Risk factors for human brucellosis among a pastoralist community in South-West Kenya, 2015

Abstract

Abstract Objective Brucellosis is one of the top five priority zoonosis in Kenya because of the socio-economic burden of the disease, especially among traditional, livestock keeping communities. We conducted a 1 year, hospital based, unmatched case–control study to determine risk factors for brucellosis among Maasai pastoralists of Kajiado County in 2016. A case was defined by a clinical criteria; fever or history of fever and two clinical signs suggestive of brucellosis and a positive competitive enzyme-linked immunosorbent assay test (c-ELISA). A control was defined as patients visiting the study facility with negative c-ELISA. Unconditional logistic regression was used to study association between exposure variables and brucellosis using odds ratios (OR) and 95% confidence intervals (CI). Results Forty-three cases and 86 controls were recruited from a population of 4792 individuals in 801 households. The mean age for the cases was 48.7 years while that of the controls was 37.6 years. The dominant gender for both cases (62.7%) and controls (58.1%) groups was female. Regular consumption of un-boiled raw milk and assisting animals in delivery were significantly associated with brucellosis by OR 7.7 (95% CI 1.5–40.1) and OR 3.7 (95% CI 1.1–13.5), respectively.

Full text

Muturietal. BMC Res Notes (2018) 11:865
https://doi.org/10.1186/s13104-018-3961-x
RESEARCH NOTE
Risk factors forhuman brucellosis
amongapastoralist community inSouth-West
Kenya, 2015
Mathew Muturi1* , Austine Bitek2, Athman Mwatondo1, Eric Osoro3, Doris Marwanga4, Zeinab Gura5,
Phillip Ngere6, Zipporah Nganga7, S. M. Thumbi3,5 and Kariuki Njenga3
Abstract
Objective: Brucellosis is one of the top five priority zoonosis in Kenya because of the socio-economic burden of
the disease, especially among traditional, livestock keeping communities. We conducted a 1 year, hospital based,
unmatched case–control study to determine risk factors for brucellosis among Maasai pastoralists of Kajiado County
in 2016. A case was defined by a clinical criteria; fever or history of fever and two clinical signs suggestive of brucellosis
and a positive competitive enzyme-linked immunosorbent assay test (c-ELISA). A control was defined as patients visit-
ing the study facility with negative c-ELISA. Unconditional logistic regression was used to study association between
exposure variables and brucellosis using odds ratios (OR) and 95% confidence intervals (CI).
Results: Forty-three cases and 86 controls were recruited from a population of 4792 individuals in 801 households.
The mean age for the cases was 48.7 years while that of the controls was 37.6 years. The dominant gender for both
cases (62.7%) and controls (58.1%) groups was female. Regular consumption of un-boiled raw milk and assisting ani-
mals in delivery were significantly associated with brucellosis by OR 7.7 (95% CI 1.5–40.1) and OR 3.7 (95% CI 1.1–13.5),
respectively.
Keywords: Brucellosis, Risk factors, Kenya
© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/
publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Introduction
Brucellosis is a debilitating febrile illness in humans and
reproductive disease of livestock, caused by bacteria
of the genus Brucella [1]. ere are six Brucella species
based on primary host preference, but only four have
zoonotic potential; B. melitensis (goats and sheep), Bru-
cella abortus (cattle), B. suis (swine) and B. canis (dogs)
[2–5]. Human infection occurs through direct contact
with infected animal tissues like products of abortion
and blood or ingestion of unpasteurized milk and dairy
products [2, 6]. Although livestock are the primary
source of human infection, wild animals may act as res-
ervoirs in regions with human-wildlife interaction [7,
8]. Human brucellosis presents as an acute to chronic
illness characterized by fever and other constitutional
symptoms such as joint pains, fatigue and muscle ache
that vary with the stage of infection and body system
affected [9, 10]. e disease has a low mortality rate, but
the relapsing and chronic nature of human infection, the
long cause of treatment and negative implication on live-
stock trade qualifies brucellosis as a serious public health
and socio-economic problem [2, 9, 11–15].
Brucellosis is the most common zoonotic infection
globally with more than half a million human cases annu-
ally, however, infection rates vary significantly between
developed and developing countries [1, 16, 17]. e
human disease has been eliminated in most developed
countries like Canada, Japan and Australia but remains
endemic in most developing countries in Asia, the Mid-
dle East, Eastern Europe, Latin America and Africa [1,
16, 18–20].
In Kenya, brucellosis is ranked as a top priority zoon-
osis due to the socio-economic burden and amenability
Open Access
BMC Research Notes
*Correspondence: muturimathew@gmail.com
1 Kenya Zoonotic Disease Unit–Ministry of Agriculture, Livestock
and Fisheries and Ministry of Health, P.O. Box 20811-00202, Nairobi, Kenya
Full list of author information is available at the end of the article

