RISK OF INFLUENZA UNDER REALISTIC INFLUENZA VACCINATION INTERVENTIONS AMONG UNIVERSITY STUDENTS

Public Deposited
Analytics

Downloadable Content

Download PDF
Request Version for Screen Reader
Creator
  • Zivich, Paul Nicholas
    • Gillings School of Global Public Health, Department of Epidemiology
Abstract
  • Seasonal influenza causes substantial morbidity and mortality each year. One important group to consider is university students since their vaccination uptake is low, they experience high attack rates, they suffer substantial negative impacts on their well-being, and they may be important for further transmission. Rather than assess direct vaccine effectiveness, this work aimed to estimate the risk of influenza under large-scale changes in the distribution of influenza vaccination.Using self-reported contact data from the eX-FLU cluster randomized trial on three-day self-isolation, we applied a recent extension of the targeted maximum likelihood estimation (TMLE) framework for dependent data. Hypothetical policies to increase vaccination focused on educational information regarding influenza, reducing non-financial barriers, and reducing financial barriers were compared. Each policy was further compared across a range of plausible shifts in the log-odds of influenza vaccination receipt. To guide the application of TMLE and approaches to account for measurement error of self-reported edges within eX-FLU, two simulation studies were conducted. To assess imputation and Bayesian procedures for measurement error, we conducted a simulation study of three network generative models with non-informative and informative measurement error. To assess TMLE for dependent data, we simulated four data-generating mechanisms in three different networks. Both imputation and Bayesian approaches reduced bias and improved confidence limit coverage for all parameters in most scenarios. The TMLE for dependent data performed wellacross scenarios with interference, but issues manifested when policies were not well-supported by the observed data. In the application of TMLE to the eX-FLU data, a reduction in the risk of laboratory-confirmed influenza was observed across policies. However, the estimates were consistent with little-to-no reduction. When accounting for measurement error of self-reported contacts via the Bayesian approach, a greater reduction in risk was observed but differences between policies were minor.Our results are a robust analysis for a parameter of public health importance. The results of our simulations and analyses will serve as an example for future applications.
Date of publication
Keyword
DOI
Resource type
Rights statement
  • In Copyright - Educational Use Permitted
Advisor
  • Aiello, Allison E
  • Brookhart, Maurice A
  • Hudgens, Michael G
  • Moody, James
  • Weber, David J
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2021
Language

Relations

Parents:

This work has no parents.

Items

Thumbnail Title Date Uploaded Visibility Actions
file details Zivich_unc_0153D_20224.pdf 2021-06-21 Public Download
file details Zivich_unc_0153D_20224_DATA.xml 2021-06-21 Public Download