Early Clinical Features of Dengue Virus Infection in
Nicaraguan Children: A Longitudinal Analysis
Hope H. Biswas1, Oscar Ortega2, Aubree Gordon1,3, Katherine Standish2, Angel Balmaseda4, Guillermina
Kuan5, Eva Harris3*
1Division of Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America, 2Sustainable Sciences Institute, Managua,
Nicaragua, 3Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, California, United States of America, 4National
Virology Laboratory, Centro Nacional de Diagno ´stico y Referencia, Ministry of Health, Managua, Nicaragua, 5So ´crates Flores Vivas Health Center, Ministry of Health,
Background: Tens of millions of dengue cases and approximately 500,000 life-threatening complications occur annually.
New tools are needed to distinguish dengue from other febrile illnesses. In addition, the natural history of pediatric dengue
early in illness in a community-based setting has not been well-defined.
Methods: Data from the multi-year, ongoing Pediatric Dengue Cohort Study of approximately 3,800 children aged 2–14
years in Managua, Nicaragua, were used to examine the frequency of clinical signs and symptoms by day of illness and to
generate models for the association of signs and symptoms during the early phase of illness and over the entire course of
illness with testing dengue-positive. Odds ratios (ORs) and 95% confidence intervals were calculated using generalized
estimating equations (GEE) for repeated measures, adjusting for age and gender.
Results: One-fourth of children who tested dengue-positive did not meet the WHO case definition for suspected dengue.
The frequency of signs and symptoms varied by day of illness, dengue status, and disease severity. Multivariable GEE models
showed increased odds of testing dengue-positive associated with fever, headache, retro-orbital pain, myalgia, arthralgia,
rash, petechiae, positive tourniquet test, vomiting, leukopenia, platelets #150,000 cells/mL, poor capillary refill, cold
extremities and hypotension. Estimated ORs tended to be higher for signs and symptoms over the course of illness
compared to the early phase of illness.
Conclusions: Day-by-day analysis of clinical signs and symptoms together with longitudinal statistical analysis showed
significant associations with testing dengue-positive and important differences during the early phase of illness compared
to the entire course of illness. These findings stress the importance of considering day of illness when developing prediction
algorithms for real-time clinical management.
Citation: Biswas HH, Ortega O, Gordon A, Standish K, Balmaseda A, et al. (2012) Early Clinical Features of Dengue Virus Infection in Nicaraguan Children: A
Longitudinal Analysis. PLoS Negl Trop Dis 6(3): e1562. doi:10.1371/journal.pntd.0001562
Editor: Benedito A. Lopes da Fonseca, Universidade de Sa ˜o Paulo, Brazil
Received October 20, 2011; Accepted January 25, 2012; Published March 6, 2012
Copyright: ? 2012 Biswas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grant VE-1 from the Pediatric Dengue Vaccine Initiative to EH and the University of California, Berkeley Center for Global
Health and Graduate Division Fellowships to HHB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Dengue virus (DENV) causes the most prevalent mosquito-
borne viral disease affecting humans, with 2.5–3 billion people at
risk for infection and approximately 50 million cases of dengue
each year [1,2]. The four DENV serotypes are transmitted to
humans by Aedes aegypti and Ae. albopictus mosquitoes, primarily in
urban and peri-urban areas in tropical and subtropical countries
worldwide. Most cases present as classic dengue fever (DF), a
debilitating but self-limited illness that manifests with high fever,
retro-orbital pain, severe myalgia/arthralgia, and rash. However,
in some cases, mainly children, illness progresses to life-threatening
dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS),
characterized by vascular leakage leading to hypovolemic shock
and a case fatality rate up to 5% [1,3,4]. Currently, no licensed
vaccine or antiviral therapy exists for dengue. Early identification
of patients at risk of developing severe dengue is critical to provide
timely supportive care, which can reduce the risk of mortality to
,1% [1,2]. However, distinguishing dengue from other febrile
illnesses (OFIs) early in illness is challenging, since symptoms are
non-specific and common to other febrile illnesses such as malaria,
leptospirosis, rickettsiosis, and typhoid fever [5–7] in dengue-
endemic countries. In addition, many distinguishing clinical
features of DHF/DSS generally emerge only after 4–5 days, at
defervescence, when the patient is already critically ill.
Although the World Health Organization (WHO) has recently
established new clinical guidelines to classify dengue severity ,
serological, virological, and molecular biological tests are required
to definitively diagnose DENV infection. In many endemic
countries, laboratory diagnosis of dengue is often problematic due
www.plosntds.org1 March 2012 | Volume 6 | Issue 3 | e1562
to lack of reagents, expense, or delay in obtaining results. Patients
with suspected dengue are often hospitalized for close monitoring to
ensure proper treatment if they begin to develop severe dengue;
however, up to 38–52% are later diagnosed with OFIs [8,9] and
thus were hospitalized unnecessarily at great financial cost to their
family and society . New tools are therefore needed to
distinguish dengue from OFIs to prevent deaths from severe dengue
and to mitigate the economic burden of excess hospitalization.
Recent approaches using multivariable logistic or linear regression
models have shown that petechiae, thrombocytopenia (platelet count
#100,000 cells/mm3), positive tourniquet test, rash, and other signs
and symptoms can distinguish dengue from OFIs [11–17]; however,
results were not consistent across studies. Only two studies considered
clinical and laboratory features according to day of illness [18–20],
but as these were hospital-based studies, the results likely reflect
patients with more severe symptoms and not the clinical spectrum of
all symptomatic cases in dengue-endemic populations. Furthermore,
none of these studies analyzed data using longitudinal statistical
methods, which account for correlations between repeated measures
analyze cohort data is essential to utilize all of the data available for
analysis and appropriately estimate the within-person and between-
person variance in measures over time.
