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RESEARCH ARTICLE
Contents Vol 3(4)

The cost effectiveness of screening for genital Chlamydia trachomatis infection in Australia

Silke Walleser A D , Glenn Salkeld B and Basil Donovan B C
+ Author Affiliations
- Author Affiliations

A NHMRC Clinical Trials Centre, University of Sydney, NSW 2050, Australia.

B STEP (Screening Test Evaluation Program) School of Public Health, University of Sydney, NSW 2006, Australia.

C National Centre in HIV Epidemiology & Clinical Research, University of New South Wales, Sydney, NSW 2001, Australia.

D Corresponding author. Email: swalleser@ctc.usyd.edu.au

Sexual Health 3(4) 225-234 https://doi.org/10.1071/SH06016
Submitted: 6 March 2006  Accepted: 13 October 2006   Published: 17 November 2006

Abstract

Background: In Australia, there is no published study on the cost effectiveness of screening for chlamydia. The aim of this study was to examine the cost effectiveness of a hypothetical screening programme for chlamydia based on annual opportunistic testing of all women 25 years of age or younger consulting a general practitioner, compared with no screening. Methods: A decision–analytic modelling approach was used to determine the incremental cost effectiveness ratio (ICER) of screening compared with no screening over 25 years. The analysis measured Australian health-care costs and benefits were assessed in terms of quality-adjusted life years (QALYs). Results: The analysis resulted in a cost per QALY of $2968 for screening. One-way sensitivity analyses on all variables, and multi-way sensitivity analyses on some variables, showed a wide range for the cost effectiveness, from dominance (where screening is effective and saves money overall) to an ICER of $67 715 per QALY. Conclusions: The results indicate that annual opportunistic screening for chlamydia in women under 25 is a potentially worthwhile undertaking. However, the analysis also highlights uncertainties around the natural history of chlamydia and the effectiveness of chlamydia screening. Given these uncertainties, the need for further primary data collection in these areas becomes apparent.


Acknowledgements

The authors acknowledge and appreciate the assistance of Ms Kirsten Howard in the use of the modelling software DATA that contributed to the design of the decision model.


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