Risk factors and algorithms for chlamydial and gonococcal cervical infections in women attending family planning clinics in Thailand

• Main Authors: Rugpao S., Rungruengthanakit K., Werawatanakul Y., Sinchai W., Ruengkris T., Lamlertkittikul S., Pinjareon S., Koonlertkit S., Limtrakul A., Sriplienchan S., Wongthanee A., Sirirojn B., Morrison C.S., Celentano D.D.
• Format: Article
• Language: English
• Published: 2014
• Online Access: http://www.scopus.com/inward/record.url?eid=2-s2.0-76349099116&partnerID=40&md5=1c4cc6f8e9e84a9fc6699b373f1ce451; http://www.ncbi.nlm.nih.gov/pubmed/20178541; http://cmuir.cmu.ac.th/handle/6653943832/2621
• Institution: Chiang Mai University

Aim: To identify risk factors associated with and evaluate algorithms for predicting Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) cervical infections in women attending family planning clinics in Thailand. Methods: Eligible women were recruited from family planning clinics from all regions in Thailand. The women were followed at 3-month intervals for 15-24 months. At each visit, the women were interviewed for interval sexually transmitted infection (STI) history in the past 3 months, recent sexual behavior, and contraceptive use. Pelvic examinations were performed and endocervical specimens were collected to test for CT and NG using polymerase chain reaction. Results: Factors associated with incident CT/NG cervical infections in multivariate analyses included region of country other than the north, age ≤25 years, polygamous marriage, acquiring a new sex partner in the last 3 months, abnormal vaginal discharge, mucopurulent cervical discharge, and easily induced bleeding of the endocervix. Three models were developed to predict cervical infection. A model incorporating demographic factors and sexual behaviors had a sensitivity of 61% and a specificity of 71%. Incorporating additional factors did not materially improve test performance. Positive predictive values for all models evaluated were low. Conclusion: In resource-limited settings, algorithmic approaches to identifying incident cervical infections among low-risk women may assist providers in the management of these infections. © 2010 Japan Society of Obstetrics and Gynecology.