Identifying etiologies of acute febrile illnesses (AFI) is challenging due to non-specific presentation and limited availability of diagnostics. Prospective AFI studies provide a methodology to describe the syndrome by age and etiology, findings that can be used to develop case definitions and multiplexed diagnostics to optimize management. We conducted a 3-year prospective AFI study in Puerto Rico. Patients with fever ≤7 days were offered enrollment, and clinical data and specimens were collected at enrollment and upon discharge or follow-up. Blood and oro-nasopharyngeal specimens were tested by RT-PCR and immunodiagnostic methods for infection with dengue viruses (DENV) 1–4, chikungunya virus (CHIKV), influenza A and B viruses (FLU A/B), 12 other respiratory viruses (ORV), enterovirus, Leptospira spp., and Burkholderia pseudomallei. Clinical presentation and laboratory findings of participants infected with DENV were compared to those infected with CHIKV, FLU A/B, and ORV. Clinical predictors of laboratory-positive dengue compared to all other AFI etiologies were determined by age and day post-illness onset (DPO) at presentation. Of 8,996 participants enrolled from May 7, 2012 through May 6, 2015, more than half (54.8%, 4,930) had a pathogen detected. Pathogens most frequently detected were CHIKV (1,635, 18.2%), FLU A/B (1,074, 11.9%), DENV 1–4 (970, 10.8%), and ORV (904, 10.3%). Participants with DENV infection presented later and a higher proportion were hospitalized than those with other diagnoses (46.7% versus 27.3% with ORV, 18.8% with FLU A/B, and 11.2% with CHIKV). Predictors of dengue in participants presenting <3 DPO included leukopenia, thrombocytopenia, headache, eye pain, nausea, and dizziness, while negative predictors were irritability and rhinorrhea. Predictors of dengue in participants presenting 3–5 DPO were leukopenia, thrombocytopenia, facial/neck erythema, nausea, eye pain, signs of poor circulation, and diarrhea; presence of rhinorrhea, cough, and red conjunctiva predicted non-dengue AFI. By enrolling febrile patients at clinical presentation, we identified unbiased predictors of laboratory-positive dengue as compared to other common causes of AFI. These findings can be used to assist in early identification of dengue patients, as well as direct anticipatory guidance and timely initiation of correct clinical management.
We conducted a prospective study of acute febrile illness (AFI) in Puerto Rico to better understand the etiology of AFI among all age groups in the tropics. Such findings could assist clinicians to identify disease-specific characteristics, which can then be used to initiate proper patient management. We enrolled 8,996 AFI patients and tested them for dengue viruses 1–4 (DENV 1–4) and 21 other pathogens. A pathogen was detected in 55% of patients, most frequently chikungunya virus (CHIKV, 18%), influenza A or B virus (FLU A/B, 12%), DENV 1–4 (11%), or another respiratory virus (ORV, 10%). Participants with dengue presented later after symptom onset and were hospitalized more often (47%) than patients with another etiology of AFI (27% with ORV, 19% with FLU A/B, and 11% with CHIKV). Predictors of patients with dengue differed by timing of presentation but included eye pain, nausea, and low white blood cell or platelet counts; negative predictors included symptoms of respiratory illness. By enrolling febrile patients at clinical presentation, we identified unbiased predictors of patients with dengue as compared to other common AFI. Findings can be used to diagnose dengue patients to provide early and appropriate clinical management.
Citation: Tomashek KM, Lorenzi OD, Andújar-Pérez DA, Torres-Velásquez BC, Hunsperger EA, Munoz-Jordan JL, et al. (2017) Clinical and epidemiologic characteristics of dengue and other etiologic agents among patients with acute febrile illness, Puerto Rico, 2012–2015. PLoS Negl Trop Dis 11(9): e0005859. https://doi.org/10.1371/journal.pntd.0005859
Editor: Shama Parveen, Jamia Millia Islamia, INDIA
Received: May 8, 2017; Accepted: August 7, 2017; Published: September 13, 2017
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: All relevant data are presented within the paper.
Funding: This work was supported by the Centers for Disease Control and Prevention cooperative agreement 5U01CK000246-03 awarded to Ponce Health Sciences University and Saint Luke's Episcopal Hospital. The funders at CDC National Center for Emerging and Zoonotic Infectious Diseases had no role in study design, data collection and analysis.
Competing interests: The authors have declared that no competing interests exist.
As malaria incidence continues to decrease throughout the tropics, a new area of research has focused on identifying etiologies of non-malaria, acute febrile illness (AFI) [1, 2]. Knowledge is limited in this area, in large part because AFIs often have similar non-specific clinical presentations early in the clinical course when most patients are likely to present for care. In addition, rapid point-of-care diagnostics are often not readily available. Surveillance for AFIs, if done, is largely passive and relies on clinical identification of cases and voluntary case reporting. Therefore, burden of disease for the etiologic agents of AFI are likely underestimated. An improved understanding of the major causes of AFI is important to guide clinical management, develop diagnostics, inform public health policy, and direct prevention efforts .
In Mexico, South and Central America, and the Caribbean, AFI are common among patients of all age groups. In the last four decades, dengue, a mosquito-borne AFI caused by four genetically distinct dengue viruses (DENV 1–4), has become an increasingly common cause of AFI [4, 5]. The burden of dengue is thought to be less in Latin America than in Southeast Asia ; however, several studies have found that the incidence of dengue is likely underestimated in Latin America due to reliance on passive case surveillance [6–8]. Understanding region-specific etiologies of AFI and estimating the true incidence of dengue is necessary to plan large scale interventional trials for assessing the impact of mosquito control measures and vaccines. In addition, collecting clinical signs and symptoms from AFI patients of all ages with identified etiologic agents has utility in developing unbiased case definitions and identifying early clinical predictors to guide clinical management.
Prospective studies enrolling patients with AFI provide a methodology to systematically identify causes of AFI in a population and describe variation in the clinical course by patient age and etiologic agents. Since 2000, nine such studies have evaluated AFIs including dengue among both pediatric and adult patients [9–18]. While these studies are comparable in many ways, the studies differ in that several excluded either infants and young children [9–13, 16], older adults [12, 13], severe or hospitalized cases [13, 16], or cases with a known source of fever [9–12, 14]. In addition, most studies were conducted in low resource settings in Southeast Asia where malaria is still endemic [9, 10, 12–18]. In fact, two studies enrolled based on a potential participant meeting national eligibility criteria for malaria testing [12, 13], and two other studies excluded cases based on malaria blood smear positivity [14, 15].
