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Bushmeat Consumption and Prediction of Ebola Risk Factors in Senegal
Alia Kroos
A thesis
submitted in partial fulfillment of the
requirements for the degree of
Master of Forest Resources
University of Washington
2016
Committee:
Patrick Tobin, Chair of the Supervisory Committee
Ivan Eastin
Aaron Wirsing
Randall Kyes
Daisuke Sasatani
Program Authorized to Offer Degree:
School of Environmental and Forest Sciences
College of the Environment
© Copyright 2016
Alia Kroos
i
University of Washington
Abstract
Bushmeat Consumption and Prediction of Ebola Risk Factors in Senegal
Alia Kroos
Chair of the Supervisory Committee:
Patrick Tobin, Assistant Professor
Department of Environmental and Forest Sciences
Over 60% of emerging infectious diseases are caused by zoonotic pathogens that are primarily
driven by anthropogenic activities such as agricultural encroachment and bushmeat practices.
The magnitude of the 2013-2016 West Africa outbreak of Ebola demonstrates the importance
of minimizing exposure risks and understanding the drivers that influence human-wildlife
conflict. In this study, I examined the risk factors that influence bushmeat consumption of
Ebola hosts in an area peripheral to a high Ebola infection zone. I conducted 100 randomized
head of household surveys in seven villages in Bassari Country in southeastern Senegal. I
found that educated individuals were significantly more likely to consume bushmeat than
ii
individuals who had never attended school, possibly due to increased income. Individuals
who actively practice the religion animism had a decreased likelihood of bushmeat
consumption and potential exposure to Ebola; this could be due to religious leaders
discouraging bushmeat consumption during the epidemic. My research demonstrates a
significant effect of the interaction between availability of domestic meat and proximity to
the bushmeat market on bushmeat consumption. Respondents who consumed domestic meat
more than one time per month and lived more than ten kilometers from the bushmeat market
were significantly less likely to consume bushmeat. Human-wildlife conflict through crop
raiding may contribute to Ebola risks indirectly by influencing hunting patterns and risk
perceptions. Underlying socioeconomic status, demographic variables and geographical
access are associated with bushmeat consumption and contribute to Ebola transmission risks.
Enhancing awareness of risks, improving animal surveillance, minimizing human-wildlife
conflict and providing sustainable alternatives for food security can help mitigate
transmission risks. Zoonotic pathogen management and educational strategies need to be
individualized and adaptive to meet regional needs.
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Table of Contents
List of Figures: .............................................................................................................................. iv
List of Tables: ............................................................................................................................... vi
List of Abbreviations ................................................................................................................. viii
Acknowledgements ...................................................................................................................... ix
1 Introduction ........................................................................................................................... 1
2 Methods ................................................................................................................................ 12
2.1 Study Setting ................................................................................................................. 12
2.1.1 Cross Sectional Study with Survey Assessment: .................................................... 12
2.1.2 Observational Bushmeat Market Study: ................................................................. 21
2.2 Selection of Study Subjects .......................................................................................... 23
2.2.1 Cross Sectional Study with Survey Assessment: .................................................... 23
2.2.2 Observational Bushmeat Market Study: ................................................................. 26
2.3 Data Collection ............................................................................................................. 26
2.3.1 Cross Sectional Study with Survey Assessment: .................................................... 26
2.3.2 Observational Bushmeat Market Study .................................................................. 28
2.4 Statistical Analyses ....................................................................................................... 29
3 Results ................................................................................................................................... 34
3.1 Demographics ............................................................................................................... 34
3.2 Crop Raiding ................................................................................................................ 36
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3.3 Bushmeat Practices ...................................................................................................... 40
3.3.1 Bushmeat Consumption .......................................................................................... 40
3.3.2 Market Availability of Bushmeat............................................................................ 44
3.4 Hypothesis Testing ....................................................................................................... 46
3.4.1 Education ................................................................................................................ 46
3.4.2 Animism .................................................................................................................. 46
3.4.3 Risk Perceptions...................................................................................................... 46
3.4.4 Domestic Meat Consumption ................................................................................. 47
3.4.5 Occupation Effect: ................................................................................................... 47
3.4.6 Location and Domestic Meat Consumption Interaction Effect .............................. 48
3.4.7 Probability Estimates of Risk Factors ..................................................................... 52
4 Discussion ............................................................................................................................. 54
4.1 Education ...................................................................................................................... 54
4.2 Animism ........................................................................................................................ 55
4.3 Risk Perceptions ........................................................................................................... 56
4.4 Domestic Meat Consumption ...................................................................................... 57
4.5 Occupation .................................................................................................................... 58
4.6 Interaction Between Location and Domestic Meat Consumption ........................... 59
4.7 Bushmeat Practices ...................................................................................................... 60
4.8 Human-Wildlife Conflict and Bushmeat .................................................................... 61
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4.9 Limitations .................................................................................................................... 63
5 Conclusions........................................................................................................................... 64
6 Citations ................................................................................................................................ 67
7 Appendixes ........................................................................................................................... 71
7.1 Methods for Randomized Household Selection ......................................................... 71
7.2 Definition of Variables ................................................................................................. 71
7.2.1 Outcome Variable ................................................................................................... 71
7.2.2 Predicator Variables ................................................................................................ 72
7.3 Expansion of Results .................................................................................................... 72
7.3.1 Testing Assumptions ............................................................................................... 72
7.3.2 Trends in Bushmeat Consumption over Time ........................................................ 76
7.3.3 Trends in Bushmeat Hunting over Time ................................................................. 78
7.3.4 Domestic Meat Consumption Patterns.................................................................... 81
7.3.5 Interactions Models for Predicators of Bushmeat Consumption ............................ 83
7.3.6 Bushmeat Consumption Probability Estimates ....................................................... 85
7.4 Survey (English) ............................................................................................................ 89
7.5 Survey (French) ........................................................................................................... 100
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List of Figures:
Figure 1-1: Diagram of Hypothesized Domestic Meat Consumption Risk Factor ....................... 10
Figure 1-2: Hypothesized Diagram of Perception Risk Factors .................................................... 11
Figure 2-1: Map of Study Site in relation to High Ebola Infection Zones in Neighboring
Countries ................................................................................................................................ 13
Figure 2-2: Bassari Country in relation to the National Park and Guinean Border ...................... 14
Figure 3-1: Most Destructive Crop Raiders reported by Bassari Farmers .................................... 37
Figure 3-2: Crops Most Affected by Non-Human- Primate Raiders .............................................. 38
Figure 3-4: Bushmeat Consumption of Ebola Host Species Proceeding and During the West
Africa Ebola Epidemic .......................................................................................................... 42
Figure 3-5: Frequency of Bushmeat Consumption Trends Before and During the West African
Ebola Outbreak ...................................................................................................................... 43
Figure 3-6: Distribution of Domestic Meat Consumption and Proximity to Bushmeat Market on
Likelihood of Consuming Bushmeat………………………………………………………..50
Figure 3-7: Inverse Logistic Regression to Estimate Probability of Bushmeat Consumption for
Adult Male Bassaris on Individual Likelihood of Consuming Bushmeat ............................. 53
Figure 7-1: Bushmeat Consumption Trends for All Bushmeat over Time in Bassari Country .... 77
Figure 7-2: Hunting Trends for All Bushmeat over Time in Bassari Country ............................. 79
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Figure 7-3: Hunting Trends of Ebola Hosts Species in Bassari Country over Time .................... 80
List of Tables:
Table 2-1: Suspected Ebola Reservoirs and Hosts in Senegal ...................................................... 17
Table 2-2: Exploratory Likelihood Ratio Test for Predicator Variables………………………..24
Table 3-1: Distribution of participants by proximity to market, socioeconomic status, age, gender,
education and religion ........................................................................................................... 33
Table 3-2: Availability of Ebola Host Bushmeat for Sale at Bushmeat Market ........................... 45
Table 3-3: Final Model: Interaction Effect between Domestic Meat and Proximity to Bushmeat
Market .................................................................................................................................... 47
Table 3-4: Exploratory Analysis for Interaction between Domestic Meat and Proximity to the
Bushmeat Market ................................................................................................................... 51
Table 7-1: Spearman Correlation Table: Examining Covariates for Multi-collinearity ................. 74
Table 7-2: Pearson Chi Test for independence .............................................................................. 75
Table 7-3: Binomial Logistic Regression Models for Predicators of Bushmeat Consumption:
Examining Different Interaction Effects ............................................................................... 79
Table 7-4: Probability Estimates for Male, Animist with No Education ....................................... 86
Table 7-5: Probability Estimate for Male, Non-Animist with No Education ................................. 86
Table 7-6: Probability Estimates for Male, Animist with Education ............................................. 87
Table 7-7: Probability Estimates for Male, Non-Animist with Education ..................................... 87
vii
Table 7-8: Probability Estimates for Male, Non-Animist with No Education with Negative Views
of Primates ............................................................................................................................. 83
Table 7-9: Probability Estimates for Male, Non-Animist with Education with Negative Views of
Primates ................................................................................................................................. 88
viii
List of Abbreviations
EBOV Ebola Virus (also known as Ebola Hemorrhagic Fever)
CHW community health worker
NHP non-human primate
OIE World Organization of Animal Health
NAHLN National Animal Health Lab Network
USAID United States Agency of International Development
WHO World Health Organization
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Acknowledgements
I would like to thank my committee members Patrick Tobin, Ivan Eastin, Aaron Wirsing,
Randall Kyes, Daisuke Sasatani and Peter Rabinowitz for their guidance and assistance in
designing and compiling this research project. Thank you, Ted, Westling and Daisuke Sasatani
for your statistical assistance and insights. Djitte Cherif, my Peace Corps Senegal Mentor, thank
you for encouraging my research and introducing me to key resources. Thank you, Jean Pierre
Lama, Boubane, my translator and research assistant, for your help, cultural insight, endless
positivity and encouragement. Thank you SEFS scholarship committee, advisors and professors
who helped fund and guide my education. Lastly, would like to thank my family and friends who
supported and encouraged me throughout the years. This work is dedicated to Colonel Ousmane
Kane, former Director of Niokolo Koba National Park who believed in my research and
promoted environmental conservation but was never able to see the final product.
1
1 Introduction
Zoonotic diseases contribute to more than 60% of emerging human infectious diseases
globally, accounting for 1 billion cases and millions of deaths annually (Karesh et al., 2012).
Anthropogenic activity, specifically bushmeat practices, often drives emergence of zoonotic
diseases such as monkey pox, simian foamy virus, Marburg and Ebola (Friant, Paige, and
Goldberg, 2015; LeBreton et al., 2006). Since December 2013 the West African countries of
Guinea, Liberia and Sierra Leone have been crippled by the largest Ebola Virus (EBOV) outbreak
to date. Virology investigations identified the virus as EBOV from the Filoviridae Family
(formerly Zaire species), one of five species in the genus Ebolavirus. With an average 50% fatality
rate in humans, (but up to 90% depending on virus strain and location), EBOV has resulted in over
28,616 cases and over 11,310 deaths since 2013 (WHO, 2016). The initial outbreak in the
Democratic Republic of Congo in 1976 and subsequent outbreaks between 1994-1997 and 2001-
2005 have been linked to a hunter infected from an animal source (Pourrut et al., 2007).
Concurrent to the 2002-2003 Republic of Congo outbreak, an EBOV epizootic reduced duikers
by 50% and the already shrinking chimpanzees populations by 88%, indicating wildlife as an
important sentinel for surveillance (Pourrut et al., 2007). Genome sequencing of EBOV in Sierra
Leone and Guinea supports the hypothesis that the West African outbreak stems from a single
spillover event from a wildlife source and is perpetuated by human-to-human transmission
(Alexander et al., 2015).
Fruit bats from the Pteropodidae family (Hypsignathus monstrosus, Eidolon helvum,
2
Myonycteris torquata) are the probable reservoir for West Africa EBOV and appear resistant to
Filoviridae pathogenicity (Alexander et al., 2015; Chippaux, 2014). The insectivorous bat Mops
condylurus from the Molossidae family may also be a reservoir and was linked to patient zero in
the West Africa 2013 outbreak (Pourrut et al, 2007, Saéz, et al, 2015). Researchers suspect non-
human primates (NHP), porcupines and duikers as intermediate hosts (WHO, 2015). Intermediate
hosts are highly susceptible and experience high rates of mortality. Recent serological evidence
in chimpanzees, however, suggests non-lethal infections are possible in NHP (Alexander et al,
2015). These intermediate hosts are suspected to play an important role in the viral transmission
chain, natural maintenance cycle, propagation, amplification and viral emergence in humans
(Wolfe, 2005, Alexander et al, 2015). EBOV is often introduced by direct contact with an
infected species’ bodily fluids (blood, secretions, organs, salvia, urine, feces or other bodily
fluids and tissues). Hunting and butchering of bushmeat (wild animals hunted for food) result in
increased risks of cross-species transmission of zoonotic pathogens (LeBreton et al., 2006).
Hunters and butchers are especially vulnerable for microbial transmission of EBOV from a
scratch, bite or direct contact with an infected animal’s tissues. According to the Centers for
Disease Control and Prevention, handling bushmeat is a primary mechanism of EBOV spillover
from wildlife sources to humans (CDC, 2016). There is insufficient research on risks factors that
influence bushmeat consumption particularly in areas peripheral to an Ebola infection zone.
A combination of favorable factors such as animal demography, seasonality, habitat
shifts, viral dynamics and animal behavior contribute to increase the probability of an outbreak.
The onset of the dry season shifts fruit-eating mammals’ behavior and migration and has been
3
closely tied to increases in virus circulation and human outbreaks (Chippaux, 2015).
Spatiotemporal clustering of frugivorous animals leads to increased competition and frequent
interactions between bats and NHP. High densities and limited food availability shift breeding
activities in fruit bats, which may increase contact and subsequently virus transmission and basic
reproduction rate (Pourrut et al., 2007). The shared ecological niche of reservoirs and intermediate
hosts and seasonal long distance migration of suspected reservoirs (>2,500km for Eidolon
helvum) influence the distribution of epidemic clusters (Chippaux, 2014). Elevated viral loads may
be due to transient resurgence of viral replication in fruit bats associated with immune function
during the end of pregnancy (Leroy, Labouba, Maganga, and Berthet, 2014). Habitat loss is the
main driver of changes in the roosting patterns of fruit bats from forests to orchards (Dieng and
Ndiaye, 2012). For example, the Eidolon helvum bat has been found in human-modified settings,
the Hypsignathus monstrosus bat has been discovered feeding on cultivated fields, and the
Myonycteris torquata bat has been identified in forest/grassland patches consuming mangos and
guavas (Alexander et al, 2015). Habitat shifts of EBOV reservoirs contribute to the contamination
of bush fruits in new regions that are adjacent to intermediate hosts and human habitats.
Consumption of fruit contaminated with bat feces, urine or salvia by intermediate hosts or
humans contributes to intraspecific viral pathways. Environmental persistence up to several days
may also increase risks of transmission via bush fruit (Leroy et al., 2014). Transmission dynamics
are also affected by specific species behavior, particularly chimpanzee group sizes, close contact,
predation of other NHP and hunting behavior (transporting meat more than a kilometer and meat
sharing amongst social group) (Alexander et al, 2015). Duikers’ propensity to graze on carcasses
and compete for bush fruit also contributes to transmission dynamics.