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Muturietal. BMC Res Notes (2018) 11:865
to control, however, as is common with other neglected
zoonotic diseases, establishing the true morbidity and
socio-economic impact of the disease is a challenge
because of misdiagnosis and underreporting [21]. Stud-
ies in Kenya indicate high prevalence in humans and
livestock although this varies with geographical region
and livestock production system [22–27]. Brucellosis is
endemic in Kenya and identifying potential risk factors
of brucellosis among the most vulnerable populations;
primarily rural livestock keeping communities is impor-
tant in defining control and prevention strategies. We
conducted a case–control study in a pastoral community
in rural Kenya to identify potential risk factors for bru-
cellosis as a step towards comprehensive understanding
of the disease among pastoralists to inform public health
interventions.
Main text
Materials andmethods
Study area andpopulation
e study was conducted in Arroi, Sultan-Hamud and
Mashuru sub-counties in Kajiado East sub-county, Kenya
(Fig. 1). e study area is an arid rangeland inhabited
primarily by the Maasai nomadic pastoralist community
[23, 28]. e site was selected because a previous study
had reported high brucellosis prevalence and because it
represent an ecosystem with high frequency of human-
livestock-wildlife interaction [23, 29, 30].
Study design
We conducted a hospital based unmatched case–con-
trol study in three health facilities that historically had
the highest patient load in the year preceding the study.
Participants were recruited from 80 randomly selected
households in the study area that were part of an ongoing
longitudinal brucellosis study in humans and livestock
(population = 4792 people). To enhance case finding at
health facilities, recruited household members were sen-
sitized on brucellosis using a community level case defi-
nition adapted from the World Health Organisation, and
provided with free treatment at the participating health
facility [2]. e community case definition for brucellosis
used was fever of undetermined origin with at least one
of the following symptoms; chills, lethargy, joint pains,
body ache, abdominal pain and headaches.
Fig. 1 Map of Kenya showing Kajiado County in red and the study site in grey