In this manuscript, we describe a 3-year prospective study of AFI among all age groups that used a pre-defined diagnostic testing algorithm for DENV 1–4 and 21 other pathogens. We conducted this study in Puerto Rico, where malaria was eradicated in 1962  and dengue has been endemic since the late 1960s . We describe the frequency of dengue and other AFIs, and the distribution of these diseases in terms of person, place and seasonality. Last, we describe clinical predictors of dengue by timing of presentation compared to other AFIs.
Materials and methods
The study was conducted in southern Puerto Rico at Saint Luke’s Episcopal Hospital (SLEH)–Ponce, a 425-inpatient bed, tertiary care teaching hospital during May 7, 2012–May 6, 2015; and SLEH—Guayama, a 161-inpatient bed hospital during February 1, 2013–May 6, 2015. SLEH-Ponce is one of four hospitals serving 481,708 residents of Ponce and 11 neighboring municipalities . SLEH-Ponce has about 50,000 emergency department (ED) visits and 11,000 inpatient admissions annually. SLEH-Guayama is one of two hospitals that serve 96,439 residents of Guayama and three adjacent municipalities. SLEH-Guayama has 35,000 ED visits and 6,000 inpatient admissions annually.
Study enrollment and procedures
Patients presenting to the ED or as a direct hospital admission were eligible for enrollment if fever was present (defined by a body temperature of ≥38.0°C [oral] or ≥38.5°C [axillary]) or they reported a history of fever of ≤7-day duration. After informed consent was administered, vital signs, signs and symptoms of current AFI, history of exposures and chronic disease, and clinical laboratory results were recorded on an enrollment case report form (CRF). A physician examined the participants and recorded the clinical diagnosis on the CRF. Participants discharged to home after enrollment were asked to return 7–10 days post-illness onset (DPO). At the follow-up visit, a second completed CRF included a report of any healthcare services received and signs and symptoms experienced since enrollment. Participants admitted to the hospital upon enrollment had their hospital course summarized on a standardized data collection form that included treatment received, results of clinical laboratory and radiologic investigations, and disease manifestations.
Prior to enrollment, informed consent was administered in accordance with Puerto Rico law (Article 13, Section 13, Regulation 7617 of the Office of Patient Ombudsman, Act #194). Specifically, written informed consent was obtained from eligible adults >20 years old and emancipated minors 14–20 years old. Written informed assent was obtained from non-emancipated minors 14–20 years old and written informed consent was obtained from the parents or guardians. Verbal informed assent was obtained from children 7–13 years old and written informed consent was obtained from the parents or guardian, and the parents or guardian of children <7 years old. The Institutional Review Boards at the Centers for Disease Control and Prevention (CDC) and Ponce Health Sciences University approved the study protocol.
All study participants had blood (5 mL in EDTA, 7 mL whole blood), urine (15 mL), nasopharyngeal (NP), and oropharyngeal (OP) specimens collected at enrollment. Convalescent blood (5 mL in EDTA, 5 mL whole blood) and urine (10 mL) were collected at the follow-up visit or hospital discharge. NP and OP specimens were placed in a vial containing viral transport medium. Serum, blood, and urine specimens and inoculated vials were kept at 4°C until transported to CDC Dengue Branch (CDC-DB) in San Juan, Puerto Rico.
Molecular diagnostic testing for DENV 1–4, influenza A and B viruses (FLU A/B), and 12 other respiratory viruses (ORV) including adenovirus (AdV), human respiratory syncytial virus (HRSV), human metapneumovirus (HMPV), parainfluenza virus 1–4 (PIV-1–4), human rhinovirus (HRV), and four human coronaviruses (HCoV) (229E, OC43, NL63 and HKU1), was performed at CDC-DB. However, testing for HRV, PIV-2, PIV-4, and the four HCoV was discontinued after the first year because of low yield (i.e., only 1 PIV-2, 37 HCoV and 4 HCoV co-infections identified). In brief, RNA was extracted from NP and OP specimens and tested for ORV and FLU A/B viral genome by real time, reverse transcriptase-polymerase chain reaction assay (rRT-PCR) . Serum specimens collected ≤6 DPO were tested by DENV-serotype specific rRT-PCR [23, 24], and those collected ≥4 DPO were tested by an antibody-capture enzyme-linked immunosorbent assay (MAC-ELISA) (InBios International, Inc., Seattle, WA)[25–27]. Beginning in May 2014, specimens collected ≤6 DPO were tested by CHIKV-specific real-time RT-PCR , and those collected ≥6 DPO were tested by anti-CHIKV MAC-ELISA . Remaining serum, whole blood, and urine were stored at -70°C until shipped to CDC in Atlanta, Georgia.
At CDC, serum specimens collected ≤3 DPO were tested in the Picornavirus Laboratory by a pan-enterovirus real-time RT-PCR assay that targets the VP1 region ; positive specimens were sequenced. Paired serum specimens from enrollment and the follow-up visit or hospital discharge were tested for Leptospira spp., and Burkholderia pseudomallei at the Bacterial Special Pathogens Branch Laboratory. Specimens were tested by microscopic agglutination test (MAT) for 20 Leptospira reference antigens representing 17 serogroups . All convalescent serum specimens were tested for presence of Burkholderia pseudomallei and Leptospira antibodies by an indirect hemagglutination assay (IHA)  and MAT respectively, and acute specimens were tested in cases for which the corresponding convalescent specimen was positive. The first 250 patients with Leptospira spp. and Burkholderia pseudomallei negative specimens and for which paired specimens were available were tested by IFA for Rickettsia spp., Ehrlichia spp., and Coxiella spp. at the Rickettsial Zoonoses Branch Laboratory. Whole blood and/or acute serum from cases with a reactive IFA were assessed for C. burnetii, R. rickettsii, R. typhi, and/or E. chaffeensis DNA by PCR.