4
Intensification of infectivity during the passage of EBOV between animal species has
been experimentally demonstrated in some species (Gale et al., 2016). Intensification, possibly
due to mutation of the virus due to coding changes in key proteins, may affect dose response in
different bushmeat species. EBOV load per unit mass of tissue or volume of fluids in infected
animals may affect species susceptibility and infectivity. Although there is little known on viral
loads of wild animals, experimental results indicate cynomolgus macaques (Macaca fasicularis)
and hamadryas baboons (Papio hamadryas) may be as high as 109 PFU g -1 in the spleen and
liver. Experimental passage of EBOV from a wild green monkey (Chlorocebus sabaeus) to a
guinea pig (Cavia porcelains) amplified four to five times order of magnitude (Gale et al, 2015).
Limited data suggest nonhuman-primate- adapted EBOV, through bushmeat, may pose a larger
risk than bat-adapted EBOV. Researchers speculate the number of intra- or interspecific passages
affects infectivity (per EBOV plaque forming unit) to humans from bushmeat (Gale et al, 2015).
These studies indicate intermediate hosts, such as primates and duikers, may have higher
lethality, which would greatly impact risk assessments for bushmeat.
Risk behavior through bushmeat hunting, storage, and consumption has yet to be
investigated in regions peripheral to EBOV infected zones. Bushmeat consumption serves as an
indicator of possible exposure to EBOV and is considered a high-risk activity. Bushmeat
consumption reports in combination with arrest records can reliably estimate hunting rates
(Knapp et al., 2010). Dietary recall of bushmeat consumption is less threatening and a viable
alternative to assess hunting in sites where arrest records are unavailable or unreliable (Knapp, et
al., 2010). Research in Senegal, bordering the high EBOV infection zone in West Africa,
5
demonstrates the integral role of bushmeat economically and culturally despite its potential high
infectivity (Ba et al., 2006). In Senegal, bushmeat is an affordable commodity with a weighted
average estimated at 400-500CFA/kg (1USD/kg) in comparison to 1,200/CFA (3USD/kg) for beef
(Ba et al, 2006). Hunting complements modest agriculture earnings and contributes to total cash
income for poorer households. Bushmeat is typically sold locally whereas game by-products are
often sold in urban centers, yielding an estimated 56 million CFA (78,300USD) per year in
Senegal’s capital Dakar (Ba et al. 2006). In 2000, southern Senegalese rural households reported
consuming bushmeat between one and three times per month (Ba et al., 2006). Frequency of
bushmeat consumption is correlated with ownership of domestic animals, whereas traditional
beliefs and religious affiliations influence the species of bushmeat consumed (Ba et al., 2006).
Meat preferences, cooking techniques and distribution of bushmeat among households can also
impact exposure. EBOV is inactivated by cooking or boiling for five minutes at 60°C but the
virus can persist in dried meat for over 50 days (Alexander et al., 2015). Bushmeat practices,
particularly hunting, decreases with increased enforcement but fines and patrols have been found
to have a small negative impact in areas where bribes and settlements are common (Knapp et al,
2010). Household income, price of substitute meats in local markets and donation of livestock
have been shown to influence bushmeat hunting and consumption (Nielsen, Jacobsen, and
Thorsen, 2014).
Little is known concerning EBOV risk behaviors that influence transmission routes.
Accordingly, my study was designed to quantify and characterize the risks of exposure to EBOV,
and elucidate the drivers for bushmeat consumption in a region peripheral to a high infection
6
zone. Although Senegal has not experienced a large-scale outbreak, with only one case to date,
the southeastern region of Kedougou called Bassari Country serves as a potential location for a
spillover event. Bassari people are spread across Gambia, Guinea, Guinea-Bissau and Senegal
with populations between 10,000 and 30,000 and majority residing between the Guinean border
and Niokolo Koba Park (ICOMOS, 2011). Bassari people, an animist tribe of agro-pastoralists
who were traditionally hunter-gatherers, are an at-risk demographic. Bassari villagers are at risk
for contracting EBOV due to their cultural and land use practices, contact with NHP and routine
bushmeat consumption. To understand demographic predictors and perceptions that contributes
to increased risk of exposure to EBOV among Bassaris I conducted two studies. The first cross-
sectional study investigated drivers of bushmeat practices by conducting household surveys on
bushmeat practices. Studies have shown dietary recall of bushmeat consumption is a reliable
surrogate to assess hunting patterns (Ceppi and Nielsen, 2014). Respondents were more likely to
admit to consuming bushmeat than hunting due to illegal nature of hunting; therefore, I chose to
use bushmeat consumption as a proxy for hunting activity. I tested the significance of
demographic variables such as occupation and availability of domestic meat sources, as well as
perceived risks and perceptions of NHP, as predictors for potential EBOV exposure through
bushmeat consumption. My second study sought to complement my first study by providing
observational data on type and quantity of available bushmeat at the Bassari bushmeat market. To
explain risk factors that drive bushmeat consumption I hypothesized the following:
1. Participants with no formal education have an increased probability of consuming
bushmeat.
7
2. Increased availability of domestic meat decreases an individuals’ likelihood of
consuming bushmeat.
3. Individuals who perceive a sufficient risk, zoonotic or injury related, associated with
wildlife contact have a decreased likelihood of consuming bushmeat.
4. Negative views of primates, as pests, have an increased likelihood of consuming
bushmeat.
5. Occupations with exposure to wildlife habitat such as hunters, palm collectors or
medicinal plant gatherers, have an increased access to wildlife and thus an increased
likelihood of consuming bushmeat.
I constructed my hypotheses based on existing research and knowledge of my study
population. I hypothesized the lack of any formal education as a predictor for increased
likelihood of consuming bushmeat based on the philosophy that education increases the
acquisition of knowledge. I speculated the lack of an education would be associated with poor
knowledge of the known risks based on a study in Nigeria that found pre-trainings education level
significantly influenced knowledge of EBOV risks (Patel, 2016). In further studies, lack of
education was postulated as a contributing factor towards the observed difference in number of
secondary cases in different socioeconomic levels (Fallah et al, 2015). Socioeconomic status was
also considered a predictor variable in the form of domestic meat consumption. I hypothesized
that increased domestic meat availability (or socioeconomic status) would decrease an
individual’s likelihood of consuming bushmeat based on EBOV transmission risk models and
previous studies (Ba et al., 2006). Transmission models found 3.5 times more EBOV transmission
in improvised settings with wider disease propagation between communities (Fallah et al., 2015).
8
Researchers in Senegal found an association between frequency of bushmeat and socioeconomic
status (Ba et al., 2006). I predicted demographic variables, such as age, education and animism, to
have minimal effect on socioeconomic status as measured by domestic meat consumption. My
hypothesis regarding occupational exposures and bushmeat consumption was based on studies
indicating proximity to harvestable wildlife increases consumption (Brashares et al., 2011). I
tested occupational effects separately from access to purchasable bushmeat and thus likelihood to
consume bushmeat (Brashares et al, 2011). I examined an individual’s proximity to the bushmeat
market as an indicator of access to purchasable bushmeat. I speculated proximity to the bushmeat
market was unrelated to socioeconomic status as measured by domestic meat consumption
(Figure 1-1).
Examining behavioral drivers, I hypothesized that increased perceived risk associated with
wildlife contact was association with decreased likelihood of bushmeat consumption based on
informal informational interviews and previous studies of perceived risk. Informal interviews in
Bassari Country in November 2014 found perceptions of risks were generally high and attitudes
toward behavioral change were positive (Kroos, 2015). In a risk perception study in Nigeria, 55%
of participants were aware of risks associated with wildlife contact, of which almost half (26%)
reported taking precautionary measures (Friant et al., 2015). I hypothesized that views of primates
were associated with an intermediate outcome of perceived risk of NHP and would ultimately
influence bushmeat consumption likelihood, regardless of demographic variables (Figure 1-2). I
based these hypotheses on interviews with cultural experts regarding the role of nature and
wildlife in animism and Bassari culture and research in southern Senegal that found an
association between religious beliefs and type of bushmeat consumed (Boubane, 2015, Ba et al.,
9
2006). To determine drivers of bushmeat consumption and EBOV risk factors I tested these
hypotheses in a region peripheral to a high infection zone.
10
Figure 1-1: Diagram of Hypothesized Domestic Meat Consumption Risk Factor
Main Hypotheses: Increased availability of domestic meat affects an individuals’ likelihood of consuming bushmeat as seen by the green pathway signifying a causal
pathway. I hypothesized village location (proximity to the bushmeat market) was unassociated with domestic meat but may affect bushmeat consumption. I further
hypothesized demographic variables were unassociated with domestic meat consumption’s impact on bushmeat consumption and on bushmeat consumption and were
tested as potential confounders (as seen with red biasing pathways). To test these hypotheses, I examined the possibilities of biasing pathways (interaction) between
domestic meat consumption and proximity to the bushmeat market on bushmeat consumption.
11
Figure
1-2: Hypothesized Diagram of Perception Risk Factors
Main Hypothesis: Negative views of primates, as pests, increased an individual’s EBOV exposure risk as seen with the green causal pathway. Perceived risk associated
with wildlife contact was associated with an intermediate outcome on the likelihood of consuming bushmeat. I hypothesized demographic variables were unassociated
with the likelihood of bushmeat consumption and were tested as potential confounders (as seen with red biasing pathways).
12
2 Methods
2.1 Study Setting
2.1.1 Cross Sectional Study with Survey Assessment:
I conducted a cross sectional study between May and July, 2015, in seven villages in
Bassari Country, in the region of Kedougou, located in the Salemata district of southeast
Senegal. The study sites were located between 1 and 25 km from the Niokolo-Koba National
Park and the Guinean border, in the northern foothills of the Fouta Djallon massif (Figure 2-1,
Figure 2-2). In 2012, 8,856 people lived in the Bassari-Salemata area. The Bassari Salemata
zone occupies 242 km² south of Salemata and is surrounded by a 1,634 km ² wide buffer
zone. The landscape features both alluvial plains and mountains that are heavily forested with
dry, deciduous woodland. Less than 10% of the land is cultivated on marginalized farmland
(ICOMOS, 2012). Most Bassari people primarily speak a Tenda language, of the minority
languages, called Bassari. The Bassari region has been classified as a United Nations
Educational, Scientific and Cultural Organization (UNESCO) World Heritage Site since 2012
due to Bassaris’ cultural integrity and complex socioeconomic cultural system involving
unique agricultural practices and traditional rituals (ICOMOS, 2012). However, this region
has continued to experience human-wildlife conflicts as a result of deforestation, land
conversion, illegal poaching, and crop raiding (e.g., the act of entering cultivated area and
stripping plants of edible parts, often damaging crops).
13
Figure 2-1: Map of Study Site in relation to High Ebola Infection Zones in Neighboring Countries
14
Figure 2-2: Bassari Country in relation to the National Park and Guinean Border
15
Southern Senegal is a potential location for an EBOV outbreak based on the spatial
distribution of environmental factors and wildlife. Senegal is located at the upper geographic
limit for the distribution of reservoir fruit bats (H. monstrosus, E. franqueti, and M. torquata) as
well as home to the Mops condylurus insectivorous bat, a suspected source of the 2013 Guinean
EBOV outbreak (Pourrut et al, 2007, Saéz, et al, 2015). Bassari Country is also home to
intermediate hosts of EBOV such as Papio papio (Guinea baboon), Erythrocebus patas (patas
monkeys), Cercopithecus aethiop (grivet monkeys), Pan troglodytes (savannah chimpanzees) and
Sylvicapra grimmia (common duikers) (Table 2-1). Intermediate hosts can cover large areas, such
as savannah chimpanzees with a projected range of 250km², which enables hosts to travel
between infected zones across the Guinean border and into non-infected zones in Bassari
Country, potentially spreading EBOV among new populations. (Kormos et al., 2003). Researchers
have hypothesized that reduction in bush fruit availability has driven chimpanzees to disperse
outside their known ranges and further into regions that are endemic to EBOV fruit bat
reservoirs. Insufficient wild food has increased crop raiding and increased territorial conflicts
between chimpanzee populations, potentially increasing the risks of inter and intra-specific
transmission. In 2010, an estimated 200 chimpanzees were residing in Niokolo Kobe Park with
approximately 100 within the Salemata region (Kane, 2015). The Fouta Djallon northern region
of Guinea, overlapping into Bassari Country, contains approximately half of the regional
chimpanzee population (8,113-29,011 nationwide). Despite chimpanzees’ protected status,
questionnaires from the Fouta Djallon region reported 6% of the population consume chimpanzee
meat, whereas 46% of people consume chimpanzee in the southern Guinea Forestiere region
(Kormos et al., 2003). Surrounding regions’ increasing demand for chimpanzee meat is
16
encroaching on the Fouta Djallon region that contains Bassari Country. Chimpanzees’
overlapping territories into Guinea, shared habitat with EBOV reservoirs and Guineans’ demand
for chimpanzee meat place Bassari Country in increased risk for an independent outbreak.
Contact with infected chimpanzees through butchering or hunting can facilitate transmission of
microorganisms. For example, the EBOV outbreak in Gabon in 1996 was linked to butchering
and consuming a chimpanzee carcass. In fact, 29 of 37 cases of EBOV in Central Africa involved
chimpanzee exposure (Wolfe et al, 2004).
17
Table 2-1: Suspected Ebola Reservoirs and Hosts Species in Senegal
Role Species Common Name Native to Senegal
Suspected Ebola Reservoirs Hypsignathus monstrosus Hammer-head fruit bat Upper Range
Epomops franqueti Franquet's epauletted fruit bat Upper Range
Myonycteris torquata Little collared fruit bat Upper Range
Mops condylurus Angolan free-tailed bat (insectivorous) Yes
Ebola intermediate Hosts Pan troglodytes Savannah chimpanzee Yes
Cercopithecus aethiops Grivet (Green monkey) Yes
Colobus badius Western red colobus Yes
Papio papio Guinea baboon Yes
Erythrocebus patas Patas (Wadi monkey) Yes
Sylvicapra grimmia Common duiker Yes
Cephlaopus rufilatus Red-flanked duiker Yes
18
Bassaris are vulnerable for a contracting EBOV due to their agricultural practices that
perpetuates human- wildlife conflict. The Bassari converted from hunter gathers to agro-pastoral
gatherers after prolonged contact with Pular farmers. They are subsistence farmers and typically
grow corn, millet, rice, fonio, cotton, peanuts, ground nuts and rice. Increased land conversion
and the close proximity of cultivated fields to primate habitat has resulted in high incidence of
crop raiding by guinea baboon, patas and grivet monkeys, and on occasion savannah
chimpanzees (Dieng and Ndiaye, 2012). These primates’ omnivorous diet and large ranges also
lends to their exploitation of agricultural fields. To prevent yield loss Bassaris protect their field
with active surveillance, particularly during harvest season. Patrolling their fields and chasing
NHP away from their field increases their contact with hosts, which escalate Bassaris risk for a
spillover event.
