

1-Hydroxypyrene (ng/mL) 0.917 0.768 0.548 0.514 0.036* 1-Hydroxypyrene/Creatinine 0.481 0.984 0.171 0.131 0.116 (µmol/mol) 1-Hydroxypyrene adjusted 0.889 0.626 0.476 0.315 0.0216* for specific gravity

Table 5-3. Results of Biometric Measurements among all participants Mchini Kalongwezi Maids Measure (n = 25) (n = 25) (n = 5) Mean SD Mean SD Mean SD Age (years) 38.04 9.68 37.28 13.74 32.17 6.34 Height (cm) 157.17 5.96 159.17 5.03 159.08 6.43 Weight (kg)** 66.23 13.63 80.66 20.43 73.19 14.4 BMI* 26.91 5.51 31.89 8.19 28.71 3.88 Systolic BP 123.99 15.81 117.92 16.47 121.53 9.76 (mmHg) Diastolic BP 84.36 11.38 80.36 10.89 84.13 9.17 (mmHg) Pulse (BPM)* 85.71 11.12 77.88 10.77 77.87 9.6 Yes No Yes No Yes No Systolic 6 19 6 19 1 4 Hypertension Diastolic 16 9 13 12 3 2 Hypertension Total 16 9 13 12 3 2 Hypertensive

* p-value ≤ 0.05; ** p-value ≤ 0.005

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Figure 5-6. Pulse rates (BPM) of participants in Kalongwezi and Mchini

Table 5-4. Summary of baseline spirometry data (all participants) Mchini Kalongwezi Maids Mchini + Maids Measure (n = 25) (n = 25) (n = 5) (n = 30) FVC (L) 2.61 2.67 2.48 2.59 FVC predicted (%) 90.52 89.24 80.6 88.66 FVC z-score -0.71 -0.75 -1.46 -0.85 FEV1 (L) 2.34 2.44 2.18 2.31 FEV1 predicted (%) 96.96 96.68 84.6 94.64 FEV1 z-score -0.23 -0.16 -1.15 -0.40 FEV1/FVC (L) 89.74 91.21 88.74 89.56 FEV1/FVC 107.24 108.12 105.4 106.90 predicted (%) FEV1/FVC z-score 1.15 1.28 0.87 1.10 PEF (L/s) 6.38 6.46 5.46 6.21 PEF predicted (%) 106.24 104.72 88.80 102.97

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Table 5-4. Continued Mchini Kalongwezi Maids Mchini + Maids Measure (n = 25) (n = 25) (n = 5) (n = 30) PEF z-score 0.24 0.19 -0.45 0.11 VC (L) 2.80 2.79 2.76 2.79 VC predicted (%) 106.44 103.32 99.80 105.20 VC z-score 0.46 0.25 -0.05 0.36

Table 5-5. Summary of baseline spirometry data (passed only) Mchini Kalongwezi Maids Mchini + Maids Measure (n = 16) (n = 8) (n = 3) (n = 19) FVC (L) 2.68 2.75 2.34 2.63 FVC predicted 92.94 92.63 81.00 91.05 (%) FVC z-score -0.53 -0.57 -1.42 -0.67 FEV1 (L) 2.35 2.47 2.05 2.31 FEV1 predicted 98.13 99.75 85.00 96.05 (%) FEV1 z-score -0.13 -0.03 -1.11 -0.29 FEV1/FVC (L) 87.76 89.90 88.37 87.85 FEV1/FVC 105.44 107.00 105.00 105.37 predicted (%) FEV1/FVC z- 0.83 1.08 0.76 0.81 score PEF (L/s) 6.51 6.48 5.71 6.38 PEF predicted 108.50 106.00 95.67 106.47 (%) PEF z-score 0.34 0.22 -0.15 0.26 VC (L) 2.84 2.68 2.57 2.80 VC predicted (%) 107.63 100.13 99.00 106.26 VC z-score 0.55 -0.02 -0.12 0.44

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Table 5-6. Results of Mary Phiri spirometry Parameters LLN Predicted Best %Predicted z-score FVC 2.05 2.66 2.32 87 -0.9 FEV1 1.65 2.17 2.01 93 -0.51 FEV1/FVC 71 81.8 86.6 106 0.84 PEF 2.98 5.62 6.48 115 0.54 ELA 47 56 119 EVC 2.05 2.66 2.31 87 -0.93 VC 1.84 2.39 2.31 97 -0.24 FEV1/VC 71 81.8 87 106 0.92 *note: PEF- peak expiratory volume; ELA- estimated lung age; EVC- expiratory vital capacity

Figure 5-7. Kalongwezi: average age to begin assisting in cooking duties for adolescent females and males

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Figure 5-8. Mchini: average age to begin assisting in cooking duties for adolescent females and males

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Figure 5-9. Roadside refuse pile seen before burning with 5 x 5 gridlines, July 21, 2019. Chipata. Courtesy of the author

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Table 5-7. Total waste split into constituent parts HDPE LDPE Polypropylene PET

5 1291 294 71

Polystyrene Organic Waste Unidentifiable Total Amount of Refuse

15 Uncountable 23 1,699

Abbreviations: HDPE = High Density Polyethylene; LDPE = High Density Polyethylene; PET = polyethylene terephthalate

Figure 5-10. Image of Rabecca Miti lighting her brazier with a crisp bag before cooking on the porch of her home, July 19, 2019. Chipata. Courtesy of the author

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CHAPTER 6 DISCUSSION

The results of this study indicate several important things concerning HAP and environmental toxicant exposure in eastern Zambia. This section focuses on three main findings from this study (1) risk perception does not seem to have a significant effect on behaviors surrounding brazier use, (2) participants in the study are exposed to levels of

AP significantly higher than WHO recommended safe levels consistently, and (3) local knowledge of disease etiology and symptoms do not connect AP with its associated diseases in the community.

Risk Perception and Brazier Use

Weber’s Global warmings papers assertions fit well into the issue of HAP and long-term health consequences to individuals because the effects of exposure to HAP are both unknown and often tied to events (e.g. cancer, COPD) far in the future that presently seem intangible (Weber, 2006). Risk perception generally falls into two categories—the first is affect, or the feeling of risk (e.g., fear, anxiety), and the second involves establishing an objective sense of risk (Weber, 2006). The second is more analytical and involves consciously assessing situation(s) to assess risk. This conscious assessment is a combination of personal experience with a risk and objective information on the probability of an event occurring (gathered from secondary sources).

Air pollution, and the health consequences of exposure, fall into the category of an intangible risk for several reasons. First, air pollution is not always detectable by human senses, mostly sight and smell. Smoke and smog are known to be dangerous, but other compounds such as CO are undetectable without equipment to measure air quality. Overall, participants in this study perceive exposure to biomass fuel emissions

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as a limited risk, which is similar to Weber’s explanatory model for how people view climate change. She outlines three aspects of risk perception applicable here in her work: (1) there is an importance to a visceral and personal response to a risk that induces behavioral responses, (2) the two main pathways that guide concern about risk, the first of which is a personal experience and the second mental simulation, and (3) visceral reactions to risk often do not correlate with the objective measures of said risk

(Weber, 2006, 2010).

Risk perception is generally influenced more by affect/emotion than an analytical breakdown of a specific activity (Loewenstein et al., 2001). This first system (affect) is considered to be more the basic, evolution-driven of the two and links concrete events with strong negative or positive feelings (Slovic et al., 2007; Weber, 2006). Affect plays an increasingly important role when the trade-offs of a risk are more difficult to calculate.

Emotion, then, may become the deciding factor (Slovic & Peters, 2006). Though, a reciprocal relationship between risk perception and affect in a feedback system is shown to explain more variance than either one in isolation (Van Der Linden, 2014). In this study, there were two instances of participants having strong feelings of risk related to charcoal cookstoves use that resulted in the exclusive use of the brazier outside. The first, Rachel, describes a trip she took with a friend from school. Their first night they cooked in the home, they used a brazier inside, and suddenly, she started to feel lightheaded. Rachel awoke on the floor after fainting caused by inhaling fumes emitted from the brazier. Since that day, she recognizes the danger of cooking indoors and makes sure that both she and her children only use the brazier outside. The second woman, Mary, describes an event from early childhood. When she was five years old,

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she resided with her grandmother, and one evening an unattended brazier caught the home on fire. Flames soon enveloped the entire structure, and the family lost most of their possessions. After this traumatic early-life event, she also makes sure that she only uses the brazier outdoors to keep her home safe. It is important to note that participants who never used a brazier indoors were a small minority (only two participants- one of which explicitly cited air quality while the other was concerned primarily with fires), and often attributed this to concrete events that left a long-lasting emotional impression. Participants used this second analytical system yet drew on different information than an environmental scientist would use to come to conclusions concerning risk.

