Detecting Signals of Seasonal Influenza Severity through Age Dynamics

Abstract

Background: Measures of population-level influenza severity are important for public health planning, but estimates are often based on case-fatality and case-hospitalization risks, which require multiple data sources, are prone to surveillance biases, and are typically unavailable in the early stages of an outbreak. To address the limitations of traditional indicators, we propose a novel severity index based on influenza age dynamics estimated from routine physician diagnosis data that can be used retrospectively and for early warning. Methods:We developed a quantitative 'ground truth' severity benchmark that synthesizes multiple traditional severity indicators from publicly available influenza surveillance data in the United States. Observing that the age distribution of cases may signal severity early in an epidemic, we constructed novel retrospective and early warning severity indexes based on the relative risk of influenza-like illness (ILI) among working-age adults to that among schoolaged children using weekly outpatient medical claims. We compared our relative risk-based indexes to the composite benchmark and estimated seasonal severity for flu seasons from 2001-02 to 2008-09 at the national and state levels. Results: The severity classifications made by the benchmark were not uniquely captured by any single contributing metric, including pneumonia and influenza mortality; the influenza epidemics of 2003-04 and 2007-08 were correctly identified as the most severe of the study period. The retrospective index was well correlated with the severity benchmark and correctly identified the two most severe seasons. The early warning index performance varied, but it projected 2007-08 as relatively severe 10 weeks prior to the epidemic peak. Influenza severity varied significantly among states within seasons, and four states were identified as possible early warning sentinels for national severity. Conclusions: Differences in age patterns of ILI may be used to characterize seasonal influenza severity in the United States in real-time and in a spatially resolved way. Future research on antigenic changes among circulating viruses, pre-existing immunity, and changing contact patterns may better elucidate the mechanisms underlying these indexes. Researchers and practitioners should consider the use of composite or ILI-based severity metrics in addition to traditional severity measures to inform epidemiological understanding and situational awareness in future seasonal outbreaks.

Double trouble? When a pandemic and seasonal virus collide

“…Early in the influenza season, influenza B was dominant, but since early February, influenza A H1N1 has been more prevalent. Influenza A H1N1 is expected to produce milder infections so this may explain the ILI reduction later in the season [32]. However, we found that influenza A dynamics were not significantly associated with ILI dynamics.…”

Characterizing an epidemiological geography of the United States: influenza as a case study

“…Additionally, the regionalization reveals a unique feature of epidemic timing across regions in which seasons are homogeneously early or late, or heterogeneous in their peak timing across regions. We note that this partitioning of influenza is consistent with the severity classification we proposed in [45] and suggests that severe seasons are homogeneously early, moderately severe seasons are uniformly late, and mild seasons are distributed in their timing, which is consistent with a previous study by Dahlgren et al [46]. The wave-like progression of flu through the U.S. also allows for the prediction of epidemic dynamics in a region based on other regions (as demonstrated by our Granger causality results) and for the prediction of region membership during the course of the epidemic (as highlighted by our partial regionalization results).…”

Contact, travel, and transmission: The impact of winter holidays on influenza dynamics in the United States

“…Finally, we observed that holiday-associated behavioral changes consistently increased the risk of disease among adults relative to that among children. Our previous work leveraged the consistency of this temporary change to detect early warning signals of seasonal influenza severity [1], and future work could examine how this early influenza testbed might signal other actionable epidemiological information about the influenza season.…”

“…Influenza epidemics are characterized by large variation in disease burden across seasons and across locations within a given season [1]. While we do not fully understand what drives this variation, contact and travel patterns have been observed to influence local and global influenza transmission [2][3][4][5].…”