Risk of bias and confounding of observational studies of Zika virus infection: A scoping review of research protocols
Risk of bias and confounding of observational studies of Zika virus infection: A scoping review of research protocols
Ludovic Reveiz 0 1
Michelle M. Haby 0
Ruth MartÂõnez-Vega 0
Carlos E. PinzoÂ n-Flores 0
Vanessa Elias 0 1
Emma Smith 0 1
Mariona Pinart 0
Nathalie Broutet 0
Francisco Becerra- Posada 0 1
Sylvain Aldighieri 0 1
Maria D. Van Kerkhove 0
0 Editor: Marcia Edilaine Lopes Consolaro, Universidade Estadual de Maringa , BRAZIL
1 Pan American Health Organization , Washington, D.C. , United States of America, 2 Department of Chemical and Biological Sciences, Universidad de Sonora, Sonora, Mexico, 3 Escuela de Medicina, Universidad de Santander, Bucaramanga, Colombia, 4 Grupo de Investigaci oÂn de Salud, Universidad de La Sabana, Bogot aÂ, Colombia, 5 SubdireccioÂn de Producci oÂn de GuÂõas de PraÂctica ClÂõnica, Instituto de Evaluaci oÂn Tecnol oÂgica en Salud (IETS), Bogot aÂ, Colombia, 6 Yale School of Public Health , New Haven , Connecticut, United States of America, 7 Cochrane Skin Group, The University of Nottingham , Nottingham , United Kingdom , 8 World Health Organization , Geneva, Switzerland, 9 Center for Global Health , Institut Pasteur , Paris , France
sure and outcomes (microcephaly and Guillain-BarreÂ Syndrome). Potential confounders
need to be measured where known and controlled for in the analysis. Selection bias due to
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files. Any additional information can be found on
the PAHO Zika Virus Research Platform on the
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
Abbreviations: CDC, Centers for Disease Control;
CONSISE, Consortium for the Standardization of
Influenza Seroepidemiology; CT, Computed
Tomography; ELISA, Enzyme-linked
immunosorbent assay; GBS, Guillain-BarreÂ
Syndrome; IMF, immunofluorescence assay;
INTERGROWTH, International Fetal and Newborn
Growth; ISARIC, International Severe Acute
Respiratory and Emerging Infection Consortium;
LAC, Latin American countries; MRI, Magnetic
Resonance Imaging; PAHO, Pan American Health
Organization; PCR, polymerase chain reaction;
TORCH, includes Toxoplasmosis, syphilis,
varicella-zoster, parvovirus B19, Rubella,
Cytomegalovirus (CMV), and Herpes infections;
WHO, World Health Organization; ZIKV, Zika virus.
non-random selection is a significant issue, particularly in the case-control design, and
losses to follow-up is equally important for the cohort design.
Observational research seeking to answer key questions on the ZIKV should consider these
restrictions and take precautions to minimize bias in an effort to provide reliable and valid
results. Utilization of the standardized research protocols developed by the WHO, PAHO,
Institut Pasteur, and CONSISE will harmonize the key methodological aspects of each study design to minimize bias at different stages of the study. Biases need to be considered by researchers implementing the standardized protocols as well as by users of observational epidemiological studies of ZIKV.
Although the Zika virus (ZIKV) was first identified in the mid twentieth century on the
African continent, only fourteen cases were documented in humans prior to the first large,
documented epidemic outbreak on the Island of Yap in 2007 [
]. This was followed by the largest
ZIKV outbreak ever previously reported in French Polynesia from October 2013 to April 2014
]. Since 2015, 76 countries and territories around the world have reported mosquito-borne
ZIKV transmission, particularly the Americas where Brazil has been hit the hardest . In
February 2016 the World Health Organization declared the ZIKV outbreak a Public Health
Emergency of International Concern .
Infection with Zika virus is asymptomatic in an estimated 50 to 80% of cases and when Zika
virus does cause illness; symptoms are generally mild and self-limited [
]. The most recent
outbreak of ZIKV has been associated with an increase in cases of microcephaly and congenital
neurological malformations and disabilities in babies [6±9] and Guillan-BarreÂ Syndrome
(GBS) and GBS-like syndrome in adults [
]. A recent systematic review of the literature
found sufficient evidence to conclude that ZIKV is a cause of congenital abnormalities and is a
trigger of GBS [
]. However, the authors acknowledged caveats in methodological aspects,
inconsistencies, and gaps in the body of evidence for both sets of conditions. ZIKV alone may
not be sufficient to cause either congenital brain abnormalities or GBS and may depend on as
yet uncharacterized cofactors being present.
Epidemiological studies can provide valuable information to understand the spectrum of
disease ZIKV infection causes and inform potential strategies to minimize its impact. In
particular, a well-designed observational study can play an important role in understanding the
associations between exposure to ZIKV and disease outcomes or other health conditions [
However, an association does not necessarily establish causation and dubious conclusions can
be drawn as a result of bias and confounding in those studies. As has been done with other
infectious diseases , it is important to assess the risk of bias and confounding in
observational studies of ZIKV.
Given the severity of the current outbreak, numerous countries have quickly developed
epidemiological studies to assess ZIKV and its potential health consequences [
]. In an effort to
ensure that studies are comprehensive, both internally and externally valid, and with reliable
results, the World Health Organization (WHO), Pan American Health Organization (PAHO),
Institut Pasteur, the Consortium for the Standardization of Influenza Seroepidemiology
2 / 17
(CONSISE), Fiocruz, the International Severe Acute Respiratory and Emerging Infection
Consortium (ISARIC) and others joined efforts to harmonize current research through discussions
on ongoing research on ZIKV and associated complications. In July 2016, the collaboration
produced a final set of six standardized protocols for cohort, case-control, and cross-sectional
studies [17±22], summarized in Table 1.
Here, we aim to characterize the risks of bias and confounding in ZIKV observational
studies and propose ways to minimize them, including the use of the standardized protocols. Biases
need to be considered by researchers implementing the standardized protocols as well as by
users of observational epidemiological studies of ZIKV infection.
The PAHO, through the creation of the Zika Virus Research Platform (http://www.paho.org/
zika-research/), maintains a database of research protocols and primary research studies,
including both observational and experimental study designs. These records contain data
elements that describe the study's purpose, recruitment status, design, eligibility criteria, and
locations, as well as other key protocol details. Resources and links to additional information
are inserted by the PAHO to enhance the overall usefulness of the database. Researchers,
policymakers, and others can now examine features and ongoing trends of Zika virus research.