Bassari traditional use of forest products, reliance on natural water sources and
ethnomedicinal practices increases their risk of microbial transmission through contact with
intermediate hosts and with contaminated plants or fruits. Their ceremonies, habits and
interpretation of illness are reflected in their beliefs in supernatural forces and the embodiment of
their ancestors in nature (ICOMOS, 2012). Bassaris believe in a metaphysical world and use
masks and traditional medicine to ward off evil spirits. Traditional medicines, provided by a
medicine man, are often worn in leather pouches (gris gris) around one’s neck (Boubane, 2014);
these ethnomedicinal beliefs can impact care seeking, prognosis and transmission chains. The
Bassari believe the forests are sacred and are regularly used for initiations, prayer or burial. They
believe in a metaphysical world where all living things are part of one’s cosmology (ICOMOS,
2012). Despite their beliefs, deforestation is occurring at 0.7% per year, primarily due to road
19
construction, gold mining, expanding settlements, and land conversion for agriculture (Dieng and
Ndiaye, 2012). The loss of buffer zones along the forest forces Bassaris to travel farther into the
forest to collect traditional medicinal plants, edible leaves, bush-fruit and palm wine. Increasing
temperature due to climate change drives competition between humans and primates for water.
During the dry season, chimpanzees from the surrounding areas converge around the Diara
River, which is regularly used by villagers for water collection, bathing and clothes washing.
Competition over water has escalated in the last ten years in the Bassari zone (Kormos et al.
2003). Interaction with EBOV hosts escalates Bassaris risk for a spillover event.
Bassaris routine consumption of bushmeat poses a significant risk for pathogen
transmission. In an area with 26% chronic malnutrition, Bassaris rely on bushmeat as a source of
protein and plays a significant role in their cultural practices (FAO, 2006). Livestock are free
ranging and are not managed to produce high yields of meat, dairy or eggs. Due to low yields,
domesticated meat (chicken, goat, sheep, cow) is typically reserved for consumption on special
occasions. According to a 2011 poverty monitoring survey, Kedougou is one of the poorest
regions in Senegal with 71.3% of inhabitants living in poverty (International Monetary Fund,
2013). Bushmeat, despite its strong demand, is a fairly inexpensive commodity and serves a
disproportionately poor community. For example, a study in Cameroon showed that the poorest
income classes spent 16-17% of their meat budgets on bushmeat in comparison to 7% in the richest
class (Wolfe, 2005). In Salemata in 2015, warthog sold for 715CFA/kg (1.2USD/kg) in comparison
to 1,750CFA (3USD/kg) for beef (Boubane, 2015). Properly cooked bushmeat is deemed safe and
EBOV-free but butchering and cleaning the meat is considered a high-risk activity (WHO, 2016).
20
Hunters or cooks traditionally only wash the carcass with water during butchering and do not
wear protective clothing or equipment. Due to the unlawful nature of hunting, selling and
consuming bushmeat, these practices are often practiced in secret and may go underreported,
posing a considerable challenge for monitoring exposures.
Hunting patterns, wildlife density and proximity to wildlife can also impact bushmeat
consumption and microbial EBOV transmission. Bordering Bassari Country, Niokolo Koba-
Badiar Park is one of the largest national parks in West Africa, covering 91,300 km² of the
Guinea and Senegal savannah (UNESCO, 2016). There is a 1 km2 buffer zone that acts as a
protective transitional land between agricultural land and the park. Hunting, capturing animals or
exploitation of flora is prohibited within the park and in the buffer zone. In 2014, park guards
caught approximately 60 poachers. Between January and July, 2015 approximately 25 illegal
poachers were apprehended (Kane, 2015). Frequency of bushmeat consumption has been shown
to decrease as distance to protected areas decreases, indicating hunting is lower in areas adjacent
to national park that have high enforcement activity (Ceppi and Nielsen, 2014). Outside the park,
within the Salemata hunting zone (60,000 hectares), customary hunting rights are recognized and
managed by the national forestry program. Hunting of warthogs and wild birds is permitted
between December and April with a hunting permit (3,000CFA/hunting season/6USD) and gun
license (>10,000CFA depending on gun model/20USD) (Foufana, 2015). Permit costs are
inhibitory for Bassari communities who live well below poverty level. According to national
forestry program records, all hunting in the last five years in Bassari Country was illegal. A food
security survey conducted in southern Senegal (Kedougou and Tambacounda and Kolda regions)
21
found that 37% of hunting was illegal, including hunting in hunting zones without a permit.
Household surveys of 17 rural communities surrounding Niokolo Koba National Park found that
most hunting (73%) takes place on the outskirts of the village all year round with 20% of kills
occurring in protected areas (Ba et al., 2006). The majority of poachers apprehended within the
national park traveled vast distances, with the majority originating from Guinea and entering
Niokolo Koba National Park through Guinea’s Badiar National Park (Kane, 2015). Poachers have
been known to pay to stay in Bassari homes peripheral to the park for easy hunting access (Kane,
2015). Sharing of bushmeat for consumption, close contact with wildlife carcasses and interacting
with high-risk individuals perpetuates EBOV risk among Bassaris.
2.1.2 Observational Bushmeat Market Study:
The exchange of bushmeat across borders and market availability supports Bassari
Country as a suitable study site for accessing EBOV through bushmeat consumption patterns. To
gauge bushmeat availability and likelihood of consumption in Bassari Country I conducted an
observational bushmeat market study concurrently with my surveys during May and July 2015 in
southern Bassari Country. The bushmeat market is located on a bush path that connects two
Bassari villages near the Guinean border. Bassaris and Guinean congregate after dusk at the
illegal market to drink palm wine and consume bushmeat. Attendees are known to also barter,
trade and sell black market pharmaceuticals, guns and household goods (Boubane, 2015).
Numerous survey respondents reported authorities have raided the market, destroying goods and
beating vendors for illegally harvesting palm wine and hunting bushmeat. Due to a history of
22
violent altercations, attendees distrust outsiders and conduct their trade on unmarked paths in the
forest. The market is relatively small with four to five sections representing different vendors’
districts. The market typically closes around midnight and serves fifty to a hundred consumers a
week. Market participants typically travel by foot or bicycle between 2-15km to attend the
bushmeat market.
Human travel also poses a risk for a potential outbreak. Despite officially closing
Senegal’s borders with Guinea in March 2014, it actually remained relatively porous particularly
on bush paths connecting Bassaris to Guinean villages. The lack of any clear demarcation of the
border further exacerbated unregulated travel. Bassari often traveled across the border to visit
friends and family as well as for commerce. Despite the closure of the weekly market in Salemata
in Bassari Country from April through August 2014, a market that is the sole legitimate trading
market for more than 80km, people still regularly moved across the Senegal-Guinea borders.
Indeed, according to my market study from May through July 2015 in Bassari Country, 40% of
the bushmeat vendors/hunters originated from Guinea. Contact with persons from infected
regions places Bassari Country at risk for an EBOV outbreak.
Little is known about risk factors that drive bushmeat consumption and zoonotic
pathogen transmission, particularly in regions peripheral to high infection zones. Bassari Country
is primed for a spillover event due to its spatial distribution of environmental factors, proximity
to the border, human-wildlife conflict, cultural rituals and bushmeat practices. The shared
ecological niche of reservoirs and hosts enables viral intraspecific transmission and interspecific
circulation. Land conversion is driving wildlife closer to human settlements, increasing human-
23
wildlife conflict. Competition for bush fruit, crops and water contribute to Bassaris’ risk of being
bitten or scratched by an EBOV host. Cultural practices place Bassaris in EBOV host habitats,
which increases their risk for exposure through contaminated fruit or fomite exposure. Bassaris
routine consumption of bushmeat poses a significant risk for pathogen transmission, particularly
due to intensification of infectivity during the passage of EBOV between animals. Bassaris close
contact with hunters from Guinea at the bushmeat market or as boarders in their homes
intensifies the risk for EBOV transmission. Transportation of raw bushmeat at the market and
human travel further exacerbates the risk for microbial transmission. Identifying risk drivers in
regions primed for a spillover event can help public health authorities strategize to prevent future
outbreaks of Ebola, Marburg and other emerging threats. Understanding how viruses behave and
surge in respond to anthropogenic land use changes is essential for predicting emerging
infectious diseases (Wolfe, 2005). Accordingly, lessons from Bassari Country are applicable to
countless regions with similar human-wildlife conflicts.
2.2 Selection of Study Subjects
2.2.1 Cross Sectional Study with Survey Assessment:
Structured questionnaires were administered to the head of household or head of
household’s spouse from seven villages in Bassari Country to investigate villagers’ risk for
contracting EBOV from wildlife hosts through bushmeat consumption. I collected information
regarding demographics, domestic meat availability, perceptions of primates and type and
frequency of bushmeat consumption and hunting. I divided my study population of adult Bassaris
24
into strata. I targeted male head of households because of their increased risk of EBOV
transmission due to their traditional gender roles as hunters and butchers and head of household
responsibilities to provide for their families. Individuals were selected using randomized
stratified purposeful sampling between May and July 2015. Approval to work with human
subjects was granted on April 3, 2015 (HSD study #49407) from University of Washington
Human Subjects Division. Participant’s homes were randomly selected using the World Health
Organization epidemiology survey coverage for rural single dwelling guidelines (WHO, 2008)
(Appendix 7.1). A central location in the village was first selected based on proximity to the
chief’s house or central water pump. I then determined the first house by spinning blindly. I
worked outwardly in a spiral formation to sample as many houses as possible. Households were
randomly selected to be sampled based upon the outcome of a coin toss.
Sites were selected based on their shared similarities in ethnic groups, proximity to
wildlife habitat, distance from road, presence of agricultural farming and population size (100-
350 people). All sites were within 3 km of natural habitat for chimpanzee, grivet monkeys, patas
monkeys and baboons. All villagers were predominantly Christian animist whom unlike
Muslims, do not have a religious taboo on bushmeat consumption. These villages had no
electricity, no sanitation facilities, no running water, poor cell phone coverage and seasonally
impassable roads. Bassari was the dominant ethnic group at all sites. Most Bassari people
primarily speak an Oniyan minority language called Bassari. Due to language barriers, a local
Bassari health worker worked as my translator and interviewed all of my subjects in Bassari.
25
Prior to surveying subjects, I met with the chief of each of the selected seven villages to
describe the goals of my research and the participants’ rights to anonymity and confidentiality.
After one week, I contacted the chief by cell phone or via bush mail. I was invited to conduct
research at all seven of the villages I initially contacted. I approached each randomly selected
home and asked to interview the head of household. Due to the high illiteracy rate, participants
provided oral consent. I asked to survey participants in private to ensure confidentiality and
encourage open discussion with the interviewer.
I surveyed the adult (over age 18 years) male head of household or, if unavailable, the
wife of head of household, who had resided in the village for at least two years and identified as
a non-Muslim Bassari. Adult males were the primary subjects; female participants, due to gender
roles, often spoke less freely and had less knowledge of hunting patterns. Women may have had a
different set of knowledge than men due to gender roles (men typically hunt and butcher, and
women typically prepare and cook food). Eligibility criteria regarding residence was set at two
years to ensure participants were familiar with daily activities and cultural ceremonies that often
only occur annually. Approximately 6 potential interviewees opted out of the survey, of which 5
were women. Approximately 10% of selected households were skipped due to the absences of
adult head of households on the premises. Approximately 3% of households were excluded
because the head of household was absent and his wife had not resided in the selected village for
more than 2 years.
26
2.2.2 Observational Bushmeat Market Study:
To assess bushmeat availability at the market and to complement self-reported bushmeat
consumption patterns I targeted hunter/vendors at the weekly bushmeat market. Our sampling
strategy focused on recording any adults selling, purchasing or consuming bushmeat at the
weekly bushmeat market. The market’s history of volatile interactions with authorities
contributed to villagers’ resistance to outsiders (Boubane, 2015). Due to distrust of outsiders
hunter/vendor interviews were not possible. Since few survey respondents identified as hunters or
bushmeat vendors I hired a local Bassari to conduct an observational study at the bushmeat
market. The observational market study supplemented the survey’s deficit in responses from self-
identified hunters and bushmeat vendors and provided information concerning species and
quantities targeted, hunting methods, cooking procedures and cost of available bushmeat.
2.3 Data Collection
2.3.1 Cross Sectional Study with Survey Assessment:
The questionnaire was designed to provide information on direct or indirect exposure to
bodily fluids of EBOV hosts through injury either during field protection or hunting, or exposure
to bodily fluids during hunting, butchering or consuming (Appendix 7.4). EBOV risk was
measured in terms of bushmeat consumption of known EBOV host or reservoir species within
the last six months. Bushmeat consumption was considered a binary response variable (non-
consumer or consumer) based on self-reports. Risk perception were determined by asking
participants about which interactions were risky and how they perceived their probability of
contracting EBOV during the identified risky behavior on a risk scale. The framework for the
27
questionnaire was adapted from Gillingham and Lee’s (2003) NHP crop raiding assessments and
from Lebreton’s (2006) behavioral questionnaire on hunting, butchering and consumption of
bushmeat, precautionary measures taken, and perceived risk of touching blood.
Data were collected between May and July 2015 in Bassari by a local translator and
transcribed in English. I extrapolated the number of adult males in each selected village using
2014 Ministry of Health census data (Camera, 2014). Based on these census data, I then estimated
my minimal sample size per site to obtain 20% of the head of households in each village, which
resulted in a sample size of 12-25 people per village. At least 20% of the head of households were
surveyed in each of the seven villages. I continued sampling a village until I reached at least 20%
of head of households. I conducted a total of 100 surveys across seven villages.
My estimates are likely conservative since my data relies of self-reports concerning an
illegal activity and behaviors discouraged by health campaigns. Underreporting by hunters is
likely due to fears of substantial fines and imprisonment. Park poaching records were shared by
the Director of Niokolo Koba Park but were not recorded in term of arrests per km patrolled,
which would be needed to estimate the total number of illegal hunters in a given area. Park
poaching records are prone to underestimation due to large under patrolled tracts of land
inaccessible by car and understaffed patrols, and therefore were not used as an indicator of
hunting incidences. Bushmeat consumption rates are a valid alternative when hunting reports are
unavailable (Ceppi and Nielsen, 2014). Bushmeat consumption rates corresponded to the number
of reported poachers and quantity of bushmeat sold at the market, which indicates that although
underreporting may have lowered my estimate of bushmeat consumption it did not significantly
28
affect my results. NHP pets were discovered in three households but all were orphans separated
from any potential infection source and rarely bite; thus, they were excluded as potential risk
factors for EBOV.
To assess and assure data accuracy, respondents were asked to identify bushmeat
consumed or hunted, and were provided photographs and illustrations of local species when they
were unsure of the species or only knew the local name for which no translation was available.
Respondents may have misidentified some bushmeat species, particularly women. To avoid
recall bias and since bushmeat is sold based on visual appraisal, not weight, respondents were
not asked to estimate the quantity of meat consumed. Since most respondents could not identify
between different antelope species, a generic group for antelope was created. Questions
concerning bushmeat consumption and hunting were posed in the middle of the interview to
avoid respondent anxiety. Sensitive questions concerning bushmeat consumption were eased by
questions referring to consumption of legal proteins. Since Bassaris do not use a Gregorian
calendar, temporality was established by referencing seasons and events. Both studies spanned
over the rainy and dry season to help reduce seasonal bias (Knapp et al., 2010). To promote trust,
I used a translator from the community who identified as Bassari and was fluent in Bassari and
English. I was a resident and established community member in the region for 18 months before
beginning the surveys and spoke the local language, Pula Fuuta.
