Adv Health Behav

Adv Health Behav

**Received:** June 13, 2018; ** Accepted: ** July 6, 2018; ** Published: ** July 9, 2018

**Correspondence
to: **Willy Chou, Ncphrology Department, Chi-Mei Medical Center, 901 Chung Hwa Road, Yung Kung Dist., Tainan 710, Taiwan; Email: hsienyiwang@gmail.com

^{1} Medical Research Department, Chi-Mei Medical Center, Tainan, Taiwan

^{2} Department of Paediatrics, Chi-Mei medical center, Tainan, Taiwan

^{3} National Taiwan University School of Medicine, Taiwan

^{4} Department of Sports Management, College of Leisure and Recreation Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan

^{5} Ncphrology Department, Chi-Mei Medical Center, Tainan, Taiwan

**
Citation: **Chien TW, Chow JC, Chang Y, et al. Detecting Dengue Fever in Children: Using Sequencing Symptom Patterns for An Online Assessment Approach. *Adv Health Behav*, 2018, **1**(1): 12-16

**
Copyright:** © 2018 Willy Chou, *et al*. This is an open access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use, dis- tribution, and reproduction in any medium, provided the original author and source are credited.

### 1. Introduction

Dengue fever (DF) is one of the most common arthropod-borne viral diseases worldwide,[ 1] especially in South East Asia, Africa, the Western Pacific, and the Americas.[ 2, 3 ]

There is, however, no accurate and speedy diagnostic screening test for DF at an early stage because its signs and symptomse.g., fever, headache, and myalgiaare similar to those of other illnesses.[4–6] Some studies[ 4, 5 ] that used a univariate analysis report that the presumptive diagnosis of DF is imprecise. Multivariate logistic regressions also do not significantly distinguish patients with Copyright: c 2018 Tsair-Wei Chien, 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. dengue from those with other febrile illnesses.[ 7 ] The multivariate discrimination analyses reported a sensitivity and a specificity 0.76, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.93, but costly laboratory tests (Dengue Duo IgM & Rapid Strips; Panbio, Queensland, Australia)[ 8–11 ] were needed before DF was serologically confirmed.

DF symptoms are usually assessed using a
dichotomous (i.e., absent versus present) evaluation. The
dependent variable (DF^{+} versus DF ) predicted using
independent evaluations with a weighted summation score
is more accurate than that using simple evaluations with
an unweighted summation score. So far, there has been
no published study that has reported using the specific
sequence of symptoms reported or observed in specific
patients suspected of having DF. All published studies
to date still report using only a standard group of
symptoms with an unweighted summation score that apply to
a general group of patients that might have DF.

The non-parametric H^{T} fit statistic has been used in
education and psychometrics to identify aberrant test
respondents.[
12, 13
] It is a transposed formulation of a
scalability coefficient for items (e.g., symptoms in this study)
and evidently the best among 36 person fit statistics for
detecting abnormal behaviors.[
14
]

In the present study, we used the H^{T} coefficient
combined with weighted and unweighted variables to
examine whether these combinations provide a valid and
reliable approach for the early detection of DF in children.

### 2. Materials and methods

#### 2.1 Sample and clinical symptoms

The sample of 177 pediatric patients (≤ 16 years old;
DF^{+}:69; DF^{-} :108) was the same as in our previous
paper.[8] Guided by the literature,[5–7
] we collected nineteen
DF^{-}related clinical symptoms from the patients
medical records to develop the initial set of itemsdesignated
as 0=absent or 1=present to screen for DF infection:
(i) personal history of DF, (ii) family history of DF,
(iii) mosquito bites within the previous 2 weeks, (iv)
fever ≥ 39 C, (v) biphasic fever, (vi) rash, (vii)
petechiae, (viii) retro-orbital pain, (ix) bone pain
(arthralgia), (x) headache, (xi) myalgia, (xii) abdominal pain,
(xiii) anorexia, (xiv) occult hematuria, (xv) stool occult
blood, (xvi) cough, (xvii) sore throat, (xviii) soft
(watery) stool, and (xix) flushed skin. Data from these
patients charts were obtained and approved by the Research
Ethics Review Board of the Chi-Mei Medical Center.