A laboratory-positive dengue case had DENV nucleic acid or anti-DENV IgM detected in a single specimen. A laboratory-negative dengue case had no anti-DENV IgM detected in serum collected ≥6 DPO. A laboratory-positive influenza case was defined by presence of FLU A/B nucleic acid in a NP or OP specimen. Laboratory-positive HMPV, HRSV, ADENO, PIV-1, PIV-2, PIV-3, PIV-4, HRV, and HCoV cases had the respective viral nucleic acid present in a NP or OP specimen. A laboratory-positive leptospirosis case was defined by ≥4-fold increase in MAT titers in paired specimens, or MAT titer ≥800 in a single specimen. A laboratory-positive melioidosis case was defined by presence of Burkholderia pseudomallei nucleic acid in a clinical specimen and/or a ≥4-fold rise in IHA titer in paired specimens. A laboratory-positive enteroviral case was defined by presence of enterovirus nucleic acid in serum collected ≤3 DPO. A laboratory-positive ehrlichiosis case was defined by presence of Ehrlichia chaffeensis IgG reciprocal titer >1:128 by IFA, a ≥4-fold rise in IgG titer in paired serum specimens, or a positive PCR on an acute whole blood or serum specimen. A laboratory-positive Rickettsia case was defined by presence of R. rickettsii or R. typhi IgG titer >1:128 by IFA, a ≥4-fold rise in IgG titer in paired serum specimens, or a positive PCR in a whole blood or serum specimen. A laboratory-positive Coxiella case was defined by presence of C. burnetii IgG titer >1:128 by IFA, a ≥4-fold rise in IgG titer in paired serum specimens, or positive PCR on an acute whole blood or serum specimen.
Leukopenia was defined as a white blood cell count ≤5,000 cells/μL. Thrombocytopenia was defined as a platelet count ≤100,000/μL. Severe hemoconcentration was defined by a hematocrit ≥20% above the U.S. population mean hematocrit for age and sex, and moderate hemoconcentration was defined by a hematocrit >97.5th percentile for age and sex to less than the cut-off for severe hemoconcentration . A skin bleed was defined by presence of skin bruising and/or petechiae. Mucosal bleeds included epistaxis, gingival bleed, hematemesis, melena, hematochezia, menorrhagia, or hematuria (>5 red blood cells per high powered field) in a male or non-menstruating female.
Frequencies were calculated for demographic characteristics and medical history by study year. Clinical and laboratory features were compared by sex, age group, and laboratory diagnostic groups including infection with DENV, FLU A/B, ORV, and CHIKV. Differences in proportions were tested by applying the chi-square test, and medians were compared using the Mann-Whitney-Wilcox test. Bonferroni correction was used to account for simultaneous multiple comparisons. The Woolf test was performed to evaluate the homogeneity of odds ratio across DPO group for death among adult participants by sex, and the Mantel-Haenzel test was used to determine significance. Multiple imputation was used to predict an independent plausible value for missing values using generalized linear regression on non-missing variables to create 40 imputed complete data sets . To identify predictors of laboratory-positive dengue as compared to all other AFI cases, stepwise Akaike Information Criterion (AIC) variable selection was used for each imputed data set. Variables retained at least once in the 40 models were included in a pooled logistic regression model . Odds ratios (OR) and 95% confidence intervals (CI) were calculated for significant early (<3 DPO) and late (3–5 DPO) predictors. Data were analyzed using the “mi” and “MASS” packages from R software (V3.3.0, R Foundation for Statistical Computing, Vienna, Austria).
During the study, sites recorded 234,221 ED visits of which 43,567 (18.6%) patients had fever or reported fever (Fig 1). Enrollment was offered to 11,505 AFI case-patients, and 10,039 (87.3%) gave their consent/assent to participate. However, 1,043 (10.4%) of those enrolled withdrew from the study or were withdrawn due to noncompliance with study enrollment procedures. Of the remaining 8,996 participants, 2,999 (33.3%) had follow-up forms completed and 24.9% had follow-up specimens collected. Half (50.3%) of the 8,996 participants were female, and the median age was 12.8 years (range 0–103) (Table 1). One-third (33.7%) of participants reported having a chronic medical condition; a higher proportion of participants enrolled in the first year reported a co-morbidity than those enrolled in subsequent years. The most common co-morbid conditions were asthma (18.6%), high blood pressure (10.7%), diabetes (7.5%), high cholesterol (4.7%), coronary heart disease (4.4%), and thyroid disease (4.2%). Participants resided in 49 of the 78 municipalities in Puerto Rico; however, most (76.3%) were residents of five municipalities: Ponce (43.0%), Guayama (15.6%), Juana Díaz (8.2%), Salinas (4.9%), and Villalba (4.6%).
Most (71.8%) participants were enrolled <3 DPO (median DPO at enrollment = 1, range: 0–8 days) (Table 2). The timing of presentation did not differ by sex but did differ by age, with a higher proportion of child participants (i.e., <20 years old) presenting <3 DPO than adult participants (74.9% child vs. 68.2% adult females, p <0.001; and 73.4% child vs. 67.4% adult males, p <0.001). One quarter (24.9%) of participants were admitted to the hospital at enrollment. Adult participants were less likely to be admitted than child participants; a higher proportion of female adult participants than male adult participants were sent home after enrollment (78.3% vs. 74.6% respectively, p <0.05). However, a higher proportion of male versus female adult participants died after enrollment (0.8% vs. 0.2% respectively), in fact, adult males were five times more likely to die than adult females when adjusting by DPO (OR = 5.4, CI: 1.5–19.0). There were no statistical significant differences between female and male participants <20 years old in terms of the timing of presentation and disposition. The most common signs and symptoms (aside from fever) at enrollment were tiredness/lethargy (73.5%), anorexia (65.0%), chills (64.5%), headache (64.3%), muscle, bone or back pain (60.0%), cough (53.4%), red conjunctiva (49.2%), rhinorrhea (49.1%), nausea (48.9%), and joint pain (48.9%).
Etiologic agents identified
Slightly more than half (54.8%, 4,930) of the 8,996 participants had a pathogen detected (Fig 2). CHIKV was detected in 1,635 (18.2%) participants and was the most common pathogen detected, followed by FLU A/B (1,074, 11.9%), DENV 1–4 (970, 10.8%), and ORV (904, 10.3%). Most chikungunya (1,499, 91.7%) and dengue (685, 70.6%) cases were confirmed by RT-PCR. Among PCR-positive cases, DENV-1 was detected most frequently (645, 94.2%), followed by DENV-4 (38, 5.5%), and DENV-2 (2, 0.3%); no DENV-3 infections were identified. The majority (736, 68.5%) of influenza cases had FLU A virus detected. Among the ORV cases, adenovirus was detected most frequently (284, 31.4%), followed by RSV (175, 19.4%), HMPV (168, 18.6%), PIV-3 (138, 15.3%), PIV-1 (101, 11.2%), HCoV (37, 4.1%), and PIV-2 (1, 0.1%). Overall, enterovirus (80, 0.9%), leptospirosis (11, 0.1%), and melioidosis (2, 0.02%) cases were infrequently identified. Positive blood, urine or other culture, taken at the discretion of the site physician, were available for 145 (1.6%) participants. Co-infection was identified by molecular detection of two pathogens in 109 participants (Table 3). Co-infections most commonly occurred among participants infected with enterovirus (13/80, 16.3% of all enterovirus cases), followed by ORV (67/904, 7.4%), FLU A/B (46/1074, 4.3%), DENV (34/970, 3.5%), and CHIKV (27/1635, 1.7%).
