A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore.

A Clinical Diagnostic Model for Predicting Influenza
among Young Adult Military Personnel with Febrile
Respiratory Illness in Singapore
Vernon J. Lee1,2,3,4*, Jonathan Yap1, Alex R. Cook5, Chi Hsien Tan1, Jin-Phang Loh7, Wee-Hong Koh7,
Elizabeth A. S. Lim7, Jasper C. W. Liaw7, Janet S. W. Chew7, Iqbal Hossain7, Ka Wei Chan7, Pei-Jun Ting7,
Sock-Hoon Ng7, Qiuhan Gao1, Paul M. Kelly4, Mark I. Chen3,6, Paul A. Tambyah8, Boon Huan Tan7
1 Biodefence Centre, Ministry of Defence, Singapore, Singapore, 2Centre for Health Services Research, National University of Singapore, Singapore, Singapore,
3Department of Epidemiology and Public Health, National University of Singapore, Singapore, Singapore, 4National Centre for Epidemiology and Population Health,
Australian National University, Canberra, Australia, 5Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore,
6Department of Clinical Epidemiology, Tan Tock Seng Hospital, Singapore, Singapore, 7Defence Medical and Environmental Research Institute, DSO National
Laboratories, Singapore, Singapore, 8Division of Infectious Diseases, National University of Singapore, Singapore, Singapore
Abstract
Introduction: Influenza infections present with wide-ranging clinical features. We aim to compare the differences in
presentation between influenza and non-influenza cases among those with febrile respiratory illness (FRI) to determine
predictors of influenza infection.
Methods: Personnel with FRI (defined as fever$37.5uC, with cough or sore throat) were recruited from the sentinel
surveillance system in the Singapore military. Nasal washes were collected, and tested using the Resplex II and additional
PCR assays for etiological determination. Interviewer-administered questionnaires collected information on patient
demographics and clinical features. Univariate comparison of the various parameters was conducted, with statistically
significant parameters entered into a multivariate logistic regression model. The final multivariate model for influenza versus
non-influenza cases was used to build a predictive probability clinical diagnostic model.
Results: 821 out of 2858 subjects recruited from 11 May 2009 to 25 Jun 2010 had influenza, of which 434 (52.9%) had 2009
influenza A (H1N1), 58 (7.1%) seasonal influenza A (H3N2) and 269 (32.8%) influenza B. Influenza-positive cases were
significantly more likely to present with running nose, chills and rigors, ocular symptoms and higher temperature, and less
likely with sore throat, photophobia, injected pharynx, and nausea/vomiting. Our clinical diagnostic model had a sensitivity
of 65% (95% CI: 58%, 72%), specificity of 69% (95% CI: 62%, 75%), and overall accuracy of 68% (95% CI: 64%, 71%),
performing significantly better than conventional influenza-like illness (ILI) criteria.
Conclusions: Use of a clinical diagnostic model may help predict influenza better than the conventional ILI definition
among young adults with FRI.
Citation: Lee VJ, Yap J, Cook AR, Tan CH, Loh J-P, et al. (2011) A Clinical Diagnostic Model for Predicting Influenza among Young Adult Military Personnel with
Febrile Respiratory Illness in Singapore. PLoS ONE 6(3): e17468. doi:10.1371/journal.pone.0017468
Editor: Benjamin Cowling, University of Hong Kong, Hong Kong
Received November 17, 2010; Accepted January 28, 2011; Published March 2, 2011
Copyright: � 2011 Lee 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: The work was supported by a Singapore Ministry of Defence funded operational research program. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: VJL has received unrelated research support from GSK. PAT has received research support and honoraria from Baxter, Adamas, Merlion
Pharma, and Novartis, as well as travel support from Pfizer and Wyeth, and sits on the boards of the Asia Pacific Advisory Committee on Influenza and the Asian
Hygiene Council. The rest of the authors declare no conflict of interests, financial or otherwise, in this study. This does not alter the authors’ adherence to all the
PLoS ONE policies on sharing data and materials.
