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LimJK, etal. BMJ Open 2018;8:e017673. doi:10.1136/bmjopen-2017-017673
Open Access
Evaluating dengue burden in Africa in
passive fever surveillance and
seroprevalence studies: protocol of eld
studies of the Dengue Vaccine Initiative
Jacqueline Kyungah Lim,1,2 Mabel Carabali,1,3 Jung-Seok Lee,4 Kang-Sung Lee,4
Suk Namkung,1 Sl-Ki Lim,1 Valéry Ridde,5 Jose Fernandes,6 Bertrand Lell,6
Sultani Hadley Matendechero,7 Meral Esen,8 Esther Andia,9 Noah Oyembo,9
Ahmed Barro,10 Emmanuel Bonnet,11 Sammy M Njenga,9
Selidji Todagbe Agnandji,6 Seydou Yaro,12 Neal Alexander,2 In-Kyu Yoon1
To cite: LimJK, CarabaliM,
LeeJ-S, etal. Evaluating
dengue burden in Africa in
passive fever surveillance and
seroprevalence studies: protocol
of eld studies of the Dengue
Vaccine Initiative. BMJ Open
2018;8:e017673. doi:10.1136/
bmjopen-2017-017673
►Prepublication history for
this paper is available online.
To view these les, please visit
the journal online (http:// dx. doi.
org/ 10. 1136/ bmjopen- 2017-
017673).
Received 10 May 2017
Revised 25 September 2017
Accepted 18 October 2017
For numbered afliations see
end of article.
Correspondence to
Jacqueline KyungahLim;
kalim@ ivi. int
Protocol
ABSTRACT
Introduction Dengue is an important and well-
documented public health problem in the Asia-Pacic and
Latin American regions. However, in Africa, information
on disease burden is limited to case reports and reports
of sporadic outbreaks, thus hindering the implementation
of public health actions for disease control. To gather
evidence on the undocumented burden of dengue in Africa,
epidemiological studies with standardised methods were
launched in three locations in Africa.
Methods and analysis In 2014–2017, the Dengue
Vaccine Initiative initiated eld studies at three sites in
Ouagadougou, Burkina Faso; Lambaréné, Gabon and
Mombasa, Kenya to obtain comparable incidence data
on dengue and assess its burden through standardised
hospital-based surveillance and community-based
serological methods. Multidisciplinary measurements of
the burden of dengue were obtained through eld studies
that included passive facility-based fever surveillance,
cost-of-illness surveys, serological surveys and healthcare
utilisation surveys. All three sites conducted case detection
using standardised procedures with uniform laboratory
assays to diagnose dengue. Healthcare utilisation surveys
were conducted to adjust population denominators in
incidence calculations for differing healthcare seeking
patterns. The fever surveillance data will allow calculation
of age-specic incidence rates and comparison of
symptomatic presentation between patients with dengue
and non-dengue using multivariable logistic regression.
Serological surveys assessed changes in immune status
of cohorts of approximately 3000 randomly selected
residents at each site at 6-month intervals. The age-
stratied serosurvey data will allow calculation of
seroprevalence and force of infection of dengue. Cost-of-
illness evaluations were conducted among patients with
acute dengue by Rapid Diagnostic Test.
Ethics and dissemination By standardising methods
to evaluate dengue burden across several sites in Africa,
these studies will generate evidence for dengue burden
in Africa and data will be disseminated as publication in
peer-review journals in 2018.
BACKGROUND
Dengue fever, a mosquito-borne flavivirus
infection caused by four related but antigen-
ically distinct dengue viruses (DENVs, sero-
types 1–4), is a major and rapidly increasing
global public health problem. Recent studies
have estimated an annual incidence of
50–100 million symptomatic infections glob-
ally.1 Dengue is a high burden disease that
disproportionately affects countries in the
tropics and subtropics, many of which have
limited healthcare resources.2 Although one
dengue vaccine has been recently licensed
in several endemic countries, the vaccine has
restricted age and epidemiological indica-
tions. Other prevention and control measures
such as vector control are suboptimal as
Strengths and limitations of this study
►There have not been population-based studies
conducted with a multidisciplinary approach (ie,
surveillance, healthcare utilisation and serosurvey
in one catchment area population). Data from the
passive surveillance will be used to calculate annual
incidences of dengue and data from the serosurvey
will estimate the force of infection and prevalence.
►The studies were conducted in three locations
in Africa, based on standardised methods and
laboratory algorithm. Thus, comparison by site
would be possible.
►This is not a cohort study. The passive facility-based
surveillance may lead to underestimation of the
burden of dengue fever by measuring incidence
based on only those that sought care at our study
facilities.
►There may be limited generalisability of our study
results to other dengue-endemic parts of Africa.
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Open Access
stand-alone interventions,3 4 and no drugs for treatment
are currently available.
Like in Asia and the Americas, epidemics of dengue
were reported from Africa in the late 19th and early 20th
centuries.5 6 Specifically for Africa, there are records of
multiple dengue case reports between 1964 and 1968 with
DENV 2 in Nigeria.7 Data from several studies conducted
in the 1960–1970s in Nigeria supported a substantially
high level of immunity in adults as well as children.8 9
In 2011, Amarasinghe et al conducted a comprehensive
review of literature on dengue in Africa and described
that dengue cases have been reported in 34 countries
in Africa, with most of these countries also having Aedes
mosquitoes.6 However, prior studies which suggested
the presence of dengue in Africa were limited by their
retrospective design or sample collection (blood donors
or sample collected from surveys of other diseases), and
often from travellers, with a small number of reported
autochthonous cases, to demonstrate the true, popula-
tion-based, burden of dengue. Also, while many dengue
endemic countries in Asia and Latin America have
mandatory reporting of dengue cases to public health
authorities and national surveillance systems in place
to monitor incidence patterns,10 most African countries
lack such established reporting mechanisms and only
sporadic outbreaks and individual case reports have been
documented. In addition, the frequently non-specific
clinical presentation of dengue may be difficult to distin-
guish from the myriad other infectious diseases present
in Africa, since dengue diagnostic assays are not widely
available. Thus, the burden of dengue remains largely
unknown in Africa.6 11 Without such dengue burden data,
informed decision-making about prevention and control
measures, including dengue vaccine introduction, in
Africa are not possible.
