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

Abstract

Background: 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.

Methods: 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.

Results: 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.

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

Figure 1

Estimated transition probabilities from a Markov regression model with a random intercept and slope.

Figure 2. Autocorrelation between visits for the empirical data and a first-order Markov model, a first-order Markov model with random effects, and a mover-stayer model

This figure compares the observed versus the predicted correlation structure for each type of model. The autocorrelation represents the correlation between all pairwise response measurements separated by 1 to 4 visits (lag).