“When the brazier stops smoking, it is safe to bring inside”

Brazier use in the home follows a consistent pattern across participants in this study, and those who live in and around Chipata generally. The cook piles charcoal into the brazier, and the individual cooking lights a plastic/crisp bag with a match and places it on top of the mounded charcoal. The plastic bag almost melts around the coals giving extra time for the coals to catch fire in a way that fast-burning sticks or dried grass do not allow. While the coals burn and smoke, the brazier is left unattended outside while the person who lit it goes about doing other chores around the home or yard. They may occasionally come and grab the brazier by the handle and swing it back and forth to increase the oxygen flow and make the fire hotter. Once the coals have burned sufficiently, and there is no longer visible smoke coming from the coals, it is then an acceptable time to start cooking. Most people I interviewed did not like smoky-tasting food. In the hot, dry season, cooking would continue outdoors in the shade, and in the cold or rainy seasons, the person cooking might bring the brazier inside or into a

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corridor to prevent getting the brazier and themselves wet. It is the moment the charcoal stops emitting smoke that the main issue of risk perception comes into play. At this moment the brazier is viewed as safe to bring indoors and leave inside for the cooking duration.

From the perspective of a person cooking on a charcoal stove, this makes intuitive sense. Once the smoke clears, immediate symptoms of exposure (e.g. burning eyes, coughing) to AP generally become less severe and may disappear entirely. Most of the PM produced when using charcoal occurs when it is smoking. The brazier is no longer producing strong smells and will no longer cause the eyes to sting or nose to run.

However, there are significant levels of CO and other compounds being produced even after this point (though it is cleaner than when actively smoking). One of the only other studies completed on air pollution in Zambia, located in Lusaka, found that charcoal users were exposed to significantly higher levels of CO than users of either wood or electricity (Ellegård & Egnéus, 1993). During just one cooking session women were exposed to the equivalent of six cigarettes worth of CO. This was consistent regardless of cooking location in/outside of the home.

In addition to the intangible future events that HAP might cause, there is the issue of familiarity with an action lessening the perceived risk associated with said action (Fischhoff et al., 1978). Similar to driving, although automobile accidents kill thousands in the US every year, it is not viewed by most as a risky activity in part because driving is a part of daily life. Almost all participants felt there was a risk in using the brazier indoors, but in their case, these conclusions were based largely on information gathered from the senses—moderated by a familiarity with the activity. The

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more an individual engages in an activity, particularly when there is no immediate

negative consequence, the perceived risk associated with said activity decreases (note

that this does not hold for familiarity and in-depth knowledge of specific health risks)

(Heideker & Steul-Fischer, 2015). Once the brazier stops smoking, it is extremely

difficult to tell by sight or smell. that emissions are causing any bodily damage. At this

point, the brazier emits a pleasant aroma and warmth, and the smoke no longer stings

the eyes. Additionally, this is an activity almost all participants had engaged in

thousands of times by the time they reached just twenty years of age, likely with no

adverse events outside the occasional burn (most girls begin to help with daily cooking

activities around ten years of age in this area). This combination of lack of affect-

inducing events and familiarity makes this activity seem safe and a mundane part of

everyday life. Of note is the one participant in Mchini who cooked exclusively outside

(her grandmother’s house burned down when she was a child do to an unattended

brazier) had the third and second lowest levels of exposure to PM10 and PM2.5

respectively. Cooking exclusively outdoors allows the emissions from the brazier to

diffuse much more rapidly, significantly lowering her levels of exposure. However, it is

3 3 still important to note that her average exposures (96 µg/m for PM10 and 20 µg/m for

PM2.5 are still elevated above annual safe levels).

These thousands of normal, daily occurrences make this an activity that is not

associated with a dreaded outcome in all but one exception. Almost universally (among

participants and informal interviews outside the study population), people make sure

never to fall asleep with the brazier inside or bring it into their bedrooms. There exists a

country-wide narrative that tells of an unfortunate family in a village that fell asleep with

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the brazier inside, and they all suffocated. No participants had a personal connection to this story (i.e., knew anyone this happened to), and the village was always unnamed, but this story persists every year, particularly in the cold season. For example, radio announcements go out during the rainy and cold seasons reminding people to be careful, as these are the most common times to bring the brazier inside. This narrative persists in part perhaps because it hits on two of the main affect-driven attributes of risk, those of (1) dread and (2) the unknown (Bond & Nolan, 2011; Fox-Glassman & Weber,

2016; Murakami et al., 2016). These two attributes are causally linked to the brazier because the effects are readily apparent. The causal pathway for cancer and COPD is not as clear. While brazier use may increase the risk of developing these diseases, they do not necessarily have a direct relationship because the etiology of these diseases is much more complicated.

Additionally, these diseases already fit into the local explanatory disease models that do not directly connect these illnesses to brazier use. These explanatory models are culturally constructed from a combination of traditional beliefs with western biomedical information. This combination creates models unique to this area when discussing disease etiology or treatment. in a similar vein to Kleinman’s work on patient explanatory models. (Kleinman, 1978). The most common illness attributed to supernatural forces in this area is kunyu, particularly if a person had epilepsy which could not be controlled well by the hospital. Frequently participants talked about first going to the local hospital or clinic for treatment, but if the medication or advice they received did not work they would then consult a traditional healer. Kunyu is frequently treated with a combination of pharmaceuticals and rituals/herbs provided by a healer.

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While this is not the only disease or incidence a person would contact a traditional healer for, it is the most common. Other maladies and accidents may be blamed on witchcraft (e.g. lightning strikes) and for protection against these, ritual scarification could be used to ward off enemy witchcraft. Drawing from Douglas’ work and the assertion that cultural groups ‘decide’, in a manner of speaking, what is a perceived risk, it appears that brazier use does not largely constitute one. When asked directly if participants viewed using the brazier indoors constituted a concern, after it had finished smoking, the results show that the majority do not perceive this as an activity to be avoided. However, more complexity and information needs to be included than the four underlying cultural groups that are still used in studies on cross-cultural risk perception

(Xue et al., 2014).While Weber’s explanatory theories applied here come from a western context, they were chosen (1) because they rely on relatively few underlying assumptions of risk cognition and (2) this model fits the empirical data gathered from participants in this area (Weber, 2006, 2010). Combining these underlying psychological processes of risk with empirical data on local explanatory models of risk and disease provides an avenue to examine risk perception across cultures in a way that can assess both psychological assumptions and provide more detailed, rich information on local risk models.

The perceived risk of the brazier is also limited by the relatively low probability of serious adverse events occurring to one individual. Fires in the home are rare and most participants had no personal experience with housefires caused by a brazier. Similarly, burns will happen (especially on children running by the brazier), but these are usually minor and do not require significant medical treatment. While AP precipitates millions of

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premature deaths every year, this is within a population of approximately 3 billion that

rely on it as their fuel source (World Health Organization, 2018). While this is an

enormous number of deaths, it is still a relatively low percentage of those reliant on

biomass fuel and may obfuscate the risk felt at the individual or community level. If the entirety of those 9 million estimated premature deaths were due entirely to biomass fuel use (which we know they are not), it represents 0.3% of the population that uses it.

‘Finite Pool of Worry’

Another facet to consider is the phenomenon of the ‘finite pool of worry’— the

concept that people can only spend a finite amount of time and energy worrying about

current or future events. Thus, limiting how many events they can worry about at a time

(J. Hansen, 2004). The ‘finite pool of worry’ concept is an extension of the work by

Linville et al., who investigate the renewable resource model for dealing with

psychologically impactful events (Linville & Fischer, 1991). In this hypothesis, when an

individual has many immediate concerns, it diminishes the relative concern of abstract

future events. Recently this work has been applied to issues of far-off events that are

difficult to conceptualize as significantly impacting an individual such as climate change

to the COVID-19 pandemic to cyber-security (Botzen et al., 2021; Weber, 2006, 2010).

Everyone has worries in their everyday lives; however, the participants who lived in

Mchini had immediate and concrete worries that would often outweigh future abstract

events. These include, but by no means were limited to, worrying about their supply of

antiretroviral drugs, worrying about the drought killing maize crops and leaving them

short of food for the coming year, dental issues like a tooth abscess that is causing pain

but they cannot afford to have fixed, etc. (this is not intended to minimize or create a

false dichotomy between those in Kalongwezi as they had similar worries but were

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relatively more financially stable). Such serious and immediate concerns largely fill this

‘finite pool of worry,’ leaving little room for concerns about what you cook with and how it might lead to a disease sometime in the future.

Exposure to Household Air Pollution

The levels of PM10 and PM2.5 in both the biomass fuel exclusive and electricity group were significantly above safe levels. Elevated exposure levels are largely due to biomass fuel use, but there are extraneous factors that increase exposure even for those that primarily rely on electricity. These additional air pollution sources are largely due to sweeping the home, burning refuse, and burning organic yard waste. In the two dry seasons, Eastern Province is marked by an abundance of fine, red dust that is easily kicked up by cars, foot traffic, or wind. This ubiquitous dust coats the ground and necessitates sweeping the household once or twice per day for the average resident of

Chipata. Most people sweep using short, fibrous brooms that require the person doing the task to bend at the waist. The dust from sweeping becomes airborne easily, and the posture of the person sweeping means their airways (mouths and noses) are close to the ground, breathing in the airborne dust. These are made of locally sourced materials and much less expensive than long-handled plastic brooms sold in stores. While sweeping introduces more PM10 than PM2.5 to the air in the household, which is not as detrimental to health, the levels are still much higher than recommended for the amount of time spent sweeping.

