Value of multinomial model in epidemiology: application to the comparison of risk factors for severely and moderately preterm births

BACKGROUND: The multinomial logistic regression model is employed to model the relationship between an outcome variable with more than two categories and a set of covariates. This model is not widely used in epidemiology. We discuss the value of the multinomial model by comparing it with the binary logistic model, and we present a statistical comparison of odds ratios (OR) using the multinomial model. We studied the associations between obstetric history and very (< 33 weeks of amenorrhea) and moderate (33-36 weeks) preterm births. METHODS: Parameters (lnOR) of very and moderate preterm births, associated with the severity of obstetric history (none=0, moderate=1, severe=2), were estimated using two logistic binary models (moderate preterm births vs full-term births (>=37 weeks), and very preterm births vs full-term births) and one logistic multinomial model which compared very and moderate preterm births to full-term births. These analyses were performed before and after adjustment for a covariate: the country of survey. Parameters of very preterm birth and moderate preterm birth, estimated from multinomial model, were compared using Wald test. These analyses were performed using data from a large case-control survey in Europe, the EUROPOP survey; 1 675 very preterm births, 3 652 moderate preterm births and 7 965 full-term births were included. RESULTS: Crude parameters of very and moderate preterm births were similar, regardless the logistic regression model, binary or multinomial. The estimated parameters slightly differ after adjustment for the covariate, but lower variance estimates were obtained using multinomial logistic regression model. Parameters of very preterm birth associated with moderate obstetric history, B(gp)=0.5040, and severe obstetric history, B(gp)'=1.545, differ significantly from those of moderate preterm birth, B(pm)=0.4434 and B(pm)'=1.223 respectively (p < 0.001). CONCLUSION: Parameters obtained in separate logistic binary models are close to those obtained in a multinomial model. The multinomial model is useful for testing the heterogeneity of risk factors for distinct health problems.