In this study, we used five years of data from an ongoing
prospective cohort study of approximately 3,800 children aged 2–
14 years in Managua, Nicaragua, to examine the frequency of
clinical signs and symptoms by day of illness and to generate
models for the association of signs and symptoms during the early
phase of illness and over the entire course of illness with testing
dengue-positive. In order to account for the longitudinal structure
of the data, odds ratios (ORs) and 95% confidence intervals were
calculated using generalized estimating equations (GEE), adjusting
for age and gender.
Study site and participants
In August and September 2004, a community-based pediatric
cohort was established in District II of Managua, a low-to-middle
income area with a population of approximately 62,500 .
Study activity was based in the Health Center So ´crates Flores
Vivas (HCSFV), a public facility that is the primary source of
health care for District II residents. Briefly, participants aged 2–9
years were recruited through house-to-house visits, and additional
two year-olds were enrolled each year to maintain the age
structure of the cohort . Children were eligible to remain in
the study until age 12 or until they moved from the study area.
The parent/legal guardian of each participant signed an informed
consent form, and children $6 years old provided verbal assent. In
2007, participants #11 years old were given the opportunity to
continue for an additional 3 years, and a second informed consent
The study was approved by the Institutional Review Boards of
the University of California, Berkeley, the Nicaraguan Ministry of
Health, and the International Vaccine Institute in Seoul, Korea.
Parents or legal guardians of all subjects in both studies provided
written informed consent, and subjects 6 years of age and older
encouraged to bring their child(ren) to the HCSFV at first sign of
illnessorfever. Studyphysiciansand nurses, trainedin identification
of possible dengue cases, provided medical care for study
participants. Febrile illnesses that met the WHO criteria for
suspected dengue (Table 1) and those without other apparent
origin (undifferentiated febrile illnesses) were treated as possible
dengue cases and followed daily while fever or symptoms persisted
through visits with study medical personnel (Figure 1). Complete
blood counts (CBCs) were completed every 48 hours or more
frequently as necessary, as indicated by the physician. Cases were
monitored closely for severe manifestations and were transferred by
study personnel to the Infectious Disease Ward of the Manuel de
Jesu ´s Rivera Children’s Hospital, the national pediatric reference
hospital, when they presented with any sign of alarm (Table 1). In
addition, an annual healthy blood sample was collected to identify
all DENV infections during the previous year and for baseline CBC
values. Study physicians in both the hospital and HCSFV
completed systematic data collection forms that contained approx-
imately 80 variables (Table 1). In the hospital, additional clinical
data, including fluid balance and treatment, were collected daily
during hospitalization or through ambulatory follow-up visits by a
team of study physicians and nurses. Data were also recorded on
medical tests ordered and treatments prescribed.
A case was considered laboratory-confirmed dengue when acute
DENV infection was demonstrated by: detection of DENV RNA
by RT-PCR; isolation of DENV; seroconversion of DENV-
specific IgM antibodies observed by MAC-ELISA in paired acute-
and convalescent-phase samples; and/or a $4-fold increase in
anti-DENV antibody titer measured using Inhibition ELISA [22–
25] in paired acute and convalescent samples. DENV serotypes
were identified by RT-PCR and/or virus isolation.
Laboratory-confirmed dengue cases were further classified by
severity. DHF and DSS were defined according to the traditional
WHO criteria (Table 1) . Additional categories of severity
were included for those cases presenting with shock without
thrombocytopenia and/or hemoconcentration (dengue with signs
associated with shock (DSAS))  or dengue fever with
compensated shock (DFCS)  (Table 1). Laboratory-confirmed
Dengue virus causes an estimated 50 million dengue cases
and approximately 500,000 life-threatening complications
annually. New tools are needed to distinguish dengue
from other febrile illnesses. In addition, the natural history
of pediatric dengue early in illness in a community-based
setting has not been well-defined. Here, we describe the
clinical spectrum of pediatric dengue over the course of
illness in a community setting by using five years of data
from an ongoing prospective cohort study of children in
Managua, Nicaragua. Day-by-day analysis of clinical signs
and symptoms together with longitudinal statistical
analysis showed significant associations with testing
dengue-positive and important differences during the
early phase of illness compared to the entire course of
illness. These findings are important for clinical practice
since outside of the hospital setting, clinicians may see
dengue patients toward the beginning of their illness and
utilize that information to decide whether their patient has
dengue or another febrile illness. The results of these
models should be extended for the development of
prediction algorithms to aid clinicians in diagnosing
Early Clinical Features of Pediatric Dengue
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cases were defined as primary DENV infections if acute-phase
antibody titer, as measured by Inhibition ELISA, was ,1:10 or if
convalescent phase antibody titer was ,1:2560, and as secondary
infections if the acute titer was $1:10 or convalescent titer was
Data from the first five years of the study (August 30, 2004–June
30, 2009) were used for analysis. The first three days after onset of
fever were considered the early febrile phase of illness. Day of
illness at presentation was determined by the date of fever onset,
which was defined as the first day of illness as reported by the
parent/guardian. Variable definitions are described in Table 1.
Positive tourniquet test was examined using cut-offs of $10
petechiae/in2and $20 petechiae/in2. Platelet count was dichot-
omized using a cut-off of #150,000 cells/mm3to enable
comparisons during days 1–3. Only data from days 1–8 of illness
were included for analysis.