Outdoor burning

Burning yard residue and refuse is common in this region and occurs both in the household and around town, most frequently along roadways where trash often builds up. Exposure from these burnings is difficult for individuals to mitigate because they

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often have no control over what/when refuse is burned. If a neighbor is burning something in their yard, and the wind is blowing in a certain direction, the smoke may enter another person’s home, leaving them with no recourse to avoid exposure. The composition of these burns is particularly problematic as they frequently include plastic waste, producing carcinogens at much higher levels than organic waste. Chipata lacks the infrastructure for home waste collection, and residents must dispose of it themselves. Many people lack the space to bury their waste and burn it to keep their homes clean and free of refuse. There are dumpsters located in town, but it is common to find dumpster contents burning. Burning the dumpster waste cuts back on necessary trips to the local landfill (located a few miles east of the main area of town) and limits disposal costs. The corner where the dumpster is in figure 5-1 is a busy crossroads, adjacent to the main shopping area. Here it is common to find men with bicycles waiting to ferry people across town, children selling fritters, and people passing through on their way to shop. Many days there is a thick, caustic smoke coming from this dumpster that makes it difficult to breathe even when walking by (Figure 5-1).

Issues of single-use plastics in developing countries

The issue of single-use plastics and their effect on the environment is an emerging concern in developing countries, particularly their impact on water runoff into oceans (Adam et al., 2020; Khan et al., 2018; Van Rensburg et al., 2020). Many African countries are adopting policies to reduce single-use plastic products and encourage peoples to utilize renewable resources (Adam et al., 2020). Worldwide, humans produce and circulate approximately 150 million tons of disposable plastics each year, and this figure is steadily on the rise (Vimal et al., 2020). A secondary issue is the lack of recycling facilities to address the issue of accumulating plastics. Africa uses relatively

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less plastic than other parts of the world (the Middle East and Africa combined accounting for less than 10%) (Knoblauch et al., 2018). However, recycling facilities are not available to most populations living in Africa, as figure 5-2 indicates.

Currently, Zambia is starting to introduce these measures in steps. For example, in grocery stores (e.g., Spar, Choppies), it is common now to only have the option of a bag made from renewable resources. Increasingly, there is a charge for purchasing a bag in addition to the price of groceries (50 ngwee for a small bag and one kwacha for a large bag) to limit the number of bags a person uses. Though their supplies of renewable bags are inconsistent and frequently, stores revert to using plastic bags when supplies run low. A lack of recycling centers, an incentive for disposing of plastics, and their utility in everyday life result in continued use after initial purchase in a store.

They are commonly used for waterproofing roofs and filling windows that do not have paned glass. As mentioned in a previous section, plastic bags are commonly burned to light braziers or gathered with other refuse littering the ground and burned roadside.

The smell of burning is ubiquitous when walking through town.

Zambia is a landlocked country, and Zambian single-use plastics are less likely to end their journey in the oceans than in the coastal countries. However, watersheds and precipitation result in significant amounts of these plastics ending up in rivers.

Rivers are an important source of food and livelihood for many Zambians and may be a source of environmental and personal exposure to microplastics. Khan et al. identified only two studies of microplastics in inland waterways, both in Lake Victoria, and highlight the need for additional data to ascertain the true nature of the problem (Khan et al., 2018). Therefore, it is uncertain but likely that single-use plastics contaminate

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rivers such as the Zambezi with microplastics affecting environmental and human

health.

Cardiovascular and Pulmonary Biometric Data

Higher pulse rates among the Mchini participants could have to do with the relatively greater daily stress participants experience in Mchini compared to many in the relatively more affluent Kalongwezi. Chronic stress is well known to overexcite the sympathetic nervous system leading to detrimental alterations to many cardiovascular measures (e.g., blood pressure, heart rate variability, and heart rate) (Kim et al., 2018).

An increase in heart rate variability is a well-documented response to stress; however,

the relationship between stress and increased resting heart rate are less clear. A 2003

study examined individuals with self-report symptoms of burnout. These included such feelings as mental exhaustion, physical fatigue, or loss of energy. Patients with these

symptoms showed significantly increased resting heart rate than healthy controls, while

blood pressure and cardiovascular reactivity remained similar between the groups (De

Vente et al., 2003). Elevated heart rate in living situations marked by chronic stress and

lack of social support (Evans & Steptoe, 2001). Chronic (nutritional) anemia may also

explain some of the variance between the two groups as anemia is known to alter pulse

pressure, hypertension, and normal cardiovascular function (Hegde et al., 2006; Yoon

et al., 2018).

Isolated diastolic hypertension (IDH) is less of a risk factor for adverse

cardiovascular outcomes than mixed systolic and diastolic hypertension (Fang et al.,

1995; Y. Li et al., 2014; Petrovitch et al., 1995). Systolic hypertension is a much greater

predictor of cardiovascular events, particularly in older individuals. It is a much more

robust measure and more important overall when discussing managing hypertension (Y.

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Li et al., 2014). Recent work in the United States found that IDH prevalence was higher than anticipated, but not significantly associated with an increased risk for adverse cardiovascular events (McEvoy et al., 2020). Oscillatory methods show an extremely high correlation with high precision measurements such as radial tonometry in supine, seated, and standing positions (Climie et al., 2012). However, pulse rate, pressure, and arterial thickness influence this method—the latter two are unaccounted for in these measurements (Van Montfrans, 2001). Given the extremely high prevalence of IDH in this sample, it is likely an artifact of using an oscillatory method that does not control for these variables.

Given the high rates of stunting in Zambia and the average age of participants placing them in an age cohort where stunting was well over 50% in the country, it is reasonable to assume that growth restriction in a significant portion of those in this study occurred at some point. The pattern these measures follow generally indicates lung function is not restricted. Rather, growth restrictions resulted in relatively small chest dimensions (Arigliani et al., 2017). The same study found that excluding those who experienced malnutrition, GLI scores of SSA populations were comparable to

African American populations. It was outside the scope of this study to identify the nutritional status of participants during development, or the timing/presence of growth restrictions among participants.

There are no significant differences between groups based on the spirometry results, and daily levels of PM did not correlate with spirometry scores (though it is important to note these measures were taken on different days so a short term increase in PM the day of spirometry could not be accounted for). However, there are individuals

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within each group that deviate significantly from the average without a history of cardiac or pulmonary disease. For example, Mary Phiri’s results for the active air quality monitor indicate that her flow-volume curve does not entirely match what is predicted based on her ancestry, age, weight etc. and her scores for FVC, FEV1, and VC fell below expected values (Table 4-18). While this is not a diagnostic tool as there was (1) no physician present and (2) no bronchial dilation and post-testing completed, the shape of the curve may indicate a mild restriction of some kind.

The Role of Biomass Fuel Use in Disease Etiology

This research takes a constructivist view investigating consensus and knowledge concerning local diseases, their causes, and treatments. in the way many past cognitive anthropologists have done (Baer et al., 2008; Dressler, 2005; Dressler et al., 2005;

Gravlee et al., 2005; Weller et al., 2012). This approach is not meant to discount the impact of structural forces as an important component of causal linkages. Structural issues affect population-level health significantly and influence individual exposure to higher or lower exposures to air pollution (these are touched on in the background section, e.g. issues related to SAPs, economic mismanagement). These structural forces and decisions, both political and economic, have shaped modern Zambia both economically and politically and influence reliance on biomass fuel—mediated through infrastructure, aid from international groups, the political capital of individuals etc.

The focus of this study is to investigate what factors at the local level govern exposures to AP in present-day Eastern Province. Specifically, this study sought to test if there were consistent underlying cultural models of local disease knowledge and cooking practices which would influence biomass fuel use. Results indicate that there

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are aspects of both cultural models that may increase or decrease exposure to HAP

(e.g. cooking indoors).

Chifua and flu are the two most common respiratory diseases participants cite in the study (outside of tuberculosis). When asked what causes these illnesses, the most common explanations revolve around seasonal changes, particularly the transition from the rainy season to the cold and dry season (this typically occurs around May).

Participants in this study are aware and cite that breathing in smoke from the brazier can precipitate chifua and the flu—though this is frequently not offered without prompting. A typical interaction might involve someone stating that the changing seasons could cause chifua, breathing in dust, or catching it from someone else. Only once participants are directly asked if breathing in smoke can cause chifua that they agree, ‘yes it can.’ However, once the brazier has ceased smoking, participants believe it is no longer able to cause any of these illnesses.

Other illnesses such as asthma, bronchitis, and pneumonia. are not commonly linked by the participants in this study to HAP despite the robust association between them in epidemiology and public health literature (Dherani et al., 2008; K. H. Kim et al.,

2011; Kurmi et al., 2010). The cause of asthma varies between participants as some think that you can catch asthma from another person, while others cite that it is a genetic condition. Bronchitis is thought to be a more serious version of chifua, requiring hospitalization if the chifua continues too long and becomes worse. Like chifua and flu, participants thought cold weather causes pneumonia, and the most common symptoms are an inability to breathe properly and sharp pains in the lungs/side. Participants did not identify non-respiratory diseases associated with biomass fuel use (e.g., cancer,

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hypertension) in association with either brazier or specific fuel use (Bruce et al., 2015;

Deng et al., 2020).

