Respiratory viral pathogens among Singapore military servicemen 2009-2012: epidemiology and clinical characteristics

Abstract

Background: Few studies have comprehensively described tropical respiratory disease surveillance in military populations. There is also a lack of studies comparing clinical characteristics of the non-influenza pathogens with influenza and amongst themselves.

Methods: From May 2009 through October 2012, 7733 consenting cases of febrile respiratory illness (FRI) (temperature [greater than or equal to]37.5 degrees C with cough or sorethroat) and controls in the Singapore military had clinical data and nasal washes collected prospectively. Nasal washes underwent multiplex PCR, and the analysis was limited to viral mono-infections.

Results: 49% of cases tested positive for at least one virus, of whom 10% had multiple infections. 53% of the FRI cases fulfilled the definition of influenza-like illness (ILI), of whom 52% were positive for at least one virus. The most frequent etiologies for mono-infections among FRI cases were Influenza A(H1N1)pdm09 (13%), Influenza B (13%) and coxsackevirus (9%). The sensitivity, specificity, positive predictive value and negative predictive value of ILI for influenza among FRI cases were 72%, 48%, 40% and 69% respectively. On logistic regression, there were marked differences in the prevalence of different symptoms and signs between viruses with fever more prevalent amongst influenza and adenovirus infections than other viruses.

Conclusion: There are multiple viral etiologies for FRI and ILI with differing clinical symptoms in the Singapore military. Influenza and coxsackevirus were the most common etiology for FRI, while influenza and adenoviruses displayed the most febrile symptoms. Further studies should explore these differences and possible interventions.

Figure 1

Distribution of weekly cases of febrile respiratory illness (FRI), viral mono-infections during study period*. *The top panel presents the weekly FRI cases together with viral mono-infection cases. The second panel is a frequency chart presenting the weekly viral mono-infection cases. The dominating virus was placed at the bottom of each bar. Viruses are shaded in different colors – Adenovirus E (ADV-E); Influenza A(H3N2) (FLU-A(H3N2)); Rhinovirus (RV); Coxsackie/Echovirus (CV); Influenza B (FLU-B) and influenza A(H1N1)pdm09 (FLU-A(H1N1)pdm09). Influenza A(H1N1) (FLU-A(H1N1) and Influenza (unknown type) (FLU-A(unknown)), Adenovirus B (ADV-B) and ADV(untyped), Enterovirus (EV), human metapneumovirus (hMPV), Parainfluenza 1 (hPIV-1), hPIV-2, hPIV-3 and hPIV-4,Coronavirus OC43 (CoV-OC43), CoV-NL63, CoV-229E, CoV-HKU1 and CoV(untyped), respiratory syncytial virus A (RSV-A) and RSV-B and Bocavirus (BV) are pooled as others in the bottom panel.

Figure 2

Multivariate analysis comparing viruses among clinical features adjusted for age, smoking status. Age, smoking status and a categorical predictor for viruses were included in the analysis before subsequently removing non-significant variables. Columns represent the categorical predictor for viruses, and each row corresponds to a virus that was chosen as the reference group. The viruses included from the top row to the bottom row are Influenza A(H1N1)pdm09 (FLU-A(H1N1)pdm09), Influenza A(H3N2) (FLU-A(H3N2)), Influenza A(unknown type) (FLU-A(unknown), Influenza B (FLU-B), Coxsackie virus (CV), Enterovirus (EV), Adenovirus E (ADV-E), ADV-B, Coronavirus OC43 (CoV-OC43), CoV-NL63, CoV-229E, Human Metapneumovirus (hMPV) and Parainfluenza 3 (hPIV-3). Color cells represent variables that are significant at the 5% level, and the thickness of the cell wall represents the p-value (thin means 0.01 < p < 0.05; medium, 0.001 < p < 0.01; and thick, p < 0.001). The odds ratios are encoded by colors where a red cell indicates an odds ratio > 1; and blue otherwise. For example, for a sore throat, FLU-A(unknown), FLU-B, CV, RV, ADV-E, CoV-OC43 and CoV-NL63 have more of the sore throat than IFLU-A(H1N1)pdm09 indicated by the red cells in the row for FLU-A(H1N1)pdm09 and corresponding columns.

Figure 3

Correlation of Symptoms and Signs Across all Viruses. Clinical signs or symptoms are listed by average frequency from the most to the least. Binomial test is used to assess the discrepancy between the observed proportion of symptom pairs and the expected proportion of symptom pairs which is the product of the two marginal distributions by assuming symptoms develop independently. Color cells represent differences that are significant at the 5% level, and the thickness of the cell wall represents the p-value (thin means 0.01 < p < 0.05; medium, 0.001 < p < 0.01; and thick, p < 0.001). The excess probability encoded by colors measures the effect size. If the observed proportion is lower than the expected proportion, the cell will be shaded by blue color, and red color otherwise.