The distribution of pathogens causing AFI varied by age (Fig 2). The proportion of chikungunya cases increased with age, accounting for 9.3% of all AFI cases in participants <5 years old versus 33.4% in participants ≥50 years old. In contrast, the contribution of ORV to AFI cases decreased with age, making up 21.6% of AFI cases in participants <5 years old, 6.4% in participants 5–19 years old, 3.7% in participants 20–49 years old, and 4.1% in participants ≥50 years old. Dengue was the most common cause of AFI in participants 5–19 years old, accounting for 20.3% of all cases; 2.8% of AFI cases in participants <5 years old were dengue, 9.8% in participants 20–49 years old, and 7.4% in participants ≥50 years old. The contribution of influenza was similar among age groups making up 8.4% of AFI cases in participants <5 years old, 13.9% in 5–19 years old, 15.4% in 20–49 years old, and 9.7% in ≥50 year-old participants.
Analysis of the temporal disease trends demonstrated that a dengue epidemic occurred in 2012 and continued through 2013, during which a total of 921 dengue cases were detected (Fig 3). In comparison, few (n = 49) dengue cases were detected in 2014 to the end of the study period in 2015. The first chikungunya case was detected in May of 2014, and was followed by a six-month outbreak during which 1,558 cases were detected. Few (n = 61) chikungunya cases were detected in 2015. A large bimodal influenza epidemic took place in 2013 with increased case detection in the dry months of January–April (n = 225), and during the rainy season, July–October (n = 302). Fewer influenza cases (n = 356) were detected in 2014 and 2015, and those detected occurred primarily in dry months with no obvious bimodal distribution. An increase in AFI cases due to ORV was detected at the same time influenza cases were detected, with the exception of 2013 when the peak time of ORV case detection appeared to follow that of influenza.
Clinical features by etiologic agent
Subject demographics at enrollment differed by subsequent laboratory diagnosis (Table 4). A lower proportion of participants with dengue and ORV illness were females when compared with participants with chikungunya. Participants with ORV illness were significantly younger (median age = 3.2 years, p <0.001) than participants with dengue (15.4 years), chikungunya (24.3 years), or influenza (14.1 years). In contrast, the median age of participants with chikungunya was significantly greater than participants in all other diagnostic groups, and they were more likely to report having a chronic medical condition. A higher proportion of participants with dengue reported having a household member with dengue at enrollment than participants with other diagnoses (11.8% of dengue cases versus ≤5% in other diagnostic groups, p <0.001). Over half of all participants reported having mosquito bites in the 30 days before enrollment; however, a higher proportion of participants with chikungunya reported mosquito bites than participants with other laboratory diagnoses (73.9% versus <55% in other diagnostic groups, p<0.001).
Clinical presentation and disposition varied by laboratory diagnostic group (Table 4). Participants with laboratory-positive dengue presented later (median = 3 days), and a higher proportion were admitted at enrollment than participants with other laboratory diagnoses; nearly half (46.6%) of dengue cases were admitted compared with 27.3% of participants with ORV illness, 18.8% with influenza, and 11.2% with chikungunya. A significantly higher proportion of participants with dengue had chills, signs of poor circulation, eye pain, headache, dizziness, anorexia, nausea, abdominal pain, and diarrhea at enrollment than participants with influenza, ORV illness or chikungunya. A higher proportion of participants with dengue versus these other diagnoses had thrombocytopenia and leukopenia. Compared to influenza and ORV illness cases, a significantly higher proportion of dengue cases had a skin rash, pruritic skin, any bleeding, a skin bleed, and muscle, bone, back, and joint pain, whereas a higher proportion of chikungunya versus dengue cases had these findings. A higher proportion of dengue versus influenza and ORV illness cases had facial and/or neck erythema and mucosal bleeding. In contrast, a significantly higher proportion of participants with influenza and ORV illness than dengue had cough, rhinorrhea, and sore throat.
Predictors by timing of presentation and age
Among 6,349 participants who presented early (<3 DPO) in the clinical course, leukopenia, thrombocytopenia, headache, eye pain, nausea, and dizziness were significant positive predictors of laboratory-positive dengue as compared to all other AFI cases across all age groups (Table 5). Presence of rhinorrhea and irritability predicted non-dengue AFI. Age group had a statistically significant effect on multiple predictors (Table 6). Rash was a positive early predictor of dengue among participants <5 years old, and being male was a positive predictor among adults 20–49 years old. Chills and cough were positive predictors for those >50 years old while cough was a negative predictor among those <20 years old. Muscle, bone or back pain was a negative predictor in those >50 years old. Pruritic skin as a predictor varied by age group, but most significantly between the <5 and 50+ year-old groups.
Among the 2,146 participants who presented 3–5 DPO, thrombocytopenia, leukopenia, facial and/or neck erythema, nausea, eye pain, signs of poor circulation, and diarrhea were significant positive predictors of dengue across all age groups (Table 7). Presence of rhinorrhea, red conjunctiva and cough predicted non-dengue AFI. Again, age group significantly affected multiple predictors (Table 8). Abdominal pain was a positive predictor for participants 20–49 years old. Red and/or swollen joints was a positive predictor among participants <5 years old but a predictor of non-dengue AFI among participants ≥50 years old. Leukopenia was a significant positive predictor across all age groups, but to varying degrees. Chills; muscle, bone, back and joint pain; and any bleeding as predictors varied depending on the age group.