* E-mail: vernonljm@hotmail.com
Introduction
Influenza infections result in a wide range of clinical
presentations, from the classical influenza-like illness (ILI), to
milder respiratory infections, and subclinical infections. Determin-
ing the clinical predictors of influenza infection is important for the
diagnosis and management of patients presenting with respiratory
illness, helping to guide appropriate antiviral therapy, and to avoid
unnecessary antibiotic use. This is particularly important in the
young adult population, which constitutes an economically
productive age group whereby early treatment may reduce work
absenteeism [1]. The recent 2009 H1N1 pandemic has shown that
young adults have a higher infection rate compared to other age
groups [2]. For essential public services such as the military, police,
civil defence, and healthcare with substantial proportions of young
adults, early recognition and treatment may reduce service
disruptions.
There has been research describing the differences in symptoms
between influenza and non-influenza cases. However, few have
been performed in tropical countries, where a large proportion of
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the world’s population reside. Influenza morbidity and mortality in
tropical countries like Singapore has been shown to be comparable
to temperate countries [3,4]. Furthermore, there has also been
substantial co-circulation of other etiologic agents that can
similarly cause acute respiratory illnesses [5]. While two recent
tropical studies sought to differentiate the symptoms of these
clinical entities, they had only limited number of cases [6,7], and
were based only on hospital attendances in the peri-pandemic
period, where inclusion criteria might be atypical.
Using data from a respiratory disease sentinel surveillance
system in the Singapore military, we compare the differences in
clinical presentation between influenza and non-influenza cases in
young adults with febrile respiratory illness to determine predictors
of influenza infection and aid case management especially where
laboratory confirmation is not possible.
Methods
Singapore is a city state in tropical South-East Asia with 5
million people, with all Singaporean males serving two years of
military service after high school. These servicemen live in
barracks-style accommodation during weekdays and return home
during weekends, maintaining continued interaction between the
military and the Singapore population.
The Singapore military began a sentinel respiratory disease
surveillance program in 4 major camps, including a recruit
training camp, on 11 May 2009 (epidemiological-week 19), just
before community spread of pandemic H1N1 in late-June 2009
[8,9]. All personnel who visited the primary healthcare clinics in
these camps during the main consultation hours with febrile
respiratory illness (FRI)—defined as the presence of fever$37.5uC
with cough or sore throat—were recruited. The use of FRI
contrasts with the usual measure of influenza-like illness (ILI,
defined as fever $38.0uC with cough or sore throat); our choice
reflected the desire to capture other febrile cases that also result in
substantial absenteeism; while limiting cases to those with fever as
an indicator of potential severity and absenteeism.
Repeat visits for the same illness episode as assessed by the
consulting physician were excluded to avoid double counting.
Nasal washes, collected separately from each side of the nose, were
taken from consenting participants by trained medical staff,
collected in viral transport media, and sent to the laboratory
within 24 hours. Nasal washes were used as they have been shown
to be equally or more sensitive than other methods such as nasal or
throat swabs, and nasopharyngeal aspirates, in the detection of
respiratory infections such as influenza [10–12].
In addition, interviewer-administered questionnaires were
completed during the medical consultation, collecting information
on patient demographics and clinical features. A follow-up phone
questionnaire was conducted 2 weeks after the initial consultation
to determine symptoms present during the entire course of illness.
Written informed consent was obtained. The study was
approved by the military’s Joint Medical Committee for Research,
and by the institutional review boards of the National University of
Singapore, and the Australian National University.
Laboratory Methods
To determine the etiology, we used the multiplex PCR strategy
based on the Resplex assays described below, and performed
additional singleplex PCR assays to determine the influenza
subtype.
Total nucleic acids were extracted from each specimen using
the DNA minikit (Qiagen, Inc, Valencia, CA, USA) according to
the manufacturer’s instructions. Five ml of extract were tested with
Resplex I and II (version 2.0, Qiagen, Inc., Valencia, CA, USA)
for the presence of respiratory micro-organisms on the LiquiChip
200 Workstation, again according to the manufacturer’s instruc-
tions. The Resplex I and II (version 2.0) assays are multiplex PCR
assays coupled with bead array detection technology and can
simultaneously detect and subtype 18 different viruses and bacteria
including influenza A and influenza B [13–15].
Specimens that were Resplex II positive for influenza A were
further subtyped with real-time PCR for H1 or H3 (Singapore
Ministry of Health), or for pandemic H1N1 [16]. Briefly, five ml of
total genetic extracts were tested with the one-step SuperscriptIII/
Platinum Taq kit (Invitrogen, Carlsbad, CA, USA) following the
manufacturer’s instructions on either the LightCycler machine
from Roche or the Applied Biosystems real-time PCR machine
(7500).
