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Determinants of Influenza Mortality Trends: Age-Period-Cohort Analysis of Influenza Mortality in the United States, 1959–2016

Author

Listed:
  • Enrique Acosta

    (Université de Montréal
    Max Planck Institute for Demographic Research)

  • Stacey A. Hallman

    (Statistics Canada)

  • Lisa Y. Dillon

    (Université de Montréal)

  • Nadine Ouellette

    (Université de Montréal)

  • Robert Bourbeau

    (Université de Montréal)

  • D. Ann Herring

    (McMaster University)

  • Kris Inwood

    (University of Guelph)

  • David J. D. Earn

    (McMaster University
    McMaster University)

  • Joaquin Madrenas

    (Harbor-UCLA Medical Center)

  • Matthew S. Miller

    (McMaster University
    McMaster
    McMaster University)

  • Alain Gagnon

    (Université de Montréal
    Université de Montréal)

Abstract

This study examines the roles of age, period, and cohort in influenza mortality trends over the years 1959–2016 in the United States. First, we use Lexis surfaces based on Serfling models to highlight influenza mortality patterns as well as to identify lingering effects of early-life exposure to specific influenza virus subtypes (e.g., H1N1, H3N2). Second, we use age-period-cohort (APC) methods to explore APC linear trends and identify changes in the slope of these trends (contrasts). Our analyses reveal a series of breakpoints where the magnitude and direction of birth cohort trends significantly change, mostly corresponding to years in which important antigenic drifts or shifts took place (i.e., 1947, 1957, 1968, and 1978). Whereas child, youth, and adult influenza mortality appear to be influenced by a combination of cohort- and period-specific factors, reflecting the interaction between the antigenic experience of the population and the evolution of the influenza virus itself, mortality patterns of the elderly appear to be molded by broader cohort factors. The latter would reflect the processes of physiological capital improvement in successive birth cohorts through secular changes in early-life conditions. Antigenic imprinting, cohort morbidity phenotype, and other mechanisms that can generate the observed cohort effects, including the baby boom, are discussed.

Suggested Citation

  • Enrique Acosta & Stacey A. Hallman & Lisa Y. Dillon & Nadine Ouellette & Robert Bourbeau & D. Ann Herring & Kris Inwood & David J. D. Earn & Joaquin Madrenas & Matthew S. Miller & Alain Gagnon, 2019. "Determinants of Influenza Mortality Trends: Age-Period-Cohort Analysis of Influenza Mortality in the United States, 1959–2016," Demography, Springer;Population Association of America (PAA), vol. 56(5), pages 1723-1746, October.
    • Handle: RePEc:spr:demogr:v:56:y:2019:i:5:d:10.1007_s13524-019-00809-y
    DOI: 10.1007/s13524-019-00809-y
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    References listed on IDEAS

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    Cited by:

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    2. Arolas, Héctor Pifarré i & Acosta, Enrique & Casasnovas, Guillem López & Lo, Adeline & Nicodemo, Catia & Riffe, Tim & Myrskylä, Mikko, 2020. "Global years of life lost to COVID-19," SocArXiv gveaj, Center for Open Science.
    3. Yann Décarie & Pierre-Carl Michaud, 2020. "Counting the Dead: COVID-19 and Mortality in Québec and British Columbia," Cahiers de recherche / Working Papers 2008, Chaire de recherche sur les enjeux économiques intergénérationnels / Research Chair in Intergenerational Economics.

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