Background: Immunization information systems (IISs) are valuable population-based surveillance tools, although in- and out-migration may potentially introduce bias to the determination of immunization levels.
Objectives: The objective of this study was to explore alternative methods for determining the eligible population for adolescent vaccination and thereby for calculating adolescent immunization levels using a statewide IIS.
Methods: Records from adolescents aged 11-18 years were abstracted from the Michigan Care Improvement Registry (MCIR) in 2010 to assess vaccination status using valid doses administered. Four methods for determining denominators were explored, including: 1) an “inclusive” denominator of all adolescents with MCIR records; 2) a more restrictive denominator that excluded adolescents identified by MCIR as having moved out of state; 3) a denominator that further excluded those with no activity on their MCIR records for at least 10 years prior to the evaluation date; and 4) a denominator based on U.S. census data. Coverage levels for four adolescent vaccines (Tdap, MCV4, HPV4 and Flu) were calculated at both state and county levels.
Results: There was nearly 20% difference in size between the most inclusive and restrictive denominator populations. Though some variability in vaccination levels (between 2 and 11 percentage-points depending on the vaccine) was seen at the state level among the four methods, there was greater variation in county-specific vaccination levels (up to 21 percentage-points depending on the county and vaccine selected). In general, vaccines with higher coverage levels had greater variation across the different methods as did counties with smaller populations.
Conclusions: At a county level, using the four denominator calculation methods resulted in substantial differences in derived adolescent immunization rates that were less apparent when aggregated at the state level. Future research is needed to ascertain how to select the most appropriate calculation method for each vaccine when studying data at more fragmented levels (i.e. the county level).