title: Chlamydia screening coverage estimates derived using healthcare effectiveness data and information system procedures and indirect estimation vary substantially.
abstract: BACKGROUND
  Screening coverage is an important determinant of chlamydial control program success.
  
  
  OBJECTIVE
  The aim of this study was to compare chlamydial screening coverage estimates.
  
  
  METHODS
  We compared 9 estimates among women aged 15 to 25 years in Washington State, 2009. Four used Healthcare Effectiveness Data and Information System (HEDIS) procedures among Group Health enrollees. Separate HEDIS estimates assessed all enrollees and the subset of women who used services; for each group, separate estimates defined the sexually active population using HEDIS methods or National Survey of Family Growth (NSFG) data. Three indirect screening estimates used census and NSFG data to define the population's size and derived the number of tests performed by dividing the number of reported cases by test positivity defined using data from different laboratories, adjusted for repeat testing. A fourth indirect estimate was adjusted for reason for testing. A direct-indirect estimate combined data on the number of tests performed in reporting laboratories and an indirect estimate of tests performed elsewhere.
  
  
  RESULTS
  Healthcare Effectiveness Data and Information System procedures and NSFG data yielded similar estimates of the percentage of women who were sexually active (60% vs. 61%). Screening coverage estimated by HEDIS was higher among Group Health users (43.6%) than among all enrollees (34.2%). Indirect screening coverage estimates varied from 46.4% to 68.7%. The direct-indirect estimate, which included a direct measure of the number of tests performed to identify 52% of reported cases, was 57.6%.
  
  
  CONCLUSIONS
  Most sexually active women aged 15 to 25 years in Washington State were screened for chlamydia in 2009. Healthcare Effectiveness Data and Information System methods may underestimate screening coverage. Health departments can derive population-based coverage estimates using data from large laboratories.
authors.affiliation: University of Washington, Seattle, WA, USA.
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Broad
authors.forename: Lisa E
authors.initials: LE
authors.lastname: Manhart
authors.forename: Roxanne P
authors.initials: RP
authors.lastname: Kerani
authors.forename: Delia
authors.initials: D
authors.lastname: Scholes
authors.forename: James P
authors.initials: JP
authors.lastname: Hughes
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Golden
mesh_terms: D000293:Adolescent; D000328:Adult; D002690:Chlamydia Infections / Q000175:diagnosis* / Q000453:epidemiology / Q000517:prevention & control; D002692:Chlamydia trachomatis / Q000302:isolation & purification*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007256:Information Systems; D008403:Mass Screening; D008962:Models, Theoretical; D015203:Reproducibility of Results; D034121:Reproductive Health Services; D018571:Sentinel Surveillance; D013223:Statistics as Topic; D014861:Washington / Q000453:epidemiology