This lack of association between fuel use and disease could have implications for

the prevalence of diseases at the local level. This even though during the month these

data were collected, the most common illnesses the clinics in Mchini and Kalongwezi

were non-pneumonia respiratory infections. Biomass fuel use is highly correlated with

respiratory infections and may contribute to the prevalence seen over the month of data

collection (particularly as this took place in the rainy season when people frequently

cook indoors) (Po et al., 2011).

Limitations

Data collection for this research occurred during the worst drought Zambia has faced in the past century (Harding, 2020). This drought resulted in electricity rationing

(load shedding) for a significant portion of data collection (June-September). The limited availability of electricity may have resulted in higher than typical AP levels for the participants who would normally have access to electricity in this sample. The increase in air pollution levels is caused by an increased reliance on the charcoal brazier when electricity is unavailable during normal times for meal preparation. During a year with rainfall levels within a historically normal range, the differences in air pollution between those reliant on biomass fuel and those who are not could be greater than documented in this study.

Five participants refused or could not wear the active air quality monitor and provide a urine sample for the study. Most participants had no issue providing samples.

Similarly, we did not conduct spirometry tests on all participants until they had trials that passed repeatability measures (quality scores calculated using winspiroPro 8.1). Rather

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each person completed at least six trials, and once they reached this number, we did

not ask them to continue unless they wanted to. This approach eased the burden on

participants since spirometry tests leave some people feeling lightheaded and out of

breath. As a result, a smaller pool of individuals passed with acceptable quality control

scores than the total number tested (see Tables 4-16 and 4-17 for more information).

This limitation reduces the power of any statistical test performed using this data.

Likewise, post-tests using bronchodilator methods would have been ideal for assessing

if certain individuals' restriction was due to an underlying condition rather than individual

physiology.

Finally, access to the University of Florida Center for Environmental and Human

Toxicology laboratories during the COVID-19 pandemic was limited, so we could not complete the PUF disk analysis in time to include it in the dissertation. The passive disk analysis is ongoing, and the results will be published in future work.

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Figure 6-1. Fire burning in a municipal dumpster on a crowded street corner near the Chipata business district, July 23, 2019. Chipata. Courtesy of the author

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Figure 6-2. Solid waste disposal OECD vs. Africa (Knoblauch et al., 2018). Note, rates of incineration may be underestimated in Africa due to unofficial burning

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CHAPTER 7 CONCLUSION

This chapter reviews the five study aims and what can be inferred from the

findings concerning biomass fuel use in Eastern Zambia. Then, I discuss the

implications of these findings in SSA, particularly in light of (1) unstable weather

patterns predicted in climate change models in the coming decades and (2) lagging

infrastructures in peri-urban and rural areas.

R1- Determine chronic cardiopulmonary disease prevalence within the

populations of interest using biological measures and interviews to elicit recent/current

illnesses. There were few statistically significant differences in lung function, biometrics,

or disease prevalence between the study populations, though this was likely an artifact

of the small sample size addressed in the limitations. ARIs were common in the

community during this period, but self-report data is limited to descriptions given by

participants such as chest pain or a persistent cough. The Mchini group did exhibit a

lower BMI on average and significantly elevated pulse rates. But these are not specific

diagnostic criteria exclusive to illnesses precipitated by exposure to AP.

R2- Determine environmental and personal toxicant exposure levels in biomass and electricity using populations. Research objective two focuses on measuring

environmental and personal exposures to AP, focusing on PAHs and PM. As measured

by PM2.5 and PM10, AP was significantly elevated in the biomass fuel group compared to

the electricity group. However, both were significantly higher than the safe

recommended levels. Cooking practices, household characteristics, and community

practices influenced exposure levels. Similarly, levels of urinary 1-hydroxypyrene were

significantly elevated in the biomass group compared to the mixed electric. 3-

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hydroxybenzo[a]pyrene was detectable in more individuals in this group as well, though not in enough quantity to perform statistical analyses. While individuals can take steps to reduce their exposures, there are numerous factors outside their control that elevate

AP and potentially place them at risk. These AP sources in the environment surrounding homes suggest that AP, and its potential health consequences, are not limited strictly to those reliant on biomass fuel as their only fuel (particularly in the months preceding the rainy season when load shedding is more likely).

R3- Determine if there are shared models of risk and disease/illness between/within communities by utilizing traditional ethnographic field methods, in-depth interviews, and cognitive anthropology methods to create and test a formal cultural consensus model. There is a reasonably strong consensus surrounding disease knowledge and cooking practices among the participants in this study. Participants in

Mchini exhibited strong consensus when discussing disease knowledge but fell slightly below the accepted threshold for one underlying cultural model for cooking practices.

Kalongwezi participants exhibited the opposite trend, agreeing on cooking practices but diverging in local disease knowledge. This difference in knowledge/agreement suggests that there may be some significant variation between and within the two groups that future studies should investigate in more depth.

R4- Determine if risk perceptions associated with exposure to biomass fuel emissions correlate with increased or decreased personal levels of exposure to toxicants by comparing self-reported perceptions of risk with the results of the toxicological analyses. Risk perception is low, and participants did not typically connect biomass fuel use with developing serious diseases. When smoke is not visible from the

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brazier, most participants believe that the stove is safe to bring indoors for use, a practice common in two of the three seasons in this region. This lack of perceived connection between brazier use and the development of pulmonary and cardiac conditions leads to an avoidable increase in AP exposures. There are frequently risks and worries that are much more immediate and take precedent in people’s minds, leaving little room for worrying about accumulated exposures over the years. These daily risks not associated with air pollution are important drivers of behaviors surrounding brazier use. For example, cooking indoors early in the morning or evening due to the fear of robbery. These phenomena may increase even more in areas that experience high levels of crime. As a result, air pollution levels among these two groups are influenced more by factors mentioned in R2 than by individual risk perceptions.

R5- Determine what utilitarian and cultural aspects of cookstoves are important in the study populations using traditional ethnographic field methods and semi-structured in-depth interviews with informants to elicit narratives. Lastly, charcoal braziers do not seem to hold significant symbolic or cultural value for participants though they function exceptionally well for cooking local foods with relative ease. While participants cite valued aspects of the brazier, all those reliant on biomass fuels expressed a desire for an electric cookstove. Mostly to ease the time burden needed to make certain foods and the ease of cooking on an electric stove allows. Charcoal braziers are the preferred instrument among all participants to cook certain foods either because of flavor or the prohibitive electric cost. Late in the year before the rainy seasons, when electricity shortages are common, it leaves the entire population little recourse except to cook with charcoal exclusively.

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These findings all have health implications in this area, particularly when climate

change adds a layer of insecurity to the region. Zambia relies on the Kariba Dam to

produce the vast majority of its electricity, which works reasonably well in years of

plentiful rainfall. However, as weather patterns in the region become more

unpredictable, this reliance may become dangerous for the country. In 2019, southern

Africa experienced the worst drought in almost a century (Harding, 2020). It is normal in

Zambia for load shedding to occur immediately before the rainy season. However, this

year, load shedding practices started months earlier (in June, near the beginning of the

cool, dry season). Beginning at four hours per day and building from there, by the time

the rainy season arrived in late 2019, many people were going sixteen hours per day

without access to electricity. Southern Zambia experienced the worst of this drought,

killing huge swaths of agricultural maize. The maize deficit led to more than two million

Zambians in need of food aid (Harding, 2020; Makondo & Thomas, 2020). The rainy

season in southern Zambia shows an overall reduction in both length and total rainfall in

the past few decades. In the future, droughts such as this will play a significant role in

the percentage of the population that relies on biomass fuels even if they can afford

electricity.

Air pollution and its associated morbidities and mortality will continue to be an issue in this region until the challenges surrounding reliance on biomass fuels are addressed through regulation and more affordable access to clean energy sources. This issue is multifactorial and involves addressing infrastructure, poverty, reliance on hydroelectric power, among other things. The complex nature of energy access means

Zambia, like much of SSA, will have a large portion of its population reliant on biomass

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fuel sources for decades (Lelieveld et al., 2015). Understanding and characterizing household exposure and how to limit exposures is important for both individual and population-level health in SSA and other areas of the world where the reliance on biomass fuel continues. This work demonstrates the utility and benefit of anthropological methods and theories in addressing global health issues with a focus on environmental pollution. It accomplishes this by explicitly focusing on local understandings of risk, disease, and cooking, in tandem with measures of both environmental and personal exposure to AP for the first time in Zambia. Exploring these topics together, it is clear that environmental and economic factors play a much larger role in AP exposure than risk perceptions.

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CHAPTER 8 FUTURE DIRECTIONS

The priority following the submission of this dissertation is the analysis of

environmental samples collected (PUF disks). The passive samplers, which sat inside

the home for two weeks in every participant’s home, collected indoor PAHs and

provided information on the background levels in homes. However, because

environmental levels of AP and inhaled levels do not correlate in a one to one manner,

these are important to compare to the urine samples collected from participants to

measure urinary metabolites of two PAHs (1-Hydroxypyrene and 3-

Hydroxybenzo[a]pyrene). Taken together, these measures, along with PM data, will give a richer description of the AP present in these two communities in Chipata. These measures also provide information on the mediators of exposure and potential health ramifications associated with exposure to these compounds.

