Risk of Pelvic Inflammatory Disease Following Chlamydia Trachomatis Infection: Analysis of Prospective Studies With a Multistate Model

Am J Epidemiol. 2013 Aug 1;178(3):484-92. doi: 10.1093/aje/kws583. Epub 2013 Jun 27.

Abstract

Our objective in this study was to estimate the probability that a Chlamydia trachomatis (CT) infection will cause an episode of clinical pelvic inflammatory disease (PID) and the reduction in such episodes among women with CT that could be achieved by annual screening. We reappraised evidence from randomized controlled trials of screening and controlled observational studies that followed untreated CT-infected and -uninfected women to measure the development of PID. Data from these studies were synthesized using a continuous-time Markov model which takes into account the competing risk of spontaneous clearance of CT. Using a 2-step piecewise homogenous Markov model that accounts for the distinction between prevalent and incident infections, we investigated the possibility that the rate of PID due to CT is greater during the period immediately following infection. The available data were compatible with both the homogenous and piecewise homogenous models. Given a homogenous model, the probability that a CT episode will cause clinical PID was 0.16 (95% credible interval (CrI): 0.06, 0.25), and annual screening would prevent 61% (95% CrI: 55, 67) of CT-related PID in women who became infected with CT. Assuming a piecewise homogenous model with a higher rate during the first 60 days, corresponding results were 0.16 (95% CrI: 0.07, 0.26) and 55% (95% CrI: 32, 72), respectively.

Keywords: Bayesian analysis; Chlamydia trachomatis; Markov model; causal effect; mass screening; meta-analysis; pelvic inflammatory disease; prospective studies.

Publication types

  • Meta-Analysis
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Causality
  • Chlamydia Infections / epidemiology*
  • Chlamydia trachomatis*
  • Comorbidity
  • Disease Progression
  • Female
  • Humans
  • Incidence
  • Markov Chains
  • Mass Screening / statistics & numerical data*
  • Models, Statistical*
  • Pelvic Inflammatory Disease / epidemiology*
  • Prevalence
  • Prospective Studies