The frequency of dengue testing results (laboratory-confirmed
dengue-positive versus dengue-negative) and disease severity
(DF versus severe dengue) was examined by year, demographics,
serotype and immune response. The frequency of the WHO
case definition for suspected dengue was examined by dengue
testing results and age, and a chi-square test for trend was
performed. The frequency of clinical signs and symptoms by day
of illness and dengue severity was also examined using chi-
To examine the association between clinical signs and
symptoms and the odds of testing dengue-positive versus
dengue-negative, odds ratios (ORs) and 95% confidence intervals
(CIs) were calculated using GEE models assuming an exchange-
able correlation structure with robust standard errors to account
for the correlations between repeated measures on the same
patients over time. First, ORs were calculated using bivariable
models that included only dengue testing results and each of the
signs or symptoms. All signs and symptoms were then examined in
multivariable models that adjusted for age and gender. Data from
the first three days of illness and from all days of illness only were
analyzed separately. Finally, for comparison, we used traditional
logistic regression models to analyze the association between signs
and symptoms and testing dengue-positive with data collapsed by
illness episode to disregard repeated measures on the same
Table 1. Definitions of clinical terminology, variables and disease classifications.
Term Signs of alarm Persistent vomiting, moderate to severe hemorrhagic manifestations, neurological
manifestations, platelet count #100,000 cells/mm3, hematocrit $20% of normal value
for age and sex
Variables collected in hospital and health center
Temperature, blood pressure, cardiac and respiratory rates, lower and upper respiratory
symptoms, gastrointestinal symptoms, indicators of dehydration, urinary tract
symptoms, musculoskeletal pain, rashes and other skin abnormalities, hemorrhagic
manifestations, nutritional status
Narrow pulse pressure
Poor capillary refill
Hypotension Systolic blood pressure ,80 mmHg for children ,5 years of age and ,90 mmHg for
children $5 years of age
LeukopeniaWBC #5000 cells/mm3
Increased hematocrit 20% increase in hematocrit (compared to the stabilized hematocrit at hospital discharge)
or hematocrit 20% above normal for age and sex
ClassificationSuspected dengueAcute febrile illness with 2 or more of the following: headache; retro-orbital pain;
myalgia; arthralgia; leukopenia (WBC #5000 cells/mm3); rash; hemorrhagic
Dengue hemorrhagic fever (DHF)a
All of the following must be present:
Fever or history of acute fever lasting 2–7 days
Hemorrhagic manifestations (positive tourniquet test; petechiae, equimosis, purpura or
bleeding from mucosa, gastrointestinal tract, injection sites or other locations;
Thrombocytopenia (#100,000 platelets/mm3)
Evidence of plasma leakage due to increased vascular permeability
Dengue shock syndrome (DSS)a
DHF with hypotension for age or narrow pulse pressure (#20 mmHg) plus one of the
following: rapid and weak pulse; cold, clammy skin; restlessness; poor capillary refill
Dengue with signs associated with shock (DSAS)a
Hypotension for age or narrow pulse pressure (#20 mmHg) plus one of the following:
poor capillary refill (.2 sec); cold extremities; weak pulse
Dengue with compensated shock (DFCS)a
DF with poor capillary refill (.2 sec) plus one of the following on the same day: cold
extremities; weak pulse; tachycardia; tachypnea
Severe dengueDHF, DSS, DSAS or DFCS
aplus laboratory confirmation of current dengue virus infection.
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Figure 1. Flowchart of clinical and laboratory protocols for study participants in the Pediatric Dengue Cohort Study. Of the 1,974
episodes of febrile illness in the Pediatric Dengue Cohort Study from August 2004 to June 2009 that met the WHO classification criteria for suspected
dengue or were diagnosed with undifferentiated fever, 405 patients presented with febrile illness on 2 occasions, 105 presented on 3 occasions, 21
presented on 4 occasions, and 5 presented on 5 occasions. One patient presented after day 8 of illness and was excluded from analysis. Twenty-nine
patients had cause of fever identified later in the course of illness. CBC, complete blood count; WHO, World Health Organization; UTI, urinary tract
Early Clinical Features of Pediatric Dengue
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patients, using the same model generation process as for the GEE
models. All analyses were conducted using STATA 10 (StataCorp
LP, College Station, TX).
From August 2004 to June 2009, 22,778 episodes of febrile
illness were evaluated, of which 1,974 episodes were suspected
dengue or undifferentiated fever (Figure 1). Of the 1,974 possible
dengue cases, 1,793 (91%) tested negative and 181 (9%) were
laboratory-confirmed as dengue-positive, of which 161 (89%) were
classified as DF, 9 (5%) as DHF, 4 (2%) as DSS, 3 (2%) as DSAS
and 4 (2%) as DFCS (Table 1). Nearly all (95%) of the severe
dengue cases but only 116 (72%) of the DF cases met the WHO
case definition for dengue. The proportion of laboratory-
confirmed DENV infections that met the WHO case definition
significantly increased by age (chi-square test for trend 5.977,
p=0.01), while younger children experienced significantly more
undifferentiated febrile illness due to DENV infection (Figure 2).
The median age for cases meeting the dengue case definition was 8
years (range 2–13) and that of undifferentiated febrile illness due to
DENV infection was 6 years (range 2–10).
The number of confirmed dengue-positive cases varied by year,
as expected (Table 2) . Both genders were equally represented,
with a slightly higher percentage of females experiencing severe
dengue, though this difference was not statistically significant. The
majority of DF cases were DENV-2 (58%), followed by DENV-1
(21%) and DENV-3 (9%), while 60% of severe dengue cases were
DENV-2, followed by DENV-3 (25%) and DENV-1 (10%). In
addition, there were nearly equal proportions of primary and
secondary immune responses among DF cases, whereas the
majority (70%) of severe dengue cases were secondary DENV
Figure 2. Frequency of dengue-positive episodes that met the
WHO classification criteria for suspected dengue by age
(n=181). Upon presentation to the health center or hospital, children
with a febrile illness were classified according to whether or not they
met the WHO classification criteria for suspected dengue. One patient
had two dengue virus infections over the course of the study and is
represented twice. n=6 for age 2, n=10 for age 3, n=18 for age 4,
n=23 for age 5, n=21 for age 6, n=16 for age 7, n=21 for age 8, n=23
for age 9, n=24 for age 10, n=19 for age 11+. Chi-square test for trend
5.977, p=0.01. WHO, World Health Organization.
