Relative timing of influenza disease by age group

Clinical features and outcomes of influenza by virus type/subtype/lineage in pediatric patients

BACKGROUND

Recently, four influenza viruses are circulating worldwide: A(H1N1)pdm09, A(H3N2), B/Victoria, and B/Yamagata. However, information on the clinical differences among pediatric patients infected with four recently circulating influenza viruses is sparse.

METHODS

Medical records of pediatric patients (<20 years of age) diagnosed with influenza between the 2014-2015 and 2018-2019 influenza seasons were retrospectively reviewed. Clinical features were compared between (I) patients infected with influenza A (FluA) and influenza B (FluB) viruses, (II) patients infected with FluA when A(H1N1)pdm09 and A(H3N2) circulated dominantly, and (III) patients infected with FluB when B/Victoria and B/Yamagata circulated dominantly.

RESULTS

A total of 1,588 patients infected with FluA and 964 patients infected with FluB were included in this study. Patients infected with FluB were older (P<0.001) and more likely to report sore throat (P=0.002) than those infected with FluA. Otherwise, there were no significant differences in the clinical symptoms, diagnoses, and outcomes between patients infected with FluA and FluB. Overall, clinical features of influenza patients were similar regardless of the dominantly circulated subtype and lineage of the virus. In children aged ≤2 years, patients infected with FluB were more like to experience lower respiratory tract infection (P=0.034) and hospitalization (P=0.001) than those infected with FluA.

CONCLUSIONS

There were no significant clinical differences among pediatric patients infected with four recently circulating influenza viruses, except that FluB infection tended to be more severe than FluA infection in children aged ≤2 years.

Linking influenza epidemic onsets to covariates at different scales using a dynamical model

Background

Evaluating the factors favoring the onset of influenza epidemics is a critical public health issue for surveillance, prevention and control. While past outbreaks provide important insights for understanding epidemic onsets, their statistical analysis is challenging since the impact of a factor can be viewed at different scales. Indeed, the same factor can explain why epidemics are more likely to begin (i) during particular weeks of the year (global scale); (ii) earlier in particular regions (spatial scale) or years (annual scale) than others and (iii) earlier in some years than others within a region (spatiotemporal scale).

Methods

Here, we present a statistical approach based on dynamical modeling of infectious diseases to study epidemic onsets. We propose a method to disentangle the role of covariates at different scales and use a permutation procedure to assess their significance. Epidemic data gathered from 18 French regions over six epidemic years were provided by the Regional Influenza Surveillance Group (GROG) sentinel network.

Results

Our results failed to highlight a significant impact of mobility flows on epidemic onset dates. Absolute humidity had a significant impact, but only at the spatial scale. No link between demographic covariates and influenza epidemic onset dates could be established.

Discussion

Dynamical modeling presents an interesting basis to analyze spatiotemporal variations in the outcome of epidemic onsets and how they are related to various types of covariates. The use of these models is quite complex however, due to their mathematical complexity. Furthermore, because they attempt to integrate migration processes of the virus, such models have to be much more explicit than pure statistical approaches. We discuss the relation of this approach to survival analysis, which present significant differences but may constitute an interesting alternative for non-methodologists.

Deploying digital health data to optimize influenza surveillance at national and local scales

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.

Influenza contributes substantially to global morbidity and mortality each year, and epidemiological surveillance for influenza is typically conducted by sentinel physicians and health care providers recruited to report cases of influenza-like illness. While population coverage and representativeness, and geographic distribution are considered during sentinel provider recruitment, systems cannot always achieve these standards due to the administrative burdens of data collection. We present spatial estimates of influenza disease burden across United States counties by leveraging the volume and fine spatial resolution of medical claims data, and existing socio-environmental hypotheses about the determinants of influenza disease disease burden. Using medical claims as a testbed, this study adds to literature on the optimization of surveillance system design by considering conditions of limited reporting and spatial aggregation. We highlight the importance of considering sampling biases and reporting locations when interpreting surveillance data, and suggest that local mobility and regional policies may be critical to understanding the spatial distribution of reported influenza-like illness.

2015-2016 Vaccine Effectiveness of Live Attenuated and Inactivated Influenza Vaccines in Children in the United States

Background

In the 2015-2016 season, quadrivalent live attenuated influenza vaccine (LAIV) and both trivalent and quadrivalent inactivated influenza vaccine (IIV) were available in the United States.