As a clinical syndrome, AFIs are a diagnostic challenge for clinicians especially early in the clinical course when anticipatory guidance and supportive care may pre-empt medical complications. Our study identified the AFI etiology in over half (55%) of participants and most were infected with one of nine viral pathogens. This detection frequency was higher than that of other recent prospective AFI studies that tested for multiple pathogens (55% versus 36–41%) [9–12, 15]. This difference may in part be explained by the greater contribution of chikungunya in our study than in the other studies that tested for this pathogen [9, 10, 17]. However, unlike other studies, we were unable to detect any evidence of disease caused by Rickettsia or Coxiella spp., which made up 4–13% of all AFIs in other studies [10–13]; and unlike other areas, malaria [9, 11–13, 18] and typhoid fever [9, 10, 12, 13, 15] were not part of our diagnostic algorithm as they are only occasionally detected among travelers returning to Puerto Rico.
While DENV was not as commonly identified as CHIKV or FLU A/B, participants with dengue were more likely to be admitted to the hospital at enrollment. The proportion of AFI cases with dengue in our study was comparable to other recent studies which found 4–9% of AFI cases had dengue [9–13, 15–18]. One exception to this was a study that found 34% of AFI patients had dengue; however, the study’s eligibility criteria likely enhanced enrollment of dengue cases . In our study, dengue incidence varied by age group with a nearly a 10-fold difference between participants <5 years old and 10–19 years old (3% versus 27%), which may be due to differences in likelihood of seeking medical care in primary versus secondary DENV infections . Of note, 6% of participants ≥65 years old had dengue as a cause of AFI, a finding comparable to a Puerto Rico study in which 5% of 17,666 laboratory-positive dengue cases detected by surveillance were ≥65 years old . In contrast, other recent prospective studies [37, 38] and a cross-sectional serosurvey  conducted in other dengue endemic countries found few, if any, symptomatic dengue cases among older participants. Whether this is due to a lower force of infection in Puerto Rico, immunosenescence, evolution of genotypes/strains of DENV, differences in prevalence of underlying chronic disease or health care seeking behavior in Puerto Rico, or lack of life-long homotypic immunity is not known [40–42]. However, DENV-1 has been in circulation in Puerto Rico since the 1970s and involved in every major outbreak since [35, 43].
Chikungunya, the most commonly identified AFI overall, was least likely to result in hospital admission, although two male participants with CHIKV infection died. These cases were older individuals (>75 years old) who had underlying co-morbidities which may have complicated their clinical course. Nonetheless, since autopsy was not performed for either case, ascertaining whether CHIKV infection played a role in either fatality is difficult. However, in our study chikungunya was disproportionally identified among older participants, with positivity increasing from <10% of pre-school aged children to about one-third of participants ≥50 years old. This pattern of disease has been seen in other areas with recent CHIKV emergence , and may be due to older individuals having an increased likelihood of complications due to preexisting co-morbidities [44, 45].
Co-infections confirmed by molecular assays were detected among 1% of our participants, most commonly involving enteroviral or ORV infections; less than one-third of all co-infections included a DENV or CHIKV infection. Another recent prospective study found that 1% of AFI participants had co-infections involving molecularly diagnosed dengue or influenza, malaria, and positive blood culture . Interestingly, we did not detect any co-infections involving CHIKV and DENV. An analysis of island-wide surveillance data from Puerto Rico during the same time period found only one CHIKV/DENV co-infection among approximately 1,000 specimens tested by RT-PCR for both DENV and CHIKV . These findings are consistent with another prospective AFI study conducted in Sri Lanka . Although a recent study has shown that Aedes aegypti can be infected with as many as three arboviruses simultaneously and can likely transmit these viruses to humans , the frequency of co- or tri-infection of mosquitoes in the wild depends upon the geographic spread and degree of circulation of each virus. During our study, DENV transmission decreased significantly before CHIKV transmission peaked, making co-infections less likely. In addition, in Puerto Rico, where Aedes aegypti is the sole vector for CHIKV and DENV, viral interaction and viral interference within the mosquito may reduce the likelihood of co-infection [48–50]. However, RT-PCR positive DENV/CHIKV co-infections have been documented at higher rates in five countries .
We identified differences in clinical predictors of laboratory-positive dengue by timing of presentation and age group highlighting the importance of considering these factors when developing prediction algorithms for clinical management [52–60]. We found, as have others , that even early (<3 DPO) in the clinical course leukopenia and thrombocytopenia are predictive of dengue across all age groups, and thrombocytopenia strengthened as a predictor over time. In our study, headache and eye pain were the only “aches and pains” that were predictive of dengue for all age groups . Eye pain was a predictor early and later in the clinical course, a finding consistent with pediatric  and adult  prospective cohort studies, as well as a surveillance study conducted in Puerto Rico . We also found that rash among children <5 years old presenting early and erythema on the face and/or neck in all age groups presenting 3–5 DPO, were positive predictors of dengue. While the presence of skin rash has been found to be a predictor of dengue in several prospective studies , few studies have evaluated erythema as a predictor [18, 64, 65]. Last, like other prospective studies [56, 66], we found that nausea is an early predictor for dengue. We were also able to show that adults aged 20–49 years presenting 3–5 DPO were more likely to have abdominal pain than those with other AFIs, and dengue cases of all ages presenting 3–5 DPO were also more likely to have diarrhea and poor circulation in addition to nausea, findings that lend support to the idea that warning signs for severe dengue develop after the early phase of the illness.
Our study, which enrolled all patients presenting with fever regardless of age, sex, or clinical characteristics, may be limited in generalizability. The study was conducted in southern Puerto Rico which may differ from neighboring islands and other parts of the island with regard to population demographics, preexisting immunity to DENV and other flaviviruses, and exposure to infections. Second, while we enrolled nearly 600 older adults (≥65 years old), we were unable to adequately evaluate predictors of dengue among this population because we had only 36 dengue cases and most presented early in the clinical course. Last, we did not systematically collect stool and test for potential enteric pathogens, and bacterial infections were likely under recognized because blood cultures were only done on patients in whom sepsis was suspected.
While our study identified an etiology in over half of all AFI cases, the etiology of 45% of AFI remained unknown even after extensive testing and the majority of diagnosed cases were caused by one of nine viral pathogens that typically do not require empiric therapy. In fact, we were unable to find any cases of Rickettsia spp., Ehrlichia spp., and Coxiella spp., and only sporadic cases of melioidosis and leptospirosis were identified. Our findings demonstrate that dengue is not only one of the leading causes of AFI in Puerto Rico, but results in greater morbidity than other AFIs as measured by need for hospitalization. Moreover, dengue affects people of all ages including older adults who may be at higher risk of developing medical complications. Clinicians should include dengue on the differential diagnosis of AFI among older adults so that timely anticipatory guidance can be offered. We found that the presence of leukopenia and thrombocytopenia were the best predictors of dengue in both time periods overall and for all age groups. Our findings suggest that eye pain should be reevaluated as a predictor of dengue. Future studies should focus on improving clinical diagnosis of AFI including dengue by timing of presentation and age of the patient.