Statistical Analysis
We compared differences in overall clinical presentation
between influenza and all non-influenza FRI cases. Univariate
comparison of demographic parameters, symptoms and signs was
conducted using logistic regression to determine statistically
significant parameters of interest. Potential confounding was
addressed by performing multivariate analyses where character-
istics found to be statistically significant in univariate analyses were
entered into a multivariate logistic regression model to identify
independent clinical predictors, with non-significant terms in the
multivariate analysis dropped one at a time starting with the
highest p-value. To address another source of potential confound-
ing among the remaining variables, we assessed for interactions
between these variables but none proved significant. All statistical
analyses were performed using Stata 9.0 (Stata Corp., College
Station, TX, USA) and R (R Core Development Team). All tests
were conducted at the 5% level of significance, with no explicit
adjustment for multiple comparisons; instead, where appropriate,
we present the expected number of false positive findings under
the assumption that all null hypotheses are correct, a strongly
conservative assumption. We report odds ratios (OR) and
corresponding 95% confidence intervals (CI) where applicable.
The final multivariate model for influenza versus non-influenza
cases was used to build a predictive probability equation as a
clinical diagnostic model to determine the likelihood of influenza
infection given the clinical characteristics. For this we developed
the receiver operating characteristic (ROC) curve whence the area
under the ROC (AUC) was calculated and two cut-off points
determined: one maximizing the sum of sensitivity and specificity,
the other maximising specificity while keeping sensitivity at 90%.
Ten-fold cross-validation was used to guard against over-fitting,
with AUC, sensitivity and specificity scores averaged over the ten
folds.
Results
A total of 2858 eligible subjects were recruited from 11 May
2009 to 25 Jun 2010. Of these 2858 subjects, 2717 (95.1%)
completed the telephone follow-up. The average age was 21 years
old (SD 3.2), and 2853 (99.8%) were male. Of the 2858 subjects,
there were 821 influenza cases, of which 434 (52.9% of all
influenza cases) were 2009 pandemic influenza A (H1N1), 58
(7.1%) seasonal influenza A (H3N2), 269 (32.8%) influenza B, and
10 (1.2%) seasonal influenza A (H1N1), with 6 co-infections and
44 unsubtypable.
There were a total of 70 influenza vaccine failures, defined as
seasonal or pandemic influenza infections that occurred despite
previous vaccination with the relevant seasonal or pandemic
Clinical Diagnostic Model for Influenza
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vaccine respectively. Of these, there were 43 pandemic H1N1
vaccine failures, although 27 (63%) were vaccinated less than 2
weeks before onset of symptoms; 11 H3N2 vaccine failures, and 16
influenza B vaccine failures. There were no statistically discernible
differences in influenza severity (fever $38.0uC or breathlessness)
for vaccine failures compared to other influenza cases.
Figure 1 shows the number of FRI cases sampled per week, and
the proportion of these cases that tested positive for influenza. For
the non-influenza FRI cases, 289 (10.1% of all subjects) were
diagnosed with coxsackie viruses/echoviruses, 247 (8.6%) rhino-
virus, 217 (7.6%) H. influenzae, 130 (4.5%) coronaviruses, 76
(2.7%) parainfluenza viruses, 47 (1.6%) human metapneumovirus,
27 N. meningitidis, 12 S. pneumoniae, 5 adenoviruses, 2 RSV,
and 1 bocavirus.
Clinical Features
Univariate analyses comparing the clinical features between
influenza and non-influenza cases are presented in Figure 2, while
the multivariate analyses adjusting for possible confounders are
presented in Table 1.
From the univariate and multivariate analyses, influenza-
positive cases were significantly more likely to present with
running nose, chills and rigors, and higher temperature, and less
likely to present with sore throat, photophobia, and injected
pharynx, compared to influenza-negative cases (Figure 2 and
Table 1). Ocular symptoms were significant on univariate but only
marginally so on multivariate analysis, while nausea/vomiting was
borderline significant on univariate but clearly significant on
multivariate analysis. Based on the final model’s maximum
likelihood estimates, we created a diagnostic index that predicted
influenza infection based on clinical presentation. The predicted
probability of influenza infection (pi) was calculated as follows:
10ln
pi
1{pi
= –31 – 5[sore throat] + 6[running nose] + 2[ocular
symptoms] – 3[nausea/vomiting] + 4[chills/rigors] – 7[photopho-
bia] + 5[fever$37.8] + 8[fever$38] – 4[injected pharynx]where
[A] = 1 if the patient presents with that symptom or sign and 0
otherwise. A score (on the right hand side) of 0 corresponds to a
50% chance of influenza infection, -10 to about a 25% chance, -5
to about a 40% chance. The fever terms are cumulative, i.e. a
fever of 37.9 adds 5 to the score, while a fever of 38.2 adds 13.
