Patterns of influenza-associated mortality among US elderly by geographic region and virus subtype, 1968-1998

Am J Epidemiol. 2006 Feb 15;163(4):316-26. doi: 10.1093/aje/kwj040. Epub 2006 Jan 4.


The regular seasonality of influenza in temperate countries is recognized, but regional differences in patterns of influenza-related mortality are poorly understood. Identifying patterns could improve epidemic prediction and prevention. The authors analyzed the monthly percentage of deaths attributable to pneumonia and influenza among people aged 65 or more years in the contiguous United States, 1968-1998. The local Moran's I test for spatial autocorrelation and correlograms assessing space-time synchrony within each influenza season were applied to detect and to characterize mortality patterns. Western US regions experienced epidemics of greater magnitude than did eastern regions. Positive spatial autocorrelation (two-sided p = 0.001) revealed the similarity in influenza mortality of neighboring states, with several western states forming a focus of high mortality. In transmission seasons dominated by virus subtype A(H3N2), mortality was correlated at a high and consistent level across the United States (mean correlation = 0.56, standard deviation = 0.134). However, when subtype A(H1N1) or type B dominated, the average synchrony was lower (mean correlation = 0.23, standard deviation = 0.058). These novel analyses suggest that causes of spatial heterogeneity (e.g., large-scale environmental drivers and population movement) have impacted influenza-associated mortality.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Geography
  • Humans
  • Influenza, Human / mortality*
  • Influenza, Human / transmission
  • Influenza, Human / virology*
  • National Center for Health Statistics, U.S.
  • Orthomyxoviridae / classification*
  • Orthomyxoviridae / isolation & purification
  • Retrospective Studies
  • Risk
  • Risk Assessment
  • Risk Factors
  • Seasons
  • Space-Time Clustering
  • Time Factors
  • United States / epidemiology