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Muturietal. BMC Res Notes (2018) 11:865
Sample size calculation
Sample size was calculated using the Kelsey Kelsey for-
mula for unmatched case control studies using an open-
Epi version 2 open source online calculator (http://www.
opene pi.com) [31]. e appropriate sample size was
determined using a power of 0.8 and significance level of
0.05 to detect an odds ratio greater than 3 for exposure
factors present in 20% of controls as estimated in other
similar studies [3, 32]. A control to case ratio of 2:1 was
used to improve study power. is yielded a sample size
of 43 cases and 86 controls.
Selection ofcases andcontrols
A case was defined as any person from the study popula-
tion presenting to any of the three health facilities with
fever or history of fever (> 37.5 °C) and at-least two of
the following signs; joint pains, joint swelling, head-
ache, backache and was negative for malaria and sal-
monellosis on rapid diagnostic tests and with a positive
c-ELISA Immunoglobulin M (IgM) or Immunoglobulin
G (IgG) result. A control was defined as a person from
the same study population presenting to the study facili-
ties with history of fever within the same study period
and was negative for brucellosis by c-ELISA IgM and IgG.
Cases were tested for malaria and Salmonellosis because
the diseases are common aetiologies of similar clinical
disease.
Laboratory testing
Laboratory testing was carried out at the Kenya Medical
Research Institute using IgM and IgG ELISA kit sourced
from Immuno-Biological Laboratories, America (Min-
neapolis, Minnesota). All assays were conducted as per
manufacturer’s instructions. Briefly, human sera were
diluted at 1:10 with sample diluent, added to microtitre
plates pre-coated withBrucella antigen (Brucella abor-
tus, strain W99; lysate of a NaCl extract) and incubated
at room temperature for 1h. Conjugate was added and
incubated for 30min before adding substrate. e con-
jugate–substrate reaction was terminated after 20 min
by adding a stop solution. Sample optical densities
(ODs) were read at 450nm. Equivocal samples were not
included in analysis.
Questionnaire andinterviewing
A study nurse was stationed in each of the three facilities.
Once a patient was identified as a member of the study
population during triage (coming from a study house-
hold), they were directed to the study nurse who exam-
ined them and administered a standard questionnaire
pre-loaded on a personal data assistant. e question-
naire collected information on patients’ demographic,
risk factors, history of illness and point of care test
results. Informed consent was obtained from all study
participants.
Data analysis
A number of risk factors were investigated including con-
sumption of goats, sheep, or cow milk, drinking fresh
livestock blood, livestock ownership, herding and slaugh-
tering animals, handling skins and hides, and helping in
animal delivery. Bivariate analysis was performed using
the Chi squared test. Variables with a p-value ≤ 0.10 in
the bivariate analysis were included in a multivariate
logistic regression model. Adjusted odds ratios and the
corresponding 95% confidence intervals along with the
p-values were reported with significance level being set
at 5%. Multivariate logistic regression was used to iden-
tify risk factors associated with brucellosis and to esti-
mate the magnitude of the adjusted odds ratios (aORs)
for each factor while controlling for other confound-
ing factors. Only the significant variables were included
in the model to control for confounding and get a final
logistic regression model. Only those variables that had
a p-value < 0.05 in the final model were considered sta-
tistically significant. Data were analyzed using Statistical
Analysis Software (SAS) version 9.2.
Results
Patient socio‑demographic characteristics
Of the 236 participants from the study population who
met the inclusion criteria, majority, 64% were majority
female. Participants had a mean age of 40years (stand-
ard deviation = 16.9, range 7–75) and 129 (54.6%) of
them were enrolled in the case control study, including
43 cases and 86 controls. e mean age for the cases was
48.7 (standard deviation = 20, range = 10–85) years while
that of the controls was 37.6 (standard deviation = 18.8,
range = 8–72). Among cases, 70% (n = 30) were between
20 and 59 years. e dominant gender for both cases
(62.7%) and controls (58.1%) was female. Majority of both
cases and controls were non-skilled laborers and there
was no significant difference in socio-demographic char-
acteristics (sex, religion, occupation, marital status and
education) between cases and controls besides age.
Clinical information
Sixty percent of the cases presented at-least 7days after
the onset of the first symptom while 37% presented
between 11 and 60 days after onset of symptoms. e
mean number of days between onset of symptoms and
visit to hospital was 12days (standard deviation = 13.3).
e most commonly reported symptoms by both cases
were headache (83.7%) back pains (62.8%) and joint pains
(60.6%). is was similar to the symptoms reported by