The AUC under ten-fold cross-validation was 69% (95% CI:
61%, 76%). Using a cut-off to maximize sensitivity and specificity,
the model had sensitivity of 65% (95% CI: 58%, 72%), specificity
of 69% (95% CI: 62%, 75%), and overall accuracy of 68% (95%
Figure 1. Weekly FRI cases, by influenza RT-PCR positivity, in 2009/10 in the Singapore military.
doi:10.1371/journal.pone.0017468.g001
Figure 2. Univariate comparison of clinical signs or symptoms
between influenza-positive and influenza-negative cases.
Symptoms or signs are ranked by frequency for non-influenza cases.
Empirical frequencies of presentation of each symptom or sign are
presented in the right column as bars, with 95% confidence intervals
represented by whiskers. Symptoms or signs that are statistically
discernibly different at the 5% level are displayed in bold font. With 21
tests, the conservative expected number of false discoveries is 1.1.
doi:10.1371/journal.pone.0017468.g002
Clinical Diagnostic Model for Influenza
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CI: 64%, 71%) under ten-fold cross validation. The model allows
for differing cut-off specifications using the indicated criteria
(Table 2). The relatively poor performance of ILI alone as a
predictor is notable.
Discussion
Differentiating between influenza infections and other febrile
respiratory illnesses is a challenge in clinical settings without
laboratory assistance. In most situations, it is not feasible or cost-
effective to perform PCR tests, while cheaper rapid tests have
limited sensitivity [17,18]. It is therefore important for clinicians to
have clinical presentation-based guides to assist in diagnosing
influenza cases for treatment and further management, especially
during an epidemic or pandemic.
Influenza-positive and negative cases had several differing
clinical parameters. We have found that influenza-positive cases
were more likely to have running nose compared to influenza-
negative cases, similar to the findings from another general
population study in the tropics [7]. This is contrary to previous
belief that running nose is less common in influenza compared to
other viral respiratory illnesses [19]. Likewise, influenza cases also
had similar prevalence of cough with sputum compared to non-
influenza cases, also contrary to previous belief [19].
At the same time, influenza cases were more likely to have
higher temperature and chills and rigors but less likely to present
with sore throat, providing supporting evidence to a previous study
by Monto and colleagues that one of the most predictive symptoms
of influenza is fever [20]. However, unlike that study, we did not
find that cough was a predictive symptom for influenza. Possible
reasons for such a difference include the potentially different
aetiologies for non-influenza cases in the tropics and other regions,
and also possible differences in influenza presentation by region. It
is therefore important to validate these predictive tools in the local
setting where they are used.
In the absence of laboratory testing, using our clinical diagnostic
model enabled accurate classification of up to 76% of all cases in
our cohort (Table 2). Keeping sensitivity at 90%, we were able to
achieve a high negative predictive value of 86%, which is useful for
clinicians in excluding influenza cases. The positive predictive
value, on the other hand, is low due to the substantial overlap in
symptoms between influenza and non-influenza cases. The clinical
diagnostic model performed significantly better than standard ILI
criteria among our subjects with febrile respiratory infections. It
can be easily adapted into various tabular or electronic formats for
easy use by clinicians. This, if taken together with specific policy
and cost evaluations in the local setting, may help guide initiation
of anti-viral treatment or isolation measures during an epidemic or
pandemic situation while reducing wrong treatment of non-
influenza cases to minimize stockpile wastages.
The strengths of our study are its large sample size, high follow-
up rate, and high diagnostic ascertainment, with etiological
confirmation of all positive influenza cases. There are some
limitations to this study, including the natural bias towards febrile
symptomatic cases due to the case definition. Influenza cases do
present with mild or asymptomatic infection, but these cases will
be difficult to identify in a surveillance program and are less severe
in clinical outcome. The results should therefore be interpreted in
the context of febrile symptomatic infection requiring physician
consultation, which capture the more severe and important cases
that affect absenteeism.
