Spatial and temporal clustering of patients hospitalized with laboratory-confirmed influenza in the United States

Abstract

Background: Timing of influenza spread across the United States is dependent on factors including local and national travel patterns and climate. Local epidemic intensity may be influenced by social, economic and demographic patterns. Data are needed to better explain how local socioeconomic factors influence both the timing and intensity of influenza seasons to result in national patterns.

Methods: To determine the spatial and temporal impacts of socioeconomics on influenza hospitalization burden and timing, we used population-based laboratory-confirmed influenza hospitalization surveillance data from the CDC-sponsored Influenza Hospitalization Surveillance Network (FluSurv-NET) at up to 14 sites from the 2009/2010 through 2013/2014 seasons (n = 35,493 hospitalizations). We used a spatial scan statistic and spatiotemporal wavelet analysis, to compare temporal patterns of influenza spread between counties and across the country.

Results: There were 56 spatial clusters identified in the unadjusted scan statistic analysis using data from the 2010/2011 through the 2013/2014 seasons, with relative risks (RRs) ranging from 0.09 to 4.20. After adjustment for socioeconomic factors, there were five clusters identified with RRs ranging from 0.21 to 1.20. In the wavelet analysis, most sites were in phase synchrony with one another for most years, except for the H1N1 pandemic year (2009-2010), wherein most sites had differential epidemic timing from the referent site in Georgia.

Conclusions: Socioeconomic factors strongly impact local influenza hospitalization burden. Influenza phase synchrony varies by year and by socioeconomics, but is less influenced by socioeconomics than is disease burden.

Keywords: SaTScan; Socioeconomics; Surveillance; Wavelet.