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Muturietal. BMC Res Notes (2018) 11:865
the controls; headache (82.6%), back pains (47.7%) and
joint pains (69.8%).
Bivariate analysis
On bivariate analysis, consuming un-boiled cow milk,
drinking fresh blood, slaughtering animals (cattle, wild
animals), assisting goats in giving birth, handling ani-
mal hides were associated with increased risk of brucel-
losis (p-value ≤ 0.1). Of these factors, handling skins and
hides, assisting goats with delivery, and consuming un-
boiled goat milk were significantly associated with dis-
ease (p-value ≤ 0.05). Having cattle in the household was
found to be protective as shown in the Table1.
Multivariable analysis results
On multivariate logistic regression analysis consum-
ing un-boiled cow milk (OR 7.7, 95% CI 1.5–40.1) and
assisting animals in delivery (OR 3.7, 95% CI 1.1–13.5)
remained significantly associated with brucellosis as
shown in Table2.
Discussion
Our case–control study identified consumption of raw
cow milk, assisting livestock in delivery, and handling
animal hides as risk factors on bivariate analysis. How-
ever, only assisting livestock in delivery and drinking
un-boiled cow milk remained significant risk facts after
multivariate analysis. e association between assisting
animals with delivery and increased risk of infection has
been reported in other studies carried out in similar set-
tings in East Africa [23, 33] Chad [34], the Middle East
[35] and in Turkey [36, 37]. Given that Brucella spp. are
known to have a predilection for reproductive organs
particularly placenta and aborted fetuses, it is logical that
assisting animals in delivery increases risk of infection
[23]. e risk of brucellosis associated with consump-
tion of un-boiled milk has been well documented [22, 23,
38]. Interestingly, even though most of the pastoralists
around the world know about this risk, majority of them
still consume raw milk as a tradition and for cultural rea-
sons [39]. Although opinion differs between authors on
whether direct contact with livestock (assisting in deliv-
ery, milking and feeding) or indirect contact with live-
stock (consumption of animal products) is a stronger risk
factor, we found greater association with disease from
consuming animal products than direct contact with ani-
mal. is finding is in agreement with other studies car-
ried out within the East Africa region [23, 40, 41]. Studies
have shown that consumption of unpasteurized milk is
a common practise in Kenya, including communities in
urban areas such as where 77% of households reported
the risky practice [42]. Some studies show education and
occupation are significant risk factors contrary to our
Table 1 Bivariate analysis of risk factors for human
brucellosis
Variable Controls
(n = 86) Cases
(n = 43) Crude
OR(95% CI) p-value
Yes Yes
Consume fresh goat milk
More than 3 times a
week 14 14 2.4 (1.0–6.0) 0.114
Less than 3 times a
week 21 8 0.9 (0.4–2.4)
No 51 21 1.0
Consume cow milk
Boiled 82 32 7.7 (1.5–40.1) 0.016
Unboiled 2 6
Consume fresh sheep milk
More than 3 times a
week 1 1 2.1 (0.1–34.1) 0.756
Less than 3 times a
week 4 3 1.6 (0.3–7.3)
No 81 39 1.0
Drink fresh blood
Yes 6 7 2.6 (0.8–8.3) 0.098
No 80 36
Had cattle in the household
Yes 55 26 0.1 (0.0–0.9) 0.035
No 31 17
Slaughter cattle at home
Occasionally 54 32 2.3 (0.8–6.2) 0.102
Never 23 6
Herding sheep
Several times a week 16 14 2.0 (0.5–7.8) 0.196
Occasionally 49 19 0.9 (0.2–3.2)
Never 9 4 1.0
Assisting sheep in delivery
Several times a week 1 1 4.0 (0.2–72.2) 0.116
Occasionally 45 30 2.7 (1.0–6.9)
Never 28 7 1.0
Slaughtering goats at home
Several times a week 1 1 4.8 (0.3–90.3) 0.115
Occasionally 53 33 3.0 (1.0–8.6)
Never 24 5 1.0
Assisting goats in delivery
Occasionally 48 31 3.7 (1.3–10.7) 0.043
Never 29 5 1.0
Slaughtering wild animals
Yes 1 3 0.073
No 82 40 6.4 (0.6–63.2)
Cleaning animal barns
Several times a week 57 5 0.4 (0.1–1.3) 0.132
Occasionally 19 14
Handle animal hides
Yes 30 23 2.1 (1.2–4.5) 0.043
No 56 20