Limited by surveillance capacity hindering continuous
reporting in the region, there had not been frequent and
systematic reporting of dengue in Africa. African ancestry
is known to be protective against severe dengue and the
candidate genes were recently identified in a Cuban
patient.12 13 Bhatt et al’s modelling of the global dengue
burden suggests high burden in Africa in terms of equal
numbers of infections (both apparent and inapparent) as
in Latin America.1 There are new findings about dengue
in Africa, but there is still much unknown about the magni-
tude of the dengue problem in the continent. To improve
estimates of population-based dengue disease burden in
Africa and validate whether the undocumented burden
of dengue is as high in Africa as in the Americas with
empirical data, the Dengue Vaccine Initiative (DVI) initi-
ated field studies at three sites in West (Ouagadougou,
Burkina Faso), West-Central (Lambaréné, Gabon) and
East Africa (Mombasa, Kenya). In each of the three sites,
a standardised package of study components, including
passive facility-based fever surveillance, healthcare utilisa-
tion surveys, cost-of-illness surveys and serological surveys
(figure 1), was initiated between December 2014 and
March 2016.
METHODS
Site selection
Study sites were selected, in part, based on their likeli-
hood of supporting DENV transmission. To select sites,
we considered dengue outbreaks and cases reports in
the literature, available seroprevalence studies as well as
country-specific dengue risk maps of the probability of
DENV transmission and the level of evidence of dengue
presence, reporting the uncertainty of the consensus
estimates of dengue in Africa.7 14 In addition, adequate
research infrastructure to implement the studies was
taken into account. Finally, inclusion of different regions
of Africa was also a factor in site selection. Thus, Ouaga-
dougou, Burkina Faso; Lambaréné, Gabon and Mombasa,
Kenya were selected, respectively, to measure the burden
of dengue in selected sites from West, (West-) Central and
East Africa.
In Burkina Faso, the first reported dengue outbreak
occurred in Ouagadougou in 1982 due to DENV-2.6
Serological prevalence of dengue antibodies among
pregnant women and blood donors was found to be
26.3% in a rural setting (Nouna village) and 36.5% in an
urban setting (Ouagadougou) in 2006.15 More recently,
an observational study conducted by Ridde et al among
febrile patients consulting at selected study facilities
in 2013–2014 showed 8.7% (33/379) to be positive by
dengue rapid diagnostic test (RDT) and 15 of 60 samples
tested by RT-PCR to be dengue-positive.16 With evidence
for the presence of dengue, along with a strong health
and demographic surveillance system (Ouaga-HDSS)
which could be used to describe the demographic charac-
teristics of the catchment area, a field study was initiated
in Ouagadougou, Burkina Faso in December 2014.
In Gabon, cases of dengue haemorrhagic fever
(DHF) caused by up to three different DENV serotypes
have been reported, and dengue seroprevalence has
been found to be between 5% and 20%.17–19 Results
of a recently published study demonstrated sero-
prevalence of 12.3% among toddlers approximately
30 months of age in semirural Lambaréné between
2007 and 2010.20 However, a different study in 2005–
2008 suggested minimal DENV transmission in rural
areas of Gabon.21 This latter study examined anti-
bodies against dengue in individuals from randomly
selected villages representing about 10% of all Gabo-
nese villages. Blood samples were tested by anti-DENV
IgG and IgM capture ELISA and found to have only
minimal IgG (0.5%) and IgM (0.5%) seroprevalence.
Based on these low prevalences, the authors concluded
that there was no active circulation of DENV in rural
Gabon. However, the low seroprevalence may have
been affected by low sensitivities of the tests used,
leading to a high rate of false negative results and/
or selection bias in the blood sample pool among the
selected villagers.22 Seroprevalence estimates in the
2007/2010 study may have also been impacted by the
possibility of false-positive results due to IgG cross-re-
activity among flaviviruses.21 Nevertheless, given the
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possibility of DENV circulation in Gabon, a field
study was initiated in Lambaréné in March 2015 in
a community with a catchment population of about
77 000 residents, using the clinical research infra-
structure of the Centre de Recherches Medicales de
Lambaréné (CERMEL), benefiting from experienced
research staff who conducted a large Phase 3 malaria
vaccine trial.23 24
In Kenya, more evidence is available for the pres-
ence of dengue based on local data. Dengue was the
most common viral pathogen in retrospectively tested
blood specimens from HIV-negative survey samples
from the 2007 Kenya AIDS Indicator Survey. Anti-
body testing for dengue as well as chikungunya and
Rift Valley fever was performed by IgG ELISA using
either commercial kits or CDC assays; 12.5% were
found to be dengue-positive.25 Similarly, a household
survey found 13% of individuals from 701 households
in Mombasa had serological evidence of either past
or current DENV infection.26 These data suggest
that there is more dengue in Kenya than indicated
by public health reporting, possibly due to misdi-
agnosis.25 26 A field study was initiated in Mombasa,
Kenya in March 2016.
Study participants
For the passive facility-based fever surveillance, individ-
uals who met the following criteria were eligible for study
enrolment:
1. Age 1–55 years old.
2. Resident of the catchment area covered by healthcare
facilities participating in the study, without plans to
move out of the catchment area within 12 months.
3. Signed informed consent and assent for those aged
between 7 (13 for Kenya) and 17 years.
4. Patients presenting with current fever (axillary tem-
perature ≥37.5°C) or history of fever for ≤7 days
duration without localising signs (fever caused by a lo-
calised infection as well as fever with a known and con-
firmed aetiology other than dengue, such as malaria
Figure 1 Description of the study components, including passive facility-based fever surveillance, healthcare utilisation
surveys, cost-of-illness surveys and serological surveys.There are two arms in the study package, composed of four parts. In
the health facility-based arm of the study package, there are passive facility-based fever surveillance and cost-of-illness survey
embedded within the surveillance. In the community arm of the study, there are serological survey and healthcare utilisation
survey.
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Open Access
confirmed by malaria RDT, as listed in the patient
identification standard operating procedure [SOP]).
For the serological survey, criteria 1–3 were applied.
For the healthcare utilisation survey, household inter-
views were conducted among the heads or representatives
of the household invited from each family participating
in the serosurvey.
