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Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza

  • Kevin Berry
  • Julia E. Anderson
  • Jude Bayham
  • Eli P. Fenichel
Original Contribution

Abstract

To inform public health and medical decision makers concerning vaccination interventions, a methodology for merging and analyzing detailed activity data and health outcomes is presented. The objective is to investigate relationships between individual’s activity choices and their decision to receive an influenza vaccination. Data from the Behavioral Risk Factor Surveillance System (BRFSS) are used to predict vaccination rates in the American Time Use Survey (ATUS) data between 2003 and 2013 by using combined socioeconomic and demographic characteristics. The correlations between the extensive (do or not do) and intensive (how much) decisions to perform activities and influenza vaccination are further explored. Significant positive and negative correlations were found between several activities and vaccination. For some activities, the sign of the correlation flips when considering either the intensive or the extensive decision. This flip occurs with highly studied activities, like smoking. Correlations between activities and vaccination can provide an additional metric for targeting those least likely to vaccinate. The methodology outlined in this paper can be replicated to explore correlation among actions and other health outcomes.

Keywords

Nontraditional vaccination campaigns American Time Use Survey Influenza Behavioral Risk Factor Surveillance System Survey Public health Vaccination 

Notes

Acknowledgments

This publication was made possible by Grant Number 1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health and NSF. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIGMS. This work was also funded by NSF Grant No. 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases program.

Supplementary material

10393_2017_1296_MOESM1_ESM.docx (103 kb)
Supplementary material 1 (DOCX 102 kb)

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Copyright information

© EcoHealth Alliance 2017

Authors and Affiliations

  • Kevin Berry
    • 1
    • 2
  • Julia E. Anderson
    • 1
    • 3
  • Jude Bayham
    • 1
    • 4
  • Eli P. Fenichel
    • 1
  1. 1.School of Forestry and Environmental StudiesYale UniversityNew HavenUSA
  2. 2.Institute of Social and Economic ResearchUniversity of Alaska AnchorageAnchorageUSA
  3. 3.School of Public HealthYale UniversityNew HavenUSA
  4. 4.Department of Agricultural and Resource EconomicsColorado State UniversityFort CollinsUSA

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