Abstract
BackgroundMeasures 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.MethodsWe 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 school-aged 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.ResultsThe 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.ConclusionsDifferences 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-015-1318-9) contains supplementary material, which is available to authorized users.
Highlights
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
Using a high coverage outpatient influenza-like illness (ILI) dataset based on medical claims data from the United States, we introduce two novel influenza severity metrics: 1) a retrospective index based on ILI age dynamics, which can aid in epidemiological analysis and the evaluation of public health responses using a commonly collected single data source; 2) an early warning index, estimated prior to the epidemic peak, which can help physicians improve patient-level communication, diagnosis, and treatment and inform decision makers on communication strategies regarding vaccination and antiviral usage
The 2006– 07 season was one of the mildest seasons according to the benchmark, and it had the lowest rates of child and adult hospitalization and pneumonia and influenza (P&I) mortality compared to other seasons, but relatively high counts in pediatric deaths, suggesting that seasons could have mixed indications of severity across different data streams
Summary
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. The United States Centers for Disease Control and Prevention (CDC) characterizes seasonal severity through influenza-associated hospitalization rates and mortality due to pneumonia and influenza (Fig. 1) From these surveillance data, CDC estimated a range of 3,000 to 49,000 influenza-associated. The CDC has recently adopted a population-level severity framework for influenza pandemics that incorporates both clinical severity and transmissibility metrics, but the clinical severity component remains closely tied to case-fatality and similar ratios [12] These measures of severity based on mortality and hospitalization only capture one facet of the experience of flu across the population [4, 17], and are limited by the availability of data. While hospitalization and mortality remain the accepted measures of influenza severity, there is no composite quantitative metric (used by the CDC or others) that synthesizes the varying acute effects imposed by the disease
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