We thank Saint Luke’s Episcopal Hospital patients for their participation in this study. We would like to thank physicians, nurses, clinical laboratory staff and administrative personnel at the Saint Luke’s Episcopal Hospitals in Ponce and Guayama for their assistance to recruiting potential participants and implementing study procedures. In addition, we would like to acknowledge the medical management information offices from Saint Luke’s Episcopal Hospitals for facilitating the review of medical records for admitted participants. We would also like to thank Dr. Brad Biggerstaff from the CDC’s Division of Vector-Borne Diseases for his critical review of the data analysis and manuscript. In addition, we would like to thank CDC staff members at the Dengue Branch, Polio and Picornavirus Laboratory Branch, Rickettsial Zoonoses Branch, and Bacterial Special Pathogens Branch (Zoonoses and Select Agent Laboratory) for processing and testing of all clinical specimens. Last, we would like to acknowledge the technical support of Ponce Health Sciences University. Without their interest in and support of this project, the SEDSS sites would have never been established and this study would never have been possible.
- 1. Acestor N, Cooksey R, Newton PN, Menard D, Guerin PJ, Nakagawa J, et al. Mapping the aetiology of non-malarial febrile illness in Southeast Asia through a systematic review—terra incognita impairing treatment policies. PloS one. 2012;7(9):e44269. pmid:22970193;
- 2. Cifuentes SG, Trostle J, Trueba G, Milbrath M, Baldeon ME, Coloma J, et al. Transition in the cause of fever from malaria to dengue, Northwestern Ecuador, 1990–2011. Emerging infectious diseases. 2013;19(10):1642–5. pmid:24047566;
- 3. Chappuis F, Alirol E, d'Acremont V, Bottieau E, Yansouni CP. Rapid diagnostic tests for non-malarial febrile illness in the tropics. Clinical microbiology and infection: the official publication of the European Society of Clinical Microbiology and Infectious Diseases. 2013;19(5):422–31. pmid:23413992.
- 4. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013;496(7446):504–7. pmid:23563266;
- 5. San Martin JL, Brathwaite O, Zambrano B, Solorzano JO, Bouckenooghe A, Dayan GH, et al. The epidemiology of dengue in the americas over the last three decades: a worrisome reality. The American journal of tropical medicine and hygiene. 2010;82(1):128–35. pmid:20065008;
- 6. Halstead SB. Dengue in the Americas and Southeast Asia: do they differ? Revista panamericana de salud publica = Pan American journal of public health. 2006;20(6):407–15. pmid:17341332.
- 7. Dayan G, Arredondo JL, Carrasquilla G, Deseda CC, Dietze R, Luz K, et al. Prospective Cohort Study with Active Surveillance for Fever in Four Dengue Endemic Countries in Latin America. The American journal of tropical medicine and hygiene. 2015;93(1):18–23. pmid:26013373;
- 8. Beatty ME, Stone A, Fitzsimons DW, Hanna JN, Lam SK, Vong S, et al. Best practices in dengue surveillance: a report from the Asia-Pacific and Americas Dengue Prevention Boards. PLoS neglected tropical diseases. 2010;4(11):e890. pmid:21103381;
- 9. Kasper MR, Blair PJ, Touch S, Sokhal B, Yasuda CY, Williams M, et al. Infectious etiologies of acute febrile illness among patients seeking health care in south-central Cambodia. The American journal of tropical medicine and hygiene. 2012;86(2):246–53. pmid:22302857;
- 10. Leelarasamee A, Chupaprawan C, Chenchittikul M, Udompanthurat S. Etiologies of acute undifferentiated febrile illness in Thailand. Journal of the Medical Association of Thailand = Chotmaihet thangphaet. 2004;87(5):464–72. pmid:15222513.
- 11. Manock SR, Jacobsen KH, de Bravo NB, Russell KL, Negrete M, Olson JG, et al. Etiology of acute undifferentiated febrile illness in the Amazon basin of Ecuador. The American journal of tropical medicine and hygiene. 2009;81(1):146–51. pmid:19556580.
- 12. Mayxay M, Castonguay-Vanier J, Chansamouth V, Dubot-Peres A, Paris DH, Phetsouvanh R, et al. Causes of non-malarial fever in Laos: a prospective study. The Lancet Global health. 2013;1(1):e46–54. pmid:24748368;
- 13. Mueller TC, Siv S, Khim N, Kim S, Fleischmann E, Ariey F, et al. Acute undifferentiated febrile illness in rural Cambodia: a 3-year prospective observational study. PloS one. 2014;9(4):e95868. pmid:24755844;
- 14. Phuong HL, de Vries PJ, Nga TT, Giao PT, Hung le Q, Binh TQ, et al. Dengue as a cause of acute undifferentiated fever in Vietnam. BMC infectious diseases. 2006;6:123. pmid:16869969;
- 15. Punjabi NH, Taylor WR, Murphy GS, Purwaningsih S, Picarima H, Sisson J, et al. Etiology of acute, non-malaria, febrile illnesses in Jayapura, northeastern Papua, Indonesia. The American journal of tropical medicine and hygiene. 2012;86(1):46–51. pmid:22232450;
- 16. Reller ME, Bodinayake C, Nagahawatte A, Devasiri V, Kodikara-Arachichi W, Strouse JJ, et al. Unsuspected dengue and acute febrile illness in rural and semi-urban southern Sri Lanka. Emerging infectious diseases. 2012;18(2):256–63. pmid:22304972;
- 17. Reller ME, Akoroda U, Nagahawatte A, Devasiri V, Kodikaarachchi W, Strouse JJ, et al. Chikungunya as a cause of acute febrile illness in southern Sri Lanka. PloS one. 2013;8(12):e82259. pmid:24312651;
- 18. Senn N, Luang-Suarkia D, Manong D, Siba PM, McBride WJ. Contribution of dengue fever to the burden of acute febrile illnesses in Papua New Guinea: an age-specific prospective study. The American journal of tropical medicine and hygiene. 2011;85(1):132–7. pmid:21734138;
- 19. Miranda Franco R, Casta Velez A. [Eradication of malaria in Puerto Rico]. Revista panamericana de salud publica = Pan American journal of public health. 1997;2(2):146–50. pmid:9312420.