2.3.2 Observational Bushmeat Market Study
In addition to conducting surveys, I conducted an observational bushmeat market study to
supplement self-reports of bushmeat consumption and hunting patterns. Due to the illegal nature
29
of the bushmeat market, I was unable to interview vendors/hunters at the market due to distrust
of outsiders. I hired a local health worker to collect observational data from a local bushmeat
market within my study site zone. My research assistant recorded type of bushmeat sold, cost,
hunter/vendor place of residence, whether people purchased the meat, hunting method and
whether it was cooked or raw. He recorded his observations for 15 weeks, May to July 2014,
which included both a rainy and dry season. To ensure criteria data validity, I selected a local
Bassari health worker based on his literacy level and his status in the village. He typically
attended the weekly market so his presence was unlikely to have introduced bias into the study.
Bushmeat market studies complemented the dietary recall reports and self-reports by hunters and
demonstrated concurrent market availability of bushmeat.
2.4 Statistical Analyses
Data were analyzed using R studio. I defined my outcome variable as non-consumer or
consumer of EBOV host bushmeat within the last six months. Predictor variables were identified
based on socioeconomic, geographic and cultural drivers of consumption among Bassaris:
education, religion, risk perceptions, domestic meat availability, occupation, and proximity to the
bushmeat market. Education, having attended any formal schooling, was examined as a predictor
of socioeconomic status and of knowledge of potential EBOV risk associated with bushmeat
consumption. Religious beliefs were examined as animist or non-animist due to the religion’s
strong involvement with nature and wildlife. Risk perceptions of NHP were categorized as non-
negative views (scared, religious, or no strong opinions) or negative views (pests or vectors for
30
disease). Domestic meat availability was used as a proxy for socioeconomic status and indicative
of access to alternative meat sources. Consumption of domestic meat was defined by frequent (>
1x/month) or infrequent consumption (<1x/month). Occupational risk was defined by exposure to
EBOV either by contact with hosts, contact with contaminated environments or exposure to
bodily fluids of sick individuals through healthcare work or through work that involved time in
the bush, such as hunters. Distance to bushmeat markets was considered a surrogate predictor
variable for access to regular bushmeat. Villages were grouped based on distance to the bushmeat
market as within ten kilometers of the bushmeat market (regular access) or more than ten
kilometers (poor access) (Appendix 7.2)
I tested my hypothesis that the lack of formal education increased risk of bushmeat
consumption with an exploratory likelihood ratio followed by a logistic regression. I tested my
hypothesis that increased domestic meat consumption decreases an individual’s likelihood of
consuming bushmeat and was associated with decreased EBOV risks with an exploratory
likelihood ratio with all of my variables and examined the bivariate association with bushmeat
consumption followed by a logistic regression. I examined the relationship between perceived
risk of NHP and views of NHP impact on an individual’s likelihood of consuming bushmeat with
the same statistical tests, assuming the relationship between the outcome and covariates within
each exposure group followed a binomial logistic function. I examined my predictor variable
occupation’s relationship to bushmeat consumption with a likelihood ratio and then binomial
logistic regression to test my hypothesis that occupations with exposure to wildlife habitat
increased individual’s risk of bushmeat consumption. I further tested my hypothesis that
demographic variables were not significantly associated with increased bushmeat consumption
31
by examining gender and religion as covariates in the binomial logistic model.
To further examine potential simultaneous influence of two variables on my outcome, I
tested my hypothesis that there was no relationship between site location and domestic meat
consumption on bushmeat consumption. I ran an exploratory analysis with an interaction between
domestic meat and proximity to the bushmeat market on likelihood of consuming bushmeat,
while excluding insignificant or highly correlated variables (age and gender). I further tested
interaction effects between age and education, views of NHP and perceived risks and age and
education but found no significant effects so they were excluded from the final model (Appendix
7.3.5, Table 7.3). I graphed this interaction effect for visual depiction of the data distributions.
After which, I ran probability estimates for potential risk factors that contribute to bushmeat
consumption using an inverse logistic regression for categorical dependent variables by
examining proximity to the bushmeat market for frequent domestic consumption (>1x/mo) and
infrequent domestic meat consumption (<1x/mo).
To apply a binomial logistic regression, I tested my data to ensure I met the assumption
that the error terms were independent. To test my hypothesis that domestic meat consumption
and perceptions of NHP were independent of villages’ proximity to the bushmeat market, I ran a
Spearman correlation test against each covariate (Appendix 7.3.1, Table 7-1). The Pearson Chi
square test results (p=0.94) indicated that I could not reject the null hypothesis that domestic meat
consumption was independent of location (Table 7-2). There was a significant interaction between
view of primates and proximity to the bushmeat market (p=<0.001) with a 39% correlation; no
corrections between variables exceeded 50%, which justified keeping these variables in my final
model. The Pearson chi square test demonstrated a significant interaction between age and
32
education (p< 0.001) with a fairly strong 45% correlation. Thus, subsequent tests omitted age to
avoid multi-collinearity.
A Pearson Chi squared test was also run to compare all covariates to ensure the
proportional odds assumption was met and there was no multi-collinearity between covariates. To
report significance of the main and interaction effects under the null hypothesis that the effect is
not a significant predictor of the response, I examined predictor variables one by one in an
exploratory analysis using the likelihood ratio Chi squared (Table 2-2). I found non-negative view
of primates were significant predicators for bushmeat consumption (p=0.04). The findings from
these exploratory analyses varied some from my final model due to later adjustments for
interaction effects.
33
Table 2-2: Exploratory Likelihood Ratio Test for Predicator Variables
Proximity
to market Age Animist Occupation
Domestic
Meat
Consumption
View of
Primates
Perceived
Risk Education Gender
Likelihood
Ratio 0.03 1.33 3.09 2.60 0.03 4.40 3.71 3.38 7.29
P value 0.87 0.52 0.08 0.11 0.87 0.04* 0.16 0.07 0.07
34
3 Results
3.1 Demographics
One hundred participants were interviewed in Bassari Country, of which 94 surveys were
completed. My data were fairly equally distributed across villages clustered within ten kilometers
to the bushmeat market and villages more than ten kilometers away. The survey targeted head of
household resulting in an underrepresentation of women; therefore, gender was excluded in the
final model as a predictor variable. All respondents in Bassari Country participated in farming.
Age groups were not equally distributed with 65% contained in the 36-60 years old age group
(Table 3-1). The median age of respondent was 48 with 70% of the population having never
attended school. Younger respondents were more likely to have attended school than older
generations; 69.6% of younger respondents (age 18-35 years old) attended school while only 14.9%
of older respondents (age > 36 years old) attended school. Practicing animism was present in a
little less than half of the respondents. Consumption of domestic meat was typically associated
with ceremonies and occurred every few months on average.
35
Table 3-1: Distribution of participants by proximity to market, socioeconomic status, age, gender, education and religion
Independent Variables
<10km from
Market (n=62) >10km from
Market (n=38) Total % (n=100)
Gender Male 53 28 81.0%
Age (years) 18-35 15 8 23.0%
36-60 43 22 65.0%
>60 4 8 12.0%
Education None 44 26 70.0%
Occupation Farmer only 38 23 61.0%
Socio-economic status Domestic meat consumption
(>1x/mo) 24 15 39.0%
Practicing Animism No 29 22 54.3%
(n=94) N/A 6
36
3.2 Crop Raiding
Cows were free ranging and were the leading source of crop damage. NHPs were
identified as the second worst crop raider of which patas monkeys were identified as the most
problematic (Figure 3-1). Millet was most susceptible to crop damage/loss, particularly by patas
monkeys but other crops such as peanuts and corn were raided frequently by patas and on
occasion by grivet monkeys and Guinea baboons (Figure 3-2, Figure 3-3). Crop were most
vulnerable to raiding during the end of rainy season, between September and October, when the
crops were ready for harvest.
37
Figure 3-1: Most Destructive Crop Raiders reported by Bassari Farmers
0
10
20
30
40
50
60
70
80
Cow Primate Sheep/Goat Wild Bird
# r
esponden
ts
Crop Raiding Species
Crop Raiding: Most Destructive Species
38
Figure 3-2: Crops Most Affected by Non-Human- Primate Raiders
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
corn ground nuts cotton millet peanut
# r
espondnet
s
ranked worst Crop raided
Worst Crop raided by Primate species
Baboon
Vevert
Patas
39
Figure 3-3: Crop Raiding Patterns by Non-Human Primates Annually
0
1
2
3
4
5
6
corn ground nuts cotton millet peanut Rice
# r
esponden
ts
Crop Raided
Frequency of Crop Raiding by all primates
Baboon
Vevert
Patas
40
3.3 Bushmeat Practices
3.3.1 Bushmeat Consumption
Comparing self-reported Bassari bushmeat consumption patterns from 1995, 2012 (prior to
the EBOV outbreak) and 2015 (during the EBOV outbreak) revealed that bushmeat consumption
was primarily driven by availability and cost and were not selected based on EBOV risks or
wildlife density (Appendix 7-3-2-7-3-3, Figure 7-1, 7-2, 7-3). When examining EBOV hosts
population data from Niokolo Koba Park, I found Guinean baboons were the most abundant
(100,000) followed by grivet monkey (28,000) and patas monkey (13,000). Total antelope species
were estimated at 51,700 of which 11,700 were EBOV hosts (8,500 red flanked duikers and
3,200 common duikers) (Becker et al., 1995). Wildlife population patterns do not appear to be
strongly interrelated to self-reported bushmeat consumption patterns by Bassaris. There was no
current wildlife census data available but Bassaris and park guards reported a large decline in
large antelope species, such as the giant eland (Taurotragus derbianus), and little change in NHP
population numbers (Kane, 2015). According to survey responses, consumption of antelope
decreased by 44% since 1995. Bushmeat consumption trends in 2012 were similar to trends during
the outbreak in 2015, despite many Bassaris reporting selective bushmeat consumption. Despite
no clear avoidance of EBOV hosts during the outbreak, Bassaris did report consuming
significantly less EBOV host bushmeat than in 2012 (Figure 3-4). Bassaris reported consuming
fruit bats, an EBOV reservoir, twenty years ago. Even before the EBOV outbreak Bassari
reported the end of this practice due to lack of availability of fruit bats as a sustainable source of
meat. Bassaris reported consuming more EBOV hosts, regardless of species, twenty years ago,
41
relative to 2012 and 2015. The majority of Bassaris reported consuming EBOV host bushmeat
one to three times per month in 2012, whereas most Bassari reported consuming bushmeat only
rarely during the outbreak (every six months or more) (Figure 3-4). Frequency of consumption of
EBOV host bushmeat declined from 1-3 times per week to rarely but consumption of EBOV
species did not change significantly.
42
Figure 3-4: Bushmeat Consumption of Ebola Host Species Proceeding and During the West Africa Ebola Epidemic
0
10
20
30
40
50
60
Antelope Porcupine Fruit Bats Patas Vevert Babbon
# r
esponse
s
Ebola Host Species
Ebola Hosts Species Consumption Trends
1995
2012
2015
43
Figure 3-5: Frequency of Bushmeat Consumption Trends Before and During the West African Ebola Outbreak
0
2
4
6
8
10
12
14
16
18
1-3x/week 1-3x/month Every few months Every six months or less
# r
esponden
ts
Frequency of Bushmeat Consumption
Frequency of Bushmeat Consumption Trends Before and During EBOV outbreak
2015
2012
44
3.3.2 Market Availability of Bushmeat
Over a 15-week period during the EBOV outbreak in neighboring countries, my assistant
observed bushmeat for sale during each visit to the weekly market ( Table 3-). Bushmeat was
purchased at least 80% of the time, and 67% of the bushmeat sold was from EBOV hosts; no
EBOV reservoirs were recorded sold or hunted. EBOV host bushmeat was transported and sold
raw 40% of the time. The most common cooking technique was boiling (29%) or grilling (14%).
Prepared bushmeat was frequently sold as a meal in a stew or as meatballs mixed with flour and
peanuts. Most hunters used guns (63%) to hunt bushmeat, whereas fewer used dogs (19%) or traps
(1%). Most of the hunters (47%) were from or hunted in Bassari Country; 19% of these individuals
were from three of my study sites. Available bushmeat was sold in smaller quantities and at
lower prices than beef sold in the Salemata market (Boubane, 2015). Patas monkeys were the
most common and affordable type of bushmeat sold (100CFA per piece (25cents)). Porcupine was
the most expensive due to its ethno-medicinal properties (Boubane, 2015). Typically, one to two
vendors sold one to two bushmeat species per night, indicating low but stable levels of bushmeat
availability for purchase and consumption.
45
Table 3-2: Availability of Ebola Host Bushmeat for Sale at Bushmeat Market
*Grey indicates potential Ebola host
Observation Date Quantity Bushmeat Type Preparation Source/home of hunter Cost
(470CFA=IUSD) Hunting
method
14-May-15 1 patas monkey raw Guinea 1500CFA/chunk gun
14-May-15 1 rabbit cooked unknown unknown unknown
21-May-15 1 patas monkey cooked Senegal 100CFA/piece dogs
28-May-15 1 porcupine raw Senegal 1500CFA/piece unknown
3-Jun-15 1 antelope (waterbuck) raw Senegal 1000/chunk gun
10-Jun-15 1 warthog cooked Senegal 100CFA/piece gun
17-Jun-15 1 patas monkey cooked Senegal 25CFA/ball dogs
24-Jun-15 1 crocodile cooked Senegal 25CFA/ball gun
1-Jul-15 2 patas monkey cooked Guinea 100CFA/piece gun
8-Jul-15 1 porcupine raw Guinea 1500/chunk gun
15-Jul-15 1 duiker (antelope) cooked Senegal 100CFA/piece gun
6-Aug-15 1 warthog cooked Guinea 100CFA/piece gun
13-Aug-15 1 civet cooked Senegal 100CFA/piece dogs
20-Aug-15 1 duiker (antelope) cooked Guinea 25CFA/ball gun
27-Aug-15 1 warthog cooked Senegal 100CFA/piece trap
30-Jul-15 1 porcupine raw Guinea 500CFA/piece gun
46
3.4 Hypothesis Testing
3.4.1 Education
I tested the hypothesis that individuals with no formal education were more likely to
consume bushmeat, and observed that education was significantly associated with bushmeat
consumption (p=0.02) but in an unexpected direction. If individuals had completed some school,
they were significantly more likely to consume bushmeat. Respondents with some schooling
were 4.3 times more likely to consume bushmeat (Table 3-3). There were no significant
interactions between age and education (p=0.86), education and animism (p=0.78) or education and
occupation (p=0.069) (Table 7-3).
3.4.2 Animism
I tested the hypothesis that demographic variables (e.g., gender and religion) were not
significantly associated with increased bushmeat consumption. Gender was excluded due to
multi-collinearity concerns. Animism did have a significant effect on bushmeat consumption
(p=0.04). Practicing animism was significantly negatively associated with bushmeat consumption,
indicating those who were actively practicing animism had a decreased likelihood of exposure to
EBOV. Respondents who reported actively practicing animism were 0.3 times less likely to
consume bushmeat (Table 3-3).
3.4.3 Risk Perceptions
Crop raiding by primates was reported as the second highest cause of crop loss after
grazing by cows. Overall, perceptions of primates were negative with 70% identifying NHP as
47
pests. Residents cited millet (grain) as the most commonly raided crop, and identified patas
monkeys as the most destructive NHP crop raider. Initially, an exploratory analysis depicted that
if individuals view primates positively or in a neutral manner (protected species or unaffected by
presence), then they were significantly less likely to consume bushmeat (p=0.05). However, when
including an interaction effect, the view of NHP was not significantly associated with bushmeat
consumption due to underlying collinearity (p=0.17) (Table 3-3). I tested my second hypothesis
involving perceptions of wildlife using a binomial logistic regression while running each
perception as an ordinal variable. I tested my hypothesis that perceived risk of wildlife is
associated with a decreased likelihood of consuming bushmeat and found no significant
association with bushmeat consumption for perceptions involving perceived risks of zoonotic
disease (p =0.87) or perceived risk of injury (p=0.19) (Table 3-3). There were no significant
interactions between perceived risk and view of primates (p=0.99) (Table 7-3).