The second priority is to disseminate the environmental and personal toxicant level results to participants after analysis (results of active air quality monitor were

immediately returned to participants the day they were collected). Disseminating results

will give everyone who participated in the study an idea of their PAH exposure level on

an average day and recommendations on how they may minimize these types of

exposures (e.g., location of cooking, time spent next to the brazier). Some exposure is

inevitable, particularly as one group is completely reliant on biomass fuel, which will

likely not change; however, steps to lessen exposure may be implemented and included

in a daily routine. In addition to financial compensation for time spent assisting in this

research, it is important to give participants information to make informed decisions

about how they would like to go forward with their brazier use. I will work with the same

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CHWs to provide information in the most appropriate and relevant manner for the two groups.

Integrating future work into a DOHaD framework will be important in populations with high fertility rates and exposure to high air pollution levels. Maternal exposure to

AP and early life exposures correlate with various respiratory symptoms that persist later in life. In addition to respiratory symptoms, of perhaps greater importance are the cognitive and neurological deficits documented in children exposed to high levels of airborne toxicants (Heindel et al., 2017; Kalkbrenner et al., 2010; Newman et al., 2013;

F. P. Perera et al., 2009). These life-long impacts are particularly relevant in the African context as the simultaneous shift from rural to urban residence patterns occur, and the transition from predominantly infectious to non-communicable disease. Efforts to focus health awareness and interventions specifically targeting critical periods of growth could have multigenerational positive effects on population health and assist many of the goals already in place such as the United Nations Sustainable Development Goals 2030

(Norris et al., 2017).

Lastly, it is important to maintain the relationships established at the University of

Zambia with Drs. Alice Ngoma and Nosiku Munyinda to continue research and work on air quality issues and pollution in Zambia. The landscape there, referring to environmental pollutants, is largely centered in the Copperbelt and focused on mining issues. These typically involve heavy metals, particularly lead, and the effect exposures have on livestock and human health. Relatively few studies focus on issues of AP in

Zambia, and the few that do center on the capital, Lusaka. A focus on Lusaka makes sense as this is the largest city in the country, and in addition to biomass fuel use, there

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is air travel, emissions from cars, to name a few sources of emissions. However, the results of this study indicate that residents in other areas of the country are routinely exposed to AP at high levels. Building on this work could potentially influence applied public health campaigns in the country and focus on practices and messaging to reduce these exposures. The combination of research, public health campaigns, and regulatory policies focused on reducing AP could significantly improve population health.

Information from this study could be used as (1) a benchmark for populations in Chipata and (2) justification for larger studies in Zambia, which would give a clearer picture of

AP in different regions of the country.

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APPENDIX A QUESTIONNAIRES

Dzina (name): Last ______,

First ______

Amuna/Akazi (male/female): Amuna □ Akazi □

Zaka (age): ______

Pomwe munafika mumaphunziro (level of education):

No Formal □ Primary □ Secondary □ College/University □

Block: ______

Camp: ______

Mwine/Mukulu wa panyumba (head of household):

Mwine □ Mukulu □

Ndimwe okwatira/okwatiriwa mwina osakwatira/osakwatiriwa (marital status):

Ndimwe okwatira □ okwatiriwa mwina osakwatira □ osakwatiriwa □

Muli ndi ana angati? (number of children):

1□ 2□ 3□ 4□ 5□ 6□ 7□ 8□

Pali anthu angati pa nyumba:

Total number of people in the house ______

Amuna (men) ______

Akazi (women) ______

Muli vipinda kapena marumu angati munyumba (number of rooms in the household):

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1□ 2□ 3□ 4□ 5□ 6□ 7□ 8□

Socioeconomic Status Questionnaire:

1. What individuals in the household have an occupation? What are these occupations?

Khodi ni anthu oti omwe ali ndi zinchinto panyumba pano ndipo anthuwa agwira nchito zotani?

…………………………………………………………………………………………

2. How much of your income would you categorize as cash? How much of your income takes a form other than cash?

Kodi zolandira zanu mungazipatule motani monga zolandira za ndalama mwina zolandira zina zache?

% Cash ______% Other ______

3. Who makes the economic decisions for the household? (Probe)

Khodi pakhoma panu nindani amene amakamba pakasebenzesedwe ka chuma?

…………………………………………………………………………………………

4. Do you own any animals? Yes □ No □

Khodi muli ndi ziweto? Yes □ No □ a. If yes: What animals do you own? How many of each type?

Ngati inde: Khodi muli ndi ziweto zotani? Mumabanja a ziweto, muli ndi ziweto zingati mu banja ili yonse?

• Chicken(s) Nkhuku □ Number ______

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• Pig(s) Nkhumba □ Number ______• Goat(s) Mbuzi □ Number ______• Sheep Mberere □ Number ______• Duck(s) Zibaka □ Number ______• Cow(s) Ngombe □ Number ______• Other(s) □ Number ______

5. Does the household own an automobile? A motorcycle? Any other forms of transportation?

Khodi pakhomo panu muli ndi galimoto? Mtututu (Honda)? Mwina zipangitso zina zache zothandizira ndi mayendedwe?

Motorcycle Yes □ No □ Automobile Yes □ No □ Other Yes □ No □

6. Do you have access to an improved water source?

Khodi musebenzesa njira zatsopano potapa madzi?

Yes □ No □

7. Do you have access to an improved sanitation facility?

Khodi musebenzesa njira zatsopano zachimbuzi?

Yes □ No □

8. Do you own a refrigerator?

Khodi muli ndi fuliji?

Yes □ No □

9. Do you own a television?

Khodi muli ndi wailesi ya kanena (T.V.)?

Yes □ No □

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10. Please list any other appliances you own.

Nenani mamakina ena omwe mulinao.

…………………………………………………………………………………………

11. Does the household have a bank account?

Kodi pakhomo muli ndi akaunti yaku banki?

Yes □ No □

Cookstove Questionnaire

1. Who is the primary cook for the household? How long would you estimate that this person spends cooking per day (hours)?

Kodi nindani amene amaphika pakhomo, ndipo munthuyu akhala nthawi monga ma ola angati kuphika?

1□ 2□ 3□ 4□ 5□ 6□

2. How would you identify your cookstove (a more traditional design or an improved design that uses a fuel source besides biomass, e.g. liquefied petroleum gas)?

Kodi musebezensa moto otani pophika? Njira zakale monga malasha ndi nkhuni ndi zina mwina njira zina zatsopano monga palafini?

Malasha/nkhuni □ Malaiti □ Palafini □ Zina zaache □ Other type ______

3. Is there a window in your kitchen? Kodi nyumba yanu ili ndi mawindo? Yes □ No □ a. If yes, do you open it during cooking? Yes □ No □

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4. Is there a door in your kitchen? Kodi nyumba yanu ili ndi Mulyango mopikila? Yes □ No □

a. If yes, do you open it during cooking? Yes □ No □

5. Do you cook outside or inside? Kodi mumapikila mukati mwanyumba mwina panja? Yes □ No □ Both □

a. What influences where you cook? …………………………………………………………………………………………

…………………………………………………………………………………………

6. If you use a traditional cookstove, what type of fuel do you use? Does this vary by season? (or some other factor)

Ngati musebenzesa njira zakale, kodi ndi moto otani omwe musebenzesa? (monga nkhuni, malasha ndi zina zache). Njira zimenezi, kodi zimasintha kulingalira ndi nthawi yachaka mwina kamba ka vilingo vina vosiyana siyana?

Wood □ Nkhuni Charcoal □ Malasha Dung □ matuvi ya ngombe Crop Residue □ Mauzu

…………………………………………………………………………………………

7. How much time per day is spent gathering and processing fuel for cooking and heating the home? Does this vary by season? (or some other factor)

Kodi pa tsiku anthu amatha kusebenzesa nthawi yotani kusakira moto (nkhuni, malasha ndi zina zache) zophikira ndi zothumisira munyumba?

1□ 2□ 3□ 4□ 5□ 6□

…………………………………………………………………………………………

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8. Is your cookstove located outside or in an enclosed space? Does this vary by season? (or some other factor)

Kodi moto pomwe muphikira uli panja mwina mukati mwamalo ozingililidwa? Kodi malowa ophikiramo mumasintha kulingalira ndi nthawi yachaka mwina kamba ka vilingo vina vosiyana siyana?

…………………………………………………………………………………………

9. Do you think there is a health risk breathing in smoke emitted by cookstoves? Why or why not?

Kodi muganiza kuti kopema chutsi cha moto pophika kungathe kubweletsa bvuto pa umoyo? Nichifukwa ninji muli ndi ganizo lotero?

…………………………………………………………………………………………

10. Are different types of people more vulnerable to health effects of smoke inhalation than others (women, men, children, etc.)? If so, why or why not?

Kodi anthu ena angathe kukhala ndi bvuto kamba kofuza chutsi cha moto kupambana ndi ena (monga azimai, azibambo ndi ana)? Nichifukwa ninji muli ndi ganizo lotero?