Our analysis was based off of information from case-control and cohort studies. Many of
these protocols were shared with us following correspondence directly with the principle
investigator. The principle investigators frequently requested that the information in their
protocol remained confidential; for that reason we analyzed the protocols generally and
highlighted common biases found within the selected protocols [23±26]. Many of these
protocols were not the final version and changes were made beyond the protocol submitted to the
PAHO Zika Virus Research Platform.
Research protocols were collected by directly liaising with a number of institutions and
organizations, such as the US Centers for Disease Control and Prevention; the US National
Institutes of Health; the Microcephaly Epidemic Research Group (MERG) from Pernambuco
Brazil; the International Research Consortium of Dengue Risk Assessment, Management, and
Surveillance (IDAMS) research group; REACTing/INSERM; the Brazil Ministry of Health;
Fiocruz and maternity hospitals in Brazil.
We additionally carried out a systematic search of registered research protocols through
several databases including the International Clinical Trials Registry Platform (ICTRP) in the
United States and the Sistema Nacional de EÂtica em Pesquisa (SISNEP) in Brazil, among others
(S1 File). As a final step, authors of published studies identified (using the term ªZikaº) in
PubMed and Embase databases were contacted directly and invited to submit information if
they were planning or conducting research related to Zika virus infection. The Zika Virus
Research Platform includes protocols for 32 cohort studies (16 full text protocols) and 13 case
control studies that were used as a foundation to analyze potential biases in observational
studies of ZIKV. We conducted a comparative analysis of the available relevant protocols and
categorized the potential biases in each of the studies.
Additionally, relevant stakeholders attempted to minimize the potential biases found in
observational studies through the development of the six standardized research protocols that
were created during the ZIKV research consultation in May 2016 and face-to-face meeting in
Mexico City, Mexico in June 2016 [
]. These meetings permitted discussions on how to best
manage common biases in observational epidemiological studies of ZIKV and allowed authors
to consider the potential bias found in observational research.
3 / 17
· Measurement of the
exposure given challenges
in ZIKV serologic assays
and the lack of
understanding of antibody
kinetics in previously
· Selection of a
representative sample of
participants from the
defined geographic area
with or without current/
previous ZIKV circulation.
· Measurement of the exposure.
· Selection of a representative
sample of participants.
· Loss to follow-up.
4 / 17
Findings and discussion
Our searches in the PAHO's Zika Virus Research Platform found 317 research protocol titles.
We applied the inclusion criteria to the titles and summaries (when available) of all records
and excluded 288 of them. We contacted researchers to obtain the full text of each one of the
32 cohort and 13 case control studies that were identified. Supporting information 1±2 show
our screening and study selection process of the 16 cohort protocols and 13 case control
studies that were finally reviewed can be found in S2 Fig and S1 File. PRISMA 2009 Checklist is
presented in S2 File.
The following analysis is based on a revision of the observational protocols that were
available. It is our intention that it serves as a guide for researchers when considering specific risks
of bias common to cohort and case-control designs in the measurement of the exposure of
interest, measurement of the outcomes of interest, confounding and effect modification. The
main biases are also noted in Table 1 for each of the standardized protocols and S1 Fig shows
the overlap in risks of bias for the analytical observational study designs.
Common biases in observational epidemiological studies
Although the randomized controlled trial (RCT) is the gold standard to measure causality in
its unique advantage of random assignment, we must consider alternative research designs
5 / 17
that permit relatively strong causal inferences. Therefore, when conducting observational
studies researchers need to ensure that the internal validity of the study is not compromised by
bias and that the results found are close to the ªtruthº [
]. Biases common to all
observational studies include selection bias and information bias (Table 2, Adapted from: Bonita et al.
, Examples of low and high risk of bias in observational studies can be found in [
Studies that attempt to show a causal association between an exposure and outcome can also
be affected by confounding±these are the observational analytical designs, usually cohort and
While all designs have their advantages and disadvantages, researchers need to be aware
that depending on the research question [
· The probability of selection bias due to inclusion bias is highest in case-control studies.
· The probability of selection bias due to loss to follow-up is highest in cohort studies.
· The probability of measurement bias due to recall bias is highest in cross-sectional and
· Information bias due to instrument error and observer bias is common to all study designs.
· Confounding is an issue that is common to all observational analytical study designs.
Measurement of the exposure of interest±challenges in accurately identifying ZIKV infection
The symptomatology of ZIKV infection includes fever, rash, and conjunctivitis, as well as
other minor symptoms commonly associated with influenza, such as body aches and chills
]. However, an individual may be infected with ZIKV but either have none or some of the
aforementioned symptoms [
]. The range of symptoms of the ZIKV infection is so vast that a
myriad of definitions have been generated as to what constitutes an infected individual (i.e.
number and severity of symptoms present to qualify as a case of ZIKV), thus limiting the
external validity of studies if they use variable criteria.
Perhaps the main critical issue for all observational studies conducted since the ZIKV
outbreak of 2015 is the lack of a standardized definition of ZIKV infection due to challenges and
limitations of available molecular and serologic diagnostic tests for ZIKV [
]. This can lead to
risks of both selection bias and information bias due to misclassification of infection. In S1 Fig,
6 / 17
this issue is depicted at the center of the diagram (E) at the intersection of the four analytical
designs [17±20] described in Table 1, and also applies to the two descriptive observational
protocols [21, 22]. In studies where ZIKV infection determines participation, there is a risk of
selection bias if individuals do not meet the inclusion criterion for infection and may therefore
be systematically excluded from participation. In studies where ZIKV infection does not
determine participation, but is still considered an exposure of interest, there is a risk of information
bias due to misclassification of exposure.
Serologic assays use certain biomarkers present in blood and other bodily fluids to test for
the presence of antibodies suggestive of previous or ongoing ZIKV infection. However,
immunology assays (immunofluorescence assay (IMF) and enzyme-linked immunosorbent assay
(ELISA)) for ZIKV may result in misclassification errors due to cross-reactivity between ZIKV
and dengue virus (DENV) or other flaviviruses co-circulating in the proximity of ZIKV
patients and in addition the cross-reactivity but also the actual presence of anti-flavivirus
antibodies for two different viruses in the same patient [
]. At the time of writing, the United
States Centers for Disease Control (CDC) stated that, ªDue to serological cross-reactivity
between flaviviruses, current IgM antibody assays cannot reliably distinguish between Zika
and dengue virus infectionsº . The lack of reliable distinction between ZIKV and DENV is a
significant source of information bias for all studies of ZIKV where DENV is circulating, or
has circulated, and could contribute to confounding of the association between exposure to
the ZIKV and disease outcomes (see section 4 of this paper). However, a recent study using a
novel anti-ZIKV ELISA based on recombinant ZIKV non-structural protein1 found that
cross-reactivity with high-level dengue virus antibodies was not detected [
Interim case definitions for suspected, probable, and confirmed cases of zika have been
defined by the PAHO/WHO and their use should enable standardization and help limit
selection bias and information bias due to misclassification [
]. In the context of research
confirmed cases are required. The standardized protocols reference the PAHO/WHO case
definitions and include clear a description of which laboratory tests should be used during the
course of infection and when, the procedures to be followed, and definitions of cases .