#### 2.2 The H^{T} fit statistic

H^{T} is defined for the persons of a dichotomous dataset
with L items (in columns) and N persons (in rows),[
12
]
where X_{ni} is the scored (0,1) response of person *n* to item
*i*, and P_{n} = S_{n}/L. Here, S_{m} is the raw score for person *m*,
and S_{n} is the raw score for person *n*.

H^{T} is the sum of the covariances between person n
and the other persons divided by the maximum possible
sum of those covariances, so that the range of H^{T} is -1
to +1. When the responses by person's are positively
correlated with those of all the other persons, then H^{T} (n)
will be positive. In contrast, when the responses by
person n are negatively correlated with those of all the other
persons, then H^{T} (n) will be negative. When person's
responses are random, H^{T} (n) will be close to zero[
11
]. We
hypothesized that DF^{+} patients have different H^{T}
coefficients than do DF patients. All DF^{+} group members
were sequenced to the DF group members to obtain an
H^{T} coefficient using equation (1).

#### 2.3 Selecting symptoms and determining predictor variables

All symptoms were examined by the probability of
Type I error using the following three steps in Figure 1 to
determine predictor variables. First, each symptom was
separately examined by the univariate approach using a
*X*^{2} test and logistic regression, respectively, for
identifying a significant association with DF. Second, two
models (i.e., the univariate and the multivariate approaches)
were investigated for determining valid predictor
variables associated with DF when the probability of Type
I error is less than 0.05. Third, the predictor variables
were used in a weighted combination for discriminating
patients suspected with dengue virus infection.

#### 2.4 Detecting dengue fever: a comparison of three models

The efficacy of three models (A, B, and C) for
detecting dengue fever was examined: (i) A comparison
was made using univariate logistic regression in Model
A to examine effects through the AUC yielded by
Unweighted (i.e., summed item) scores, Weighted (i.e.,
logistic regression) scores, and H^{T} coefficients,
respectively, (ii) Multivariate logistic regression with the three
aforementioned factors combined was used in Model B,
(iii) after selecting the significant variables in Model B,
the combined predictive variables were analyzed using
multivariate logistic regression in Model C to obtain
effective weighted coefficients, and (iv) finally, we wanted
to use a single continuous variable yielded by the
combined predictive variables in Model C to compare the
AUC with the counterparts in Model A and C.

#### 2.5 Statistical tools and data analyses

SPSS 15.0 for Windows (SPSS Inc., Chicago, IL)
and MedCalc 9.5.0.0 for Windows (MedCalc Software,
Mariakerke, Belgium) were used to calculate (i) the
probability of false positives (Type I error) using a *X*^{2} test
and logistic regression, (ii) Youden J index (the higher,
the better), AUC (area under the ROC curve),
sensitivity, specificity, and the cutoff point at maximal
summations of specificity and sensitivity, (iii) correlation
coefficients among variables of unweighted, weighted, and H^{T}
scores.

### 3 Results

Sixty-nine pediatric patients clinically diagnosed with
DF and 108 with no evidence of DF infection were
included in this study (Table 1). A *X*^{2} test and logistic
regression analyses showed that only six symptoms
(Family History, Fever ≥ 39°C, Skin Rash, Petechiae,
Abdominal Pain, and Weakness) were significant for
assessing the likelihood of DF (Table 2).

Comparisons of the AUCs for the three study models
(A, B, and C) showed that the weighted variable (derived
by the Logistic regression) and the H^{T} coefficient can be
jointly used for predicting DF risk using equation (2):

The risk probability can be computed using the transformed equation (3):

*logit*denotes a unit of log odds.

A cutoff point of 1.03 (P = 0.26) was determined using
the combined predictive variables in Model C:
sensitivity = 0.91, specificity = 0.76, and AUC = 0.88 (Figure
2 and Table 3). Predictive power was better: specificity
was 5.56% (i.e., 75.93-70.37 shown in Table 3) higher
than when using traditional logistic regression; however,
the AUC was slightly lower (0.72) than when using the
unweighted (0.84) and the weighted (0.87) variables
((Table 2)). The H^{T} coefficients related to the weighted and
unweighted scores were 0.26 and 0.22, respectively. The
weighted score has a higher correlation coefficient than
does the unweighted score to the H^{T} coefficients.