3.4.4 Domestic Meat Consumption
Domestic meat consumption frequency alone had no significant effect on bushmeat
consumption (p=0.32) (Table 3-3). There was also no significant interaction between education and
domestic meat consumption (p=0.55) (Table 7-3).
3.4.5 Occupation Effect:
I found that farmers with additional jobs that involve bush exposure, such as hunting,
harvesting palm wine or collecting medicinal plants, had no significant association with
bushmeat consumption (p=0.43) (Table 3-3). Occupations located in EBOV hosts’ habitat did not
influence EBOV risk through increased bushmeat consumption. There was also no significant
48
interaction between domestic meat consumption and occupation on bushmeat consumption
(p=0.87), indicating occupations with bush exposure did not significantly affect an individual’s
income and thus likelihood to consume domestic meat (Table 7-3).
3.4.6 Location and Domestic Meat Consumption Interaction Effect
I observed a significant effect of education (p=0.02), animism (p=0.04), and a significant
interaction effect between location differences and domestic meat consumption (p=0.01) on
bushmeat consumption (Table 3-3). Strong interaction between location and domestic meat
consumption demonstrates access to a bushmeat market and socioeconomic status varies based
on location. To determine a trend, I graphed my raw data and I applied a logistic regression
model to control variables (Figure 3-6). When testing for significant interactions, I found a
significant interaction effect (p=0.01) between availability of domestic meat and proximity to the
bushmeat market on bushmeat consumption (Table 3-4). The likelihood of bushmeat consumption
declined when individuals lived far from the market (>10km) and consumed domestic meat
frequently (> 1x/mo). Respondents who consumed domestic meat more than one time per month
and lived more than ten kilometers from the bushmeat market were 0.08 times less likely to
consume bushmeat with all other covariates held constant (Table 3-4).
49
Table 3-3: Final Model: Interaction Effect between Domestic Meat and proximity to Bushmeat Market on an Individual’s Likelihood of
Consuming Bushmeat using Logistic Binomial Regression
Coefficients: Estimate (β) P value Odds Ratio
95% Confidence
Interval
(Intercept) -1.84 0.12 0.16 0.02 1.61
Perceived Risk: Fear of Zoonotic Diseases 0.20 0.87 1.22 0.11 13.30
Perceived Risk: Fear of Injury 1.28 0.19 3.60 0.52 24.84
Domestic Meat Availability: >1x/Month 0.76 0.32 2.14 0.47 9.73
View of Primates: Non-Negative -1.39 0.17 0.25 0.03 1.80
Distance to Bushmeat Market:>10 km 0.97 0.26 2.65 0.49 14.27
Education 1.45 0.02* 4.28 1.22 15.01
Gender: Female -1.94 0.10 0.14 0.01 1.41
Animist: Practicing -1.18 0.04* 0.31 0.10 0.97
Occupation: Farmer with Exposure 0.50 0.43 1.64 0.48 5.65
Domestic meat*proximity to market -3.31 0.01* 0.04 0.00 0.52
50
Figure 3-6: Distribution of Domestic Meat Consumption and Proximity to Bushmeat Market on Likelihood of Consuming Bushmeat
Note: There are only two responses on domestic meat consumption, rarely and often. In order to visually understand, plots were vertically jitter end in order to
emphasize the number of respondent.
51
Table 3-4: Binomial logistic Regression Analysis using an Interaction between Domestic Meat and Proximity to Market on Likelihood of
Consuming Bushmeat
Coefficients: Estimate (β) P value Odds Ratio 95% Confidence Interval
(Intercept) -1.84 0.12 0.16 0.02 1.61
Perceived Risk: Fear of Zoonotic Diseases 0.20 0.87 1.22 0.11 13.30
Perceived Risk: Fear of Injury 1.28 0.19 3.60 0.52 24.84
Often domestic meat*<10km to bushmeat market 0.76 0.32 2.14 0.47 9.73
Often domestic meat*>10km to bushmeat market -2.55 0.02* 0.08 0.01 0.68
View of Primates: Non-Negative -1.39 0.17 0.25 0.03 1.80
Distance to Bushmeat Market >10 km 0.97 0.26 2.65 0.49 14.27
Education 1.45 0.02* 4.28 1.22 15.01
Gender: Female -1.94 0.10 0.14 0.01 1.41
Animist: Practicing -1.18 0.04* 0.31 0.10 0.97
Occupation: Farmer with Exposure 0.50 0.63 1.64 0.48 5.65
52
3.4.7 Probability Estimates of Risk Factors
Individual's who lived more than 10 km to the bushmeat market and consumed domestic
meat less than once a month were 3.1% less likely to consume bushmeat than those who
consumed domestic meat more than once per month. For individuals who lived less than 10 km
from the bushmeat market there was no significant association with frequent domestic meat
consumption and bushmeat consumption. If the interaction between domestic meat consumption
and proximity to the bushmeat market was significant the inverse logistic regression model
predicted a 18.6% decrease in bushmeat consumption in individuals who consumed domestic
meat infrequently. Risk for EBOV via bushmeat consumption almost doubled when domestic
meat consumption changed from rarely (<1x/mo) to often (>1x/mo) in both locations (Figure 3-7).
Likelihood of bushmeat consumption was significantly affected by education and practicing
animism among male Bassaris. Factoring in gender, views of primates, education status and
domestic meat consumption variations, I predicted that risk would be highest, regardless of
proximity to the market, among educated, non-animist who consumed domestic meat frequently.
My model calculated the lowest risks to be among uneducated, animist who consume domestic
meat rarely. Educated non-animist living close to the market who consumed domestic meat
frequently were 64% more likely to consume bushmeat than individuals who were uneducated,
animists living far from the market and who consumed domestic meat rarely (Appendix 7.3.6,
Tables 7-4-7-9).
53
Figure 3-7: Inverse Logistic Regression to Estimate Probability of Bushmeat Consumption for Adult Male Bassaris on Individual
Likelihood of Consuming Bushmeat
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
Rarely
(<1x/mo)
Often
(>1x/mo)
Rarely
(<1x/mo)
Often
(>1x/mo)
Rarely
(<1x/mo)
Often
(>1x/mo)
Rarely
(<1x/mo)
Often
(>1x/mo)
No Education, Animist No Education, Non-Animist Educated, Animist Educated, Non-
Animist
Pro
bab
ilit
y E
stim
ates
Domestic Meat Consumption
Probability Estimates for Bushmeat Consumption for Adult Male Bassaris
Close to bushmeat market (<10km) Far from bushmeat market (>10km)
54
4 Discussion
My findings are consistent with research in other African countries showing that
education, socioeconomic status and access increases an individual’s likelihood of consuming
bushmeat and thus EBOV risk (De Merode, Homewood, and Cowlishaw, 2004, Ceppl and
Nlelsen, 2014). Respondents with some schooling had increased odds of consuming bushmeat,
whereas hunters typically lacked formal education. Education status’ impact is most likely due to
its strong association with socioeconomic status, indicating income as a driver for bushmeat
consumption. Actively practicing animism decreased an individual’s likelihood to consume
bushmeat almost by half, demonstrating the influence that religious beliefs can have on bushmeat
practices. I found a significant interaction effect of domestic meat availability and proximity to
the bushmeat market on bushmeat consumption patterns, indicating socioeconomic status and
bushmeat access may influence bushmeat patterns. My model predicted risk of EBOV
transmission through bushmeat consumption to be highest in educated, non-animist males who
consume domestic meat frequently (e.g., >1x/month) and live close (e.g., < 10 km) to the bushmeat
market. Interestingly, risk perceptions were high overall but had no significant effect on
bushmeat consumption patterns. Moreover, significantly increased awareness did not induce
behavior change. Since the EBOV outbreak, Bassaris have decreased the frequency of consuming
EBOV host bushmeat but have not adopted selective bushmeat behavior that excludes EBOV
hosts. Market availability is consistent with self-reported behavior and demonstrates continued
access and consumption of EBOV hosts during the epidemic.
4.1 Education
55
Lack of education has been postulated as a contributing factor towards the observed
difference in number of EBOV human propagated cases in different socioeconomic levels (Fallah
et al., 2015). I hypothesized education level may also influence exposure to EBOV. I found a
significant increase in bushmeat consumption with completion of at least some elementary
school. According to my probability estimates, EBOV risk via bushmeat consumption more than
doubled among male Bassaris with some formal education, regardless of religious beliefs (Table
7-5- Table 7-7). In Kedougou, 86.9% of the adult population were uneducated (32.3% were men,
54.6% were women) (Knoema, 2016). Among Bassaris sampled with a median age of 48 years, I
found 70% were uneducated (no formal schooling). My research findings corroborate research in
Cameroon, which found individuals with higher levels of education were more likely to consume
and butcher bushmeat (LeBreton, 2006). Education is strongly associated with socioeconomic
status, indicating educated individuals may have more means to purchase bushmeat or have more
acreage of fields to protect from crop raiders. Middle to high income households in rural settings
consume and hunt bushmeat at higher rates than poorer households (De Merode, Homewood, and
Cowlishaw, 2004). Interestingly, studies in Nigeria found lower education levels were
significantly associated with becoming a hunter, which was also corroborated by my findings
(Friant et al., 2015). Among the Bassari hunters surveyed none had more than an elementary
education and 75% had never attended school. These findings indicate lower socioeconomic
classes may be more likely to become hunters but higher socioeconomic classes may be more
likely to consume bushmeat.
4.2 Animism
56
Animist places of worship are associated with nature such as trees, forest or caves or
sometimes with man-made structures for ceremonies (ICOMOS, 2011). Actively practicing
animism, engaging in prayer, and providing offerings to the spirits of nature were negatively
associated with bushmeat consumption, suggesting that practicing animism is associated with
decreased EBOV risk. The probability of bushmeat consumption almost doubled when
comparing adult male animist to non-animist (Table 7-4, Table 7-5). When asked about their
bushmeat beliefs regarding NHP, religious reasons were most often reported. In a previous study
in the area, practicing animism was significantly associated with bushmeat behavioral change
(purchasing, consuming or hunting bushmeat) and was affected by proximity to the bushmeat
market, indicating geographic cultural differences may play a pivotal role in bushmeat practices
(Kroos, 2016). Reasons for the protective nature of practicing animism may be attributed to
religious teachings regarding animal spirits or attributed to religious leaders promoting
preventive behavior by encouraging selective bushmeat consumption.
4.3 Risk Perceptions
Hunters reported typically washing carcasses with water and avoiding contact with blood.
They did not use any protective equipment in the hunting or butchering process. Most hunters
identified the killing and butchering as the riskiest process of hunting but only rated the overall
risk of injury or pathogen transmission as low. My findings support previous research in
Cameroon that reported 74% rural villagers considered touching animals’ bodily fluids to be
dangerous but only 4% of hunters took any precautionary measures. Individuals with no
perception of the risks associated with the handling of bushmeat were 27% more likely to
57
participate in the butchering process (LeBreton et al., 2006). Among Ghanaians studied, 49% of
bushmeat consumers were aware of the risks associated with bushmeat consumption but were
not dissuaded from continued consumption (Kuukyi et al., 2014). Among Bassaris surveyed, 79%
believed NHP bushmeat should be avoided due to fear of disease of EBOV. This difference is
most likely attributable to my study coinciding with a large ongoing EBOV epidemic in which
levels of public awareness were high. This suggests that the risk perceptions associated with NHP
bushmeat consumption was most likely in response to EBOV health campaigns in the area that
actively discouraged bushmeat consumption. For example, each of my study sites received at
least one health visit by a health worker within the last four months and had access to consistent
radio broadcasts concerning the ongoing EBOV epidemic. Despite active health campaigns in
Bassari Country promoting preventive zoonotic behavioral changes (avoid contact with wildlife
and cease bushmeat consumption) and high rates of perceived risks, there was no significant self-
reported preventive behavioral change documented (Kroos, 2016). Perceived risks and behavior
change do not seem to be strongly associated.
4.4 Domestic Meat Consumption
My study assessed domestic meat availability as an indicator of socioeconomic status and
potential driver for EBOV risk. Socioeconomic heterogeneity among villagers was considered as
a determinant of EBOV risk. Transmission models found poor regions were associated with three
times more EBOV transmission with wider disease propagation between communities (Fallah et
al, 2015). Rural areas have higher rates of consumption of bushmeat and in greater variety.
Despite the strong link between socioeconomic status and bushmeat consumption, I found no
58
significant association between availability of domestic meat and bushmeat consumption. The
majority of respondents (61%) reported consuming domestic meat rarely (every few months to
every six months or more), of which chicken and goat where the most common (Appendix 7.3.4,
Figure 7-4). Only 9% of respondents reported consuming domestic meat at least every two weeks.
Low consumption rates of domestic meat and relatively uniform poverty incidence may
contribute to the lack of domestic meat’s direct effect on bushmeat consumption. Subsistence
farming was the primary source of income (74% identified as farmers), most likely resulting in
fairly equal income levels across individuals in the seven sites. Due to low representation of
higher socioeconomic classes, socioeconomic impacts may not have been captured. This trend
corroborates previous research in Nigeria that found no effect of wealth on hunting with low
variation in economic status (Friant et al., 2015). Supplemental income from alternative
livelihoods is speculated to reduce hunting frequency (Friant et al., 2015). Increased livestock
production as supplemental domesticated meat sources and dairy products may affect bushmeat
trade through cost-benefit considerations due to reduced prices of domestic meat and increased
availability. A study in Tanzania inferred that a donation of four cows could reduce hunting by
20% (Nielsen et al. 2014); their model predicted that if domestic meat prices were reduced by 33%,
then the bushmeat trade would be reduced by 30%. Due to the lack of variation in socioeconomic
levels, I would recommend further analysis before ruling out domestic meat consumption or
socioeconomic status as a potential driver of EBOV risk.
4.5 Occupation
59
According to the WHO, healthcare workers were 21-32 times more likely to become
infected with EBOV during the West African outbreak (World Health Organization, 2015); little,
however, is known concerning occupational risks associated with exposure to EBOV reservoirs,
hosts or contaminated environments. Respondents with occupations that involved exposure to
wildlife habitat and increased access to wildlife, such as hunters and palm wine collectors, were
not significant associated with increased bushmeat consumption. All respondents with potential
increased EBOV exposure occupations also had secondary jobs, mostly as farmers. The lack of
clear demarcation between occupations may have influenced the impact of this predictor
variable. Typically, EBOV high risk occupations increased exposure in short time intervals (6-48
hours/week) and were rarely reported as their primary occupation. The amount of time spent
performing these higher risk jobs may be a better indicator to test this relationship in the future.
Interestingly, exposure to environmental contaminates, such as bush fruit contaminated with fruit
bat salvia, may have a protective quality. In a study in Gabon, ZEBOV-specific IgG
seroprevalence was significantly higher in forested areas (19.4%), indicating higher hemoral and
cellular immunity among people living closer to EBOV reservoirs or hosts’ habitat (Becquart et
al., 2015). These findings raise questions regarding an occupation’s role in increasing risk or
increasing natural protective immunization.