These include lab algorithms to rule out other flaviviruses, such as DENV and the collection of
samples for all study participants, regardless of symptom status or case status. They also
include recommendations for reporting of results stratified by symptom status. Further, all of
the protocols include a standardized questionnaire  to ensure standardization of collection
of information on symptoms and other relevant information. Questionnaires were specifically
developed for each of the six zika standardized research protocols based on existing case report
While the test with highest specificity and sensitivity to detect ZIKV-infected patients is
reverse-transcriptase polymerase chain reaction (RT-PCR), which detects the presence of
ZIKV RNA in human body fluid samples [
], the use of this method is limited as ZIKV
can only be reliably detected in up to the first 7 days after infection [
]. RT-PCR is
expensive and samples must be sent to specialized laboratories for analysis, which are not yet readily
accessible in all countries affected by ZIKV. Thus, for epidemiological studies, seropositivity is
mostly defined as positive anti-ZIKV IgM antibodies AND plaque reduction neutralization
(PRNT90) for ZIKV titers 20 and four or more times higher than for other flavivuruses;
AND exclusion of other flavivirus . Where the sensitivity and specificity of the serologic
test is known, these can be adjusted for to correct for misclassification. Alternatively,
sensitivity analysis can be undertaken using a range of plausible estimates for the misclassification.
The testing strategy for patients presenting 7days after onset of symptoms focuses on IgM
serology due to the availability of reagents. The WHO Interim Guidance on laboratory testing
for Zika virus infection indicates that IgM detection should be performed for pregnant women
7 / 17
in areas of endemic transmission or pregnant women who could have had contact with
vectorborne or sexually transmitted Zika virus .
However, some issues remain and need to be addressed by the research community. These
include development of better diagnostic tools; determination of the sensitivity, specificity and
predictive value of serum ZIKV IgM; and assurance of reliable, accurate, and standardized
]. Other issues which are generally found with biomarkers are exacerbated by
problems specific to the countries and regions where the zika epidemic is currently found. For
example, there are limited labs with the capability of testing biological samples for ZIKV
infection, which may lead to difficulties shipping samples to the labs in a timely manner and may
put the integrity of the samples at risk due to the time of collection, maintaining of the cold
chain, and material transfer issues [
]. Until more laboratories have the ability to test for
ZIKV infection in biological samples, the possibility of reduced integrity of samples should be
considered a potential source of information bias as it can lead to misclassification of exposure
Measurement of the outcomes of interestÐChallenges in defining the
spectrum of diseases caused by ZIKV infection: Microcephaly and other
congenital birth defects and Guillain-BarreÂ Syndrome (GBS)
Measurement of the outcomes of interest is a risk of bias common to the observational
analytical studies on ZIKV infection [17±20]. Research conducted to date has used a variety of case
definitions for microcephaly, congenital ZIKV syndrome and GBS [
], which makes the
comparison and pooling of results difficult.
For microcephaly±the International Fetal and Newborn Growth (INTERGROWTH)
standard is considered the best method to classify a case of microcephaly in a fetus or newborn
. The WHO recognizes that there is a large variability in the levels of microcephaly present
in different geographic populations, but it is likely that some of the variability is due to
differences in measurement and subsequent classification, which would be a potential source of
information bias . Several methods can be used to determine head size, such as the
ultrasound (during pregnancy), Magnetic Resonance Imaging (MRI) scan, and Computed
Tomography (CT) scan, where available (see S1D Fig) [
For Guillain Barré Syndrome±WHO has published an interim guidance on the
Identification and management of GBS syndrome in the context of ZIKV, which provides
recommendations for clinical assessment based on the Brighton Collaboration criteria case definitions for
]. In the standardized protocol relevant to GBS, the definition of a case is: GBS
meeting levels 1±3 of diagnostic certainty for the Brighton Collaboration criteria case
definitions for GBS [
18, 45, 47
Standardized and validated methods for measuring outcomes will help to prevent
misclassification of outcomes leading to information bias. For the case-control study designs [17, 18],
misclassification of outcome can also lead to selection bias, with those not meeting the
inclusion criteria for a ªcaseº and thus being systematically excluded from participation. To help
overcome this issue clear definitions of cases are given in the ZIKV standardized protocols,
though both protocols include a note that the studies will be used to refine and update
recommendations for surveillance and case definitions [17, 18].
Confounding and effect modification
In addition to selection and information bias due to challenges with currently available ZIKV
diagnostic tests and clear definitions of key outcomes, for the analytical designs researchers
need to account for potential confounders [17±20]. In order to prove causation between an
8 / 17
exposure (in this case ZIKV infection) and an outcome (e.g., microcephaly in the newborn or
GBS in adults) confounding needs to be minimized or, if possible, ruled out.
Confounding can occur when another exposure exists in the study population and is
associated with both the disease and the exposure under study. Potential confounders include
independent risk factors for the outcomes of interest, though to be confirmed as confounders they
also need to be associated with the exposure of interest in the particular study and not be on
the causal pathway between the exposure and outcome [
Potential confounders need to be considered at both the design and analysis stages of
observational studies [
]. At the design stage, approaches that can be taken to prevent confounding
include matching and restriction. Randomization is the best approach for preventing
confounding but its use is limited to experimental designs. At the analysis stage confounding can
be controlled by stratification or multivariate modeling. However, all of these approaches
require that all potential confounders are identified and measured. Potential confounders are
included as part of the standardized questionnaires for each of the standardized protocols
and/or in the laboratory testing regime. In addition, for the case-control designs controls are
matched to cases on some key variables.
In the case of microcephaly and other congenital birth defects of the newborn independent
risk factors include certain infections during pregnancy, such as rubella, toxoplasmosis,
syphilis, varicella-zoster, rubella, cytomegalovirus, and herpes infections (TORCH) in utero (see
S1D Fig) [
]. Testing of serum for these potential confounders is recommended in the
standardized protocols relevant for microcephaly. Other independent risk factors for microcephaly
may include: severe malnutrition; maternal alcohol and tobacco use, maternal
sociodemographic characteristics including age, race, education, marital status, body mass index and
interruption of the blood supply to the baby's brain during development and these are
included in the standardized questionnaires [
Given the possibility of misclassification of DENV and ZIKV, DENV should also be
considered as a potential confounder in all cohort and case-control studies of ZIKV [
to address exposures to these potential risk factors and others are included in the standardized
questionnaires and/or in the laboratory testing regimes for the cohort and case-control studies
for microcephaly [17, 19, 20].
