Predictors of influenza immunization in persons over age 65.
BACKGROUND
Numerous individual characteristics have been found to be associated with rates of obtaining flu shots. This study creates a predictive model that assesses the relative impact of each of these factors on increasing rates of flu shots in a population.


METHODS
The Medicare beneficiary survey from 1998 and 1999 was used. Sixteen factors present in 1998 were compared between subjects who did and who did not receive flu shots. Significant factors were then used in a logistic regression to predict the probability of receiving a flu shot in 1998 and 1999.


RESULTS
Seven demographic and 7 health status measures were significantly different between subjects who did and who did not receive flu shots in 1998. Logistic regression showed that twelve of these variables were associated with a subject receiving a flu shot in 1998 and explained 11.4% of the variability in who did and who did not receive a flu shot. For the following year, 1999, 7 measures were significantly associated with receiving a flu shot and explained 64% of the variability in who did and who did not receive a flu shot. One variable, if the subject had received a flu shot in 1998, was highly predictive of a subject receiving a flu shot in 1999, explaining 63% of the observed variability in who did and who did not receive a flu shot in 1999.


CONCLUSIONS
The major predictor of getting a flu shot in future years is having received one in the current year (63% of predictive power). Six other behavior and demographic factors increase the predictive power modestly. Programs that target nonrecipients may increase the overall flu shot rates of a community.
Xakellis
George C
GC
University of California Davis, Health System Family Practice Residency Program, Ellison Ambulatory Care Center, Sacramento, CA 95817, USA. gxakelli@att.net
D000368:Aged; D015438:Health Behavior; D006304:Health Status; D006801:Humans; D007251:Influenza, Human / Q000517:prevention & control*; D016015:Logistic Models; D008962:Models, Theoretical; D014611:Vaccination / Q000706:statistics & numerical data*