A Multiyear Model of Influenza Vaccination in the United States

Arnold Kamis, Yuji Zhang and Tamara Kamis
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Arnold Kamis: Information Systems and Operations Management Department, Sawyer Business School, Suffolk University, Boston, MA 02108, USA
Yuji Zhang: Alivia Technology, Boston, MA 02114, USA
Tamara Kamis: Lexington High School, Lexington, MA 02421, USA

IJERPH, 2017, vol. 14, issue 8, 1-14

Abstract: Vaccinating adults against influenza remains a challenge in the United States. Using data from the Centers for Disease Control and Prevention, we present a model for predicting who receives influenza vaccination in the United States between 2012 and 2014, inclusive. The logistic regression model contains nine predictors: age, pneumococcal vaccination, time since last checkup, highest education level attained, employment, health care coverage, number of personal doctors, smoker status, and annual household income. The model, which classifies correctly 67 percent of the data in 2013, is consistent with models tested on the 2012 and 2014 datasets. Thus, we have a multiyear model to explain and predict influenza vaccination in the United States. The results indicate room for improvement in vaccination rates. We discuss how cognitive biases may underlie reluctance to obtain vaccination. We argue that targeted communications addressing cognitive biases could be useful for effective framing of vaccination messages, thus increasing the vaccination rate. Finally, we discuss limitations of the current study and questions for future research.

Keywords: adults; influenza vaccination; public health; communication; cognitive bias (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
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