- 20. Rymzo WT Jr., Cline BL, Kemp GE, Sather GE, Craven PC. Dengue outbreaks in Guanica-Ensenada and Villalba, Puerto Rico, 1972–1973. The American journal of tropical medicine and hygiene. 1976;25(1):136–45. pmid:1259078.
- 21. U.S. Census Bureau PD. Puerto Rico Intercensal Estimates (2000–2010): Population Division; 2011 [cited 2015 July 10]. http://www.census.gov/popest/data/intercensal/puerto_rico/pr2010.html.
- 22. Kilpatrick DR, Yang CF, Ching K, Vincent A, Iber J, Campagnoli R, et al. Rapid group-, serotype-, and vaccine strain-specific identification of poliovirus isolates by real-time reverse transcription-PCR using degenerate primers and probes containing deoxyinosine residues. Journal of clinical microbiology. 2009;47(6):1939–41. pmid:19386844;
- 23. Santiago GA, Vergne E, Quiles Y, Cosme J, Vazquez J, Medina JF, et al. Analytical and clinical performance of the CDC real time RT-PCR assay for detection and typing of dengue virus. PLoS neglected tropical diseases. 2013;7(7):e2311. pmid:23875046;
- 24. CDC. 2013. http://www.cdc.gov/dengue/clinicalLab/realTime.html.
- 25. Martin DA, Muth DA, Brown T, Johnson AJ, Karabatsos N, Roehrig JT. Standardization of immunoglobulin M capture enzyme-linked immunosorbent assays for routine diagnosis of arboviral infections. Journal of clinical microbiology. 2000;38(5):1823–6. pmid:10790107;
- 26. Miagostovich MP, Nogueira RM, dos Santos FB, Schatzmayr HG, Araujo ES, Vorndam V. Evaluation of an IgG enzyme-linked immunosorbent assay for dengue diagnosis. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology. 1999;14(3):183–9. pmid:10614855.
- 27. Namekar M, Ellis EM, O'Connell M, Elm J, Gurary A, Park SY, et al. Evaluation of a new commercially available immunoglobulin M capture enzyme-linked immunosorbent assay for diagnosis of dengue virus infection. Journal of clinical microbiology. 2013;51(9):3102–6. pmid:23824771;
- 28. Lanciotti RS, Kosoy OL, Laven JJ, Panella AJ, Velez JO, Lambert AJ, et al. Chikungunya virus in US travelers returning from India, 2006. Emerging infectious diseases. 2007;13(5):764–7. pmid:17553261;
- 29. Nix WA, Oberste MS, Pallansch MA. Sensitive, seminested PCR amplification of VP1 sequences for direct identification of all enterovirus serotypes from original clinical specimens. Journal of clinical microbiology. 2006;44(8):2698–704. pmid:16891480;
- 30. Dikken H, Kmety E. Serological typing methods of leptospires. In: Bergan T, Norris JR, editors. Methods in Microbiology. London: Acadmic Press; 1978. p. 259–307.
- 31. Alexander AD, Huxsoll DL, Warner AR Jr., Shepler V, Dorsey A. Serological diagnosis of human melioidosis with indirect hemagglutination and complement fixation tests. Applied microbiology. 1970;20(5):825–33. pmid:5530276;
- 32. Hollowell JG, van Assendelft OW, Gunter EW, Lewis BG, Najjar M, Pfeiffer C, et al. Hematological and iron-related analytes—reference data for persons aged 1 year and over: United States, 1988–94. Vital and health statistics Series 11, Data from the national health survey. 2005;(247):1–156. pmid:15782774.
- 33. Rubin DB. Multiple imputation for nonresponse in surveys. Sons JW, editor. New York1987.
- 34. Wood AM, White IR, Royston P. How should variable selection be performed with multiply imputed data? Stat Med. 2008;27(17):3227–46. pmid:18203127.
- 35. Sharp TM, Hunsperger E, Santiago GA, Munoz-Jordan JL, Santiago LM, Rivera A, et al. Virus-specific differences in rates of disease during the 2010 Dengue epidemic in Puerto Rico. PLoS neglected tropical diseases. 2013;7(4):e2159. pmid:23593526;
- 36. Garcia-Rivera EJ, Rigau-Perez JG. Dengue severity in the elderly in Puerto Rico. Revista panamericana de salud publica = Pan American journal of public health. 2003;13(6):362–8. pmid:12880516.
- 37. Alera MT, Srikiatkhachorn A, Velasco JM, Tac-An IA, Lago CB, Clapham HE, et al. Incidence of Dengue Virus Infection in Adults and Children in a Prospective Longitudinal Cohort in the Philippines. PLoS neglected tropical diseases. 2016;10(2):e0004337. pmid:26845762;
- 38. Ellis RD, Fukuda MM, McDaniel P, Welch K, Nisalak A, Murray CK, et al. Causes of fever in adults on the Thai-Myanmar border. The American journal of tropical medicine and hygiene. 2006;74(1):108–13. pmid:16407353.
- 39. Yap G, Li C, Mutalib A, Lai YL, Ng LC. High rates of inapparent dengue in older adults in Singapore. The American journal of tropical medicine and hygiene. 2013;88(6):1065–9. pmid:23610157;
- 40. Endy TP. Human immune responses to dengue virus infection: lessons learned from prospective cohort studies. Frontiers in immunology. 2014;5:183. pmid:24795725;
- 41. Forshey BM, Reiner RC, Olkowski S, Morrison AC, Espinoza A, Long KC, et al. Incomplete Protection against Dengue Virus Type 2 Re-infection in Peru. PLoS neglected tropical diseases. 2016;10(2):e0004398. pmid:26848841;
- 42. Forshey BM, Stoddard ST, Morrison AC. Dengue Viruses and Lifelong Immunity: Reevaluating the Conventional Wisdom. The Journal of infectious diseases. 2016;214(7):979–81. pmid:26984147.
- 43. Morens DM, Rigau-Perez JG, Lopez-Correa RH, Moore CG, Ruiz-Tiben EE, Sather GE, et al. Dengue in Puerto Rico, 1977: public health response to characterize and control an epidemic of multiple serotypes. The American journal of tropical medicine and hygiene. 1986;35(1):197–211. pmid:3946738.
