Chantel Sloan1, Rameela Chandrasekhar2, Edward Mitchel3, Danielle Ndi4, Lisa Miller5, Ann Thomas6, Nancy M Bennett7, Shua Chai8, Melanie Spencer9, Seth Eckel10, Nancy Spina11, Maya Monroe12, Evan J Anderson13, Ruth Lynfield14, Kimberly Yousey-Hindes15, Marisa Bargsten16, Shelley Zansky17, Krista Lung18, Monica Schroeder19, Charisse N Cummings20, Shikha Garg21, William Schaffner22, Mary Lou Lindegren23. 1. Brigham Young University, Provo, UT, USA. Electronic address: chantel.sloan@byu.edu. 2. New Mexico Department of Health, NM, USA. Electronic address: rameela.chandrasekhar@vanderbilt.edu. 3. Vanderbilt University School of Medicine, Nashville, TN, USA. Electronic address: ed.mitchel@vanderbilt.edu. 4. Vanderbilt University School of Medicine, Nashville, TN, USA. Electronic address: danielle.n.ndi@vanderbilt.edu. 5. University of Colorado, School of Public Health, Aurora, CO, USA. Electronic address: lisa.miller@ucdenver.edu. 6. Oregon Department of Public Health, OR, USA. Electronic address: ann.thomas@state.or.us. 7. University of Rochester School of Medicine and Dentistry, NY, USA. Electronic address: nancy_bennett@urmc.rochester.edu. 8. California Emerging Infections Program, Oakland, CA, USA. Electronic address: shua.chai@cdph.ca.gov. 9. Salt Lake County Health Department, CO, USA. Electronic address: melspencer@sclo.org. 10. Michigan Department of Health and Human Services, MI, USA. Electronic address: eckels1@michigan.gov. 11. New York State Department of Health, NY, USA. Electronic address: nancy.spina@health.ny.gov. 12. Maryland Department of Health, MD, USA. Electronic address: maya.monroe@maryland.gov. 13. Emory University School of Medicine and the Georgia Emerging Infections Program, Atlanta VAMC, Atlanta, GA, USA. Electronic address: evanderson@emory.edu. 14. Minnesota Department of Health, MN, USA. Electronic address: ruth.lynfield@state.mn.us. 15. Connecticut Emerging Infections Program, Yale School of Public Health, New Haven, CT, USA. Electronic address: kimberly.yousey-hindes@yale.edu. 16. New Mexico Department of Health, NM, USA. Electronic address: marisa.bargsten@gmail.com. 17. New York State Department of Health, NY, USA. Electronic address: shelley.zansky@health.ny.gov. 18. Ohio Department of Health, Columbus, OH, USA. Electronic address: krista.lung@odh.ohio.gov. 19. Council of State and Territorial Epidemiologists, Atlanta, GA, USA. Electronic address: mschroeder@cste.org. 20. Influenza Division, CDC, Atlanta, GA, USA. Electronic address: yta8@cdc.gov. 21. Influenza Division, CDC, Atlanta, GA, USA. Electronic address: izj7@cdc.gov. 22. Vanderbilt University School of Medicine, Nashville, TN, USA. Electronic address: william.schaffner@vanderbilt.edu. 23. Vanderbilt University School of Medicine, Nashville, TN, USA. Electronic address: mllindegren@gmail.com.
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.
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.