In addition, this study predominantly considered young male
adults. While we felt that there is no evidence that shows any
differences in presentation by gender, further studies are required
to determine if similarly high diagnostic ascertainment can be
Table 1. Multivariate analysis comparing clinical features of
influenza-positive with all influenza-negative FRI cases.
Influenza Positive vs Negative*
Parameters Adjusted Odds Ratio (95% CI) p value
Sore throat 0.62 (0.48, 0.82) ,0.001
Running nose 1.86 (1.52, 2.29) ,0.001
Chills/rigors 1.52 (1.20, 1.91) ,0.001
Photophobia 0.49 (0.29, 0.83) 0.007
Fever ($37.8uC)
Fever ($38uC)
1.64 (1.19, 2.26)
2.15 (1.65, 2.80)
0.003
,0.001
Injected pharynx 0.69 (0.56, 0.86) ,0.001
Nausea/Vomiting
Eye symptoms
0.74 (0.59, 0.92)
1.25 (1.01, 1.55)
0.007
0.04
*Age, sore throat, running nose, sore eyes or eye pain, chills/rigors,
photophobia, Fever $37.8uC, Fever $38.0uC, and injected pharynx were
included in the analysis before non-significant terms were sequentially
removed. With nine tests, the conservative expected number of false
discoveries is 0.45.
doi:10.1371/journal.pone.0017468.t001
Table 2. Utility of the predictive probability equation as a clinical diagnostic model in this study under 10-fold cross-validation
compared with commonly used ILI criteria (for which no cross-validation is needed).
Variable
Sensitivity (%,
and 95% CIs)
Specificity (%,
and 95% CIs)
PPV (%, and
95% CIs)
NPV (%, and
95% CIs)
Overall accuracy
(%, and 95% CIs)
Predictive probability equation, maximising
total sensitivity and specificity
65
(58, 72)
69
(62, 75)
43
(39, 47)
85
(83, 87)
68
(64, 71)
Predictive probability equation,
maximising accuracy
18
(8, 29)
96
(93, 99)
67
(57, 76)
77
(75, 80)
76
(74, 77)
Predictive probability equation,
setting sensitivity to 90%
90
(89, 90)
26
(20, 23)
30
(28, 33)
86
(83, 89)
43
(38, 48)
Fever $37.8uC, cough or
sore throat
84
(78, 83)
36
(34, 38)
34
(31, 35)
84
(80, 85)
48
(47, 51)
ILI (Fever $38.0uC, cough or
sore throat)
69
(64, 71)
55
(53, 57)
37
(35, 40)
81
(79, 83)
58
(57, 60)
doi:10.1371/journal.pone.0017468.t002
Clinical Diagnostic Model for Influenza
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achieved in other age groups. Similarly, consultation biases may
exist as the military population have medical consultation patterns
that differ from the general population. We re-emphasize that
diagnostic tools should be developed in the setting where they are
used. Other potential biases include presentation biases from cases
which rejected recruitment, presentations after recruitment hours
which were not included, and losses to follow-up. Recall biases
may exist as we obtained final clinical history two weeks after
enrolment into the study, which we felt struck a balance between
the risk of recall bias and the desire to capture comprehensively all
symptoms during the illness period.
Different diagnostic scores may need to be developed to account
for local FRI aetiologies and socio-cultural-demographic differ-
ences, but so doing will rely on well-designed local surveillance
programs. The best clinical syndrome to be used for surveillance is
a potentially interesting question that may be explored by further
related studies.
Use of a predictive equation as a clinical diagnostic model can
help better predict influenza than the conventional influenza-like
illness definition among young adult military personnel with febrile
respiratory illnesses. Until cheap, rapid and reliable point-of-care
tests become widely available, clinical scores derived from large
cohort studies may be of reasonable clinical utility.
Author Contributions
Conceived and designed the experiments: VJL JY JPL MIC PAT BHT.
Performed the experiments: VJL JY CHT JPL WHK EASL JCWL JSWC
IH. Analyzed the data: VJL JY ARC CHT JPL IH QHG PMK MIC PAT
BHT. Contributed reagents/materials/analysis tools: ARC JPL WHK
EASL JCWL JSWC IH KWC PJT SHN BHT. Wrote the paper: VJL JY
ARC CHT JPL PMK MIC PAT BHT.
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