Studies of the accuracy of syndromic management have demonstrated widely varying results depending upon gender, location of infection, risk group, organisms, among other factors.
To review current knowledge about syndromic management of sexually transmitted diseases (STDs) and to make recommendations about the strengths and weaknesses of different syndromic management algorithms.
The authors identified articles and abstracts about the syndromic management of STDs. Evaluation of the effectiveness of algorithms for urethral discharge, genital ulcer disease, and vaginal discharge was based primarily on published sensitivity and specificity data.
Overall, algorithms for the diagnosis and treatment of urethral discharge and genital ulcer disease in men had high sensitivities or cure rates (urethral discharge, 87-99%; genital ulcer disease, 68-98%). The sensitivities for the algorithms for vaginal discharge ranged from 73% to 93% among women presenting with symptoms of vaginal discharge, and from 29% to 86% among women not presenting with symptoms. Vaginal discharge was not found to be an effective indicator of cervical infection and, therefore, is not an independently effective screening tool to detect women with cervical infection, especially in low-risk or asymptomatic populations. Incorporating risk scores can improve the accuracy of algorithms to detect cervical infection.
Algorithms for urethral discharge and genital ulcer disease can be effective in STDs. The current algorithms for vaginal discharge are not highly effective in detecting gonorrhea and chlamydia in women; risk scores can improve their efficacy, but must be tailored to reflect community risks. Without attention to the qualitative aspects of STD syndromic management, these methods will likely have even less accuracy than the studies reviewed above. There remains an urgent need for the development of an affordable, rapid, and effective diagnostic technique that will improve STD detection in resource-poor settings.
From the School of Public Health, University of California, Berkeley, Berkeley, California
The authors thank the funders of this project, Data for Decision Making Project of The United States Agency for International Development (USAID), Washington DC, The David and Lucile Packard Foundation, Los Altos, CA, The Fred H. Bixby Foundation, Los Angeles, CA, Katherine Gergen who helped a tremendous amount with the background research for this paper, and Dr. Jack Colford for his advice and input.
Reprint requests: Julia Walsh, MD, DTPH, University of California, Berkeley, School of Public Health, 303 Earl Warren Hall, Berkeley, CA 94729-7360.
Received September 9, 1999, revised December 14, 1999, and accepted December 21, 1999.