Evidence map›Paper›PMID 31502229›Full record

ArticleDemography2019

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

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 and 1 more

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Demography, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. The 1968 Influenza Pandemic and COVID-19 Outcomes.medRxiv : the preprint server for health sciences · 2022
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Tissue-resident CD8Science immunology · 2020
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 7 institutions in 3 countries.

Enrique AcostaDépartement de Démographie, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada.
Stacey A HallmanDemography Division, Statistics Canada, Ottawa, Canada.
Lisa Y DillonDépartement de Démographie, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada.
Nadine OuelletteDépartement de Démographie, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada.
Robert BourbeauDépartement de Démographie, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada.
D Ann HerringDepartment of Anthropology, McMaster University, Hamilton, Canada.
Kris InwoodDepartment of History, University of Guelph, Guelph, Canada.
David J D EarnDepartment of Mathematics and Statistics, McMaster University, Hamilton, Canada.
Joaquin MadrenasLos Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA, USA.
Matthew S MillerMichael G. DeGroote Institute for Infectious Diseases Research, McMaster University, Hamilton, Canada.
Alain GagnonDépartement de Démographie, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada. alain.gagnon.4@umontreal.ca.
Université de Montréal · CAMcMaster University · CAMax Planck Institute for Demographic Research · DEMcMaster University Medical Centre · CAStatistics Canada · CAThe Lundquist Institute · USUniversity of Guelph · CA

Funding

CIHR
6 · The paper itself

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.

Indexed as

AdolescentAdultAgedAged, 80 and overAge FactorsChildChild MortalityChild, PreschoolFemaleHumansInfantInfluenza A virusInfluenza, HumanMaleMiddle AgedSeasonsAge-period-cohort analysisAntigenic imprintingCohort morbidity phenotypeInfluenza mortalityLexis surfaces

Identifiers

PMID31502229
PMCPMC6797638
OpenAlexW2972928697

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.