Table 2. Characteristics of study participants by dengue testing results and disease severity (n=1,974).
N (%)N (%)N (%)
2004–05 312 (95)16 (5)1 (0)
2005–06516 (89) 63 (11)2 (0)
2006–07 397 (97) 12 (3)1 (0)
2007–08328 (84) 53 (13)11 (3)
2008–09 240 (92) 17 (6)5 (2)
DemographicsFemale 864 (48)75 (47) 11 (55)
Male 929 (52) 86 (53)9 (45)
Median age in years (range)6 (2–13) 7 (2–13)9 (4–12)
Median day of illness at presentation (range) 2 (1–8) 2 (1–8) 3.5 (1–6)
SerotypeDENV-1N/A 33 (21)2 (10)
DENV-2N/A 94 (58)12 (60)
DENV-3 N/A14 (9)5 (25)
DENV-4 N/A0 (0) 1 (5)
MultipleN/A 2 (1)c
IndeterminateN/A18 (11)0 (0)
Immune response Primary N/A71 (44) 6 (30)
Secondary N/A87 (54)14 (70)
Indeterminate N/A3 (2)0 (0)
Numbers represent episodes of febrile illness. DENV, dengue virus; OFI, other febrile illness; DF, dengue fever; Severe dengue=dengue hemorrhagic fever (DHF),
dengue shock syndrome (DSS), dengue with signs associated with shock (DSAS), or dengue fever with compensated shock (DFCS).
aIncludes 9 DHF, 4 DSS, 3 DSAS, and 4 DFCS cases.
bPercentages are calculated horizontally for dengue season.
cIncludes 1 case each of DENV-1/DENV-2 and DENV-1/DENV-4.
Early Clinical Features of Pediatric Dengue
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infection (Table 2). The median day of illness at presentation was
day 2 for all patients, and almost all presented on days 1–3 of
illness (90%). The total follow-up time of all children in the cohort
was 17,931 person-years with a median follow-up of 3.9 years per
As shown in Figure 3, several signs and symptoms appeared to
differentiate OFIs from DF cases, and DF cases from severe
dengue cases, according to day of illness. In particular, higher
proportions of DF and severe dengue cases experienced petechiae,
platelets #150,000 cells/mm3, leukopenia, and positive tourniquet
test compared to patients with OFIs. Higher proportions of severe
cases experienced petechiae, platelets #150,000 cells/mm3,
myalgia/arthralgia and abdominal pain compared to DF cases
and patients with OFIs. Abdominal pain differentiated severe
dengue cases from DF and OFI only beginning on day 3 of illness
(for severe dengue compared to DF: chi-square 0.144, p=0.70 for
days 1–2 versus chi-square 16.910, p,0.0001 for day $3).
Bivariable and multivariable analyses were performed using
GEE models to examine signs and symptoms early in illness and
over the course of illness (Table 3). On days 1–3 of illness,
dengue-positive cases had up to 2-fold increased odds of fever,
headache, retro-orbital pain, myalgia, arthralgia, and vomiting
compared to patients with OFIs. They also had from 3-fold to 9-
fold increased odds of rash, petechiae, positive tourniquet test
with cut-offs of $10 and $20 petechiae/in2, leukopenia,
platelets #150,000 cells/mm3, poor capillary refill, cold
extremities and hypotension compared to patients with OFIs.
In contrast, they had decreased odds of abdominal pain, likely
Figure 3. Frequency of signs and symptoms by day in patients with OFI, DF and severe dengue. Over the course of an episode of febrile
illness, signs and symptoms were observed by medical personnel or reported by children and/or their parent/guardian. Selected signs and symptoms
are shown here. A, Petechiae; OFI versus DF: chi-square test for trend 21.313, p,0.0001; day 1, n=606; day 2, n=1,243; day 3, n=1,066; day 4,
n=876; day 5, n=675; day 6, n=481; day 7, n=291; day 8, n=175; B, Platelet count #150,000 cells/mm3; OFI versus DF: chi-square test for trend
14.928, p=0.0001; day 1, n=604; day 2, n=970; day 3, n=615; day 4, n=568; day 5, n=348; day 6, n=234; day 7, n=122; day 8, n=65; C, Myalgia/
arthralgia; OFI versus DF: chi-square test for trend 4.569, p=0.03; day 1, n=612; day 2, n=1,253; day 3, n=1,075; day 4, n=877; day 5, n=671; day 6,
n=477; day 7, n=289; day 8, n=181; D, Leukopenia; OFI versus DF: chi-square test for trend 6.449, p=0.01; day 1, n=604; day 2, n=971; day 3,
n=615; day 4, n=568; day 5, n=348; day 6, n=234; day 7, n=122; day 8, n=65; E, Positive tourniquet test; OFI versus DF: chi-square test for trend
20.124, p,0.0001; day 1, n=256; day 2, n=496; day 3, n=402; day 4, n=308; day 5, n=202; day 6, n=156; day 7, n=78; day 8, n=38; F, Abdominal
pain; OFI versus DF: chi-square test for trend 9.149, p=0.002; DF versus severe dengue: chi-square test for trend 4.127, p=0.04; day 1, n=609; day 2,
n=1,245; day 3, n=1,066; day 4, n=877; day 5, n=675; day 6, n=482; day 7, n=290; day 8, n=174; All other chi-square tests for trend comparing DF
to severe dengue were non-significant. OFI, other febrile illness; DF, dengue fever; Severe dengue=dengue hemorrhagic fever, dengue shock
syndrome, dengue with signs associated with shock, or dengue fever with compensated shock. Leukopenia is defined as WBC #5000 cells/mm3and
positive tourniquet test is defined as $10 petechiae/in2.
