Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models - Bacci, Silvia; Bartolucci, Francesco; Minelli, Liliana; Chiavarini, Manuela (Frontiers in Public Health, 2016)
Background The literature about the determinants of a preterm birth is still controversial. We effort the analysis of these determinants distinguishing between woman's observable characteristics, which may change over time, and unobservable woman's characteristics, which are time invariant and explain the dependence between the typology (normal or preterm) of consecutive births. Methods We rely on a longitudinal dataset about 28,603 women who delivered for the first time in the period 2005-2013 in the Region of Umbria (IT). We consider singleton physiological pregnancies originating from natural conceptions with birthweight of at least 500 grams and gestational age between 24 and 42 weeks; the overall number of deliveries is 34,224. The dataset is based on the Standard Certificates of Life Birth collected in the region in the same period. We estimate two types of logit models for the event that the birth is preterm. The first model is pooled and accounts for the information about possible previous preterm deliveries including the lagged response among the covariates. The second model takes explicitly into account the longitudinal structure of data through the introduction of a random effect that summarizes all the (time-invariant) unobservable characteristics of a woman affecting the probability of preterm birth. Results The estimated models provide evidence that the probability of a preterm birth depends on certain woman's demographic and socio-economic characteristics, other than on the previous history in terms of miscarriages and the baby's gender. Besides, as the random-effects model has a significant better goodness-of-fit than the pooled model with lagged response, we conclude for a spurious state dependence between repeated preterm deliveries. Conclusions The proposed analysis represents a useful tool to detect profiles of women with a high risk of preterm delivery. Such profiles are detected taking into account observable woman's demographic and socio-economic characteristics as well as unobservable and time-constant characteristics, possibly related to the woman's genetic makeup.