For GBS, although the exact cause is unknown, most cases of GBS occur after a i) virus
infection±such as the cytomegalovirus (a member of the herpes group), the Epstein-Barr virus
or HIV; or ii) bacterial infection±such as infection from Campylobacter bacteria, a common
cause of food poisoning [
]. Questions to address exposures to these potential risk factors
and others are included in the standardized case-control study for GBS .
Despite the attempts to account for all potential confounders in the ZIKV standardized
protocols, researchers using these protocols need to ensure that appropriate statistical analyses are
undertaken to test and control for these. Some variables may also be modifiers of the effect and
this need to be considered at the analysis stage of the study. Both users of the protocol and of
the research should also be aware of unknown confounders.
Risks of bias common to cohort designs of ZIKV infection
The risk of selection bias due to loss to follow-up is higher in cohort studies than in other
observational designs (Table 2). This is particularly important if there is differential loss to
follow-up due to the exposure. In the case of ZIKV infection, cohort studies are ideal for
investigating multiple outcomes from a single exposure [
], which is applicable when the primary
exposure of interest is ZIKV infection and there is a wide range of outcomes of interest [19,
20]. If performed in a conscientious and rigorous manner, cohort studies can be used to
9 / 17
determine causality, given that exposure and outcome are investigated in a temporal
], i.e. the exposure (ZIKV) clearly comes before the outcome (e.g. microcephaly or
The two main sources of selection bias in cohort studies arise during the recruitment and
the follow-up phases (see S1G Fig). In the recruitment phase, the considerations regarding
classification of ZIKV infection are similar to those for all observational designs as described
in section 3. In addition, care is needed to ensure that the exposed and unexposed individuals
are recruited from the same source population so that they are similar in all regards except for
the particular exposure of interest [
14, 52, 54
]. If the exposed and unexposed individuals differ
in extraneous factors (potential confounders), then internal validity is at risk and any
differences observed in the outcome of interest may not be attributable to a causal relationship [
], unless these factors can be controlled for in the analysis.
In the follow-up phase, time is crucial to minimize losses to follow-up. In the case of ZIKV
infection, follow-up is important for analyzing causal considerations such as temporality,
dose-response, and consistency [
]. The follow-up period must be long enough for a
sufficient number of outcome events to occur or be observed, increasing the statistical power to
detect differences between the exposed and unexposed individuals [
]. Depending on the
type of outcomes sought, minimum length to follow-up may vary. However, even if intended
follow-up times are appropriate for the outcomes of interest, loss to follow-up still represents a
significant risk of selection bias and a threat to validity if there is differential loss between the
exposed and unexposed cohorts. That is, if loss is classified as ªmissing at randomº, then
theoretically, there is no risk for selection bias, but if the loss is classified as ªmissing not at
randomº, then one must make the conservative assumption that the loss is related to exposure
status, which then introduces selection bias [
Cohort study of ZIKV-infected patients to measure the persistence of ZIKV in body
fluids. This is a prospective observational cohort study of men and women, aged 18 years and
above, who have ZIKV positive RT-PCR blood or urine samples and their symptomatic or
asymptomatic household contacts . Participants will be followed for 12 months in order to
evaluate the persistence of virus, reactivation and reinfection at regular intervals (9 visits in
total following the baseline visit). The risk of selection bias and loss to follow-up in this study
needs to be considered by researchers using the protocol and by users of the results. Given the
frequency of visits and the type of measurements (blood and body fluids) there may be
reluctance to participate and/or continue to final follow-up. This reluctance may be greater in
asymptomatic household contacts than in the symptomatic index cases. In addition
researchers need to be aware of the risk of bias in the measurement of the exposure (as detailed above).
Cohort study of pregnant women and newborns exposed to ZIKV during pregnancy.
The main risks of bias in this study are in measurement of the exposure, outcomes and
confounders (as detailed above). There is also a risk of selection bias due to possible
underrepresentation of ZIKV negative and asymptomatic ZIKV positive women if they perceive that their
babies are not at risk of fetal abnormalities. Bias due to loss to follow-up should also be
For this study, pregnant women are recruited as soon as possible once the pregnancy has
begun regardless of the development of symptoms, with follow-up visits at least once per
trimester, ideally more often . Measurements of the fetus or newborn are made at birth and
newborn infants should be followed for a minimum of 1 month following birth  (see
Cohort study of newborns for longer duration of follow-up). In this protocol IgM and IgG
serological methods and real-time RT-PCR have been recommended.
Given the short duration of this study (approximately 10 months), the chance of loss to
follow-up are relatively low. A concern however is that, given the severity of potential birth
10 / 17
outcomes in babies exposed to ZIKV in utero, a mother may choose to terminate a pregnancy
after learning of a probable or positive ZIKV exposure [
]. To account for this, researchers
using the ZIKV standardized protocol are requested to record the outcome of the pregnancy
(whether miscarriage, termination or live birth), including any birth defects if detected.
Among the Latin American countries (LAC) countries that are currently afflicted with ZIKV,
there is a wide range of acceptability for the termination of a pregnancy, so the availability of
that option would be highly dependent on the national and cultural context [
standardized ZIKV protocol provides guidance on information that can be provided to pregnant
women if they are exposed, infected and/or if an abnormality is identified so that she and her
partner can make informed decisions .
In the ZIKV standardized protocol, the frequency of follow-up is suggested to be at least
once per trimester to allow for the analysis on the association between timing (trimester) of
ZIKV infection in the mother and resulting frequency of abnormalities in the fetus. More
frequent follow-up will allow researchers to obtain better data about the timing of appearance of
congenital abnormalities in the fetus. Whatever the chosen frequency, this needs to be
consistent between women both exposed and unexposed to ZIKV.
Cohort study of newborns and infants born to mothers exposed to ZIKV during
pregnancy. In the event that a cohort study of pregnant women has also taken place in the region
of study, it is strongly recommended and preferred (logistically and scientifically) to follow-up
the newborn of mothers enrolled in that study  for a minimum of 2 years after birth.
Ideally, the cohort study will follow the development of the children up to the age of 5 years, if
resources permit . The newborns of both ZIKV positive and negative women are
recommended to be included for follow-up visits at 1, 3, 6, 9, 12, 18 and 24 months. At each visit,
developmental evaluations will include assessments of epilepsy, hearing, vision, swallowing
and spasticity/movement in the infant, following WHO guidelines.
