An evaluation of the natural history of bacterial vaginosis using transition models

The natural history of bacterial vaginosis (BV) is complex given the variability across and within women over time. This article considers 3 different transition models for analyzing longitudinal BV data.

Data from the Longitudinal Study of Vaginal Flora were used to evaluate 3 transition modeling strategies: (1) a Markov regression, (2) a Markov regression with random effects, and (3) a mover-stayer model. The effect of covariates on the transition process of BV, defined as a Nugent score of 7 to 10, was estimated using a logistic regression parameterization. Models were compared using various model assessment techniques. We analyzed a subset of women completing all 5 visits (n = 1731) as well as the complete data (n = 3626), in which 1 or more visit measurements were missing.

The Markov regression model had a poor fit to the data. A random-effects or mover-stayer model accounted for additional unexplained heterogeneity and had a better fit to the data. Across all models, douching was significantly associated with BV fluctuation. In the mover-stayer model, both douching and number of sexual partners were associated with persisting with (λ11 = 0.90, P < 0.001; λ12 = -0.41, P < 0.03, respectively) or without (λ01 = -0.73, P < 0.001; λ02 = -0.33, P = 0.023, respectively) BV across all visits. Using a random-effects model, we demonstrated that an individual propensity to initiate BV was positively associated with their propensity to resolve BV.

Transition models that account for additional heterogeneity provide an attractive approach for describing the effect of covariates on the natural history of BV.