Methods

This study, conducted according to a test-negative case-control design, enrolled children aged 2-17 years presenting to outpatient settings with fever and respiratory symptoms for <5 days at 8 sites across the United States between 30 November 2015 and 15 April 2016. A nasal swab was obtained for reverse-transcriptase polymerase chain reaction (RT-PCR) testing for influenza, and influenza vaccination was verified in the medical record or vaccine registry. Influenza vaccine effectiveness (VE) was estimated using a logistic regression model.

Results

Of 1012 children retained for analysis, most children (59%) were unvaccinated, 10% received LAIV, and 31% received IIV. Influenza A (predominantly antigenically similar to the A/California/7/2009 strain) was detected in 14% and influenza B (predominantly a B/Victoria lineage) in 10%. For all influenza, VE was 46% (95% confidence interval [CI], 7%-69%) for LAIV and 65% (48%-76%) for IIV. VE against influenza A(H1N1)pdm09 was 50% (95% CI, -2% to 75%) for LAIV and 71% (51%-82%) for IIV. The odds ratio for vaccine failure with RT-PCR-confirmed A(H1N1)pdm09 was 1.71 (95% CI, 0.78-3.73) in LAIV versus IIV recipients.

Conclusions

LAIV and IIV demonstrated effectiveness against any influenza among children aged 2-17 years in 2015-2016. When compared to all unvaccinated children, VE against influenza A(H1N1)pdm09 was significant for IIV but not LAIV.

Clinical Trials Registration

NCT01997450.

Synchronicity of influenza activity within Phoenix, AZ during the 2015-2016 seasonal epidemic

Background

Variability in the timing of influenza epidemics has been observed across global and regional scales, but this variability has not been studied extensively at finer spatial scales. As such, the aim of this study was to test whether influenza cases were synchronized across sites and/or age-groups within a major city.

Methods

We used influenza cases identified by rapid influenza tests from a network of clinics across Phoenix, AZ during the 2015–2016 influenza A season. We used a combination of KS tests and a bootstrapping approach to evaluate whether the temporal distribution of cases varied by site and/or age group.

Results

Our analysis indicates that the timing of influenza cases during the 2015–2016 seasonal influenza epidemic were generally synchronized across sites and age groups. That said, we did observe some statistically significant differences in the timing of cases across some sites, and by site and age group. We found no evidence that influenza activity consistently begins or peaks earlier in children than in adults.

Conclusions

To our knowledge, this is the first study to investigate differences in the intra-urban timing of influenza using influenza-specific case data. We were able to show evidence that influenza cases are not entirely synchronized across an urban area, but the differences we observed were relatively minor. It is important to understand the geographic scale at which influenza is synchronized in order to gain a better understanding of local transmission dynamics, and to determine the appropriate geographic scale that influenza surveillance data should be aggregated for prediction and warning systems.

Predictors of hospitalization for lower respiratory tract infection in children aged <2 years in the province of Quebec, Canada

Young age, adverse environmental conditions and infectious agents are established risk factors of lower respiratory tract infection (LRTI), whereas pneumococcal conjugate vaccines may be protective. To explore their relative role as predictors of hospitalizations under the continental climate prevailing in the province of Quebec, Canada, an ecological study was performed. Records with a main diagnosis of LRTI in children born during 2007–2010 and observed up to their second-year anniversary were extracted from the provincial hospital administrative database. Respiratory virus surveillance data and statistics on ambient air temperature were obtained. Vaccine use in different birth cohorts was derived from the Quebec City Immunization Registry. Additive and multiplicative Poisson regression models were applied to estimate attributable fractions. Age, month of birth, ambient temperature, and respiratory syncytial virus (RSV), human metapneumovirus (hMPV) and influenza-positive test proportions were significant predictors of LRTI hospitalizations. No substantial differences were observed in cohorts exposed to the 7-valent or 10-valent pneumococcal conjugate vaccines. In the additive model, the fraction of hospitalizations explained by temperature variation was 37%, whereas RSV circulation explained 28%, hMPV 4% and influenza 1%. Complex interplay between biological, environmental and social mechanisms may explain the important role of ambient air temperature in predicting LRTI hospitalization risk in young children.