…………………………………………………………………………………………

11. Who in the household, besides the main cook, is around the cooking area for an extended period of time?

Kuposa amene aphika, kodi ndi anthu ati ena omwe amapezeka pamalo pophikira kwanthawi yaitali?

………………………………………………………………………………………………

Epidemiology Questionnaire

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1. Do you now of have you ever smoked cigarettes?

Kodi panthawi yino mukoka fodya mwina kale munakokapo fodya?

Yes □ No □

2. Do you ever experience wheezing or shortness of breath performing everyday activities?

Kodi mumamva kulila muchifuwa popema mwina kumva kupelewera kwa mphepo pamene mugwira nchito zamasiku onse?

Yes □ No □ a. If yes, approximately how long do you have to rest before resuming activities

Ngati inde, kodi mumapumula nthawi yotani mukalibe kupitiliza nchito (minutes)?

< 10□ 10□ 15□ 20□ 25□ 30□

3. Have you ever been woken in the middle of the night because of difficulty breathing?

Kodi munaukapo usiku kamba kovutikila nid kupema? a. If yes, how long did the episode last?

Ngati inde, khodi munakhala ovutikila usikuwo kwanthawi yotani?

< 10□ 10□ 15□ 20□ 25□ 30□

4. Do you have any chronic cardiac or pulmonary diseases that you are aware of? E.g. hypertension, tuberculosis, arrhythmia, etc. (yes/no)

Kodi muli ndi matenda ovutikila kupema amene mudziwa monga hypertension (mwina kuti B.P), tuberculosis (mwina kuti T.B), arrhythmia (kuchaya kwamutima mosayenera).

Yes □ No □ a. If yes: what is the illness? How long have you been aware that you have had this illness?

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Ngati inde: Ndimatenda otani? Khodi mwakhala nthawi yotani kudziwa kuti muli ndi matenda amenewa.

…………………………………………………………………………………………

5. Do you have any other diseases that you are aware of? (yes/no)

Kodi muli ndi matenda ena amene mudziwa?

Yes □ No □

a. If yes: what is the illness? How long have you been aware that you have had this illness?

Khodi ndimatenda otani? Khodi mwankhala nthawi yotani kudziwa kuti muli ndi matenda amenewa.

…………………………………………………………………………………………

6. Have you been sick in the past month? (yes/no)

Kodi munadwalapo uyu mwezi watha?

Yes □ No □

a. If yes: What was the illness? What was the duration of the illness?

Ngati inde: Kodi munadwala matenda otani? Kodi munadwala kwanthawi yotani?

…………………………………………………………………………………………

7. Has anyone else in the household been sick in the past month?

Kodi pali ena apakhomo panu amene anadwala uyu mwezi watha?

Yes □ No □

a. If yes: What was the illness? What was the duration of the illness?

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Ngati inde: Kodi anali anadwala matenda otani? Kodi anali anadwala kwanthawi yotani?

…………………………………………………………………………………………

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Disease consensus analysis questions: True/False Questions (circle one) 1. Diarrhea can be caused by erupting teeth in infants True/False

2. Matukumwa is caused by light and heat during the summer True/False months 3. Tuberculosis is a hereditary/genetic disease True/False

4. Keeping flowers and grasses outside the home increases the risk True/False of getting malaria 5. I can catch asthma if I sleep next to someone who has it True/False

6. The flu can be caused by breathing in dust True/False

7. The flu is more contagious than chifua (cough) True/False

8. Chifua (cough) can be caused by breathing in dust True/False

9. HIV is common in my community True/False

10. Tuberculosis is common in my community True/False

11. Often, there are medications for diseases I cannot afford it if the True/False clinic runs out 12. Women who have sex with uncircumcised men can get cancer True/False from unprotected sex 13. Cancer can be contagious, and one person can give it to another True/False

14. HIV can turn into tuberculosis True/False

15. I know that I have chifua when there is throat and chest pain True/False caused by my cough 16. Fitting (kunyu) is common in my community True/False

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17. Syphilis may turn into HIV if left untreated True/False

18. Tuberculosis is more common in my community than it used to be True/False because of the prevalence of HIV 19. I sometimes choose prayer over medication to treat diseases I True/False have had or currently have 20. Breathing in smoke can cause tuberculosis True/False

21. Breathing in smoke can cause chifua True/False

22. Breathing in smoke can cause the flu True/False

23. There is a stigma/discrimination against people who are positive True/False for HIV 24. Malaria is not something that worries me because it can be True/False treated at the clinic 25. Chifua is a minor illness and not something that worries me True/False

26. I can contract malaria from eating cold food True/False

27. I can contract malaria from walking in the rain or being soaked True/False

28. BP (hypertension) is more common than it was 10 years ago True/False

29. Sugar (diabetes) is more common than it was 10 years ago True/False

30. I am more worried about getting BP (hypertension) than I am of True/False getting sugar (diabetes) 31. There is a stigma/discrimination against people who are positive True/False for Tuberculosis 32. Fitting (kunyu) can be caused by being bewitched True/False

33. Fitting (kunyu) can be caused by being born with the disease True/False

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34. Fitting (kunyu) caused by cerebral malaria is different than the True/False fitting (kunyu) caused by bewitchment or the kind you are born with 35. I do not personally worry about developing cancer True/False

36. Smoking cigarettes is worse for your health than breathing in the True/False smoke emitted from braziers 37. It is possible to contract HIV if a mosquito bites a HIV positive True/False person, then bites you 38. Dirty bedding can cause chifua True/False

39. Diseases such as flu, chifua, pneumonia, etc. are primarily True/False caused by the changing weather 40. I am more scared of getting syphilis than HIV True/False

41. I am more scared of getting gonorrhea than HIV True/False

42. BP (hypertension) is caused by thinking too much True/False

43. You can get HIV from sharing sharp tools with someone such as True/False razorblades 44. I often combine medication from the clinic with traditional True/False medicine obtained from a traditional healer 45. Tooth pain is very dangerous if not treated True/False

46. Sniffing tobacco can be used to treat BP (hypertension) True/False

47. Asthma is a disease that you are born with, it cannot develop True/False

48. I have gotten sick from breathing in smoke from the brazier True/False before 49. The clinic frequently runs out of drugs True/False

50. If you know any, what are some causes of bronchitis and/or asthma?

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Cooking Consensus Analysis Questions: True/False Questions (circle one) 1. Breathing the smoke emitted from charcoal or wood cookstoves is True / False harmful to your health 2. Drinking milk after cooking with charcoal or wood, will counteract True / False the effects of the smoke and help heal your lungs/stomach 3. Charcoal braziers are safe to bring inside after they have finished True / False smoking 4. It is preferable to bring the charcoal brazier inside when it’s raining True / False even if you have a covered area 5. It is preferable to bring the charcoal brazier inside when it is cold True / False outside 6. Food cooked using electricity tastes better than food cooked over True / False wood or charcoal

7. Food that has a smoky flavor tastes bad True / False

8. Food cooked over charcoal tastes the same as food cooked using True / False electricity 9. There might be a health risk from smoke inhalation using charcoal True / False braziers, but it is small and likely will not affect me

10. Wood is cheaper to cook with than charcoal or electricity True / False

11. In the cold season I cook inside more than outside True / False

12. In the rainy season I cook inside more than outside True / False

13. Breathing in smoke from the brazier can cause tuberculosis True / False

14. Women are most at risk of health consequences from smoke from True / False the charcoal brazier or wood

15. Charcoal is more expensive in the rainy and cold seasons True / False

16. The only danger children face from the brazier is getting burned True / False

17. Foods that cook quickly are best prepared on the electric stove True / False

18. The blue flame that sometimes appears when burning charcoal is True / False the carbon monoxide burning off

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19. Of all the meals prepared throughout the day lunch takes the True / False longest to make 20. It is better to cook meats over charcoal for a long time, so that it True / False becomes tender

21. During the hot season I prefer to cook outside more than inside True / False

22. Smoke from the charcoal brazier, or wood, can change the color of True / False a person’s lungs

23. Using a brazier inside can cause suffocation by carbon monoxide True / False

24. I know someone who has fainted from using a charcoal brazier True / False

25. The heat that enters the body during cooking can cause illness, True / False especially when it is the cold season

26. I cook with charcoal everyday True / False

27. I cook with wood every day True / False

28. I cook with electricity every day True / False

29. If I stop cooking with a charcoal brazier or wood, any effects to my True / False lungs and health will be reversed 30. If electricity prices were not an issue, I would prefer to use primarily True / False a stove to cook 31. Buying 50kg bags of charcoal is cheaper than buying small bags True / False each day

32. Using charcoal braziers over time can cause lung cancer True / False

33. Carbon monoxide is the biggest risk from using the brazier inside True / False

34. When it starts to rain and the brazier is still smoking, I bring it inside True / False anyway 35. When it is windy, I bring the charcoal brazier inside, so the charcoal True / False does not burn too fast 36. The longer it takes to prepare a dish the more likely I am to use True / False charcoal or wood, rather than electricity 37. It is easier to cook over a charcoal brazier than an electric stove because I have more control over the heat and distance from the True / False coals