Study area and population
Burkina Faso, located in West Africa, has a population of
14 017 462. The country is mainly rural with about 29%
of the population reported to be living in urban areas in
2014. However, Burkina Faso is urbanising rapidly and is
positioned as the country with the fourth fastest urbani-
sation in the last 25 years.27 28 The capital, Ouagadougou,
has a population of 2 741 128. The majority of the popu-
lation live in urban settings. About 45% of the popula-
tion are under 15 years of age.29 The city is divided into
12 districts and 52 sectors. Ouagadougou is the country’s
largest city and the cultural and economic centre. The
city is part of the Soudano-Sahelian area, with a rainfall
of about 800 mm per year. The rainy season is from May
to October, with a mean temperature of 28°C (82°F).
The cold season runs from December to January, with a
minimum average temperature of 16°C (61°F). During
the hot season, which runs from March to May, the
temperature can reach as high as 43°C (109°F).
The HDSS is in place in Ouagadougou. Ouaga-HDSS
monitors a population of 81 717 residents; according
to this surveillance system, the city population is very
stable with a rate of migration of 4.1% and more than
80% of the inhabitants with ownership of their houses
[20]. A map of the city and the study area is shown in
figure 2.
Gabon, located on the west coast of Central Africa, has
an area of nearly 270 000 square kilometres (100 000 sq.
mi) with a population estimated at 1.5 million. Its capital
and largest city is Libreville. In 2014, it is reported that
87% of the Gabonese population lived in urban areas.28
The sixth largest city, Lambaréné, the capital of Moyen-
Ogooué province, is located 75 km south of the equator,
with a population of 25 257 in 2009. The majority of
Lambaréné residents live in semirural areas. About 42%
of the Gabonese population is under 15 years of age.29
Similarly, Lambaréné’s population is relatively young with
about 50% under 20 years of age.
The health services of Gabon are mostly public, but
there are some private institutions as well. With one of
the best medical infrastructure in the region, almost
90% of the population have access to healthcare services.
Albert Schweitzer Hospital (ASH) is a private institution
which served as a study site for the passive fever surveil-
lance study.30 31 The study area in Lambaréné is shown in
figure 3.
Figure 2 Map of the study area in Ouagadougou, Burkina Faso.
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Kenya, located in East Africa, lies on the equator,
covering 581 309 km2 (224 445 sq. mi), with a popula-
tion of approximately 45 million people in 2014.32 Kenya
generally has a warm and humid tropical climate but is
diverse, ranging from the cooler climate around the
capital city, Nairobi, to a hot and dry climate inland as
well as a desert-like climate in the north-eastern regions
along the border with Somalia and Ethiopia.32 The
capital, Nairobi, is a regional commercial hub. The main
industries include agriculture, exporting tea and coffee
as well as the service industry.
Kenya is divided into 47 semiautonomous counties.
Mombasa is the country’s second largest city after Nairobi
and is located on the east coast of the country.32 Admin-
istratively, Mombasa is the capital of Mombasa County,
which was formerly called Coast Province. This overall
Coast region covers over 80 000 km2 in the south-eastern
part of Kenya, constituting about 15% of the country's
land area, with a population of 3 325 307 residents.
The main economic driver of Mombasa is tourism
and trading industry. Mombasa itself has a population
of about 1.3 million with almost 50% of the population
under 15 years of age.29 Increasingly, the population of
the province lives in urban areas; at present about 45%
live in Mombasa and other urban centres. The ‘long
rains’ period begins around April and the ‘short rains’
period begins in October.32 Mean annual temperature
ranges from 24°C to 27°C, but maximum temperature
averages over 30°C between January and April.
Figure 4 shows the area of Mvita subcounty of Mombasa,
which was the catchment area for the study in Kenya, with
a catchment population of 74 735 residents. The map
indicates the three facilities involved in the study.
Sample size
Given the paucity of available age-specific dengue inci-
dence data in the study countries or nearby countries, it
was difficult to obtain population-based incidence to make
assumptions when calculating sample sizes. The required
catchment population for the passive facility-based fever
surveillance was roughly estimated based on the limited
data available in the literature. Annual incidence esti-
mates were calculated based on available prevalence esti-
mates with the assumption that the outcome of interest
has zero prevalence at age zero, and that force of infec-
tion is constant. It was assumed that prevalence estimates
found for one particular age group would be adjusted as
the annual incidence and used across all ages.
Wichmann et al calculated an expansion factor for
children by comparing data from three cohort studies
to national surveillance data in Southeast Asia.33 For
children in Thailand, the age-specific expansion factors
Figure 3 Map of the study area in Lambaréné, Gabon.
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calculated were 11.85 for <5 years, 8.76 for 5–9 years
and 7.81 for 10–14 years.33 The results show that, even
for Asia where better reporting and surveillance systems
are available, there is a considerable degree of under-re-
porting. For Africa, there may be more dengue cases
under-ascertained (not seeking care) and under-reported
(not reported even if a patient with dengue seeks care,
given that dengue is not one of the routinely notifiable
diseases in Africa), but such information on the extent
of underestimation of dengue was not available.34 35 Also,
the incidence estimates used in our sample size calcula-
tions were not from population-based studies. While it
would have been ideal to adjust the incidence further
for likely underestimation, the annual incidence used
in sample size calculations could not be adjusted for
possible under-reporting due to the lack of data. The
sample sizes were calculated with 95% confidence levels
and a margin of error at a fixed significance level within
25% of the true proportion of incidence. This gives rela-
tive precision of 75%, considering the gap in evidence
for dengue incidence in the study areas. The final sample
sizes were calculated by assuming 10%–20% (variable by
site) non-response rate or loss to follow-up. The required
catchment population size for the fever surveillance study
in Burkina Faso was estimated to be 100 000, Gabon to be
77 000 and Kenya to be 70 000. In these catchment popu-
lations, the number of enrolled subjects depends on the
number of eligible patients who seek care at the study
facilities. How many eligible febrile episodes would actu-
ally present at our study facilities was difficult to predict;
but after assessment of the volume of febrile patients at
the facilities, a realistic upper limit for enrolment for a
study period of approximately 1.5 years was set at 3000
subjects to offer enrolment to all consenting eligible
patients.
