Estimating chlamydia re-infection rates: an empirical example. Issue 5 (4th May 2013)

• Record Type: Journal Article
• Authors: Torrone, Elizabeth A; Satterwhite, Catherine L; Scholes, Delia; Yu, Onchee; Berman, Stuart; Peterman, Thomas A
• Is Part Of: Sexually transmitted infections. Volume 89:Issue 5(2013)
• Page Start: 388
• Page End: 390
• Publication Date: 2013-05-04
• Subjects: Chlamydia Infection -- Surveillance -- Bacterial Infection; Sexually transmitted diseases -- Periodicals; HIV infections -- Periodicals; 616.951005
• DOI: 10.1136/sextrans-2012-050970
• Languages: English
• ISSNs: 1368-4973

Abstract

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.