A snapshot on a smart phone responding to questions
(Figure 3, top) was generated and the results for
assessing whether the patient has DF (Figure
3, bottom)
were determined, which indicated that patients suspected
of having DF can directly scan the QR-code to obtain
their DF *logit* scores (or the risk probability) and
examine whether these 6 symptoms are useful for
predicting a high DF risk (>1.03 *logits* or P> 0:26 =
exp( -1.03 *logits*)/(1 + exp( -1.03 *logits*)).

### 4 Discussion

We found that using the H^{T} coefficient yielded
predictions that were 5.56% more specific (i.e., 75.93-70.37
shown in Table 3) than those of traditional logistic
regression. The H^{T} index is promising when the patient
sequence symptom pattern is compared with the DF^{+}
group to detect dengue fever in children. It can be
combined with the weighted summation score to jointly
predict the DF risk and then to report that risk on
smartphones.

The H^{T} coefficient has been used in education and
psychometrics to identify aberrant test respondents.[
12, 13
]
Although some have used item response theory (IRT)
fit statistics (e.g., outfit mean square error > 2.0) to
select abnormal responses that indicate cheating,
careless responding, lucky guessing, creative responding, or
random responding,[
15
] our literature review revealed no
published papers that reported using the H^{T} coefficient
in medical settings, especially for detecting individual
aberrant response patterns different from the study
reference sample, or, like the current study, identifying the
DF risk by comparing their sequence symptom pattern to
that of the DF^{+} group.

A diagnosis of DF is usually confirmed by three
steps: (i) observing DF^{-}related symptoms, (ii) testing
laboratory data such as white blood cells (WBCs) and
platelets (PLTs), and (iii) serologically verifying DF
using dengue IgM and IgG antibodies, polymerase chain
reaction (PCR) analysis, and virus isolation tests. The
latter two are relatively expensive. It is needed to develop
a self-assessment approach (e.g., scanning QR-code,
responding questions, and obtaining the DF risk on his/her
smart phone) (1) helping patients for consultation at an
earlier stage, (2) prompting doctors sampling patient
laboratory data when he/her DF risk reaches a cutpoint of
P=0.26=exp(-1.03 *logits*)/(1+exp(-1.03 *logits*)).

We found that the weighted score was a better
predictor than was the unweighted score (see Model A and
Model B in Table 3). However, we still see so many
scales in medical setting using unweighted summation
scores to determine the presence or absence of disease.
Along with the smartphones popularly used in the
technical age, the way of obtaining the DF risk on
smartphones using the combined H^{T} coefficient and weighted
scores is available and worth recommending to
healthcare providers to use for detecting the risk for DF.

This study has some limitations. First, the DF
cutpoint based on the symptoms of our study sample might
be biased toward that population. Moreover, we did not
remove abnormal data when the H^{T} coefficient was less
than the critical value of 0.22, which best identifies
aberrantly responding examinees.[
14
] Second, although the
sample size was small, using the Rasch H^{T} coefficient
combined with the AUC yielded highly accurate
discriminatory screening. This finding, however, requires
confirmation in prospective studies of other regions with a
substantial incidence of DF.

### 5 Conclusions

Analyzing six simple symptoms using logistic
regression is useful and valid for the early detection of DF risk
in children. Combining the Rasch H^{T} coefficient with
the weighted score yields a prediction that is 5.56% more
specific than does traditional logistic regression. A
selfassessment app using patient smartphones is available to
help people suspected of having DF, and it might
eliminate the need for costly and time-consuming laboratory
tests.

### 6 Competing interests

The authors declare that they have no competing interests.

### 7 Authors contributions

T.-W.C. and S-C.K. conceived and designed the study, performed the statistical analyses and were in charge of recruiting study participants. W.-S.L. and T.-W.C. helped design the study, collected information and interpreted data. All authors read and approved the final article. This research was supported by grant ChiMei Foundation Hospital research CMFCR10593 from the Chi-Mei Medical Center. The authors have no other funding or conflicts of interest to disclose.

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