For the serological survey, the sample size was calcu-
lated similarly using the prevalence proportion based
on published literature. Seroprevalence of 0.304 for
Burkina Faso,15 0.123 for Gabon,21 and 0.144 for Kenya36
were used. With the same confidence levels and allowed
margin of error and assuming 10%–30% (variable by site)
non-response rate, the sample size was calculated to be
3000 participants at each site. Again, with the scarcity of
data from the selected countries, there were no other
Figure 4 Map of the study area in Mombasa, Kenya.
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prevalence estimates reported or estimates from different
age groups. As prevalence is expected to increase with age
and higher prevalence would give a smaller sample size,
our calculations are likely to be conservative.
Study components
Fever surveillance—design and methods
To determine burden due to symptomatic dengue in
each of the three sites in Burkina Faso, Gabon and
Kenya, passive facility-based fever surveillance was imple-
mented in a well-defined catchment area population.
In Burkina Faso, the surveillance study was initiated
in December 2014 in five selected primary healthcare
centres, locally called ‘Centre de Santé et de Promotion
Sociale’, in the municipality of Ouagadougou, with a
catchment population of 105 000 residents. This project
was implemented in collaboration with Centre Muraz in
Bobo-Dioulasso, EQUITE sante programme (a collabo-
rative programme between University of Montreal and
Action-Gouvernance-Integration-Reinforcement, AGIR,
based in Ouagadougou, funded by the Canadian Insti-
tute of Health Research) and DVI. In Gabon, the surveil-
lance study was initiated in the ASH serving a catchment
population of 130 000 residents in the Moyen-Ogooué
and surroundings within Lambaréné, in collaboration
with CERMEL and Institute of Tropical Medicine in
Tubingen, Germany. In Kenya, the surveillance study was
implemented at Ganjoni dispensary, Tudor subcounty
Hospital and Coast Provincial General Hospital, serving a
catchment population of 70 000 residents in Mombasa, in
collaboration with Kenya Medical Research Institute and
Ministry of Health of Kenya.
As described in figure 5, both outpatients and inpa-
tients at the designated study facilities, who meet inclu-
sion criteria as mentioned earlier were tested for dengue,
first with SD Dengue Duo RDT. Dengue confirmation
was done by detection of dengue virus in serum samples
using PCR as well as antidengue IgM and IgG antibodies
in acute and convalescent serum by ELISA (SD Dengue
IgM & IgG capture ELISA tests, Standard Diagnostics,
Yongin-Si, Korea).10 37 Every consecutive patient meeting
inclusion criteria was eligible for enrolment during the
study period. Infants<1 year old were not included due
to operational limitations, such as difficulty of infantile
bleeding.
In Ouagadougou, Burkina Faso, the fever surveillance
was initiated in December 2014 and continued until
February 2017 (approximately 2 years). In Lambaréné,
Gabon, the fever surveillance was initiated in April
2015 and continued until January 2017 (approximately
1.5 years). In Mombasa, Kenya, the fever surveillance
was initiated in March 2016 and continued until May
2017 (15 months).
Among subjects enrolled in the fever surveillance,
those who were positive by dengue rapid diagnostic test
were offered further enrolment in the cost-of-illness
survey, consisting of interviews on the day of acute illness
visit, day 10–14 from the first visit and day 28, if illness
continues. The cost-of-illness survey questionnaire was
designed to estimate the direct medical, direct non-med-
ical and indirect costs associated with dengue-positive
patients identified at study facilities. This survey also
estimates the cost of treating dengue at the facility level.
Data were gathered by linking patients’ medical records
concerning outpatient visits, inpatient visits and service
consumption (eg, diagnostic tests, medication and other
services provided to patients). The cost-of-illness portion
of the study will be described separately.
Fever surveillance—laboratory testing
As shown in figure 6, in all three sites, acute samples
were tested using a commercial RDT for dengue NS1 and
IgM/IgG (Dengue Duo, Standard Diagnostics, Yongin-Si,
Korea). Dengue Duo RDT was used on the day of acute
illness visit at the site of patient presentation (day 1). The
acute and convalescent samples were subsequently tested
at a local laboratory using dengue IgM/IgG ELISA (SD
Dengue IgM & IgG Capture ELISA, Standard Diagnostics,
Yongin-Si, Korea). The serum was separated and stored in
4 aliquots of about 500 µL for various laboratory tests, as
indicated in consent documents.
After ELISA testing, samples were shipped to the
International Vaccine Institute (IVI) in Korea. Samples
with positive results by RDT or ELISA, as well as a small
number of samples with negative results, undergo further
testing by RT-PCR at the Clinical Immunology Labo-
ratory of IVI. Four DENV serotype-specific real-time
RT-PCR assays are used for laboratory confirmation of
dengue and serotyping.38 The DENV 1–4 RT-PCR assays
are carried out in 25 µL reaction mixtures containing
5 µL template RNA, TagMan Fast Virus 1-step mastermix
(Applied Biosystems), 0.9 µM of each primer and 0.2 µM
probe.38 Amplification and detection are performed in a
StepOne Plus real-time PCR system, and the baseline and
threshold are determined using the auto-baseline and
threshold feature in StepOne Software V.2.2.2 (Applied
Biosystems). Thermocycling parameters are as follows:
reverse transcription at 50°C for 5 min, inactivation at
95°C for 20 s, followed by 45 cycles of fluorescence detec-
tion at 95°C for 3 s and annealing at 60°C for 30 s.38 A
specimen is considered positive if target amplification is
recorded within 40 cycles.