Given the longer duration of this cohort study there is greater potential for loss to
followup. If the loss to follow-up is greater for newborns without apparent abnormalities and/or
related to ZIKV exposure this could introduce selection bias. There is also a risk of selection
bias due to underrepresentation of babies born to asymptomatic women if infection is missed
(see section 2). Researchers using the ZIKV standardized protocol need to be aware of this risk
and make all reasonable efforts to maximize recruitment, follow-up and account for
dropouts. The risks of bias due to measurement of the exposure, outcomes and confounders (as
detailed above) also apply to this protocol.
Risks of bias specific to case-control designs
In addition to challenges in accurate diagnosis of ZIKV infection (section 2), the main source
of bias common to all case-control studies of ZIKV infection occurs at the point of selection of
cases and controls to be included in the study. Both cases and controls should represent the
same base set or same source population . That is, cases from one population should not
be compared with controls arising from a different population. However, the cases and
controls do not necessarily need to be representative of the total underlying source population, but
to have a similar baseline level of risk .
The selection of controls is a particular challenge, and there are a few different ways to
recruit them. Controls can be recruited from the general population, from amongst friends
and family of the cases, or from the same hospital where cases were recruited . However,
care must be exercised when selecting controls from hospitals since they may have comorbid
conditions or exposures that make them systematically different from the cases, increasing the
risk of confounding [63±65].
11 / 17
The selection of controls provides an important opportunity to reduce the risk of
confounding in the study design. Because the important outcomes of ZIKV infection are relatively
rare, matching should be strongly considered. Matching is a method used to ensure
comparability between cases and controls with regard to potential confounding factors. In an individual
matching scheme, each case is matched to a control based on various potential confounding
factors, such as age, sex, or other variables that are known to be associated with the outcome of
interest [62, 65, 66]. However, over-matching can occur when cases and controls are matched
by a non-confounding variable associated to the exposure but not to the disease, and may
result in bias. Over-matching can underestimate associations and cannot be corrected in the
analysis. Also, it is difficult to match for every possible confounder and unmatched
confounders must still be considered in the analysis phases of the study.
Given the retrospective measurement of exposure status and potential confounders in
casecontrol studies the risk of bias due to measurement error is also greater than in the prospective
cohort studies. The advantage, however, of the case-control study is that smaller sample sizes
are needed than for cohort studies and the follow-up time much shorter±which reduces the
risk of selection bias due to loss to follow-up. The risks of bias due to measurement of the
exposure, outcomes and confounders (as detailed above) also apply to these two protocols
Effect modifiers (interaction variable) can over- or underestimate the risk of bias by
modifying the observed effect of a risk factor on disease status. In the case of Zika, this includes the
variable gestational age, and it is not recommendable to match for this type of variable as it is
necessary to show the directionality and the magnitude of the modification of the effect
We have enumerated the potential risks of bias and confounding in all observational studies,
which are applicable to observational studies of ZIKV. We have developed six standardized
protocols for observational studies whose aim is to minimize bias by specifying clear criteria
for selection of participants; standardized measurement methods and definitions for both
exposures (ZIKV) and outcomes (microcephaly, GBS); and specification of potential
confounders, along with standardized methods to measure and control for them. Concerted
efforts must be made in the design and analysis stages of observational studies related to ZIKV
infection. The use of these standardized protocols will help minimize potential risk of bias of
future studies on ZIKV infection. The use of the standardized protocols by researchers in the
field will increase the quality of their data. In addition, it will facilitate comparability of data
and thus the possibility of performing joint analyses to answer complex questions that
individual studies cannot answer yielding more reliable, valid and generalizable results. Users of the
ZIKV research, including public health decision-makers, should critically appraise future
observational studies for known risks of bias and use this information in their
Implications for policy and research
· Researchers undertaking ZIKV infection studies should use the standardized protocols and
contribute to efforts to share and pool data.
· Funders of ZIKV research should prioritize research that uses these protocols and/or fills
gaps in knowledge related to the protocol.
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· Policy makers should use the information from research undertaken using the standardized
protocols to guide decision making regarding public health advice and resource allocation.
· Researchers, policy makers and funders can help to improve the protocols by contributing to
the generation of better information to calculate sample size, to determine the
epidemiological spread of ZIKV and timing for research, expertise needed to run some tests, and
legislation / regulations that enable the sharing of samples.
S1 Fig. Venn-diagram showing the risks of bias as they relate to the analytical observational
study designs. Circles represent the different study designs. A: represents the risk of bias
common in case-control studies for microcephaly and for the cohort of newborns; B: represents the
risk of bias common in both case-control studies and for the cohort of newborns; C: represents
the risk of bias common in both case-control studies; D: represents the risk of bias common in
the case-control study for microcephaly and both cohort study designs; E: represents the risk of
bias common in all four study designs; F: represents the risk of bias common in both
case-control studies and the cohort of pregnant women; G: represents the risk of bias common in both
cohort studies; H: represents the risk of bias common in both cohort studies and the
case-control study of Guillain-BarreÂ Syndrome; I: represents the risk of bias common in the case-control
study of Guillain-BarreÂ Syndrome and the cohort of pregnant women. Risks of bias were found
for D, E and G. No specific risks of bias were detected for A, B, C, F, H, I.
S2 Fig. Flow diagram of the search and protocol selection.
S1 File. Protocols identified through a systematic search of clinical trial databases.
S2 File. Prisma Checklist.
We thank Jairo Mendez from the Pan American Health organization for comments on the
final versions of this manuscript. We also thank all researchers that shared their research
Conceptualization: Ludovic Reveiz, Carlos E. PinzoÂn-Flores, Vanessa Elias.
Data curation: Ludovic Reveiz, Michelle M. Haby, Ruth MartÂõnez-Vega, Carlos E.
PinzoÂn-Flores, Vanessa Elias, Mariona Pinart, Nathalie Broutet, Maria D. Van Kerkhove.
Formal analysis: Ludovic Reveiz, Michelle M. Haby, Ruth MartÂõnez-Vega, Carlos E.
PinzoÂnFlores, Vanessa Elias, Emma Smith, Mariona Pinart, Nathalie Broutet, Francisco
BecerraPosada, Sylvain Aldighieri, Maria D. Van Kerkhove.
Investigation: Ludovic Reveiz, Michelle M. Haby, Ruth MartÂõnez-Vega, Carlos E.
PinzoÂn-Flores, Vanessa Elias, Emma Smith, Mariona Pinart, Maria D. Van Kerkhove.
13 / 17
Methodology: Ludovic Reveiz, Carlos E. PinzoÂn-Flores, Vanessa Elias, Emma Smith.
Project administration: Ludovic Reveiz, Michelle M. Haby, Vanessa Elias, Sylvain Aldighieri.
Supervision: Ludovic Reveiz, Nathalie Broutet, Francisco Becerra-Posada, Sylvain Aldighieri.