4.6 Interaction Between Location and Domestic Meat Consumption
Despite domestic meat having no direct effect on bushmeat consumption there was a
strong interactive effect between the proximity to the bushmeat market and frequent domestic
meat consumption on bushmeat consumption. Namely, as distance from the bushmeat market
60
increased (>10 km) with more frequent domestic meat availability (> 1x/mo), respondents were
significantly less likely to consume bushmeat. This interaction demonstrates that bushmeat
consumption is influenced by access to purchasable bushmeat and domestic meat consumption
frequency. The difference in proximity to the bushmeat market may be attributed to the
availability of bushmeat and wildlife density among villages peripheral to the bushmeat market.
Frequency of bushmeat consumption has been shown to decrease as distance to protected areas
decreases, indicating that hunting is lower in areas adjacent to national parks that have higher
enforcement activity (Ceppl and Nlelsen, 2014). The villages clustered near the bushmeat market
were farthest from the national park (>10 km), potentially contributing to bushmeat availability.
Consuming domestic meat frequently indicates higher socioeconomic status. Increased capital
enables participants, particularly living near the bushmeat market, to purchase and consume
bushmeat more frequently. Undersupply and underproduction of domestic animal meat will
likely perpetuate bushmeat practices despite health and conservation concerns (LeBreton et al.,
2006).
4.7 Bushmeat Practices
In West Africa, bushmeat is traditional and often associated with proper nutrition. Despite
low self-reported hunting rates, my observational bushmeat market study demonstrated recurring
hunting in the area. Of the few hunters identified in my survey, most traveled between 2-4 km
from their home and targeted wild birds, rabbits, rats, antelope, warthog, baboons and patas
monkeys (Figure 7-2). Self-reported hunters tended to sell their kills to their neighbors or share it
61
among their family. Only one hunter admitted that hunting significantly impacted his income. A
prior study found hunting contributed to an average cash income of about 250,000CFA (500
USD) per household (Ba et al., 2006). Hunting frequency ranged from twice per week to every
few months. Most participants reported consuming bushmeat every six months or rarely during
ceremonies, but these estimates may be conservative based on my market findings. According to
my survey responses, Bassaris reported consuming bushmeat once to three times a month prior
to the EBOV epidemic. EBOV selective bushmeat consumption was not the primary driver in
species selection as seen by large declines in antelope consumption since 1995. All of the survey
respondents preferred domestic meat but cited affordability and access as the primary reasons for
bushmeat consumption. The lower price of bushmeat may be attributed to its illegal nature
because hunters need to quickly turn over their stocks, which may impact their selling price and
hinder their negotiations. Anecdotal evidence reported by villagers testified to recent violent
interactions between authorities concerning the illegal nature of the bushmeat market. Numerous
villagers reported being beaten, imprisoned or having their belongings confiscated or camp
burned when they were caught inside the park, hunting or attending the bushmeat market.
Enforcement and fear of fines or prison contributes to underreporting and guarded bushmeat
behavior.
4.8 Human-Wildlife Conflict and Bushmeat
More than half of the Senegalese population relies on agriculture as their main source of
income (Dieng and Ndiaye, 2012). Agriculture contributes to 10% of gross domestic production
and accounts for about 10% of the public investment program (Dieng and Ndiaye, 2012). This
62
finding supports that crop losses to wildlife, particularly NHP, can be a strong motivation for
active field protection and a possible reason for bushmeat hunting. Some hunters reported killing
NHP while protecting their fields. Of the survey respondents who identified as hunters, food was
cited as the primary driver with crop protection as the second most common purpose for hunting.
A study in Uganda found no significant increase in bushmeat consumption in households that
had experienced major crop losses (Olupot & Plumptre, 2009). These findings further suggest that
crop protection is not the sole driver for hunting practices.
Initially, I had considered actively surveying ones’ field to prevent crop raiding as a
potential risk factor for EBOV. Based on the data distribution and the lack of any reports of
injury associated with field protection, however, field surveillance was considered redundant and
excluded as a risk factor. Although surveillance for crop raiding may not be a direct risk factor
for EBOV, 65% of respondents reported NHP crop raiding, of which all used active surveillance
to deter NHP (Figure 3-1). Active surveillance involves beating on buckets, patrolling the field or
using guns or dogs to kill or frighten NHP away from crops. Respondents reported sending both
children and adults of varying ages with no gender preferences to protect the fields, particularly
during harvest season. Most villagers (73%) identified the end of the rainy season (September -
October) as the most vulnerable time of year for crop raiding. The high rates of reported crop
raiding and participation of all members of community in active surveillance demonstrates the
high level of human–wildlife conflict.
Land conversion increases crop raiding behaviors and human-wildlife conflicts. Forest
cover has decreased by 45,000 hectares since 1990 throughout Senegal (FAO, 2006). Road
building, gold mining, expanding settlements and land conversion for agriculture creates
63
corridors, edge effects and access to wildlife habitat that increases human-wildlife contact (Dieng
and Ndiaye, 2012). The loss of buffer zones adjacent to the forest also forces Bassaris to travel
farther into the forest to collect traditional medicinal plants, edible leaves, bush-fruit and palm
wine. Crop raiding behavior by NHP may escalate negative attitudes towards NHP and may
contribute to increased hunting or injuries related to active field protection.
4.9 Limitations
Although the variables in the hypothesized models are structurally related, this study used
binomial regression and focused on the partial correlation between the bushmeat consumption and
other variables. My regression models were limited by endogeneity, which assumes bushmeat
consumption (dependent variable) was influenced by the independent variables in a causal manner
and does not control for simultaneous effects on the dependent variables by independent variables.
Some variables were excluded or may have been skewed due to multi-collinearity. Uncontrolled
confounding factors may have existed and affected my results. My study assumed all important
variables were investigated and omitted variables could not affect the estimation of the
coefficients. My study investigated available factors and identified potential genuine factors that
impacted bushmeat consumption for future studies. My study lacks a comparison to a baseline
survey on bushmeat consumption prior to EBOV. Small sample size may limit the generalizability
to other EBOV infected peripheral zones. Sustained bushmeat consumption patterns are unknown.
Despite these limitations, my sample size per village covered more than 20% of subjects,
confounding was minimized with randomization and exclusion of variables, questionnaires were
64
culturally appropriate, trust was well established and validity was assured with a simultaneous
observational market study.
5 Conclusions
To understand and prepare for the emergence of zoonotic disease, such as EBOV, one
must understand population biology of wildlife reservoirs, human demography, economic trends,
land conversion dynamics, drivers of human-wildlife conflict and related risk factors. My findings
reveal that underlying socioeconomic and demographic variables as well as geographic access are
associated with bushmeat consumption and may contribute to EBOV transmission risks. These
findings provide insight into the design of future interventions to promote preventive behavioral
change in an effort to minimize pathogen transmission and prevent future outbreaks. For example,
based on my findings, health campaigns in Bassari Country should prioritize targeting adult,
educated non-animist males from higher socioeconomic classes. Inventions should begin with
targeting those living closest to the bushmeat market and with capital to purchase bushmeat. Since
religious beliefs strongly influence bushmeat consumption, religious leaders must be encouraged to
engage in participatory teaching and be involved in disseminating information in their communities.
Educational campaigns need to emphasize which bushmeat species to avoid and stop advising to
cease all bushmeat consumption. Legal hunting of non-EBOV hosts should be encouraged to
preserve cultural practices. Vouchers for the exemption of required hunting permits should be
instituted to enable low yield harvest by Bassari hunters of EBOV free, abundant species, such as
warthog and birds. Since cost and access are the main drivers of bushmeat consumption, donating
65
livestock, improving husbandry techniques or providing improved varieties of goats and fowl for
larger returns can help boost yields and drive down costs of domestic meat alternatives.
In general, alternatives to EBOV host bushmeat should focus on enhancing risk
awareness, providing sustainable alternatives, reducing human-wildlife conflict and improving
animal surveillance. Educational material must highlight the risks of zoonotic transmission and
techniques to mitigate exposure. Health campaigns need to dispel misconceptions concerning routes
of transmission and promote accessible strategies to reduce contact with EBOV hosts, particularly
for hunters and butchers. Avoidance strategies should emphasize preventive protective equipment,
avoiding butchering or handling meat if they have injuries on their hands or arms and washing bites
and scratches immediately. When developing health education modules, it is imperative to field-test
them in different communities to ensure content and communication are understood, that education
efforts respect local culture and traditions, that do not rely on levels of literacy or higher education
that are beyond the level of target population, and are feasible with available resources. Adoption of
live fencing and application of food security initiatives, such as improved crop seeds, can reduce
crop-raiding losses. Limiting firearm availability and promoting decentralized management of forest
resources can further discourage human-wildlife conflict. Lastly, wildlife mortality surveillance could
function as an early warning system that helps trigger rapid implementation of prevention techniques
(WHO, 2016). Determining the factors that influence human-wildlife conflict, particularly drivers of
bushmeat supply and demand, is necessary to mitigate and manage emerging pathogens. Local
authorities and leaders must be engaged in finding sustainable solutions to food security and
mobilizing and educating communities concerning EBOV risks.
66
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7 Appendixes
7.1 Methods for Randomized Household Selection
Adapted WHO Epidemiological Survey Methods: Method 1: Randomly select households to be
visited when household lists are unavailable (WHO, 2008)
1. Select a central location in the village or town, such as a large ceremonial tree with
sacrificing stone or chief’s house. The location should be near the approximate
geographical center of the village or area.
2. Randomly select the direction from the center; for example, spin in a circle with eyes closed.
3. Walk in the selected direction until you reach the first house.
4. Flip a coin to determine if you will conduct a survey at this household.
5. The second household you should visit will be the one which is nearest to the first.
The next nearest household is the one whose front door is closest to the front door of
the household you have just visited. Work outwardly in a spiral formation.
7.2 Definition of Variables
7.2.1 Outcome Variable
The bushmeat score was run as a binary variable (non-consumer or consumer) based on self-
reports of consumption EBOV host species within the last six months. Frequency of consumption
was collected and discussed but not weighted in the risk score.
72
7.2.2 Predicator Variables
Occupation was grouped based on EBOV risk: farmer only versus farmer with EBOV exposure.
Recipients reported two EBOV high-risk occupations: farmers with bush exposure or farmers
with health exposure. Bush exposure was defined as palm wine collectors, hunters or gathers.
Health exposure was defined as work as a CHW, as a midwife or working on the health hut or
health post level.
Distance to bushmeat was divided by 10km because villages were clustered south and north of
Salemata. None of the villages were accessible via public transport and roads were seasonal and
often only passable by motorcycle or bicycle. Ten kilometers, as the crow flies, under these
conditions is significant distance to distinguish between the areas. The clustering of villages was
also defined by cultural practices. Each village cluster attended ceremonies in different religious
centers, furthering their distinctions.
All respondents identified as either Catholic or Protestant. They did not seem to equate animism
with a religion. In order to determine if they were actively practicing animism I asked if they had
a spiritual alter in their home or yard.
7.3 Expansion of Results
7.3.1 Testing Assumptions
73
The spearman correlation test examined independent variables against each other in order
to minimize multi-collinearity. Age and education had the highest correlation at 45% (p=<0.001) so
age was excluded from the final model. View of primates and proximity to the bushmeat were
correlated at 39% but both variables were kept in the final model due to low correlation. Low
correlation between a few variables limits my study; interaction between correlation variables
cannot be ruled out. Typically, variables with more than 70% correlation must be excluded to
avoid collinearity. The Chi test variables corroborate the correlation test findings.
74
Table 7-1: Spearman Correlation Table: Examining Covariates for Multi-collinearity
Proximity to
Market Age Animist Occupation
Domestic
Meat
View of
Primates
Perceived
Risk Education
rs p rs p rs p rs p rs p rs p rs p rs p
Proximity to
market - - - - - - - - - - - - - - - -
Age 0.14 0.17 - - - - - - - - - - - - - -
Animist -0.06 0.57 0.03 0.79 - - - - - - - - - - - -
Occupation -0.12 0.24 -0.05 0.61 -0.15 0.16 - - - - - - - - - - Domestic
Meat
Consumption 0.01 0.94 -0.02 0.82 -0.08 0.42 -0.08 0.41 - - - - - - - - View of
Primates -0.39 <.001 -0.08 0.43 -0.12 0.24 0.12 0.25 -0.25 0.01 - - - - - - Perceived
Risk -0.20 0.05 -0.01 0.95 0.01 0.93 0.10 0.32 0.04 0.72 0.03 0.80 - - - -
Education 0.03 0.79 -0.45 <.001 -0.11 0.32 0.08 0.46 0.15 0.14 0.06 0.59 -0.03 0.80 - -
Gender 0.15 0.15 -0.12 0.23 0.07 0.52 -0.28 0.01 -0.02 0.83 0.01 0.90 -0.28 0.01 -0.15 0.14
75
Table 7-2: Pearson Chi Test for independence on Predictor Variables
Proximity
to market Age Animist Occupation
Domestic
Meat
availability
View of
Primates
Perceived
Risk Education Gender
Proximity to market - - - - - - - - -
Age 4.76 - - - - - - - -
Animist 0.34 0.521 - - - - - - -
Occupation 1.5 7.78 2 - - - - - - Domestic Meat
Consumption 0.006 1.89 0.67 1.59 - - - - -
View of Primates 15.3 0.71 1.38 1.46 6.05 - - - -
Perceived Risk 8.38 0.92 0.20 7.16 0.34 0.09 - - -
Education 0.639 34.99 1.085 13.76 2.32 0.506 2.62 - -
Gender 0.213 3.19 0.43 7.89 0.05 0.02 7.55 2.47
76
7.3.2 Trends in Bushmeat Consumption over Time
Self-reports demonstrated bushmeat consumption has significantly declined in species
diversity and quantity since 1995. Bushmeat consumption patterns in 2016 (during the EBOV
epidemic) were slightly higher than 2012 (prior to the EBOV epidemic) of non-EBOV species,
which followed the same trend of consumption of EBOV host species. The 2012 and 2015 trends
appear comparable; the small increase in bushmeat consumption in 2015 was most likely due to
recall bias.
77
Figure 7-1: Bushmeat Consumption Trends for All Bushmeat over Time in Bassari Country
0
10
20
30
40
50
60
# o
f re
sponse
s
Species
Bushmeat Consumption Trends over Time
1995
2012
2015
78
7.3.3 Trends in Bushmeat Hunting over Time
Hunting trends corroborated consumption patterns and also illustrate a decline in
diversity and quantity of bushmeat species. In 2016, porcupine, squirrel and grivet monkey were
reportedly never hunted. These reports conflicted with my bushmeat market study that recorded
the presence of porcupine for sale. Reports of 2016 hunting trends were most likely conservative
and based on a small sample size due to the illegal nature of hunting. Respondents were more
likely to divulge hunting patterns from 1995.