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because this feature appears later in the entire course of dengue
illness. On all days of illness, dengue-positive cases had
increased odds of the same signs and symptoms as on days 1–
3 of illness; however, the magnitude of the point estimates
tended to be higher. This difference was most pronounced for
rash and platelets #150,000 cells/mm3, which had ORs
approximately double in magnitude. In addition, dengue-
positive cases had increased odds of three additional signs and
symptoms: poor appetite, absence of cough, and increased
hematocrit. When GEE analyses on data with the longitudinal
structure preserved were compared to traditional logistic
regression analyses on data collapsed on febrile episode, the
point estimates for the ORs were similar, although the 95%
confidence intervals for the logistic regression models tended to
be slightly narrower (data not shown).
In this study, we describe the clinical spectrum of pediatric
dengue starting early in illness in a community setting.
Longitudinal statistical analysis of day-by-day clinical signs
and symptoms revealed significant associations with testing
dengue-positive and important differences during the early
phase of illness compared to the entire course of illness. These
results stress the importance of considering day of illness when
developing prediction algorithms for real-time clinical manage-
The early identification of dengue cases and particularly those
at risk for severe dengue is critical for preventing severe illness and
death. We found that 25% of laboratory-confirmed dengue cases
did not meet the WHO case definition, suggesting that the WHO
criteria are not sufficient to identify dengue at younger ages.
Younger children may experience different signs and symptoms
from adults or may be unable to communicate their symptoms to
their parents, health care providers, or both. Previous studies
demonstrated that children may experience significantly more
cough, vomiting, abdominal pain, rash, epistaxis, oliguria,
thrombocytopenia, hepatomegaly, and shock compared to adults,
although the direction of these differences was not consistent
across studies [13,15,29–34]. A recent study of dengue in adults
showed significant differences in clinical features and outcomes
across ten-year age groups, indicating that signs and symptoms
associated with DENV infection may continue to evolve past
childhood . If these differences are confirmed, the WHO case
Table 3. Signs and symptoms associated with testing DENV-positive among patients using generalized estimating equation
Days 1–3 All days
OR (95% CI) aOR (95% CI)a
OR (95% CI) aOR (95% CI)a
Fever (.37.8uC) 1.7 (1.2–2.4)** 1.9 (1.3–2.7)***1.8 (1.3–2.5)***2.0 (1.4–2.7)***
Headache 2.0 (1.3–3.0)**1.7 (1.1–2.7)* 2.0 (1.3–3.0)**1.7 (1.1–2.6)*
Retro-orbital pain1.8 (1.3–2.5)**1.6 (1.2–2.3)** 2.2 (1.6–2.9)***2.0 (1.4–2.7)***
Myalgia2.0 (1.4–2.8)***1.8 (1.3–2.6)***2.4 (1.8–3.3)***2.2 (1.7–3.1)***
Arthralgia2.2 (1.6–3.0)*** 2.0 (1.5–2.8)***2.5 (1.9–3.5)*** 2.4 (1.7–3.2)***
Rash6.4 (4.0–10.2)***6.6 (4.1–10.6)*** 12.3 (8.4–18.0)*** 12.5 (8.5–18.5)***
Petechiae5.1 (3.2–8.3)***5.1 (3.2–8.1)***7.9 (5.3–11.8)***7.8 (5.3–11.6)***
Positive tourniquet test ($10
9.3 (5.6–15.6)***9.1 (5.4–15.3)*** 13.5 (8.2–22.1)***13.3 (8.1–21.8)***
Positive tourniquet test ($20
3.4 (2.4–4.9)*** 3.3 (2.3–4.7)*** 5.0 (3.7–6.9)***4.9 (3.6–6.7)***
Abdominal pain0.6 (0.4–0.9)** 0.6 (0.4–0.9)**0.9 (0.6–1.3)0.9 (0.6–1.2)
Poor appetite 1.4 (0.9–2.1)1.5 (1.0–2.3) 2.0 (1.3–3.1)**2.1 (1.4–3.3)**
Nausea1.1 (0.6–1.9) 1.0 (0.6–1.8)1.3 (0.8–2.1)1.2 (0.7–2.0)
Vomiting 2.4 (1.6–3.6)***2.4 (1.6–3.6)*** 1.2 (1.1–1.3)**1.2 (1.1–1.4)**
Sore throat erythema 1.2 (0.8–1.6)1.1 (0.8–1.6) 1.2 (0.9–1.6) 1.2 (0.8–1.6)
Absence of cough1.4 (0.8–2.6) 1.4 (0.8–2.5)2.2 (1.0–4.6)* 2.2 (1.0–4.6)*
Leukopenia4.7 (3.3–6.6)***4.4 (3.1–6.4)*** 7.6 (5.5–10.6)***7.3 (5.3–10.1)***
Platelet count #150,000 cells/mm3
5.3 (2.6–10.7)*** 5.2 (2.5–10.6)***12.6 (7.9–20.1)*** 11.9 (7.4–19.0)***
Increased hematocrit1.4 (0.6–3.4)1.2 (0.5–2.9) 2.7 (1.5–4.7)***2.2 (1.2–3.9)**
Poor capillary refill 4.1 (1.3–13.3)*4.7 (1.5–14.6)** 4.6 (1.6–13.3)**5.1 (1.8–14.1)**
Cold extremities6.2 (1.4–26.3)*5.5 (1.4–21.8)*4.8 (1.9–11.9)**4.2 (1.8–10.0)**
Hypotension2.8 (1.4–5.4)**3.1 (1.6–6.0)**2.6 (1.5–4.4)***2.7 (1.6–4.6)***
Narrow pulse pressure0.9 (0.5–1.5)0.9 (0.5–1.5)1.2 (0.8–1.7)1.2 (0.8–1.7)
Generalized estimating equation models assume an exchangeable correlation structure with robust standard errors. DENV, dengue virus; OR, odds ratio; CI, confidence
interval; aOR, adjusted odds ratio.
aORs are adjusted for age and gender.