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38. Charcoal is cheaper than electricity to use for cooking True / False

39. Even if electricity were cheap enough to cook all my food on the True / False stove, I would still use the brazier for some foods 40. If the brazier is inside, I am more concerned about burns or catching True / False something on fire than what impact it might have on my health

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APPENDIX B DESCRIPTION REFUSE IMAGES

Table B-1. Description of individual images of refuse piles in Chipata township Image Description

7221 Refuse lining a paved roadside. Almost all frames had sugar cane and organic debris mixed in

7223 Almost all frames had sugar cane and organic debris mixed in

7224 Almost all frames had sugar cane and organic debris mixed in 7230 This image is of a roadside drainage ditch leading to a collection area where women and children wash clothes and dishes 7242 This is a roadside swath of grass and tree sections that were burned- everything appears to be organic matter 7278 This is a relatively smaller roadside burn with scattered trash surrounding it that appear to have blown out of the original pile 7326 This is a roadside dirt ditch filled with trash and organic matter

7328 Almost all frames had sugar cane and organic debris mixed in 7330 This is a large pile of refuse next to the road mixed in with a lot of organic matter like sugar cane refuse 7332 This is a large pile of refuse next to the road mixed in with a lot of organic matter like sugar cane refuse 7377 This is a large assortment of trash in a waterlogged drainage ditch

7378 Trash on top of a large number of leaves swept into a drainage ditch

7383 Trash and leaved scattered next to a cinderblock wall

7389 Trash scattered along a roadside

7391 Trash scattered in a roadside ditch

7392 Dead leaves and trash on concrete

7393 Tash left in a ditch

7394 Trash left on the side of the road

7395 Trash left on the side of a wall

7477 Trash left on the road

7479 Trash left on grass

7520 Pile of burnt trash

7521 Small trash pile on the road

7522 Pile of trash, dead leaves, and peanut shells

7523 Scattered trash in a ditch

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Table B-1. Continued Image Description

7524 Scattered trash in the bushes

7527 Pile of trash, dead leaves, and peanut shells

7531 Pile of trash left by the dumpster

8062 Burning tire

8082 Scattered trash left in the grass

8162 Pile of burning trash

Table B-2. Counts of different types of plastics visible in refuse piles for each individual image taken in Chipata township Image HDPE LDPE Polypropylene PET PVC Polystyrene Org. UnID Total Waste 7221 1 123 6 0 2 1 0 0 133 7223 1 186 11 0 0 0 0 1 199 7224 1 140 7 2 0 0 0 1 151 7230 0 26 1 5 0 0 0 2 34 7242 0 0 0 0 0 0 4 0 4 7278 0 15 9 1 0 0 0 0 25 7326 0 57 2 1 0 0 0 0 60 7328 0 56 2 1 0 0 0 2 61 7330 0 163 10 4 0 3 0 0 180 7332 0 171 21 0 0 0 0 2 194 7377 0 37 8 4 0 7 0 1 57 7378 0 19 5 0 0 0 0 1 25 7383 1 9 3 2 0 1 0 0 16 7389 1 13 5 1 0 0 0 0 20 7391 0 31 13 1 0 0 0 0 45 7392 0 18 9 0 0 0 0 0 27 7393 0 58 45 9 0 0 0 6 118 7394 0 19 9 0 0 0 0 0 28 7395 0 8 11 1 0 1 0 0 21 7477 0 7 6 7 0 0 0 0 20 7479 0 13 11 1 0 0 0 0 25

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Table B-2. Continued Image HDPE LDPE Polypropylene PET PVC Polystyrene Org. UnID Total Waste 7520 0 6 12 0 0 0 0 0 18 7521 0 2 0 3 0 0 0 0 5 7522 0 11 5 1 0 1 0 0 18 7523 0 12 23 4 0 0 0 4 43 7524 0 24 28 2 0 0 0 0 54 7527 0 8 7 0 0 0 0 0 15 7531 0 30 12 7 0 0 0 0 49 8062 0 2 2 1 0 0 0 1 6 8082 0 22 8 4 0 0 0 2 36 8162 0 5 3 9 0 1 0 0 18 Abbreviations: HDPE = High Density Polyethylene; LDPE = High Density Polyethylene; PET = polyethylene terephthalate; PVC = polyvinyl chloride; Org Waste = organic waste; UnID = unable to identify

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APPENDIX C COMPOUND PILE SORT NMDS, ANTHROPAC PILE SORTS 1.0

Figure C-1. Visualization of Kalongwezi Pile Sort in nMDS produced using Anthropac 1.0

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Figure C-2. Visualization of Mchini Pile Sort in nMDS produced using Anthropac 1.0

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APPENDIX D SPIROGRAPH OF MARY PHIRI

Figure D-1. Output of the spirograph of Mary Phiri demonstrating mild restriction in her airways

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APPENDIX E TOXICOLOGY SUPPLEMENTARY DATA

Table E-1. Final urinary creatinine concentrations for all participants who provided urine for metabolite analysis Corrected Blank Conc. in Dilution Final Participant Absorbance Sub. assay Factor Conc. DC 48-143 0.127 0.076 2.01 20 40.24 FM 20-96 0.421 0.37 10.66 20 213.18 MM 31-48 0.415 0.364 10.48 20 209.65 RB 43-101 0.228 0.177 4.98 20 99.65 AN 17-114 0.381 0.33 9.48 20 189.65 CP 60-53 0.494 0.443 12.81 20 256.12 EB 42-201 0.363 0.312 8.95 20 179.06 GN 39-39 0.376 0.325 9.34 20 186.71 TL 70-148 - - - - - JB 34-18 0.622 0.571 16.57 40 662.82 NC 3-142 0.506 0.455 13.16 20 263.18 MP 18-68 0.301 0.25 7.13 40 285.18 SB 41-162 0.084 0.033 0.75 20 14.94 ZM 69-33 0.461 0.41 11.84 20 236.71 CT 20-8 0.339 0.288 8.25 20 164.94 JB 12-53 0.379 0.328 9.42 20 188.47 MB 6-52 0.421 0.37 10.66 20 213.18 MM 3-41 - - - - - MB 18-1 0.271 0.22 6.25 20 124.94 MMM 63-72 0.332 0.281 8.04 20 160.82 MN 34-24 0.167 0.116 3.19 20 63.76 LZ 13-179-1 0.361 0.31 8.89 40 355.76 HB 17-114-1 0.368 0.317 9.10 20 182.00 BZ 23-38 0.544 0.493 14.28 20 285.53 MZ 51-31 0.237 0.186 5.25 20 104.94 CD 62-2-1 0.106 0.055 1.39 20 27.88 AP 39-48 0.4 0.349 10.04 40 401.65 MP 5-85 0.339 0.288 8.25 20 164.94 LK 10-130 - - - - - TM 17-20 0.467 0.416 12.01 20 240.24 CN 9-14 0.166 0.115 3.16 20 63.18 AM 40-40 0.154 0.103 2.81 20 56.12 AM 21-35 - - - - - JM 37-189 0.317 0.266 7.60 20 152.00 CT 20-8 0.185 0.134 3.72 80 297.41 SN 57-43-1 0.157 0.106 2.89 100 289.41 MT 52-24 0.559 0.508 14.72 20 294.35 EZ 5-63 0.291 0.24 6.84 80 546.82

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Table E-1. Continued Corrected Blank Conc. in Dilution Final Participant Absorbance Sub. assay Factor Conc. NN 44-58 0.263 0.212 6.01 20 120.24 AP 22-30 0.307 0.256 7.31 40 292.24 TN 59-26 0.151 0.1 2.72 100 271.76 VB 7-43 0.428 0.377 10.86 100 1086.47 EC 41-28 0.109 0.058 1.48 100 148.24 EZ 19-59 - - - - - PN 15-46 - - - - - EL 70-148-1 - - - - - EB 46-151 0.175 0.124 3.42 20 68.47 GC 2-19 0.325 0.274 7.84 40 313.41 RM 13-33 0.068 0.017 0.28 20 5.53 *Corrected absorbance is calculated by subtracting the final absorbance from the initial absorbance to account for any background interference. Concentrations that returned values far outside the normal range for humans (25-400 mg/dl for a single sample collection) were not included