Serological survey—design and methods
While the facility-based fever surveillance studies provide
estimates of the burden of medically attended dengue
disease, evaluation of all DENV infections in a popu-
lation—including subclinical and mildly symptomatic
infections, which impact immune status—is needed
to capture the overall impact of dengue. As part of the
study package, population-based serological surveys
were conducted in the same catchment population
used for the fever surveillance. At each of the three sites
in Africa, the serosurvey was conducted on a cohort of
approximately 3000 randomly selected residents of urban
and semiurban parts of Ouagadougou, Lambaréné and
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Mombasa. Without individual-level census information
on all residents of Lambaréné and Mombasa, with help
of community/village health workers, randomisation
was done based on neighbourhoods (or defined areas
for which the health workers/volunteers are respon-
sible) as cluster units. As the community/village health
workers are familiar with the villages and their residents,
they are good entry points into the communities. With
these health workers, the field team screened houses in
the selected villages by knocking on doors of every 5–7
houses, depending on the household density per neigh-
bourhood. Also, demographic information collected in
previous research projects conducted in the same area
was used as a guide, if available. In the case of the site
in Ouagadougou, HDSS data were available and the
EQUITE SANTE, a CIHR funded research programme of
the University of Montreal, had set up a geographic infor-
mation system database of houses in the study area. Using
these data, households of potential enrolees of the sero-
survey were preselected randomly and household visits
were made in Ouagadougou. In the three sites, about 45%
of the serosurvey samples were targeted to be collected
from children 1 to 14 years of age, and 55% were targeted
to be collected from adults between 15 and 55 years of
Figure 5 Patient ow in the fever surveillance.Eligible febrile patients identied and enrolled as study subjects followed these
steps to complete participation in the passive fever surveillance.* A small number of those samples that are negative on ELISA
or NS1 are tested with PCR to exclude false negative results of the ELISA. CRF, case report form.
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age to reflect the age distribution of the general popula-
tion of the area. Household-based enrolment was offered
to the head of the household until the specific cap for the
age-group was reached in Lambaréné and Mombasa.
Randomly selected subjects 1–55 years of age under-
went phlebotomy (5 mL for children and 7 mL for adults)
twice—before the rainy season and after the rainy season,
at approximately 6-month intervals. The sera were eval-
uated using IgG indirect ELISA at baseline and after
6 months. The presence of dengue IgG antibodies at
6-month intervals will be used to estimate the level of
occurrence of inapparent DENV infection and to calcu-
late the rate of infection in the catchment population.
Flow cytometry-based DENV neutralisation assays will be
applied to a subset of samples to assess for presence of
dengue neutralising antibodies and seroconversion over
the 6-month interval. In addition to overall seroconver-
sion, age-specific seroconversion estimates in the catch-
ment population as well as the proportion of inapparent
infections will be determined.
Serological survey—laboratory testing
From the samples collected in the serosurvey, about
200 µL of serum were used and tested at a local labora-
tory using dengue IgG ELISA (Panbio Dengue IgG Indi-
rect ELISA, Alere North America, Florida, USA). After
ELISA testing for dengue IgG at the local laboratories,
samples were shipped to IVI. Given potential serological
cross-reactivity among flaviviruses,39 flow cytometry-based
neutralisation assays will be performed against selected
flaviviruses to include yellow fever virus, West Nile virus,
Zika virus and Japanese Encephalitis virus, in addition
to DENV 1-4, at the Clinical Immunology Lab of IVI.40 41
About 50 samples per bleed for four bleeds in Burkina
Faso and two bleeds in Gabon and Kenya will be tested.
About 1000 µL of serum is allotted for this proce-
dure. The flow cytometry-based neutralisation assays
are performed in duplicate in 96-well cell culture plates
with flat-bottom wells, each containing DC-SIGN-ex-
pressing U937 cells.40 The amount of virus used in the
assay infects between 7% and 15% of the cells. Human
immune sera are serially diluted and the virus is prein-
cubated with the sera for 1 hour at 37°C.40 The cells are
washed, the virus and serum mixture is added to the cells
for 1 hour at 37°C and the cells are further incubated
for 24–48 hours at 37°C in 5% CO2. The cells are fixed,
permeabilised and stained with fluoresce-conjugated
monoclonal antibody 4G2, which recognises the flavivirus
E protein.42 FACScan flow cytometer (Becton Dickinson,
San Diego, California, USA) is used to analyse the cells.40
The serum dilution that neutralises 50% of the viruses is
calculated by nonlinear, dose-response regression analysis
with Prism 4.0 software (GraphPad Software, San Diego,
California, USA).
In addition, a Luminex-based multiplex immunoassay
will be performed on a randomly selected subsample
to assess for IgG to different flaviviruses.43 About 200
samples per bleed for four bleeds in Burkina Faso and
two bleeds in Gabon will be tested. Detection of IgG
against ZIKV and each of the four DENV serotypes will be
performed on patient serum samples using an in-house
microsphere-based multiplex immunoassay (arbo-MIA)
at the Clinical Immunology Lab of IVI.44 45 The arbo-MIA
is based on a mixture of microspheres covalently coupled
with either DENV-1, DENV-2, DENV-3, DENV-4 or ZIKV
Figure 6 Laboratory testing algorithm for dengue.Samples from subjects of the passive fever surveillance would follow these
steps of the testing algorithm for conrmation of dengue.*Dengue Duo®test is performed on enrolled febrile patients to identify
dengue cases for immediate follow-up of dengue-conrmed cases in the cost-of-illness survey. **Selected samples, including
those that were found positive by IgM and NS1 on Dengue Duo®,as well as those positive by IgM and IgG capture ELISA, will
be tested with RT-PCR.
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recombinant antigens (E protein domain III) produced
in Drosophila S2 expression system. Briefly, microsphere
mixtures were sequentially incubated in the dark under
constant shaking with a 1:400 dilution of patient serum
samples, with 2 µg/mL antihuman IgG biotin-conjugated
antibody (Jackson Immunoresearch, West Grove, Penn-
sylvania, USA) and with 2 µg/mL streptavidin-R-phyco-
erythrin conjugate (Life technologies). After the final
incubation, the median fluorescence intensity (MFI)
of each microsphere set is quantified using a BioPlex
200 instrument (Bio-Rad Laboratories, Hercules, Cali-
fornia, USA). Samples are considered seropositive if the
ratio of MFI values obtained for the viral antigen to the
control antigen is superior to the defined cut-off. The
cut-off of the MIA is determined for each viral antigen by
receiving operating characteristic (ROC) curve analysis
using well-characterised sera.
In Lambaréné, the enrolment bleed took place in
November–December 2015, while the second blood
collection occurred in May 2016. In Ouagadougou,
the enrolment bleed took place in May–June 2015 with
follow-up blood collections in December 2015, June 2016
and January 2017. In Mombasa, the enrolment bleed
took place in May 2016 with the second blood collection
in November 2016–February 2017.
