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Estimating chlamydia re-infection rates: an empirical example
  1. Elizabeth A Torrone1,2,
  2. Catherine L Satterwhite1,
  3. Delia Scholes3,
  4. Onchee Yu3,
  5. Stuart Berman4,
  6. Thomas A Peterman1
  1. 1Division of STD Prevention, Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Atlanta, Georgia, USA
  2. 2Division of Applied Sciences, Epidemic Intelligence Service, Centers for Disease Control and Prevention, Scientific Education and Professional Development Program Office, Atlanta, Georgia, USA
  3. 3Group Health Research Institute, Group Health Cooperative, Seattle, Washington, USA
  4. 4Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Atlanta, Georgia, USA
  1. Correspondence to Dr Elizabeth A Torrone, Division of STD Prevention, Centers for Disease Control and Prevention, 1600 Clifton Rd NE, M/S E-02, Atlanta, GA 30333; igf0{at}cdc.gov

Abstract

Objective Chlamydia re-infection data are used to inform and evaluate chlamydia control programmes. We quantitatively investigated the effect of denominator selection on estimating re-infection rates and trends.

Methods Using data on women aged 15–44 years enrolled in Group Health Cooperative (GH), a Pacific Northwest health plan, annual chlamydia re-infection rates from 1998 to 2006 were calculated. Three different denominators were compared using person-years contributed by: (1) all women; (2) women with a prior documented chlamydial infection regardless of whether they were retested; and (3) women with a prior chlamydial infection who were retested within 14 months.

Results From 1998 to 2006, among all women 15–44 years enrolled in GH, re-infection rates increased from 64 to 149 cases per 100 000 person-years. Among women with a prior infection, rates decreased from 10 857 to 8782 cases per 100 000 person-years. Among women with a prior infection who were retested, rates increased from 29 374 to 42 475 cases per 100 000 person-years.

Conclusions Using the same dataset, we demonstrate that it is possible to tell three different stories about the magnitude of rates and trends in chlamydia re-infection among women by using different denominators. All of these strategies have limitations, but restricting the denominator to women with a prior infection who are retested may best represent the population at-risk for re-infection. Still, rates do not account for additional factors influencing the number of re-infections diagnosed, including screening coverage and changes in test technology. Caution is needed in examining and comparing re-infection data.

  • Chlamydia Infection
  • Surveillance
  • Bacterial Infection

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