Validation: Ludovic Reveiz, Michelle M. Haby, Ruth MartÂõnez-Vega, Carlos E. PinzoÂn-Flores,
Vanessa Elias, Emma Smith, Mariona Pinart, Nathalie Broutet, Francisco Becerra-Posada,
Sylvain Aldighieri, Maria D. Van Kerkhove.
Writing ± original draft: Ludovic Reveiz, Michelle M. Haby, Ruth MartÂõnez-Vega, Carlos E.
PinzoÂn-Flores, Vanessa Elias, Emma Smith, Mariona Pinart, Nathalie Broutet, Francisco
Becerra-Posada, Sylvain Aldighieri, Maria D. Van Kerkhove.
Writing ± review & editing: Ludovic Reveiz, Michelle M. Haby, Ruth MartÂõnez-Vega, Carlos
E. PinzoÂn-Flores, Vanessa Elias, Emma Smith, Mariona Pinart, Nathalie Broutet, Francisco
Becerra-Posada, Sylvain Aldighieri, Maria D. Van Kerkhove.
14 / 17
WHO, Institut Pasteur. Standardized Protocol: Case-control study to assess potential risk factors
related to microcephaly caused by Zika virus infection during pregnancy: World Health Organization
and Institut Pasteur; 2016. http://origin.who.int/reproductivehealth/zika/zika-virus-research-agenda/en/.
WHO, Institut Pasteur. Standardized Protocol: Case-control study to assess potential risk factors
related to Guillan-BarreÂ Syndrome caused by Zika virus infection: World Health Organization and
Institut Pasteur; 2016. http://origin.who.int/reproductivehealth/zika/zika-virus-research-agenda/en/.
WHO, Institut Pasteur. Standardized Protocol: Prospective longitudinal cohort study of newborns and
infants born to mothers exposed to ZIKV during pregnancy: World Health Organization and Institut
Pasteur; 2016. http://origin.who.int/reproductivehealth/zika/zika-virus-research-agenda/en/.
WHO, Institut Pasteur. Standardized Protocol: Prospective longitudinal cohort study of women and
newborns exposed to ZIKV during the course of pregnancy: World Health Organization and Institut
Pasteur; 2016. http://origin.who.int/reproductivehealth/zika/zika-virus-research-agenda/en/.
WHO, Institut Pasteur. Standardized Protocol: Prospective longitudinal cohort study of Zika-infected
patients to measure the persistence of Zika virus in body fluids: World Health Organization and Institut
Pasteur; 2016. http://origin.who.int/reproductivehealth/zika/zika-virus-research-agenda/en/.
WHO, Institut Pasteur. Standardized Protocol: Cross-sectional seroprevalence study of Zika virus
infection in the general population: World Health Organization and Institut Pasteur; 2016. http://origin.who.
15 / 17
WHO, Institut Pasteur. Harmonization of ZIKV research protocols to address key public health
concerns: World Health Organization and Institut Pasteur; 2016. http://origin.who.int/reproductivehealth/
WHO. Identification and management of Guillain-BarreÂ syndrome in the context of Zika virus. Interim
guidance update. 22 August 2016. WHO/ZIKV/MOC/16.4 Rev.1. World Health Organization, 2016.
1. Duffy MR , Chen TH , Hancock WT , Powers AM , Kool JL , Lanciotti RS , et al. Zika virus outbreak on Yap Island, Federated States of Micronesia. N Engl J Med . 2009 ; 360 ( 24 ): 2536 ± 43 . https://doi.org/10.1056/ NEJMoa0805715 PMID: 19516034 .
2. Cao-Lormeau VM , Roche C , Teissier A , Robin E , Berry AL , Mallet HP , et al. Zika virus, French polynesia , South pacific, 2013 [Letter]. Emerg Infect Dis . 2014 ; 20 ( 6 ): 1085 ±6. https://doi.org/10.3201/eid2006. 140138 PMID: 24856001 ;
WHO. Zika Situation Report: Zika virus, Microcephaly and Guillain-BarreÂ syndromeÐ27 October 2016 : World Health Organization; 2016 [ 31 October 2016 ]. http://www.who.int/emergencies/zika-virus/ situation-report/27-october-2016/en/.
WHO Director- General summarizes the outcome of the Emergency Committee regarding clusters of microcephaly and Guillain±BarreÂ syndrome [Internet] . World Health Organization; 2016; February 1, [cited February 2 , 2016 ]. http://www.who.int/mediacentre/news/statements/2016/emergencycommittee-zika-microcephaly/en/
5. CDC. Revised diagnostic testing for Zika, chikungunya, and dengue viruses in US Public Health Laboratories Centers for Disease Control, Division of Vector-Borne Diseases, 2016 7 February 2016 . Report No.
6. Barreto de Araujo TV , Rodrigues LC , de Alencar Ximenes RA , de Barros Miranda-Filho D , Montarroyos UR , de Melo AP , et al. Association between Zika virus infection and microcephaly in Brazil, January to May, 2016 : preliminary report of a case-control study . Lancet Infect Dis . 2016 ; 16 ( 12 ): 1356 ± 63 . https:// doi.org/10.1016/S1473- 3099 ( 16 ) 30318 - 8 PMID: 27641777 .
7. Brasil P , Pereira JP Jr., Moreira ME , Ribeiro Nogueira RM , Damasceno L , Wakimoto M , et al. Zika Virus Infection in Pregnant Women in Rio de Janeiro. N Engl J Med . 2016 ; 375 ( 24 ): 2321 ± 34 . https://doi.org/ 10.1056/NEJMoa1602412 PMID: 26943629 .
8. Cauchemez S , Besnard M , Bompard P , Dub T , Guillemette-Artur P , Eyrolle-Guignot D , et al. Association between Zika virus and microcephaly in French Polynesia, 2013 ± 15 : a retrospective study . Lancet . 2016 ; 387 ( 10033 ): 2125 ± 32 . https://doi.org/10.1016/S0140- 6736 ( 16 ) 00651 - 6 PMID: 26993883;
9. Cuevas EL , Tong VT , Rozo N , Valencia D , Pacheco O , Gilboa SM , et al. Preliminary Report of Microcephaly Potentially Associated with Zika Virus Infection During PregnancyÐColombia , JanuaryNovember 2016 . MMWR Morb Mortal Wkly Rep . 2016 ; 65 ( 49 ): 1409 ± 13 . https://doi.org/10.15585/ mmwr.mm6549e1 PMID: 27977645 .
10. Cao-Lormeau VM , Blake A , Mons S , Lastere S , Roche C , Vanhomwegen J , et al. Guillain-Barre Syndrome outbreak associated with Zika virus infection in French Polynesia: a case-control study . Lancet . 2016 ; 387 ( 10027 ): 1531 ±9. https://doi.org/10.1016/S0140- 6736 ( 16 ) 00562 - 6 PMID: 26948433 .
