A case-control study developing a model for predicting risk factors for high SeM-specific antibody titers after natural outbreaks of Streptococcus equi subsp equi infection in horses.

Abstract

OBJECTIVE To develop a risk prediction model for factors associated with an SeM-specific antibody titer ≥ 3,200 in horses after naturally occurring outbreaks of Streptococcus equi subsp equi infection and to validate this model. DESIGN Case-control study. ANIMALS 245 horses: 57 horses involved in strangles outbreaks (case horses) and 188 healthy horses (control horses). PROCEDURES Serum samples were obtained from the 57 cases over a 27.5-month period after the start of outbreaks; serum samples were obtained once from the 188 controls. A Bayesian mixed-effects logistic regression model was used to assess potential risk factors associated with an antibody titer ≥ 3,200 in the case horses. A cutoff probability for an SeM-specific titer ≥ 3,200 was determined, and the model was externally validated in the control horses. Only variables with a 95% credibility interval that did not overlap with a value of 1 were considered significant. RESULTS 9 of 57 (6%) case horses had at least 1 titer ≥ 3,200, and 7 of 188 (3.7%) of control horses had a titer ≥ 3,200. The following variables were found to be significantly associated with a titer ≥ 3,200 in cases: farm size > 20 horses (OR, 0.11), history of clinically evident disease (OR, 7.92), and male sex (OR, 0.11). The model had 100% sensitivity but only 24% specificity when applied to the 188 control horses (area under the receiver operating characteristic curve = 0.62.) CONCLUSIONS AND CLINICAL RELEVANCE Although the Bayesian mixed-effects logistic regression model developed in this study did not perform well, it may prove useful as an initial screening tool prior to vaccination. We suggest that SeM-specific antibody titer be measured prior to vaccination when our model predicts a titer ≥ 3,200.