Early Clinical Features of Pediatric Dengue
www.plosntds.org7 March 2012 | Volume 6 | Issue 3 | e1562
definition may need to be adjusted to be age-specific to function
effectively for younger children and older age groups.
Retro-orbital pain and low platelets were among the clinical
features independently associated with DENV infection in this
study. These results are supported by a study of dengue patients in
Puerto Rico in which data were recorded at the time of initial
consult rather than at hospitalization , and by a study of Thai
children . Moreover, our results showing increased frequency
of abdominal pain in patients beginning at day 3 of illness are
consistent with a prospective study of adults admitted to an
emergency department in Martinique . A positive tourniquet
test using cut-offs of $10 and $20 petechiae/in2was also
independently associated with DENV infection. Both cut-offs were
used because studies have indicated that a cut-off of $10 may
improve discrimination of DENV infection [20,36]; however, the
1997 WHO classification scheme specified a cut-off of $20 .
Our results support using a cut-off of $10 petechiae/in2, and this
cut-off has been specified in the 2011 WHO clinical guidelines
A major strength of this study is the use of statistical models
designed for analysis of longitudinal data. Few other prospective
community-based cohort studies have analyzed early clinical
features in pediatric dengue compared to OFI [20,38–40], and
none that we are aware of were analyzed using longitudinal
statistical methods that account for correlations between repeated
measures on patients. Here, we preserved the longitudinal
structure of the dataset by using statistical models that support
repeated measurements on subjects over time and account for
correlations between signs and symptoms experienced within the
same individual on different days of illness and in multiple
episodes. Longitudinal data have long been collected in dengue
research but have rarely been analyzed using appropriate
statistical methods. This may introduce bias into findings, as
studies may overestimate the magnitude of association or reduce
the statistical power of the study as data are lost when they are
collapsed for non-longitudinal analysis.
An additional strength of this study is that it is community-based
, enabling day-by-day capture of information on the early
course of illness and on the full clinical spectrum of symptomatic
dengue. In contrast, nearly all previous studies enrolled patients
upon presentation to a hospital , where patients present later;
thus, these studies were unable to capture information on the early
days of illness or on mild disease. By examining the clinical
spectrum of dengue by day of illness, we were able to detect
differences in the prevalence of signs and symptoms that could not
be revealed by simply analyzing whether they ever occurred over
the course of illness. In addition, through multivariable longitu-
dinal models, we were able to identify distinguishing features of
dengue during the early phase of illness compared to the entire
course of illness. These findings are important for clinical practice
since outside of the hospital setting, clinicians may see dengue
patients toward the beginning of their illness and utilize that
information to decide whether their patient has dengue or another
febrile illness. The results of these models should be extended for
the development of prediction algorithms to aid clinicians in
diagnosing suspected dengue.
This study was not without its limitations. Some participants
migrated out of the study area or withdrew from the study;
however, our retention rate was approximately 95% per year
, suggesting that any bias from loss to follow-up would be
minimal. It is also possible that we did not capture all
symptomatic dengue cases. However, in yearly participant
surveys, only an average of 2–3% of participants reported having
attended a health-care provider outside of the study or having an
illness and not attending any medical provider , and
approximately 20-fold more laboratory-confirmed dengue cases
were captured in the cohort study than by the National
Surveillance System . Unfortunately, due to the low number
of severe dengue cases, this study did not have sufficient statistical
power to compare severe dengue cases to DF cases using GEE
models, and these low numbers may have influenced the lack of
significant association of signs of severe dengue with testing
dengue-positive. For this study, we used the 1997 WHO
classification scheme for disease severity. In 2009, the WHO
updated its guidelines for classification of dengue disease severity
[1,37]; it would be interesting to re-analyze the data in a future
study using the new classification scheme. Studies of the
usefulness and applicability of the revised guidelines have been
recently performed [42,43].
In summary, this study is one of the few cohort studies to
provide early data on the full clinical spectrum of pediatric dengue.
Though we found significantly increased odds for association of
several clinical signs and symptoms with testing dengue-positive,
these increases were more modest for the early phase of illness
compared to the course of illness, suggesting that caution should
be taken when using the results from the entire course of illness to
develop prediction algorithms. Non-parametric methods such as
decision tree analysis overcome some of the limitations of
traditional logistic regression models and have recently been
applied to develop algorithms for prediction of dengue diagnosis
and disease severity [9,44,45]. These and other data-adaptive
approaches such as Super Learner  that are less subject to bias
should be further explored to develop prediction algorithms for
early identification of dengue cases and improved clinical
STROBE checklist for cohort studies.
