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87 – Weighting and Estimation of Complex Survey Data
Estimation Methodology for Weekly Surveys of Influenza Vaccination Rates
Kennon R. Copeland
NORC at the University of Chicago
Nicholas Davis
NORC at the University of Chicago
Lin Liu
NORC at the University of Chicago
Nadarajasundaram Ganesh
NORC at the University of Chicago
James A. Singleton
National Center for Infectious and Respiratory Diseases
Tammy A. Santibanez
National Center for Infectious and Respiratory Diseases
Surveys of influenza vaccination coverage that enable production of estimates within the influenza season often involve collection of weekly survey data, typically based on relatively small sample sizes yielding relatively high variability. Such variability also adversely affects the stability of estimates across time, the result being estimated trends that may show occasional declines, even though the true population trends are by definition non-decreasing. Composite estimation, utilizing data and combining estimates across time periods, offers the opportunity for more stable estimates of coverage levels and trends as well as estimated trends less subject to period-to-period decreases. Use of survival analysis techniques is another alternative that ensures non-decreasing estimated trends. This paper profiles variability associated with direct estimates of levels and trends associated with the influenza module of the National Immunization Survey, proposes a composite estimation and a survival analysis approach for combining data across time, assesses the variability associated with composite and survival estimates of weekly influenza vaccination rates, and discusses potential error associated with use of data collected in different survey periods.