79
Figure 7-2: Hunting Trends for All Bushmeat over Time in Bassari Country
0
5
10
15
20
25
30
35
# r
esponden
ts
Bushmeat Species
Hunting Trends over Time
1995
2015
80
Figure 7-3: Hunting Trends of Ebola Hosts Species in Bassari Country over Time
0
5
10
15
20
25
30
35
Antelope Porcupine Patas Vevert Babbon
# r
esponden
ts
EBOV Hosts Species
Hunting Trends over Time for Ebola Hosts
1995
2015
81
7.3.4 Domestic Meat Consumption Patterns
Domestic meat frequency and species type were examined as an indicator of
socioeconomic status, which acts as a risk factor for bushmeat consumption. Chicken was the
most frequently consumed domestic meat source, which was consumed less than once per month
by the majority of respondents. Live chicken cost between 2000-3000CFA (4-6USD) in
comparison to beef that sold for 1750CFA/kg (3USD/kg). Domestic meat consumption was
associated with ceremonies and celebrations. Domestic meat was preferred over bushmeat but
bushmeat was attractive because of its accessibility and affordability.
82
Figure 7-4: Domestic Meat Consumption Trends in 2015
0
10
20
30
40
50
60
70
80
90
Goat Chicken Beef Sheep Guinea Fowl
# r
esponden
ts
Type of Domestic Meat
Domestic Meat Consumption
83
7.3.5 Interactions Models for Predicators of Bushmeat Consumption
Multiple binomial logistic regression models were run to test for predicators of bushmeat
consumption. Age despite correlation with education was included as an ordinal variable in one
model to ensure no significant effects were missed. Based on demographic analysis, age and
education were associated; younger respondents were more likely to have attended school. I
tested an interaction effect between age and education and found no significant effect (p= 0.86). I
tested my hypothesis that view of NHP may influence perceived risk impact and result in an
intermediate outcome and together may influence bushmeat consumption. There was no
significant impact of perceived risk and view of NHP interaction effect on bushmeat
consumption (p=0.99). Education and domestic meat consumption both serve as proxies for
socioeconomic status. To understand socioeconomic status impact on bushmeat consumption I
ran a domestic meat consumption and education interaction effect and found no significant
associations (p=0.55). Insignificant variables and interactions were excluded in the final model.
84
Table 7-3: Binomial Logistic Regression Models for Predicators of Bushmeat Consumption: Examining Different Interaction Effects
Model
1 2 3 4
Coefficients: Estimate (β) Estimate (β) Estimate (β) Estimate (β) (Intercept) -0.74 -0.98 -0.8 -0.88
Perceived Risk: Fear of Zoonotic Diseases 0.08 0.1 -0.01 0.14
Perceived Risk: Fear of Injury 0.99 1 0.82 0.98
Domestic Meat Availability: >1x/Month -0.46 -0.47 -0.44 -0.72
View of Primates: Non-Negative -2.02* -1.94 -26.77 -1.93* Distance to Bushmeat Market:>10 km -0.61 -0.45 -0.46 -0.49
Education: elementary 1.03 1.16* 1.1 0.83
Education: >elementary -1.35 -1.52 -1.54 -1.57
Sex: Female -0.95 -0.98 -1 -1.01
Animist: Practicing 0.63 .60* 0.58 0.7
Occupation: Farmer with Exposure -0.38
Age (18-35 years) 0.3
Age (36-60) 0.22
Interaction: Age* Education
Interaction: Non-Negative View of Primate*Perceived Risk 12.51
Interaction: Domestic Meat Consumption *Education 0.7
85
7.3.6 Bushmeat Consumption Probability Estimates
Using an inverse logistic regression for categorical dependent variables I calculated an
estimated probability for bushmeat consumption by proximity to the market for frequent
domestic meat consumption (>1x/mo) and infrequent domestic meat consumption (<1x/mo). I
examined risk probability in male Bassaris and varied significant variables identified from earlier
analysis. I examined how different combinations of potential risk factors affected an individual’s
likelihood to consume bushmeat. I examined education, practicing animism and views of
primates (positive or negative) affect in relation to proximity to the bushmeat market and
consumption of domestic meat. I found educated non- animist had the highest probability of
consuming bushmeat, particularly among those who live close to the bushmeat market and ate
domestic meat frequently. The least likely were male, non-animist with negative view of primates
with no formal education. Views of primates were considered because they were significant in an
earlier model that excluded an interaction effect. According to my probability estimate views of
primates had minimal effect on the probability of consuming bushmeat so it was excluded in the
final model.
86
Table 7-4: Probability Estimates for Male, Animist with No Education on an Individual’s Likelihood of Consuming Bushmeat
Close to bushmeat
market (<10km) Far from bushmeat
market (>10km)
Domestic Meat Consumption
Rarely (<1x/mo) 5.6% 0.2%
Often (>1x/mo) 11.3% 0.5%
Difference in Bushmeat Consumption 5.7% 0.3% 1
Table 7-5: Probability Estimate for Male, Non-Animist with No Education on an Individual’s Likelihood of Consuming Bushmeat
Close to bushmeat
market (<10km) Far from bushmeat
market (>10km)
Domestic Meat Consumption
Rarely (<1x/mo) 16.0% 0.7%
Often (>1x/mo) 29.0% 1.5%
Difference in Bushmeat Consumption 13.0% 0.8%
1 function(x) {1/(1+exp(-x))} applied to below formula for no education and non-animist: y=1.84+1*.20+0*1.28-0*1.39+0*.97+0*1.45-0*1.94-0*1.18+0*.50+0*.76-0*3.31
y=1.84+1*.20+0*1.28-0*1.39+0*.97+0*1.45-0*1.94-0*1.18+0*.50+0*.76-1*3.31
y=1.84+1*.20+0*1.28-0*1.39+0*.97+0*1.45-0*1.94-0*1.18+0*.50+1*.76-0*3.31
y=1.84+1*.20+0*1.28-0*1.39+0*.97+0*1.45-0*1.94-0*1.18+0*.50+0*.76-1*3.31
Formula adjusted for different scenarios including education (1*1.45) or animist (1*1.18)
87
Table 7-6: Probability Estimates for Male, Animist with Education on an Individual’s Likelihood of Consuming Bushmeat
Close to bushmeat
market (<10km) Far from bushmeat
market (>10km)
Domestic Meat Consumption
Rarely (<1x/mo) 13.7% 0.6%
Often (>1x/mo) 25.4% 1.2%
Difference in Bushmeat Consumption 11.7% 0.6%
Table 7-7: Probability Estimates for Male, Non-Animist with Education on an Individual’s Likelihood of Consuming Bushmeat
Close to bushmeat
market (<10km) Far from bushmeat
market (>10km)
Domestic Meat Consumption
Rarely (<1x/mo) 45.3% 2.9%
Often (>1x/mo) 63.9% 6.0%
Difference in Bushmeat Consumption 18.6% 3.1%
88
Table 7-8: Probability Estimates for Male, Non-Animist with No Education with Negative Views of Primates on an Individual’s Likelihood
of Consuming Bushmeat
Close to bushmeat
market (<10km)
Fa
r from bushmeat
market (>10km)
Domestic Meat Consumption
Rarely (<1x/mo) 4.6% 0.2%
Often (>1x/mo) 9.4% 0.4%
Difference in Bushmeat Consumption 9.2% 0.2%
Table 7-9: Probability Estimates for Male, Non-Animist with Education with Negative Views of Primates on an Individual ‘s Likelihood of
Consuming Bushmeat
Close to bushmeat
market (<10km) Far from bushmeat
market (>10km)
Domestic Meat Consumption
Rarely (<1x/mo) 17.0% 0.7%
Often (>1x/mo) 30.6% 1.5%
Difference in Bushmeat Consumption 29.9% 0.8%
89
7.4 Survey (English)
Pre-survey introductions
My research strives to evaluate the risks associated with Ebola to help prevent a future Ebola
outbreak. The surveys are optional. The surveys will be anonymous. Your village name and your
name will not be recorded. There will be no legal ramifications for sharing information
concerning hunting practices or interactions with wildlife. I am trying to understand how to
protect Bassari culture and wildlife while preventing Ebola in Senegal. I hope you cooperate and
answer my questions honestly. At any point, you can end the survey or skip questions. Do you
agree to participant in my survey?
Number:………………… Date:
……………………………
………..
Village Name:
……………………………
…………
Start time:…………….. End Time:…………………. Sex
F M
Name of Interviewer:
……………………………………………………………
……………………
1. Age
a. 18-25
b. 26-35
c. 36-45
d. 46-55
e. >60
2. Ethnicity
a. Bassari
b. Pular
c. Bedik
d. Other
3. Religion
a. Animalist
b. Muslim
c. Other: specify
3i. Do you have any animalistic offerings or sacrifices in your home?
Yes No
90
4. Education level
a. Less than primary school
b. Some elementary school
c. Some middle
d. Some high school
e. some university
5. How long have you lived in this village?
a. All my life
b. >20 years
c. 10-20year
d. 10-5 years
e. <5 years
6. What do you do for work?
a. Farmer
b. Vendor
c. Teacher
d. Health worker
e. Pastoralist
f. Carpenter
g. Monitor for school
h. Other: 7. How far is your field from your home?
a. <1km
b. 2-4km
c. 5-10km
d. >10km
8. What types of crops do you cultivate?
a. Corn
b. Peanut
c. Millet
d. Fonio
e. Beans
f. Rice
g. Cotton
h. Ground nuts
9. Do you sell your harvest or use it solely for household consumption?
a. Selling
b. Consumption
c. Both
10. Does anything limit your crop yield?
a. Insects
b. disease
c. rainfall
d. primates
e. cows
f. sheep/goats
g. wild birds
91
h. chickens
i. rabbits
j. rats
i. If multiple: Rank their importance
ii. If primates are listed: What type of primates cause crop damage?
a. Chimpanzees
b. Baboons
c. Grivet monkeys
d. Red Colobus Monkey
e. Other: describe: v If multiple primates rank their importance
11. What crops are affected by crop invasive?
a. Corn
b. Peanut
c. Rice
d. Millet
e. Fonio
f. Nothing
g. Ground nuts
i. If multiple rank by importance
12. What time of year are crops most vulnerable?
a. End of rainy season (Sept –Oct) b. Beginning of rainy season (June-July) c. Middle of rainy season to end of rainy season (Aug-Oct) d. All the time
e. All of rainy season (June-Oct) 13. What method do you use to protect your crops?
a. Surveillance
i. Age
ii. Gender
iii. Quantity
b. dog
c. monkey blood
d. people blood
e. clothes in trees
f. monkey skin
g. nothing
h. other
14. How do you view primates in the area? a. Sacred
b. Protected
c. source of meat
d. pest
e. vector of disease
f. I don’t know
g. Distant /far from here
92
15. Do you think your home’s proximity to the National Park, Niokolo Kobe/Baja, affect the
presence of wildlife?
Yes No I don’t know
16. Do the parks restrictions on hunting affect your practices?
a. Illegal so restricts hunting
b. Protected status
c. No bushmeat available outside park
d. Because of Ebola
e. Not safe
f. Customs concerning bushmeat have changed
g. No impact
h. other
17. Do you own a gun?
Yes No
18. Do you hunt any type of bushmeat? ( if NO skip to Question 19) Yes No
i. If yes, what type?
ii. Do you hunt any type of primate?
iii. What is the propose of hunting these species?
a. Food
b. protect crops
c. health concerns
d. Other: describe:
iv. What type of hunting technique is used?
a. Snare
b. Trap
c. Bow/arrow
d. Gun
e. Dog
f. Poison
g. Other: describe:
v. How far do you travel from your village to hunt bushmeat?
a. <1km
b. 2-4km
c. 5-10km
d. >20km
vi. Do you take any precautions during hunting or butchering? If yes, describe
a. avoid touching blood
b. washing hands
c. draining blood from carcass
d. suitable clothing
e. nothing
93
vii. What process is the riskiest?
a. Butchering
b. Hunting
c. Killing
d. Other
viii. How risky do you perceive touching primate blood to be?
a. very risky
b. risky
c. somewhat risky
d. not risky
e. I don’t know
ix. Do you sell the primate bushmeat?
a. If so, where
b. To whom?
c. Does it affect your income?
19. 20 years ago, did you hunt any type of bushmeat? ( if NO skip to Question 20) Yes No
i. If yes, what type?
ii. Do you hunt any type of primate?
iii. What is the propose of hunting these species?
a. Food
b. protect crops
c. health concerns
d. Other: describe:
iv.What type of hunting technique is used?
a. Snare
b. Trap
c. Bow/arrow
d. Gun
e. Dog
f. Poison
g. Other: describe:
v.How far do you travel from your village to hunt bushmeat?
a. <1km
b. 2-4km
c. 5-10km
d. >20km
vi. Do you take any precautions during hunting or butchering? If yes, describe
94
a. avoid touching blood
b. washing hands
c. draining blood from carcass
d. suitable clothing
e. nothing
f. wash with water
vii. What process is the riskiest?
a. Butchering
b. Hunting
c. Killing
d. No risk
e. other
viii.How risky do you perceive touching primate blood to be?
a. very risky
b. risky
c. somewhat risky
d. not risky
e. I don’t know
ix.Do you sell the primate bushmeat?
i. If so, where
ii. To whom?
iii. Does it affect your income?
20.Do you consume bushmeat? ( If NO skip to question 21)
Yes No
i. If yes, which species do you eat?
a. Warthog
b. Duiker
c. Rabbit
d. Monitor lizard
e. Porcupine
f. Cat Rat
g. Wild bird (quall)
ii. Do you eat any type of primate? What type?
a. Chimpanzees
b. Baboons
c. Grivet monkeys
d. Red Colobus Monkey
e. Other: describe:
95
iii. How regularly do you consume bushmeat?
a. Everyday
b. Once a week
c. Every month
d. Every few months (once in a while) e. Every six months
f. Every year
g. Rarely (less than once a year) iv. Primate meat?
a. Everyday
b. Once a week
c. Every month
d. Every few months (once in a while) e. Every six months
f. Every year
g. Rarely (less than once a year) v. Do you eat bushmeat that was found dead?
vi. If yes, how often?
a. Everyday
b. Once a week
c. Every month
d. Every few months (once in a while) e. Every six months
f. Every year
g. Rarely (less than once a year) vii. Do you prefer bushmeat to domestic meat? Domestic or bushmeat
viii. If bushmeat, why?
a. low in fat
b. more nutritious
c. religious reasons
d. cheaper
e. easy access
f. Other: ix. What type of cooking technique is used for bushmeat?
a. Smoking
b. Grilling
c. Boiling
d. Raw
e. Salting/drying
x. Is there any type of bushmeat you do not consume?
a. Chimp
b. Snake
c. Hyena
d. Bat
e. Other
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xi. Why?
a. religious
b. prohibited/illegal
c. Ebola fears
d. Access
e. Chimpanzees resemble humans/looks human
f. Smells bad
g. Customs
h. Venomous (snakes) i. Scavengers (hyena) j. Don’t know
k. Doesn’t taste good (snake) xii. Is there any religious significance to bushmeat consumption?