Healthcare utilisation survey
As the passive fever surveillance was conducted at study
facilities, patients with potential dengue could be missed
if they seek care elsewhere. To identify the proportion of
fever and dengue cases potentially missed by the passive
surveillance system due to patients living in the study
area but seeking care outside of study facilities, a popu-
lation-based healthcare utilisation survey was conducted
in 400 randomly selected households from the study
catchment area to characterise the healthcare utilisation
patterns of the households when they have (self-reported)
febrile episodes among the family members. In addition
to assessing health-seeking behaviours of the residents,
preferences in terms of health-seeking behaviour and
respective reasons for their preferences were investi-
gated. The questionnaire was administered to 400 heads
of households. Among 3000 residents who participated
in the serosurvey, there were about 600 households.
From these households, 400 heads of households were
randomly selected and offered enrolment in the health
utilisation survey. Heads of households or a senior repre-
sentative within the household were asked questions on
health seeking patterns of their family members.
Study questionnaires
For the fever surveillance study, questionnaires were
administered at the acute illness visit and the convalescent
visit. The convalescent visit may take place at the health-
care facility (10–14 days later) or at the patient’s home
(15–21 days after the acute visit), according to patient
preference and availability. The questionnaires were
completed by medical staff of the study facilities, including
demographic and clinical information (eg, signs, symp-
toms, past medical history, treatments prescribed and
diagnoses). The same staff also completed the follow-up
questionnaire at the convalescent visit within 21 days
from the acute visit. Study nurses completed surveillance
enrolment log. Lab technicians completed the lab section
(mostly dengue-related diagnostics) and the forms were
compiled by the study coordinator on site.
For the serosurvey component, questionnaires were
administered at the household by trained field team staff
at each serosurvey visit. Study nurses completed the ques-
tionnaire after a brief physical and medical examination.
At the follow-up visit(s) in about 6 months, the same staff
made the household visits to complete the follow-up ques-
tionnaire. Enrolment log was maintained by the study
coordinator on site.
Variables of the surveillance questionnaires
The variables collected are listed in table 1.
Planned statistical analysis
From the fever surveillance data, incidence of symptom-
atic dengue among patients that seek healthcare at the
study facilities will be calculated. Age-specific incidence
rates in all the children and adults will be determined by
referring to the size and distribution of the general popu-
lation of the study area at the time of surveillance as the
denominator in calculation of the incidence of symptom-
atic dengue cases. Each person residing in the study area
is assumed to contribute 12 months of person time to the
denominator. Although the study areas all report a low
migration rate, the in-migration is assumed to balance the
out-migration of the population during the study period.
Age-specific incidence of symptomatic dengue will be
calculated by using age-specific denominators and the
number of symptomatic dengue cases in eligible individ-
uals as the numerator.
Using the data collected in the Healthcare Utilisation
Survey, the proportion of febrile cases missed by the
passive surveillance system will be determined. Then
using the proportion, the numerator will be further
adjusted in recognition of those missed fever cases from
the study area, which could have been dengue. Also,
comparison will be made between those that agreed to
participate and those that declined participation among
the eligible potential enrolees. The enrolment log, which
records basic information obtained during the screening
process of potential enrolees, will be reviewed. In addi-
tion to checking that our sample of febrile cases is repre-
sentative of febrile patients of the general population
in the catchment area, refusal rates will be determined
based on information in the log. Then, the refusal rates
will be used to adjust the numerator.
SPSS software will be used for analysis of the fever surveil-
lance data. Multivariable logistic regression will be used to
compare confirmed patients with dengue versus patients
with non-dengue febrile in terms of symptomatic presen-
tation, based on signs and symptoms collected from all
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patients with laboratory-confirmed dengue by serology and
RT-PCR, adjusting for possible confounders, such as age,
days since onset of fever, primary versus secondary infection,
inpatient versus outpatient and so on. Differences in symp-
tomatic complex of dengue fever (DF) (and DHF, if data
allows) by age and serotype will be also determined using
multivariable logistic regression.
As outpatient disease accounts for the greater part of
dengue disease burden, clinical profile of individuals with
DENV infection will be characterised by the type of treat-
ment (hospitalised vs outpatients) as well as by severity
of the disease (severe vs non-severe by the 2009 WHO
criteria).46 Classification is determined after the course
of illness is completed (typically during the convalescent
visit). Symptomatic dengue is classified as outpatient or
hospitalised. Progression of dengue is recorded as DF,
DHF I, DHF II, DHF III or DHF IV, and clinical patterns
will be compared by the severity grade.46 47 These will be
compared with results obtained from other DVI studies in
Latin America (Colombia) and Asia (Thailand, Vietnam
and Cambodia). Overall, comparisons will be made across
Burkina Faso, Gabon and Kenya.
Table 1 List of variables collected in the passive fever surveillance data collection form
Topic Description Items
Basic information Demographic and basic information about the
patient and the treatment received
Type of treatment, where patient is enrolled (IPD vs OPD)
Date of fever onset, duration of fever
Current temperature
Tourniquet test results
Patient’s address (district and village-level)
Date of visit, date of birth, age and sex
Weight and height
General health
condition
Current condition of the patient (self-report) and
underlying diseases of the patient
How well the patient could handle daily activities
Pre-existing conditions
Signs and
symptoms during
this illness
A set of signs and symptoms that may be
related to fever and dengue (dengue fever and
dengue haemorrhagic fever) at both visits 1 and
2
Rash, fatigue, headache, retro-orbital pain, neck/ear pain,
sore throat, breathing difculty, cough, expectoration,
gastrointestinal signs (nausea/vomiting, diarrhoea,
abdominal pain and so on), haemorrhagic signs (nose/
gum bleeding, ecchymosis, petechiae and so on), signs
of shock (cyanosis, capillary rell), arthralgia, myalgia,
loss of appetite, jaundice and so on
Medical history Previous dengue-related or other avivirus
infection as well as vaccination history (self-
report)
Previous dengue infection and related hospitalisation
Previous infection to other commonly circulating arboviral
infection in the area (ie, Yellow fever vaccination history)
Laboratory
ndings
Records from the routine laboratory tests
widely used in clinical fever/dengue patient
management, as part of the hospital care
procedure
Platelet count, haematocrit, haemoglobin, leucocytes,
neutrophils, protein level, AST, ALT, urine test results and
so on
Clinical diagnosis Clinician’s diagnosis with or without referring to
the RDT
Diagnosis given by the physician based on clinical
presentation after physical examination of the patient
Dengue testing
results
Results from the dengue tests, mainly RDTs for
dengue as well as other commonly circulating
arbovirus in the area
Dates of blood draw
Test results of the RDT
IgM/IgG capture ELISA results
PCR results (if available)
Treatment Medicine(s) prescribed and the starting and end
dates
Antibiotics, paracetamol, ibuprofen, aspirin and others
that may be site-specically prescribed
Outcome Outcome of this particular visit Hospitalised, returned home or referral
Hospitalisation Information collected only among hospitalised
patients in the surveillance to record other
severe signs and progression of illness
Admission and discharge diagnoses
Presence of haemorrhagic signs or shock syndrome
Hospital charges Expenses and hospital charges incurred by
patient on the visit 1
Amount of the out of pocket payment by the patient or
the family/or guardian
Breakdown of the hospital charges (laboratory,
medication, admission-related charges)
Final outcome Outcome of the patient’s illness at the second
visit
Final diagnosis given for the patient, outcome of illness
Completion of study participation (early termination and
the reason and so on)
ALT, Alanine AminoTransferase; AST, Aspartate aminotransferase; IPD, Inpatient department; OPD, Outpatient department; RDT, Rapid
Diagnostic Test.
