Epidemiologic Features of Vaginal Infections among Reproductive-age Women in South India
Background: Vaginal discharge syndrome is a clinical condition characterized by leucorrhea, which can be caused by reproductive tract infections (RTIs). Three RTIs are referred to as vaginal infections: bacterial vaginosis (BV), trichomoniasis (TV), and vulvovaginal candidiasis (VVC). Vaginal infections are known to increase susceptibility to sexually transmitted infections, including HIV, and to be associated with low birth weight and premature birth. The prevalence and incidence of and risk factors for vaginal infections are not well-studied in many settings. Further, the aforementioned consequences of these infections are highly prevalent in many low-income settings, providing ample justification for additional inquiry into the diagnosis and risk factors of these vaginal infections.
Methods: The Prerana dataset was collected as part of a prospective cohort study of 898 non-pregnant, reproductive-aged women living in and around Mysore, India between 2005 and 2006. The primary study objective was to evaluate the relationship between abnormal vaginal flora and the incidence of Herpes Simplex Virus - type 2 infection. Participants completed three study visits - conducted at baseline, and at three and six months - each of which involved an interviewer-administered questionnaire in Urdu or Kannada; a pelvic examination; and collection of vaginal and blood specimens for laboratory testing for reproductive tract infections.
Analyses investigating separate research aims will be conducted over three papers, as follows: Paper 1: Bacterial vaginosis and risk of Trichomonas vaginalis infection, Paper 2: Epidemiologic features of vulvovaginal candidiasis, and Paper 3: Syndromic diagnosis of vaginal infections using logic regression.
Discussion and significance: The Prerana cohort dataset is well-suited to filling in multiple gaps in the research literature. The analyses are among the first to test (or re-test) specific hypotheses concerning vaginal infection using a community-based sample based in a low-income setting. Paper 1 estimates the risk of Trichomonas vaginalis infection associated with the presence of bacterial vaginosis, which fills a conspicuous gap in the research literature. If susceptibility to Trichomonas vaginalis infection is found to be heightened among women with bacterial vaginosis, the burden of sexually transmitted infections attributable to bacterial vaginosis will increase dramatically, given that both conditions are highly prevalent. Next, paper 2 identifies factors associated with increased prevalence of vulvovaginal candidiasis, particularly with respect to the presence of vaginal Lactobacillus. If vulvovaginal candidiasis is associated with decreased presence of Lactobacillus, there will be support for identifying interventions to enhance the presence of Lactobacillus, such as with probiotics, after women are treated with antibiotics. Finally, paper 3 examines the sensitivity, specificity, positive predictive value and negative predictive value of vaginal infections in this population using a World Health Organization diagnosis algorithm. Separate syndromic diagnosis models for vaginal infections are developed using logic regression, a machine-learning procedure; the models are evaluated for their diagnostic performance against laboratory-confirmed diagnoses of vaginal infections. Logic regression models offer the potential to improve upon the performance of the WHO algorithm, using parsimonious models which are easy to develop, comprehend, and implement in clinical facilities in low-income settings.