Volume 24, Number 5—May 2018
Heterogeneous and Dynamic Prevalence of Asymptomatic Influenza Virus Infections
In Response: We thank Leung and Cowling (1) for taking time to comment on our article (2). One problem with the random effects model is the rapid decline in performance of the model as the heterogeneity within studies increases. Extensive heterogeneity for asymptomatic (Ι2 = 97%; Τ2 = 0.31) and subclinical (Ι2 = 97%; Τ2 = 0.45) infection was identified. However, the model selected to pool the prevalence estimates—inverse variance heterogeneity—maintains its coverage at the nominal level, even when large heterogeneity is present (3).
Regarding inclusion criteria, we elected to review all publications detailing asymptomatic influenza prevalence in humans, as is made clear from the original article’s title onward. This method included experimental studies, as well as newly emerging zoonotic strains. We note further that the 2 experimental studies in our review had subclinical influenza infection levels within the range identified in the pooled estimate of the meta-analysis (43.4%, 95% CI 25.4%–61.8%). Also, because antibody titers can vary drastically with technique used and between laboratories, we used the antibody titer threshold defined by each individual study.
The results/conclusions from the study published by Leung et al. (4) cannot be compared with those reported in our meta-analysis (2) for 2 important reasons. First, the case definition for asymptomatic was different; Leung et al. grouped patients without signs and symptoms (asymptomatic in our meta-analysis) with patients that did not fulfill the criteria of influenza-like illness (subclinical in our meta-analysis). We explained in our article why pooling asymptomatic and subclinical cases is inappropriate and likely to provide spurious results. As an example of how the case definition can affect the results, Pascalis et al. found that in the same group of patients, 30.6% had subclinical infection (not fulfilling criteria for influenza-like illness) but only 1.6% had no symptoms at all (5). Second, the number of studies included in the 2 meta-analyses was different: our comprehensive review comprised 55 studies, whereas Leung et al. included a subset of only 30 studies pertaining specifically to seasonal influenza. The different studies included and different meta-analytical methods unsurprisingly yielded different outcomes.
Dr. Furuya-Kanamori is an assistant professor at Qatar University and a visiting fellow at the Australian National University. His research interests include infectious diseases epidemiology, methodologic issues in clinical epidemiology, and meta-analysis.
Dr. Yakob is an assistant professor at the London School of Hygiene & Tropical Medicine. His research interests include infectious disease epidemiology and modelling.
- Leung NHL, Cowling BJ. Heterogeneous and dynamic prevalence of asymptomatic influenza virus infections. Emerg Infect Dis. 2018 May [cited 2018 Mar 5]. https://doi.org/10.3201/eid2405.160782.
- Furuya-Kanamori L, Cox M, Milinovich GJ, Magalhaes RJ, Mackay IM, Yakob L. Heterogeneous and dynamic prevalence of asymptomatic influenza virus infections. Emerg Infect Dis. 2016;22:1052–6.
- Doi SA, Barendregt JJ, Khan S, Thalib L, Williams GM. Simulation comparison of the quality effects and random effects methods of meta-analysis. Epidemiology. 2015;26:e42–4.
- Leung NHL, Xu C, Ip DKM, Cowling BJ. Review article: the fraction of influenza virus infections that are asymptomatic: a systematic review and meta-analysis. Epidemiology. 2015;26:862–72.
- Pascalis H, Temmam S, Turpin M, Rollot O, Flahault A, Carrat F, et al. Intense co-circulation of non-influenza respiratory viruses during the first wave of pandemic influenza pH1N1/2009: a cohort study in Reunion Island. PLoS One. 2012;7:e44755.