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

• Main Authors: Sungwal Rugpao, Kittipong Rungruengthanakit, Yuthapong Werawatanakul, Wanida Sinchai, Tosaporn Ruengkris, Surachai Lamlertkittikul, Sutham Pinjareon, Sompong Koonlertkit, Aram Limtrakul, Somchai Sriplienchan, Antika Wongthanee, Bangorn Sirirojn, Charles S. Morrison, David D. Celentano
• Format: Journal
• Published: 2018
• Subjects: Medicine
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=76349099116&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/51111
• 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.