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Int J Environ Res Public Health. 2017 Jul 28;14(8). pii: E849. doi: 10.3390/ijerph14080849.
A Multiyear Model of Influenza Vaccination in the United States.
Kamis A1, Zhang Y2, Kamis T3.
Author information
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; cognitive bias; communication; influenza vaccination; public health
PMID: 28788069 DOI: 10.3390/ijerph14080849
Free full text
A Multiyear Model of Influenza Vaccination in the United States.
Kamis A1, Zhang Y2, Kamis T3.
Author information
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; cognitive bias; communication; influenza vaccination; public health
PMID: 28788069 DOI: 10.3390/ijerph14080849
Free full text