A Time-Series Approach for Estimating Emergency Department Visits Attributable to Seasonal Influenza: Results from Six U.S. Cities, 2005-06 to 2016-17 Seasons.
IF 5 2区 医学Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Xucheng Fred Huang, A Danielle Iuliano, Stefanie Ebelt, Carrie Reed, Howard H Chang
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引用次数: 0
Abstract
Emergency department (ED) visits during influenza seasons represent a critical yet less examined indicator of the acute burden of influenza. This study investigates the burden of influenza-associated ED visits in six U.S. cities during influenza seasons from 2005-06 to 2016-17. Using a time-series design, we estimated associations between daily ED visits and weekly influenza activity data from the Influenza Hospitalization Surveillance Network (FluSurv-NET). A counterfactual approach was then used to calculate attributable expected ED. Highest influenza-associated rates were observed among the youngest (0-4 years) and oldest (65+ years) age groups. Combining estimates across seasons, the influenza-associated ED visit rate for respiratory diseases was almost six times larger compared to the subset of ED visits that resulted in hospitalization: 364 per 100,000 population (95% CI: 294-435) for total ED visits versus 58 per 100,000 population (95% CI: 45-71) for hospitalization. This difference was particularly large for the 0-4 year age group: 911 per 100,000 population (95% CI: 558-1,263) for total ED visits versus 43 per 100,000 population (95% CI: 15-71) for hospitalization. This study highlights the substantial burden of influenza on emergency healthcare services and the importance of integrating such data into public health planning and influenza management strategies.
期刊介绍:
The American Journal of Epidemiology is the oldest and one of the premier epidemiologic journals devoted to the publication of empirical research findings, opinion pieces, and methodological developments in the field of epidemiologic research.
It is a peer-reviewed journal aimed at both fellow epidemiologists and those who use epidemiologic data, including public health workers and clinicians.