We thank our study team based at the Centro de Salud So ´crates Flores
Vivas, the Hospital Infantil Manuel de Jesu ´s Rivera, the Sustainable
Sciences Institute, and the National Virology Laboratory in the Centro
Nacional de Diagno ´stico y Referencia, for their dedication and excellent
work to ensure high-quality medical attention and study performance,
tireless data entry, top-notch laboratory work, and stellar database
management and support, particularly Magaly Amador, Sonia Arguello,
William Avile ´s, Yahoska Buitrago, Jose ´ Ramon Cisneros, Douglas
Elizondo, Carolina Flores, Nicole Fitzpatrick, Gamaliel Gutierrez,
Samantha Hammond, Jaqueline Herrera, Brenda Lo ´pez, Roger Lo ´pez,
Juan Carlos Matute, Julia Medina, Juan Carlos Mercado, Berman Moraga,
Mirtha Monterrey, Azucena Munguia, Federico Narvaez, Grethel Navas,
Andrea Nun ˜ez, Sergio Ojeda, Zoila Orozco, Leonel Pe ´rez, Maria Angeles
Pe ´rez, Miguel Reyes, Carlos Romero, Crisanta Rocha, Cinthia Saborio,
Saira Saborio, Leyla Saenz, Nery Sanchez, Sheyla Silva, Yolanda Tellez,
Maria Jose ´ Vargas, Ubania Vargas, and other study personnel. We are
especially thankful to Dr. Alcides Gonzalez for his continued support over
the years. Finally, we are indebted to the children, who participated in the
study, and their parents.
Conceived and designed the experiments: HHB OO AG KS EH.
Performed the experiments: OO GK. Analyzed the data: HHB OO AG
KS. Contributed reagents/materials/analysis tools: AB GK EH. Wrote the
paper: HHB KS AG EH.
Early Clinical Features of Pediatric Dengue
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1. World Health Organization (2009) Dengue: Guidelines for diagnosis, treatment,
prevention and control. Geneva: WHO Press.
Gibbons RV, Vaughn DW (2002) Dengue: An escalating problem. BMJ 324:
Guzman MG, Halstead SB, Artsob H, Buchy P, Farrar J, et al. (2010) Dengue:
A continuing global threat. Nat Rev Microbiol 8: S7–16.
World Health Organization (2000) Strengthening implementation of the global
strategy for dengue fever/dengue haemorrhagic fever prevention and control.
Geneva: WHO Press.
Zavala-Velazquez JE, Yu XJ, Walker DH (1996) Unrecognized spotted fever
group rickettsiosis masquerading as dengue fever in Mexico. Am J Trop Med
Hyg 55: 157–159.
Watt G, Jongsakul K, Chouriyagune C, Paris R (2003) Differentiating dengue
virus infection from scrub typhus in Thai adults with fever. Am J Trop Med Hyg
Ellis RD, Fukuda MM, McDaniel P, Welch K, Nisalak A, et al. (2006) Causes of
fever in adults on the Thai-Myanmar border. Am J Trop Med Hyg 74: 108–113.
Balmaseda A, Hammond SN, Perez L, Tellez Y, Saborio SI, et al. (2006)
Serotype-specific differences in clinical manifestations of dengue. Am J Trop
Med Hyg 74: 449–456.
Potts JA, Gibbons RV, Rothman AL, Srikiatkhachorn A, Thomas SJ, et al.
(2010) Prediction of dengue disease severity among pediatric Thai patients using
early clinical laboratory indicators. PLoS Negl Trop Dis 4: e769.
10. Clark DV, Mammen MP, Jr., Nisalak A, Puthimethee V, Endy TP (2005)
Economic impact of dengue fever/dengue hemorrhagic fever in Thailand at the
family and population levels. Am J Trop Med Hyg 72: 786–791.
11. Potts JA, Thomas SJ, Srikiatkhachorn A, Supradish PO, Li W, et al. (2010)
Classification of dengue illness based on readily available laboratory data.
Am J Trop Med Hyg 83: 781–788.
12. Low JG, Ong A, Tan LK, Chaterji S, Chow A, et al. (2011) The early clinical
features of dengue in adults: Challenges for early clinical diagnosis. PLoS Negl
Trop Dis 5: e1191.
13. Ramos MM, Tomashek KM, Arguello DF, Luxemburger C, Quinones L, et al.
(2009) Early clinical features of dengue infection in Puerto Rico. Trans R Soc
Trop Med Hyg 103: 878–884.
14. Binh PT, Matheus S, Huong VT, Deparis X, Marechal V (2009) Early clinical
and biological features of severe clinical manifestations of dengue in Vietnamese
adults. J Clin Virol 45: 276–280.
15. Gregory CJ, Santiago LM, Arguello DF, Hunsperger E, Tomashek KM (2010)
Clinical and laboratory features that differentiate dengue from other febrile
illnesses in an endemic area–Puerto Rico, 2007-2008. Am J Trop Med Hyg 82:
16. Chadwick D, Arch B, Wilder-Smith A, Paton N (2006) Distinguishing dengue
fever from other infections on the basis of simple clinical and laboratory features:
Application of logistic regression analysis. J Clin Virol 35: 147–153.
17. Wilder-Smith A, Earnest A, Paton NI (2004) Use of simple laboratory features to
distinguish the early stage of severe acute respiratory syndrome from dengue
fever. Clin Infect Dis 39: 1818–1823.
18. Potts JA, Rothman AL (2008) Clinical and laboratory features that distinguish
dengue from other febrile illnesses in endemic populations. Trop Med Int Health
19. Deparis X, Murgue B, Roche C, Cassar O, Chungue E (1998) Changing clinical
and biological manifestations of dengue during the dengue-2 epidemic in French
Polynesia in 1996/97–description and analysis in a prospective study. Trop Med
Int Health 3: 859–865.
20. Kalayanarooj S, Vaughn DW, Nimmannitya S, Green S, Suntayakorn S, et al.
(1997) Early clinical and laboratory indicators of acute dengue illness. J Infect
Dis 176: 313–321.
21. Kuan G, Gordon A, Aviles W, Ortega O, Hammond SN, et al. (2009) The
Nicaraguan pediatric dengue cohort study: Study design, methods, use of
information technology, and extension to other infectious diseases.
Am J Epidemiol 170: 120–129.
22. Balmaseda A, Hammond SN, Tellez Y, Imhoff L, Rodriguez Y, et al. (2006)
High seroprevalence of antibodies against dengue virus in a prospective study of
schoolchildren in Managua, Nicaragua. Trop Med Int Health 11: 935–942.