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With the age-stratified sera that reflect the age distribu-
tion of the general population of the country, the serolog-
ical survey sampling strategy ensures sufficient subjects to
obtain precise age-specific estimates of seropositivity and
seroconversion of the catchment area population. The
seroconversion rate and change in the immune status will
be determined by age group during the study period. The
age-stratified serosurvey data will also allow calculation of
the force of infection of dengue in the study population.
After enrolment, there are subjects who drop out in the
follow-up bleeds about 6 months later. Basic demographic
information will be compared between those that completed
participation and those with incomplete participation to
check whether study subjects represent the catchment area
population. Comparisons will be made among Burkina Faso,
Gabon and Kenya.
Ethical considerations
To minimise inconvenience of the study to patients, clini-
cians and nurses were sensitised and trained regarding
the study requirements and procedures in order for data
collection to be integrated into routine patient care. The
clinicians and nurses selected for the study receive coordi-
nated support from study field staff throughout the study
process. Written informed consent and assent for partic-
ipants 7 (13 for Kenya)−17 years of age were obtained
from patients by study staff. Study staff go through
consent and assent documents for short summary of the
disease, detailed description of study procedures and
information on reimbursement. Patient data are docu-
mented in the study designated office; only the study staff
have access to the data that are de-identified. Data are
exclusively handled in the study office and stored safely in
a protected database in the study office as well as on the
DVI main server.
DISCUSSION
Dengue cases have been detected since the 1960s in
Africa, and there has been continued presence of Aedes
vectors in the continent.5 7 However, very few dengue
studies have been conducted in Africa, and little evidence
is based on population-based studies.6 Compared with the
volume of evidence from SE Asia and the Americas, there
is critical data scarcity on dengue in Africa. Suspicion of
substantial dengue burden in Africa is based on limited
reports of outbreaks and a handful of seroprevalence
studies testing different viruses among samples that likely
do not represent the general population. In the three
countries selected for our field studies, somewhat more
data are available, but are still very limited. In Burkina
Faso, a recent observational study conducted in 2013
reported that 8.7% of the febrile patients showed positive
results on dengue RDT.16 In Gabon, one study suggested
minimal DENV circulation in rural areas,21 while another
study reported 12.3% seroprevalence, by IgG antibodies
against dengue, among toddlers 30 months of age in
semirural parts of Lambaréné.20 In Kenya, about 13% of
the individuals in Mombasa have been reported to have
evidence of past or current DENV infection by RT-PCR
and IgM antidengue ELISA after the 2013 outbreak.26
Despite the limited scope and generalisability of these
studies, they suggest that there may be more dengue
than previously appreciated due to underestimation and
misdiagnosis.25 26
These studies suggest the presence of dengue and
some level of underlying seroprevalence in the coun-
tries of our field studies. However, often these studies are
limited by their retrospective design or sample collection
(blood donors or sample collected from surveys of other
diseases) to demonstrate the true, population-based,
burden of dengue. We proposed to address this gap by
population-based dengue surveillance and seropreva-
lence studies in West, (West-) Central and East Africa.
The present studies at three sites in Africa will provide
important information on undocumented DENV circu-
lation in Africa. Such data will help to strengthen the
evidence base for dengue burden in Africa. Better
defined disease burden data based on our studies could
be used to assess the relative need for dengue prevention
and control measures, such as whether a dengue vaccine
would be a cost-effective public health intervention for
countries in Africa. Clinical findings from our studies
could also be used as a guide for dengue case detection
and case management.
The studies have some important limitations. We recog-
nise variability of dengue epidemiology over time and
by region. Due to resource constraints, our studies are
limited in terms of time frames and geographical extent.
These constraints may limit the generalisability of our
study results.
One potential source of bias in estimating the incidence
of symptomatic dengue is under-ascertainment due to
the community residents with relevant symptoms seeking
care from other healthcare providers and facilities than
the study facilities. As the study design remains passive
surveillance, cases are ascertained only at our study facil-
ities. By estimating the proportion of febrile patients
seeking care elsewhere as well as refusal rates among
the potential enrolees that were screened for eligibility
criteria, the degree of febrile patients missed by the study
can be determined. Inverse probability weighting will be
used to account for these potential subjects missed by
the surveillance as adjustments in incidence calculation.
Also, depending on the transmission volume of dengue
or other cocirculating diseases with onset of fever, there
may be patients that are diagnosed with other diseases
and ruled out for dengue. Furthermore, with respect to
dengue diagnostics for our serological surveys, there are
other circulating flaviviruses in Africa leading to chal-
lenges in identifying antibodies to past dengue infec-
tions. While our testing plan assesses for some flaviviruses,
others known to circulate in Africa, such as Banzi and
Usutu viruses, are not part of the testing plan.48–50 Due to
resource limitations, serological testing will be limited to
yellow fever virus, West Nile virus, Zika virus and Japanese
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Encephalitis virus as well as DENV 1–4. Therefore, in
some cases, it may be difficult to determine prior expo-
sure to DENV versus other flaviviruses based on serolog-
ical data. This cross-reactivity may lead to overestimation
of dengue force of infection.
