  Recently, the media and public health officials have become increasingly
aware of the rise in anti-vaccine sentiment. Vaccinations have numerous health
benefits for immunized individuals as well as for the general public through
herd immunity. Given the rise in immunization-preventable diseases, a
consequence of people opting out of their routine vaccinations, we determined
that Canadian health data can identify individuals over the age of 60 who chose
not to get vaccinated (80.1% negative predictive value) and individuals under
the age of 60 who have recently been vaccinated (96.4% positive predictive
value). Using the 2009-2014 Canadian Community Health Surveys (CCHS), a probit
model identified the variables that were most commonly associated with flu
vaccination outcomes. Of 1,381 variables, 47 with the most significant marginal
effects were selected, including the presence of diseases (e.g. diabetes and
cancer), behavioral characteristics (e.g. smoking and exercise), exposure to
the medical system (e.g. whether the individual gets a regular check-up), and a
person's living situation (e.g. having young children in the household). These
variables were then used to generate a Random Forest classification model,
trained on the 2009-2013 dataset, and tested on the 2014 dataset. We achieved
an overall accuracy of 87.8% between the two final models, each using 25
classification trees with bounded depth of 20 nodes, randomly selecting from
all 47 variables. With the two proposed policies, this model can be leveraged
to efficiently allocate vaccination promotion efforts. Additionally, it can be
applied to future surveys, only requiring 3.6% of the variables in the CCHS for
successful prediction.