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Muturietal. BMC Res Notes (2018) 11:865
data that shows there was no significant difference on
the two variables between cases and controls. A possible
explanation is the study area is a rural, predominantly
Maasai agro-pastoral community where most house-
holds practise a traditional livestock rearing lifestyle. is
means that cases and controls have similar occupation
and education levels.
Conclusion andrecommendations
e findings of this study show a significant association
between infection and consumption of unpasteurized
milk and assisting animals with delivery. is findings
show that animal handlers; primarily farmers and animal
health workers and people who consume unpasteurized
milk; a common practise in Kenya, are at the greatest
risk. We recommend Public health education on bru-
cellosis transmission and prevention, specifically use of
protective personal equipment when assisting animals in
delivery and boiling of milk should be offered to farmers
and the general public, respectively.
Limitations
ere were some limitations to the study. Case–control
studies are prone to selection bias but we took measures
to minimise the same; we recruited cases and controls
from households participating in an ongoing cohort study
of brucellosis in livestock. is meant cases and controls
were recruited from households with similar character-
istics, which in turn minimises selection bias. Another
significant limitation is the limited sample size. e study
only recruited cases and controls from an ongoing study
that had recruited 810 households with 4792 people; this
limited the number of study participants who could be
included in our analysis.
Abbreviations
AOR: adjusted odds ratio; C-ELISA: competitive enzyme-linked immunosorb-
ent assay; CI: confidence interval; IgM: immunoglobulin M; IgG: immunoglob-
ulin G; OD: optical density; OR: odds ration.
Authors’ contributions
MM was part of the team that designed the study, conducted the field work,
data analyses, and did the first draft of the manuscript. AB, AM, EO, DM, ZG, PN
ZN, SM, KN supervised the field work and contributed to the study design and
manuscript. KN supervised all the work, analyses, and manuscript writing; and
designed the study. All authors read and approved the final manuscript.
Author details
1 Kenya Zoonotic Disease Unit–Ministry of Agriculture, Livestock and Fish-
eries and Ministry of Health, P.O. Box 20811-00202, Nairobi, Kenya. 2 Food
and Agriculture Organization of the United Nations, Nairobi, Kenya. 3 Paul G.
Allen School for Global Animal Health, Washington State University, Pullman,
WA, USA. 4 Kenya Medical Research Institute, Nairobi, Kenya. 5 Kenya Field
Epidemiology and Laboratory Training Program, Nairobi, Kenya. 6 County Gov-
ernment of Kajiado, Kajiado, Kenya. 7 Jomo Kenyatta University of Agriculture
and Technology, Nairobi, Kenya.
Acknowledgements
We thank the Kenya Directorate of Veterinary Services, Kenya Ministry of
Health, County Governments of Kajiado, United States’ Centers for Disease
Control and Prevention - Kenya Dr. Peninah Munyua (US CDC) for her mentor-
ship and advice during the study and Kenya Field Epidemiology and Labora-
tory Training Program for their participation in the study.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The dataset used and/or analysed during this study is available from the cor-
responding author on reasonable request.
Consent for publication
Not applicable.
Disclaimer
The findings and conclusions in this report are those of the authors and do
not necessarily represent the official position of the United States’ Defence
Threat Reduction Agency or US Centers for Disease Control and Prevention or
the Government of Kenya.
Ethics approval and consent to participate
This study was reviewed and approved by the Kenyatta National Hospital
Ethical Review committee. Cases and controls were enrolled after verbal and
written consent and no personal identifiers were recorded on the question-
naire. After questioning, participants were provided free medical treatment.
Funding
Financial support was provided by the United States’ Defence Threat Reduc-
tion Agency, Kenya Ministry of Agriculture, Livestock and Fisheries, Kenya
Ministry of Health and the United States’ Centers for Disease Control and
Prevention.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in pub-
lished maps and institutional affiliations.
Received: 29 August 2018 Accepted: 29 November 2018
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Table 2 Multivariate logistic regression of factors
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Drinking un-boiled cow milk 7.7 (1.5–40.1) 0.036

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