23. Harris E, Videa E, Perez L, Sandoval E, Tellez Y, et al. (2000) Clinical,
epidemiologic, and virologic features of dengue in the 1998 epidemic in
Nicaragua. Am J Trop Med Hyg 63: 5–11.
24. Ferna ´ndez RJ, Va ´zquez S (1990) Serological diagnosis of dengue by an ELISA
inhibition method (EIM). Mem Inst Oswaldo Cruz 85: 347–351.
25. Reed LJ, Muench H (1938) A simple method of estimating fifty percent
endpoints. Am J Hyg 27: 493–497.
26. World Health Organization (1997) Dengue haemorrhagic fever: Diagnosis,
treatment, prevention and control. Geneva: World Health Organization.
27. Gutierrez G, Standish K, Narvaez F, Perez MA, Saborio S, et al. (2011) Unusual
dengue virus 3 epidemic in Nicaragua, 2009. PLoS Negl Trop Dis 5: e1394.
28. Balmaseda A, Standish K, Mercado JC, Matute JC, Tellez Y, et al. (2010)
Trends in patterns of dengue transmission over 4 years in a pediatric cohort
study in Nicaragua. J Infect Dis 201: 5–14.
29. Wang CC, Lee IK, Su MC, Lin HI, Huang YC, et al. (2009) Differences in
clinical and laboratory characteristics and disease severity between children and
adults with dengue virus infection in Taiwan, 2002. Trans R Soc Trop Med Hyg
30. Hanafusa S, Chanyasanha C, Sujirarat D, Khuankhunsathid I, Yaguchi A, et al.
(2008) Clinical features and differences between child and adult dengue
infections in Rayong province, southeast Thailand. Southeast Asian J Trop Med
Public Health 39: 252–259.
31. Kittigul L, Pitakarnjanakul P, Sujirarat D, Siripanichgon K (2007) The
differences of clinical manifestations and laboratory findings in children and
adults with dengue virus infection. J Clin Virol 39: 76–81.
32. Hammond SN, Balmaseda A, Perez L, Tellez Y, Saborio SI, et al. (2005)
Differences in dengue severity in infants, children, and adults in a 3-year
hospital-based study in Nicaragua. Am J Trop Med Hyg 73: 1063–1070.
33. Wichmann O, Hongsiriwon S, Bowonwatanuwong C, Chotivanich K,
Sukthana Y, et al. (2004) Risk factors and clinical features associated with
severe dengue infection in adults and children during the 2001 epidemic in
Chonburi, Thailand. Trop Med Int Health 9: 1022–1029.
34. Suwandono A, Kosasih H, Nurhayati, Kusriastuti R, Harun S, et al. (2006) Four
dengue virus serotypes found circulating during an outbreak of dengue fever and
dengue haemorrhagic fever in Jakarta, Indonesia, during 2004. Trans R Soc
Trop Med Hyg 100: 855–862.
35. Thomas L, Brouste Y, Najioullah F, Hochedez P, Hatchuel Y, et al. (2010)
Predictors of severe manifestations in a cohort of adult dengue patients. J Clin
Virol 48: 96–99.
36. Cao XT, Ngo TN, Wills B, Kneen R, Nguyen TT, et al. (2002) Evaluation of
the world health organization standard tourniquet test and a modified
tourniquet test in the diagnosis of dengue infection in Viet Nam. Trop Med
Int Health 7: 125–132.
37. World Health Organization (2011) Comprehensive guidelines for prevention
and control of dengue and dengue hemorrhagic fever. India: WHO Regional
Office for South-East Asia.
38. Phuong CX, Nhan NT, Kneen R, Thuy PT, van Thien C, et al. (2004) Clinical
diagnosis and assessment of severity of confirmed dengue infections in
Vietnamese children: Is the world health organization classification system
helpful? Am J Trop Med Hyg 70: 172–179.
39. Phuong HL, de Vries PJ, Nga TT, Giao PT, Hung le Q, et al. (2006) Dengue as
a cause of acute undifferentiated fever in Vietnam. BMC Infect Dis 6: 123.
40. Karande S, Gandhi D, Kulkarni M, Bharadwaj R, Pol S, et al. (2005)
Concurrent outbreak of leptospirosis and dengue in Mumbai, India, 2002.
J Trop Pediatr 51: 174–181.
41. Standish K, Kuan G, Aviles W, Balmaseda A, Harris E (2010) High dengue case
capture rate in four years of a cohort study in Nicaragua compared to national
surveillance data. PLoS Negl Trop Dis 4: e633.
42. Barniol J, Gaczkowski R, Barbato EV, da Cunha RV, Salgado D, et al. (2011)
Usefulness and applicability of the revised dengue case classification by disease:
Multi-centre study in 18 countries. BMC Infect Dis 11: 106.
43. Narvaez F, Gutierrez G, Perez MA, Elizondo D, Nunez A, et al. (2011)
Evaluation of the traditional and revised WHO classifications of dengue disease
severity. PLoS Negl Trop Dis 5: e1397.
44. Tanner L, Schreiber M, Low JG, Ong A, Tolfvenstam T, et al. (2008) Decision
tree algorithms predict the diagnosis and outcome of dengue fever in the early
phase of illness. PLoS Negl Trop Dis 2: e196.
45. Lee VJ, Lye DC, Sun Y, Leo YS (2009) Decision tree algorithm in deciding
hospitalization for adult patients with dengue haemorrhagic fever in Singapore.
Trop Med Int Health 14: 1154–1159.
46. van der Laan MJ, Polley EC, Hubbard AE (2007) Super learner. Statistical
Applications in Genetics and Molecular Biology 6: Article 25.
Early Clinical Features of Pediatric Dengue
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