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Table E-2. Urine samples adjusted for 1-Hydroxypyrene based on levels of urinary creatinine 1- 3- 1- Hydroxypyrene Sample Compound hydroxybenzo[a]pyrene Hydroxypyrene creatinine Name (ppb) (ppb) adjusted (µmol/mol) EB-46-151 Kalongwezi ND 0.561 0.42487 DC-48-143 Kalongwezi ND 0.223 0.28687 FM-20-96 Kalongwezi ND 1.019 0.24777 MM-31-48 Kalongwezi ND 0.234 - RB-43-101 Kalongwezi ND 0.066 0.03432 AM-17-114 Kalongwezi ND 0.140 0.03827 CP-60-53 Kalongwezi ND 0.327 0.06616 EB-42-201 Kalongwezi ND 0.178 0.05140 GN-39-39 Kalongwezi ND 0.776 0.21540 JB-34-18 Kalongwezi ND 1.125 0.08799 TL-70-148 Kalongwezi ND 1.994 - MMM-63-72 Kalongwezi ND 0.217 0.06992 SB-41-162 Kalongwezi ND 0.119 0.41174 MP-18-68 Kalongwezi 0.0008 0.413 0.07513 ZM-69-33 Kalongwezi ND 0.342 0.07478 JM-37-189 Kalongwezi ND 0.711 0.24246 MP-5-85 Kalongwezi ND 0.445 0.13995 LK-10-130 Kalongwezi ND 0.132 - NC-3-142 Kalongwezi ND 1.380 0.27187 GC-2-19 Mchini 0.0030 0.704 0.11651 RM-13-33 Mchini 0.0031 0.524 4.91274 MT-52-24 Mchini 0.0018 0.734 0.12928 SN-57-43-1 Mchini 0.1465 0.656 0.11745 EZ-5-63 Mchini ND 1.308 0.12397 AP-22-30 Mchini 0.0059 2.167 0.38433 TN-59-26 Mchini 0.0061 0.801 0.15277 EL-70-148-1 Mchini 0.0021 0.401 - EZ-19-59 Mchini 0.0040 0.397 - VB-7-43 Mchini ND 0.388 0.01849 EC-41-28 Mchini ND 2.286 0.79914 PN-15-46 Mchini ND 0.355 - NN-44-38 Mchini 0.0011 0.511 0.22008 HB-17-114-1 Mchini ND 1.236 0.35196 BZ-23-38 Mchini ND 0.648 0.11756 MC-51-31 Mchini ND 0.243 0.11981 CD-62-2-1 Mchini ND 0.094 0.17384 MB-18-1 Mchini ND 0.320 0.13290 MB-6-52 Mchini ND 0.655 0.15925

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Table E-2. Continued 1- 3- Hydroxypyren Sample Compoun 1-Hydroxypyrene hydroxybenzo[a]pyren e creatinine Name d (ppb) e (ppb) adjusted (µmol/mol) MM-3-41 Mchini ND 0.156 - CT-20-8 Mchini 0.0008 2.982 0.93719 JB-12-53 Mchini ND 0.861 0.23685 MN-34-24 Mchini ND 0.304 0.24685 LZ-13-179-1 Mchini ND 0.973 0.14169 AM-21-35 Mchini ND 2.906 - CN-9-14 Mchini ND 1.368 1.12258 AM-40-40 Mchini 0.0000 0.507 0.46822 TM-17-20 Mchini 0.0002 1.071 0.23100 AD-39-48 Mchini 0.0001 1.027 0.13255

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Table E-3. Urine samples adjusted for 1-Hydroxypyrene based on specific gravity Specific Sample Name Compound 1-Hydroxypyrene (ppb) SG Adjusted Gravity

EB-46-151 Kalongwezi 0.561 1.024 0.56100 DC-48-143 Kalongwezi 0.223 1.008 0.66900 FM-20-96 Kalongwezi 1.019 1.033 0.74109 MM-31-48 Kalongwezi 0.234 1.037 0.15178 RB-43-101 Kalongwezi 0.066 1.007 0.22629 AM-17-114 Kalongwezi 0.14 1.018 0.18667 CP-60-53 Kalongwezi 0.327 1.011 0.71345 EB-42-201 Kalongwezi 0.178 1.032 0.13350 GN-39-39 Kalongwezi 0.776 1.033 0.56436 JB-34-18 Kalongwezi 1.125 1.038 0.71053 TL-70-148 Kalongwezi 1.994 1.041 1.16722 MMM-63-72 Kalongwezi 0.217 1.023 0.22643 SB-41-162 Kalongwezi 0.119 1.017 0.16800 MP-18-68 Kalongwezi 0.413 1.04 0.24780 ZM-69-33 Kalongwezi 0.342 1.051 0.16094 JM-37-189 Kalongwezi 0.711 1.023 0.74191 MP-5-85 Kalongwezi 0.445 1.021 0.50857 LK-10-130 Kalongwezi 0.132 1.019 0.16674 NC-3-142 Kalongwezi 1.38 1.033 1.00364 GC-2-19 Mchini 0.704 1.024 0.70400 RM-13-33 Mchini 0.524 1.023 0.54678 MT-52-24 Mchini 0.734 1.026 0.67754 SN-57-43-1 Mchini 0.656 1.028 0.56229 EZ-5-63 Mchini 1.308 1.04 0.78480 AP-22-30 Mchini 2.167 1.038 1.36863 TN-59-26 Mchini 0.801 - - EL-70-148-1 Mchini 0.401 1.014 0.68743 EZ-19-59 Mchini 0.397 1.029 0.32855 VB-7-43 Mchini 0.388 1.018 0.51733 EC-41-28 Mchini 2.286 1.038 1.44379 PN-15-46 Mchini 0.355 1.022 0.38727 NN-44-38 Mchini 0.511 1.006 2.04400 HB-17-114-1 Mchini 1.236 1.025 1.18656 BZ-23-38 Mchini 0.648 1.038 0.40926 MC-51-31 Mchini 0.243 1.02 0.29160 CD-62-2-1 Mchini 0.094 1.004 0.56400 MB-18-1 Mchini 0.32 1.011 0.69818 MB-6-52 Mchini 0.655 1.023 0.68348 MM-3-41 Mchini 0.156 1.031 0.12077 CT-20-8 Mchini 2.982 1.028 2.55600

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Table E-3. Continued

Sample Name Compound 1-Hydroxypyrene (ppb) Specific Gravity SG Adjusted

JB-12-53 Mchini 0.861 1.025 0.82656 MN-34-24 Mchini 0.304 1.048 0.15200 LZ-13-179-1 Mchini 0.973 1.037 0.63114 AM-21-35 Mchini 2.906 1.043 1.62195 CN-9-14 Mchini 1.368 1.014 2.34514 AM-40-40 Mchini 0.507 1.01 1.21680 TM-17-20 Mchini 1.071 1.03 0.85680 AD-39-48 Mchini 1.027 1.037 0.66616

Methods for analysis of passive air samples: passive air samplers (TSE-200)

were used to measure the levels of PAHs present in the home of each participant. Prior

to use, and between each household, the samplers were cleaned with pure acetone to

ensure no contamination between houses occurred. Inside the samplers sat a

polyurethane foam (PUF) disk. PAHs in the air, when it flows through the sampler, stick

the PUF disks. Due to the passive nature of the sampler, they were placed inside the

homes of each participant for two weeks at a time to collect analytically detectable PAH

levels. A total of eight samplers were deployed that rotated between households for 3 ½

months (Figure E-1). After the samplers sat in the household, the PUF disk was

collected, wrapped in tin foil (foil also cleaned with acetone), and stored at -80°C in the

Microbiology Lab at Chipata General Hospital. Four control PUF disks were placed in samplers after cleaning, handled using the same instruments and stored in the same manner as PUF disks deployed in participants' homes. The control disks were handled this way to ensure that there was no significant contamination during the collection or storage process that would artificially inflate the PAH levels on the PUF disks. Samples

219

were transported from Zambia to the Center for Human and Environmental Toxicology at the University of Florida.

For analysis, the PUF disks will be brought to room temperature (approximately

21°C) and placed into stainless steel extraction cells for the automated solvent extractor

(ASE). Automated solvent extraction consists of several steps to ensure all PAHs are removed from the PUF disks. PUF disks will be rolled in steel canisters and placed in the ASE. Next, the ASE fills each canister with HPLC grade dichloromethane (DCM) and heated to 100°C under 1500 PSI. The samples stay under pressure in a static state for five minutes, and then the DCM and eluted PAHs is pumped out and collected in sample vials. Two PUF disks will be sacrificed (one from each group) to establish a range for analysis on gas chromatography-mass spectrometry (GC-MS) that would fall within a prepared standard curve. The eluted samples were blown to dryness under a nitrogen stream and warm water bath. The dried samples will then be reconstituted in 1 mL of DCM for analysis. This analysis will be performed using GC-MS for the standard suite of 16 PAHs identified as a known or potential carcinogens/mutagens. The focus of the results will likely be on the heavier PAHs (e.g. pyrene, benzo[a]pyrene) as lighter

PAHs such as naphthalene will have likely volatilized off the PUF disks while they sat in participants homes given their light molecular weight.

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Figure E-1. Passive air quality monitor in the kitchen of a participant residing in Kalongwezi. May 31, 2019. Chipata. Courtesy of the author.

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BIOGRAPHICAL SKETCH

David T. Dillon received his Bachelor of Arts in anthropology with dual minors in religion and Middle Eastern/Islamic studies in 2012. Fall 2013 he enrolled in the anthropology graduate program at the University of Florida under advisor Dr. Alyson

Young. In Spring 2015 he graduated with a Master of Arts in anthropology and was accepted into the PhD program in anthropology. Simultaneously he began a Master of

Public Health with a concentration in epidemiology under Dr. Mattia Prosperi. He completed his Master of Public Health in the Spring of 2017. During this time, from 2015 to 2017, he was also a Foreign Language and Area Studies Fellow in Swahili through the Center for African Studies. He received his PhD in anthropology in 2020 under Dr.

Christopher McCarty.

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