11. Dos Santos T , Rodriguez A , Almiron M , Sanhueza A , Ramon P , de Oliveira WK , et al. Zika Virus and the Guillain-Barre SyndromeÐCase Series from Seven Countries . N Engl J Med . 2016 ; 375 ( 16 ): 1598 ± 601 . https://doi.org/10.1056/NEJMc1609015 PMID: 27579558 .
12. Krauer F , Riesen M , Reveiz L , Oladapo OT , Martinez-Vega R , Porgo TV , et al. Zika Virus Infection as a Cause of Congenital Brain Abnormalities and Guillain-Barre Syndrome: Systematic Review . PLoS Med . 2017 ; 14 ( 1 ):e1002203. https://doi.org/10.1371/journal.pmed.1002203 PMID: 28045901 .
13. Concato J , Shah N , Horwitz RI . Randomized, controlled trials, observational studies, and the hierarchy of research designs . N Engl J Med . 2000 ; 342 ( 25 ): 1887 ±92. https://doi.org/10.1056/ NEJM200006223422507 PMID: 10861325
14. Grimes DA , Schulz KF . Bias and causal associations in observational research . The Lancet . 2002 ; 359 ( 9302 ): 248 ± 52 .
15. Gonzalez U , Pinart M , Reveiz L , Rengifo-Pardo M , Tweed J , Macaya A , et al. Designing and reporting clinical trials on treatments for cutaneous leishmaniasis . Clin Infect Dis . 2010 ; 51 ( 4 ): 409 ± 19 . https://doi. org/10.1086/655134 PMID: 20624067 .
16. Van Kerkhove MD , Reveiz L , Souza JP , Jaenisch T , Carson G , Broutet N , et al. Harmonisation of Zika virus research protocols to address key public health concerns . Lancet Glob Health . 2016 . https://doi. org/10.1016/ S2214 -109X( 16 ) 30255 - 8 PMID: 27815145 .
23. Benchimol EI , Smeeth L , Guttmann A , Harron K , Hemkens LG , Moher D , et al. [ The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement] . Z Evid Fortbild Qual Gesundhwes . 2016 ; 115 ± 116 : 33 ± 48 . PMID: 27837958 ;
24. Bossuyt PM , Reitsma JB , Bruns DE , Gatsonis CA , Glasziou PP , Irwig L , et al. STARD 2015 : An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies . Clin Chem . 2015 ; 61 ( 12 ): 1446 ± 52 . https://doi.org/10.1373/clinchem. 2015 .246280 PMID: 26510957 .
25. Fitchett EJ , Seale AC , Vergnano S , Sharland M , Heath PT , Saha SK , et al. Strengthening the Reporting of Observational Studies in Epidemiology for Newborn Infection (STROBE-NI): an extension of the STROBE statement for neonatal infection research . Lancet Infect Dis . 2016 ; 16 ( 10 ):e202± 13 . https:// doi.org/10.1016/S1473- 3099 ( 16 ) 30082 - 2 PMID: 27633910 .
26. von Elm E , Altman DG , Egger M , Pocock SJ , Gotzsche PC , Vandenbroucke JP , et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies . PLoS Med . 2007 ; 4 ( 10 ):e296. https://doi.org/10.1371/journal.pmed.0040296 PMID: 17941714 ;
27. Porta M , Greenland S , HernaÂn M , Silva IdS , Last JM , for the International Epidemiological Association, editors. A dictionary of epidemiology . 6th ed. New York, NY: Oxford University Press; 2014 .
28. Bonita R , Beaglehole R , Kjellstrom T. Basic epidemiology . 2nd ed. Geneva: World Health Organization; 2006 . 212 p.
29. MAGIC. Tool to Assess Risk of Bias in Cohort Studies [January 22 , 2017 ]. http://help.magicapp.org/ knowledgebase/articles/327941-tool -to-assess-risk-of-bias-in-cohort-studies.
30. Viswanathan M , Berkman ND , Dryden DM , Hartling L . Assessing risk of bias and confounding in observational studies of interventions or exposures: Further development of the RTI item bank . Methods research report. (Prepared by RTI±UNC Evidence-based Practice Center under Contract No. 290- 2007-10056-I) . AHRQ Publication No. 13 - EHC106 -EF. Rockville, MD: Agency for Healthcare Research and Quality, 2013 .
31. Paixao ES , Barreto F , Teixeira Mda G , Costa Mda C , Rodrigues LC . History, epidemiology, and clinical manifestations of Zika: a systematic review . Am J Public Health . 2016 ; 106 ( 4 ): 606 ± 12 . https://doi.org/ 10.2105/AJPH. 2016 .303112 PMID: 26959260 ;
32. Haug CJ , Kieny MP , Murgue B. The Zika Challenge. N Engl J Med . 2016 ; 374 ( 19 ): 1801 ± 3 . https://doi. org/10.1056/NEJMp1603734 PMID: 27028782 .
33. Priyamvada L , Quicke KM , Hudson WH , Onlamoon N , Sewatanon J , Edupuganti S , et al. Human antibody responses after dengue virus infection are highly cross-reactive to Zika virus . Proc Natl Acad Sci U S A . 2016 ; 113 ( 28 ): 7852 ±7. https://doi.org/10.1073/pnas.1607931113 PMID: 27354515 ;
34. Steinhagen K , Probst C , Radzimski C , Schmidt-Chanasit J , Emmerich P , van Esbroeck M, et al. Serodiagnosis of Zika virus (ZIKV) infections by a novel NS1-based ELISA devoid of cross-reactivity with dengue virus antibodies: a multicohort study of assay performance, 2015 to 2016 . Euro Surveill. 2016 ; 21 ( 50 ): 30426 . https://doi.org/10.2807/ 1560 - 7917 .ES. 2016 . 21 .50.30426 PMID: 28006649 ;
35. PAHO . Case definitions: Pan American Health Organization; 2016 [ 28 October 2016 ]. http://www.paho. org/hq/index.php ?option=com_content&view=article&id=11117:2015-zika-case-definitions- & Itemid= 41532&lang=en.
37. Corman VM , Rasche A , Baronti C , Aldabbagh S , Cadar D , Reusken CB , et al. Assay optimization for molecular detection of Zika virus . Bulletin World Health Organization. 2016 ; 94 ( 12 ): 880 ± 92 . https://doi. org/10.2471/BLT.16.175950 PMID: 27994281 ;
38. Goebel S , Snyder B , Sellati T , Saeed M , Ptak R , Murray M , et al. A sensitive virus yield assay for evaluation of Antivirals against Zika Virus . J Virol Methods . 2016 ; 238 : 13 ± 20 . https://doi.org/10.1016/j. jviromet. 2016 . 09 .015 PMID: 27678028 .