21. 20 years ago, did you consume bushmeat? ( If NO skip to question 20)
Y N
If yes, which species do you eat?
a. Warthog
b. Duiker
c. Rabbit
d. Monitor lizard
e. Porcupine
f. Cat Rat
g. Wild bird (quail)
i. Do you eat any type of primate? What type?
a. Chimpanzees
b. Baboons
c. Grivet monkeys
d. Red Colobus Monkey
e. Other: describe:
ii. How regularly do you consume bushmeat?
a. Everyday
b. Once a week
c. Every month
d. Every few months (once in a while) e. Every six months
f. Every year
g. Rarely (less than once a year) iii. Primate meat?
a. Everyday
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b. Once a week
c. Every month
d. Every few months (once in a while) e. Every six months
f. Every year
g. Rarely (less than once a year) iv. Do you eat bushmeat that was found dead?
v. If yes, how often?
a. Everyday
b. Once a week
c. Every month
d. Every few months (once in a while) e. Every six months
f. Every year
g. Rarely (less than once a year)
vi. Do you prefer bushmeat to domestic meat? Domestic or bushmeat
vii. If bushmeat, why?
a. low in fat
b. more nutritious
c. religious reasons
d. cheaper
e. easy access
f. Other: viii. What type of cooking technique is used for bushmeat?
a. Smoking
b. Grilling
c. Boiling
d. Raw
e. Salting/drying
ix. Is there any type of bushmeat you do not consume?
a. Chimp
b. Snake
c. Hyena
d. Bat
e. Other
x. Why?
a. religious
b. prohibited/illegal
c. Ebola fears
d. Access
e. Chimpanzees resemble humans/looks human
f. Smells bad
g. Customs
h. Venomous (snakes) i. Scavengers (hyena)
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j. Don’t know
k. Doesn’t taste good (snake) xi. Is there any religious significance to bushmeat consumption?
22. Is there any religious significance to primate consumption?
i. If yes, explain?
23. Are there domestic meat sources available?
24. Type?
a. Chicken
b. Beef
c. Sheep
d. Goat
e. Pig
f. Other: List
25. How regularly do you eat domesticated meat?
a. Everyday
b. Once a week
c. Every two weeks
d. Every month
e. Every few months (once in a while) f. Every six months
g. Every year
h. Rarely (less than once a year) i. Only for ceremonies/special occasion: frequency?
26. Has there been a decline in available bushmeat in the last twenty years? ( If NO skip to
question 26) Y N
i.
a. Market
b. Bush
c. both
ii. If yes, where?
iii. What type of bushmeat?
a. Warthog
b. Duiker
c. Rabbit
d. Monitor lizard
e. Porcupine
f. Hippopotamus
g. Cat Rat
h. Baboon
i. giant eland
j. Columbus monkey
k. Grivet monkey
l. All animals
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iv. If yes, what do you think contributed to the decline in available bushmeat?
a. Overharvesting
b. park borders
c. habitat decline
d. Disease
e. Drought
f. Availability of domestic meat
g. Herbicide use for cotton framing
h. Access
i. I don’t know
j. other
27. What do you think would help increase the population of these animals?
a. nothing
b. God can help
c. decrease/stop hunting
d. stop consuming bushmeat
28. Do you perceive any risk in interacting with primates?
i. Is this perception
a. Religious
b. Medical(diseases) c. fear of injury
d. legality
e. I don’t know
f. other
ii. Have you ever been harmed or know of anyone harmed by primates? Yes or no
iii. If yes,
a. Age
b. Gender
c. Circumstances
d. date
29. Prior to Ebola in West Africa last year did you hunt, consume or butcher bushmeat more
than now?
i. How much more?
ii. Before Ebola did you eat or hunt different animals than now? If so which ones?
a. baboon
b. Patas
c. Grivet
d. Warthog
e. Duiker
f. Porcupine
g. Rabbit
h. Squirrel
i. Eland
j. rats
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7.5 Survey (French)
FICHE DE SONDAGE DE L’IMPACT DES SENSIBILISATIONS SUR EBOLA
Code de la Fiche/Numéro de
l’entrevue :………………
…
Date de l’enquête :
……………………………
………..
Lieu de l’enquête :
……………………………
…………
Heure de
début :…………….. Heure de
fin :…………………. Sexe de la cible
F M
Nom de l’enquêteur :
……………………………………………………………
……………………
Bonjour Monsieur/Madame,
je m'appelle………………………………………………., je suis au Sénégal depuis deux ans. Dans le contexte de la lutte contre Ebola, je voudrais participer à ma façon et c'est à travers une
enquête très simple et volontaire, si vous le permettez.
Mes recherches s’efforcent à évaluer l’efficacité des campagnes de santé sur Ebola pour
aider à prévenir un futur déclenchement d’Ebola. L’enquête est facultative. L’enquête est
anonyme. Le nom de votre village et votre nom ne seront pas enregistrés. Les résultats ne seront
pas publiés. J'utiliserais seulement les résultats pour compléter ma Maitrise en Amérique. Les
résultats seront partagés avec les ONG de la place, pour qu’ils puissent améliorer leurs
campagnes de sensibilisation. Il n'y aura aucune ramification légale pour partager les
informations de pratiques de chasse ou interactions avec la faune. J’essaie de comprendre
comment protéger la culture Bassari et la faune tout en prévenant Ebola au Sénégal. J’espère que
vous coopérerez et répondrez honnêtement à mes questions.
Qui est le chasseur pour ce village et peut-on l’interviewer ?
1. Age
a. 18-25
b. 26-35
c. 36-45
d. 46-60
e. >60
2. ethnie
a. Bassari
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b. Pular
c. Bedik
d. Autre : ____________
3. Religion
a. Animiste
b. Musulmane
c. chrétienne (Christian)
d. Autre : ________________
4. Education
a. achèvement du primaire
b. collège
c. lycée
d. université
e. Maîtrise ou doctorat
5. Combien de temps avez-vous vécu dans ce village ?
a. Toute ma vie
b. > 20 années
c. 10-20 années
d. 5-9 années
e. <5 ans
6. Quelle est votre profession ?
a. Agriculteur
b. Vendeur
c. Professeur
d. Agent de santé
e. Autre : ___________
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7. Combien de kilomètres y a t-il entre votre maison et votre lieu de travail ?
a. <1 km
b. 2-4km
c. 5-10km
d. > 10 km
8. Que cultivez-vous ?
a. Blé
b. Arachide
c. Mil
d. Fonio
e. Haricots
f. Riz
g. Autres : ____________
9. Vendez-vous votre récolte ou l'utilisez-vous uniquement pour la consommation du ménage ?
a. vend la récolte b. utilise uniquement pour la consommation
10. Qu’est-ce qui cause la diminution de votre rendement agricole ?
a. Insectes
b. Maladie
c. Précipitations
d. Primates
e. Vaches
f. Ovins / caprins
g. Autres : décrire:……………………………………………………………………………
i. Si plusieurs : Classez leur importance
______________________________
_______________________________
ii. Si la liste de réponses se rapporte à la faune demander :
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iii. Quels animaux sont problématiques ?
………………………………………………………………………………………………………
…………………
iv. Si les primates sont répertoriés : Quels types de primates endommagent les cultures ?
a. Les chimpanzés
b. Les babouins
c. Singes Grivet
d. Singes rouges colobus
e. Autres : décrire:
v. S’il y a plusieurs rangs, déclinez leur importance
11. Quelles cultures sont touchées par ces dévastateurs ? (culture invasive?)
a. Maïs
b. Arachide
c. Riz
d. Mil
e. autres :______________________________________
i. S’il y a plusieurs rangs, déclinez-les par ordre d'importance
12. Quelle est la période de l'année où les cultures sont les plus vulnérables ?
13. Quelle méthode utilisez-vous pour protéger vos cultures ?
a. Surveillance
Si les gens sont impliqués : Qui ?
i. Âge
ii. Sexe
iii. Quantité
b. Chen
c. le sang de singe
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d. sang humain
e. des vêtements dans les arbres
f. la peau de singe
g. autres : ______________________________________
14. Comment voyez-vous les primates dans la région ?
a. Effrayés
b. Protégés
c. Sources de viande
d. Ennuyeux
e. Vecteurs de maladie
f. Autres : décrire : 15. Pensez-vous que la proximité de votre domicile avec le parc national, Niokolo Koba, affecte la
présence de la faune ?
Oui Non
16. Et- ce que les restrictions/règlementation de la chasse,
Affectent vos pratiques ?
17. Possédez-vous une arme ?
18. Chasserez-vous tous les types d’animaux sauvages ? (Si NON passer à la question20)
i. Si oui, quel type ?
ii. Chassez-vous tout type de primate ?
iii. Porque chassez-vous ces animaux ?
a. Nourriture
b. Protéger les cultures
c. Problèmes de santé
d. Autres : décrire:________________________________________
iv. Quel type de technique de chasse est utilisé ?
a. Piège
b. Arc/flèche
c. Arme/fusil
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d. Chien
e. Poison
f. Autres : décrire: ______________________________________________
v. Quelle distance faites-vous pour chasser les animaux sauvages ?
a. <1 km
b. 1-5km
c. 5-10km
d. > 10 km
vi. Prenez-vous des précautions pendant la chasse ou quand vous découpez le gibier ? Si
oui, décrivez
a. Éviter de toucher le sang
b. Se laver les mains
c. Drainage du sang de carcasse
d. Vêtements appropriés
e. Autres : décrire:______________________________________________
vii. Quel est le processus le plus risqué ?
a. Boucherie
b. Chasse
c. Tuer
d. Autres_______________________________________________________
viii. Pensez- vous que manipuler le sang des primates est sans risque ?
Comment percevez-vous risqué sang primates touchante être ?
a. Très risqué
b. Risquée
c. Peu risqué
d. Pas risquée
e. Je ne sais pas
ix. Avez-vous déjà vendu de la viande de primates ?
a. Si oui, où ? :………………………………….
b. A qui ? …………………………………………
c. Est-ce que cela affecte votre revenu ? …………………………………..
19. Consommez-vous la viande de brousse ? (Si NON passez à la question 21)
106
i. Si oui, quelles espèces mangez-vous ?
a. Phacochère
b. Céphalophe
c. Lapin
d. varan
e. Porc-épic
f. Rat
g. Autres : Décrivez__________________________________________
ii. Mangez-vous tout type de primate ? Quel type ?
a. Les chimpanzés
b. Babouins
c. Singes Grivet
d. Colobe
e. Autres : décrire :
iii. Consommez- vous régulièrement la viande de brousse ?
a. Tous Les Jours
b. Une fois par semaine
c. Chaque mois
d. Tous les quelques mois de (temps en temps)
e. Tous les six mois
f. Chaque année
g. Rarement (moins d'une fois par année)
__________________________________________________
iv. Et la Viande de primates ?
a. Tous Les Jours
b. Une fois par semaine
c. Chaque mois
d. de temps en temps
107
e. Tous les six mois
f. Chaque année
g. Rarement (moins d'une fois par année)
v. Consommez-vous de la viande de brousse qui a été retrouvé mort ?
Non Oui
vi. Si oui, Combien de fois ?
a. Tous Les Jours
b. Une fois par semaine
c. Toutes les deux semaines
d. Chaque mois
e. Tous les deux mois (de temps en temps)
f. Tous les six mois
g. Chaque année
h. Rarement (moins d'une fois par année)
vii. Préférez-vous la viande de brousse à la viande domestique ?
Non Oui
viii. Si oui, pourquoi ?
a. moins grasse
b. plus nutritive
c. des raisons religieuses
d. moins cher
e. accès facile
f. Autres : ________________________________________________
ix. Quel type de technique de cuisson est utilisé ?
108
a. séchage
b. Grillage
c. Ébullition
d. Brut
e. Salaison
x. Yat-il un type de viande de brousse que vous ne consommez pas ?
xi. Pourquoi ?
a. religieuse
b. interdit / illégal
c. Craintes Ebola
d. accès
xii. Ets- ce que votre religion vous permet où vous interdit la consommation de viande de
brousse ?
Non Oui
20. Ets- ce que votre religion vous permet où vous interdit la consommation de viande de primates ?
Non Oui
21. Y a-t-il des sources de viande domestiques disponibles ?
Non Oui
22. Quel Type est disponible chez vous ?
a. Poulet
b. Bœuf
c. Mouton
d. Chèvre
e. Porc
f. Autres : Liste
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23. Mangez-vous régulièrement la viande domestique ?
Oui Non
i. Si oui, voir la fréquence
a. Tous Les Jours
b. Une fois par semaine
c. Toutes les deux semaines
d. Chaque mois
e. Tous les deux mois (de temps en temps)
f. Tous les six mois
g. Chaque année
h. Rarement (moins d'une fois par année)
24. Y a-t-il eu une baisse de la consommation de viande de brousse, dans les vingt dernières années
? (Si NON passez à la question 26)
i. Si oui, où ? ………………………………………………………………
ii. Quel type de viande de brousse ?
a. Phacochère
b. Céphalophe
c. Lapin
d. varan
e. Porc-épic
f. Hippopotame
g. Rat
h. Primate : Spécifier_______________________
i. Autres : Décrire________________________
iii. Selon vous, qu’est- ce qui e contribué à la baisse consommation de la viande de
brousse ?
a. La surexploitation
110
b. Frontières du parc
c. Baisse de l'habitat
d. Maladie
e. Sécheresse
f. Disponibilité de viande domestique
g. Autres_________________________________
25. Que pouvez- vous faire pour accroître la population de ces animaux ?
………………………………………………………………………………………………………
……………………...
26. Est- ce que vous percevez tous les risques encourus dans l'interaction avec les primates ?
i. Est-ce la perception
a. Religieuse
b. Médicales (maladies zoonotiques) c. Peur de se blessure
d. Loi
e. Autre_______________________________
ii. Est- ce que cette perception a changé depuis l'épidémie d’Ebola ?
Non Oui
iii. Avez-vous déjà été blessé ou connaissez- vous toute personne blessée par les
primates ?
iv. Si oui,
a. Âge
b. Sexe
c. Conditions
27. Avez-vous une radio ?
Non Oui
28. Avez-vous entendu des campagnes de santé déconseillant la consommation de viande de
brousse ?
i. Quelle source ?
a. Ami
b. ii. Radio : spécifier Salemata ou en Guinée
c. iii. poste de santé
d. iv. Croix Rouge
e. v. ONG
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f. vi. L'agent de santé (Relay) g. vii. Autre : ____________________________________
29. Avez-vous entendu les campagnes de santé sur Ebola ? (Si NON passez à la question 30)
Non Oui
i. Qu'avez-vous compris du message sanitaire sur Ebola ?
_______________________________________________________
ii. Leur message de ne pas interagir avec la faune et de ne pas consommer de la viande de
brousse, a-t-il affecté votre comportement ?
Non Oui
iii. Quel message sanitaire/ONG était le plus effectif ?
a. Croix de rouge
b. Caritas
c. District Sanitaire de Salemata
d. Radio
e. Autres_____________________________
30. Selon vous, qu’est- ce qui provoque Ebola ?
31. Est-ce que le virus Ebola a changé ou touché votre communauté ? (Si Non passez à la question
33)
Non Oui
i. Si oui, comment ?
a. Salutation
b. ii. La viande de brousse
c. iii. Tourisme
d. iv. Autre
ii. Si la réponse viande de brousse est donnée, qu’est-ce qui a changé par rapport à ce type
de comportement ?
a. Consommation
b. Vente
c. Techniques de boucherie / cuisson
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d. Comportement dissimulé
e. Autres : ___________________________________
32. Quand avez-vous noté ce changement de comportement ?
a. Dans la première semaine de flambée de fièvre Ebola
b. Après 2-4 semaines
c. Après 2 mois
d. Après 3 mois
e. 4-6 mois
f. > 6 mois
33. Avant Ebola en Afrique de l'Ouest, l'an dernier avez- vous chassé, consommé la viande de
brousse plus que maintenant ?
Non Oui
i. Si oui, montrer combien plus ?
ii. Maintenant mange vous ou chassez-vous différents animaux ? Si oui, lesquels ?