- 44. Economopoulou A, Dominguez M, Helynck B, Sissoko D, Wichmann O, Quenel P, et al. Atypical Chikungunya virus infections: clinical manifestations, mortality and risk factors for severe disease during the 2005–2006 outbreak on Reunion. Epidemiology and infection. 2009;137(4):534–41. pmid:18694529.
- 45. Hoz JM, Bayona B, Viloria S, Accini JL, Juan-Vergara HS, Viasus D. Fatal cases of Chikungunya virus infection in Colombia: Diagnostic and treatment challenges. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology. 2015;69:27–9. pmid:26209372.
- 46. Sharp TM, Ryff KR, Alvarado L, Shieh WJ, Zaki SR, Margolis HS, et al. Surveillance for Chikungunya and Dengue During the First Year of Chikungunya Virus Circulation in Puerto Rico. The Journal of infectious diseases. 2016;214(suppl 5):S475–S81. pmid:27920177;
- 47. Ruckert C, Weger-Lucarelli J, Garcia-Luna SM, Young MC, Byas AD, Murrieta RA, et al. Impact of simultaneous exposure to arboviruses on infection and transmission by Aedes aegypti mosquitoes. Nat Commun. 2017;8:15412. pmid:28524874;
- 48. Salas-Benito JS, De Nova-Ocampo M. Viral Interference and Persistence in Mosquito-Borne Flaviviruses. Journal of immunology research. 2015;2015:873404. pmid:26583158;
- 49. Nuckols JT, Huang YJ, Higgs S, Miller AL, Pyles RB, Spratt HM, et al. Evaluation of Simultaneous Transmission of Chikungunya Virus and Dengue Virus Type 2 in Infected Aedes aegypti and Aedes albopictus (Diptera: Culicidae). Journal of medical entomology. 2015;52(3):447–51. pmid:26334820;
- 50. Kramer LD, Ciota AT. Dissecting vectorial capacity for mosquito-borne viruses. Current opinion in virology. 2015;15:112–8. pmid:26569343;
- 51. Furuya-Kanamori L, Liang S, Milinovich G, Soares Magalhaes RJ, Clements AC, Hu W, et al. Co-distribution and co-infection of chikungunya and dengue viruses. BMC infectious diseases. 2016;16:84. pmid:26936191;
- 52. Gregory CJ, Santiago LM, Arguello DF, Hunsperger E, Tomashek KM. Clinical and laboratory features that differentiate dengue from other febrile illnesses in an endemic area—Puerto Rico, 2007–2008. The American journal of tropical medicine and hygiene. 2010;82(5):922–9. pmid:20439977;
- 53. Hammond SN, Balmaseda A, Perez L, Tellez Y, Saborio SI, Mercado JC, et al. Differences in dengue severity in infants, children, and adults in a 3-year hospital-based study in Nicaragua. The American journal of tropical medicine and hygiene. 2005;73(6):1063–70. pmid:16354813.
- 54. Hanafusa S, Chanyasanha C, Sujirarat D, Khuankhunsathid I, Yaguchi A, Suzuki T. Clinical features and differences between child and adult dengue infections in Rayong Province, southeast Thailand. The Southeast Asian journal of tropical medicine and public health. 2008;39(2):252–9. pmid:18564710.
- 55. Kittigul L, Pitakarnjanakul P, Sujirarat D, Siripanichgon K. The differences of clinical manifestations and laboratory findings in children and adults with dengue virus infection. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology. 2007;39(2):76–81. pmid:17507286.
- 56. Low JG, Ong A, Tan LK, Chaterji S, Chow A, Lim WY, et al. The early clinical features of dengue in adults: challenges for early clinical diagnosis. PLoS neglected tropical diseases. 2011;5(5):e1191. pmid:21655307;
- 57. Ramos MM, Tomashek KM, Arguello DF, Luxemburger C, Quinones L, Lang J, et al. Early clinical features of dengue infection in Puerto Rico. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2009;103(9):878–84. pmid:19111871.
- 58. Suwandono A, Kosasih H, Nurhayati , Kusriastuti R, Harun S, Ma'roef C, et al. Four dengue virus serotypes found circulating during an outbreak of dengue fever and dengue haemorrhagic fever in Jakarta, Indonesia, during 2004. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2006;100(9):855–62. pmid:16507313.
- 59. Wang CC, Lee IK, Su MC, Lin HI, Huang YC, Liu SF, et al. Differences in clinical and laboratory characteristics and disease severity between children and adults with dengue virus infection in Taiwan, 2002. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2009;103(9):871–7. pmid:19500813.
- 60. Wichmann O, Hongsiriwon S, Bowonwatanuwong C, Chotivanich K, Sukthana Y, Pukrittayakamee S. Risk factors and clinical features associated with severe dengue infection in adults and children during the 2001 epidemic in Chonburi, Thailand. Tropical medicine & international health: TM & IH. 2004;9(9):1022–9. pmid:15361117.
- 61. Potts JA, Rothman AL. Clinical and laboratory features that distinguish dengue from other febrile illnesses in endemic populations. Tropical medicine & international health: TM & IH. 2008;13(11):1328–40. pmid:18803612;
- 62. World Health Organization. Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. Geneva: WHO; 2009. http://whqlibdoc.who.int/publications/2009/9789241547871_eng.pdf.
- 63. Biswas HH, Ortega O, Gordon A, Standish K, Balmaseda A, Kuan G, et al. Early clinical features of dengue virus infection in nicaraguan children: a longitudinal analysis. PLoS neglected tropical diseases. 2012;6(3):e1562. pmid:22413033;
- 64. Tuan NM, Nhan HT, Chau NV, Hung NT, Tuan HM, Tram TV, et al. Sensitivity and specificity of a novel classifier for the early diagnosis of dengue. PLoS neglected tropical diseases. 2015;9(4):e0003638. pmid:25836753;
- 65. Chadwick D, Arch B, Wilder-Smith A, Paton N. Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features: application of logistic regression analysis. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology. 2006;35(2):147–53. pmid:16055371.
- 66. Kalayanarooj S, Vaughn DW, Nimmannitya S, Green S, Suntayakorn S, Kunentrasai N, et al. Early clinical and laboratory indicators of acute dengue illness. The Journal of infectious diseases. 1997;176(2):313–21. pmid:9237695.