WHO. Laboratory testing for Zika virus infection . Interim guidance. 23 March 2016 . WHO/ZIKV/LAB/ 16.1. World Health Organization, 2016 .
WHO. Zika virus research agenda . October 2016 2016 [ cited 2017 10 January] . http://www.who.int/ reproductivehealth/zika/zika-virus-research-agenda/en/.
41. Landry ML , St George K. Laboratory diagnosis of zika virus infection . Arch Pathol Lab Med . 2017 ; 141 ( 1 ): 60 ±7. https://doi.org/10.5858/arpa.2016-0406 -SA PMID : 27763787 .
42. Mayeux R. Biomarkers : Potential Uses and Limitations . NeuroRx . 2004 ; 1(2):182±8 . https://doi.org/10. 1602/neurorx.1.2.182 PMID: 15717018
WHO , Institut Pasteur . Screening, assessment and management of neonates and infants with complications associated with Zika virus exposure in utero . Interim Guidance Update. World Health Organization , 2016 .
44. Russell K , Oliver SE , Lewis L , Barfield WD , Cragan J , Meaney-Delman D , et al. Update: Interim Guidance for the Evaluation and Management of Infants with Possible Congenital Zika Virus InfectionÐ United States , August 2016 . MMWR Morb Mortal Wkly Rep . 2016 ; 65 ( 33 ): 870 ±8. https://doi.org/10. 15585/mmwr.mm6533e2 PMID: 27559830 .
45. Sejvar JJ , Kohl KS , Gidudu J , Amato A , Bakshi N , Baxter R , et al. Guillain-Barre syndrome and Fisher syndrome: case definitions and guidelines for collection, analysis, and presentation of immunization safety data . Vaccine . 2011 ; 29 ( 3 ): 599 ± 612 . https://doi.org/10.1016/j.vaccine. 2010 . 06 .003 PMID: 20600491 .
48. Stegmann BJ , Carey JC. TORCH Infections. Toxoplasmosis , Other (syphilis, varicella-zoster, parvovirus B19), Rubella, Cytomegalovirus (CMV), and Herpes infections . Curr Womens Health Rep . 2002 ; 2 ( 4 ): 253 ± 8 . Epub 2002/08/02. PMID: 12150751 .
49. Cragan JD , Isenburg JL , Parker SE , Alverson CJ , Meyer RE , Stallings EB , et al. Population-based microcephaly surveillance in the United States , 2009 to 2013: An analysis of potential sources of variation . Birth Defects Res A Clin Mol Teratol . 2016 ; 106 ( 11 ): 972 ± 82 . https://doi.org/10.1002/bdra.23587 PMID: 27891783 .
50. Krauss MJ , Morrissey AE , Winn HN , Amon E , Leet TL . Microcephaly: an epidemiologic analysis . Am J Obstet Gynecol . 2003 ; 188 ( 6 ): 1484 ± 9; discussion 9±90 . PMID: 12824982 .
51. Malone RW , Homan J , Callahan MV , Glasspool-Malone J , Damodaran L , Schneider Ade B , et al. Zika virus: medical countermeasure development challenges . PLoS Negl Trop Dis . 2016 ; 10 ( 3 ):e0004530. https://doi.org/10.1371/journal.pntd.0004530 PMID: 26934531 ;
52. Grimes DA , Schulz KF . Cohort studies: marching towards outcomes . Lancet . 2002 ; 359 ( 9303 ): 341 ±5. https://doi.org/10.1016/S0140- 6736 ( 02 ) 07500 - 1 PMID: 11830217
53. Mann CJ . Observational research methods. Research design II: cohort, cross sectional, and case-control studies . Emerg Med J. 2003 ; 20 ( 1 ): 54 ± 60 . https://doi.org/10.1136/emj.20.1.54 PMID: 12533370 62 .
White E , Hunt JR , Casso D. Exposure measurement in cohort studies: the challenges of prospective data collection . Epidemiol Rev . 1998 ; 20 ( 1 ): 43 ± 56 . Epub 1998/10/08. PMID: 9762508 .
55. Hill AB . The Environment and Disease: Association or Causation? Proc R Soc Med . 1965 ; 58 ( 5 ): 295 ± 300 .
56. Sedgwick P. Prospective cohort studies: advantages and disadvantages . BMJ . 2013 ; 347 : f6726 .
57. Kristman V , Manno M , Cote P . Loss to follow-up in cohort studies: how much is too much? Eur J Epidemiol . 2004 ; 19 ( 8 ): 751 ± 60 . Epub 2004/10/08. PMID: 15469032 .
58. Aiken AR , Scott JG , Gomperts R , Trussell J , Worrell M , Aiken CE . Requests for abortion in Latin America related to concern about zika virus exposure . N Engl J Med . 2016 ; 375 ( 4 ): 396 ±8. https://doi.org/10. 1056/NEJMc1605389 PMID: 27331661 ;
59. Diniz D , Gumieri S , Bevilacqua BG , Cook RJ , Dickens BM . Zika virus infection in Brazil and human rights obligations . Int J Gynaecol Obstet . 2017 ; 136 ( 1 ): 105 ± 10 . PMID: 28099714 .
60. Harville EW , Althabe FA , Breart G , Buekens P. The Difficult Design of Epidemiologic Studies on Zika Virus and Pregnancy . Paediatr Perinat Epidemiol . 2016 . https://doi.org/10.1111/ppe.12312 PMID: 27766638 .
61. The Center for Reproductive Rights . The World's Abortion Laws: 2016 2016 [updated 20166 July 2016 ]. http://worldabortionlaws.com/map/.
Principles. Am J Epidemiol . 1992 ; 135 ( 9 ): 1019 ± 28 . PMID: 1595688
Wacholder S , Silverman DT , McLaughlin JK , Mandel JS . Selection of controls in case-control studies: II. Types of controls . Am J Epidemiol . 1992 ; 135 ( 9 ): 1029 ± 41 . PMID: 1595689 64 . Ury HK. Efficiency of case-control studies with multiple controls per case: continuous or dichotomous data . Biometrics . 1975 ; 31 ( 3 ): 643 ± 9 . PMID: 1100136
Woodward M. Epidemiology : Study Design and Data Analysis . 2nd ed. London: Chapman & Hall/ CRC; 2005 .
Wacholder S , Silverman DT , McLaughlin JK , Mandel JS . Selection of controls in case-control studies: III. Design options . Am J Epidemiol . 1992 ; 135 ( 9 ): 1042 ± 50 . PMID: 1595690