In addition, the serosurvey and healthcare utilisation
survey are conducted on a randomised subsample of the
catchment area population and there may be limited
generalisability of the data collected from these surveys.
With unknown differences among those that agree to
participate and those that do not agree, the data may not
be representative of the general population of the study
countries.
CONCLUSION
The data collected from our studies will contribute to
the assessment of the unknown dengue disease burden
in Burkina Faso, Gabon and Kenya. These data can fill a
gap in undocumented burden of dengue in the region
and, collectively, may be used to infer dengue burden in
other areas of Western, Central and Eastern Africa. Coun-
tries in Africa may not consider introduction of a dengue
vaccine as a priority in the near future due to many other
competing public health problems and limited resources.
For cost-effective implementation of public health inter-
ventions, accurate data on dengue burden from epide-
miological studies would be needed for policy makers to
make evidence-based decisions on control and prevention
of dengue. Our studies will provide some much needed
information based on population-based research to assess
dengue burden in Africa.
Author afliations
1Global Dengue and Aedes-transmitted Diseases Consortium, International Vaccine
Institute, Gwanak-gu, The Republic of Korea
2Faculty of Epidemiology and Population Health, London School of Hygiene and
Tropical Medicine, London, UK
3Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal,
QC, Canada
4Development and Delivery, International Vaccine Institute, Gwanak-gu, The
Republic of Korea
5School of Public Health, University of Montreal, Montreal, Quebec, Canada
6Centre de Recherches Médicales de Lambaréné, Fondation Internationale de
l’Hôpital Albert Schweitzer, Lambaréné, Gabon
7Department of Communicable Disease Prevention and Control, Ministry of Health,
Nairobi, Kenya
8Institute of Tropical Medicine, University of Tübingen, Tübingen, Germany
9Eastern and Southern Africa Centre of International Parasite Control (ESACIPAC),
Kenya Medical Research Institute, Nairobi, Kenya
10Program Equité, Action-Gouvernance-Integration-Reinforcement, Ouagadougou,
Burkina Faso
11UMI Résiliences, Institut de recherche pour le developpement (IRD), Paris, France
12Centre Muraz, Bobo Dioulasso, Hauts Bassins, Burkina Faso
Acknowledgements We thank the doctors and laboratory staff of CSPS of
Ouagadougou, ASH, Ganjoni dispensary, Tudor subcounty Hospital and Coast
Provincial General Hospital as well as at Centre Muraz, CERMEL and KEMRI. We
thank collaborators at AGIR, IRD France and University of Montreal, and University
of Tubingen. Last, we would like to thank the DVI team as well as statisticians,
laboratory, and administrative staff at the International Vaccine Institute for their
helpful comments during the preparation of this manuscript and support during the
studies.
Contributors JKL designed the study, is overseeing data collection and was a
major contributor in writing the manuscript. MC codesigned the study, oversaw
some parts of data collection and supported in writing of the manuscript. JSL was
a contributor in designing of the study and oversight of parts of data collection. KSL
was a contributor in oversight of data collection. SN, SKL, EA, NO and AB supported
in data collection. VR supported in designing of the study and was a major
contributor in nalisation of the manuscript. JF was a contributor in data collection.
BL was a contributor in designing of the study and data collection. SHM was a
contributor in designing of the study and site establishment. ME was a contributor
in designing of the study. EB supported in data generation. SMN was a contributor
in designing of the study and site establishment. STA and SY were contributors
in designing of the study and site establishment. NA was a major contributor in
providing oversight of the data collection and nalisation of the manuscript. IKY was
a major contributor in designing of the study and nalisation of the manuscript. All
authors read and approved the nal manuscript.
Funding The current study was supported by funds from the Bill and Melinda
Gates Foundation (OPP 1053432). NA receives support from the United Kingdom
Medical Research Council (MRC) and Department for International Development
(DFID)(MR/K012126/1). VR holds a CIHR-funded Research Chair in Applied Public
Health (CPP-137901).
Disclaimer The funding body had no role in the design of the study and collection,
analysis and interpretation of data and in writing the manuscript.
Competing interests None declared.
Patient consent Obtained.
Ethics approval The protocol for each study obtained ethical approvals from the
Institutional Review Boards (IRBs) of the International Vaccine Institute, the London
School of Hygiene and Tropical Medicine and the Ethics Committee of host country
institutions, including KEMRI Scientic and Ethical Review Unit in Kenya, Gabon
National Ethics Committee and Institutional Ethics Committee, Scientic Review
Board of CERMEL in Gabon and the IRB of Centre Hospitalier de l'Universitéde
Montréal (CRCHUM) at University of Montreal and the National Health Ethical
Committee of Burkina Faso.
Provenance and peer review Not commissioned; externally peer reviewed.
Open Access This is an Open Access article distributed in accordance with the
terms of the Creative Commons Attribution (CC BY 4.0) license, which permits
others to distribute, remix, adapt and build upon this work, for commercial use,
provided the original work is properly cited. See: http:// creativecommons. org/
licenses/ by/ 4. 0/
© Article author(s) (or their employer(s) unless otherwise stated in the text of the
article) 2018. All rights reserved. No commercial use is permitted unless otherwise
expressly granted.
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studies of the Dengue Vaccine Initiative
seroprevalence studies: protocol of field
passive fever surveillance and
Evaluating dengue burden in Africa in
Todagbe Agnandji, Seydou Yaro, Neal Alexander and In-Kyu Yoon
Oyembo, Ahmed Barro, Emmanuel Bonnet, Sammy M Njenga, Selidji
Lell, Sultani Hadley Matendechero, Meral Esen, Esther Andia, Noah
Lee, Suk Namkung, Sl-Ki Lim, Valéry Ridde, Jose Fernandes, Bertrand
Jacqueline Kyungah Lim, Mabel Carabali, Jung-Seok Lee, Kang-Sung
doi: 10.1136/bmjopen-2017-017673
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