Bacci Silvia, Bartolucci Francesco, Minelli Liliana, Chiavarini Manuela
Department of Economics, University of Perugia , Perugia , Italy.
Department of Experimental Medicine, University of Perugia , Perugia , Italy.
Front Public Health. 2016 Dec 23;4:278. doi: 10.3389/fpubh.2016.00278. eCollection 2016.
The literature about the determinants of a preterm birth is still controversial. We approach 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.
We rely on a longitudinal dataset about 28,603 women who delivered for the first time in the period 2005-2013 in the Umbria Region (Italy). We consider singleton physiological pregnancies originating from natural conceptions with birthweight of at least 500 g 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 model 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 a preterm birth.
The estimated models provide evidence that the probability of a preterm birth depends on certain woman's demographic and socioeconomic characteristics, other than on the previous history in terms of miscarriages and the baby's gender. Besides, as the random-effects model fits significantly better than the pooled model with lagged response, we conclude for a spurious state dependence between repeated preterm deliveries.
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 socioeconomic characteristics as well as unobservable and time-constant characteristics, possibly related to the woman's genetic makeup.
Not applicable.
关于早产决定因素的文献仍存在争议。我们在分析这些决定因素时，区分了女性可观察到的特征（可能随时间变化）和不可观察到的女性特征（时间不变，解释连续分娩类型（正常或早产）之间的相关性）。
我们依据一个纵向数据集，该数据集涉及28603名于2005年至2013年期间在意大利翁布里亚地区首次分娩的女性。我们考虑单胎自然受孕的生理性妊娠，出生体重至少为500克，孕周在24至42周之间；分娩总数为34224例。该数据集基于同期该地区收集的标准出生证明。我们针对早产事件估计了两种类型的logit模型。第一个模型是混合模型，考虑了可能的既往早产分娩信息，包括协变量中的滞后响应。第二个模型通过引入一个随机效应明确考虑了数据的纵向结构，该随机效应总结了影响早产概率的女性所有（时间不变）不可观察到的特征。
估计模型表明，早产概率取决于女性的某些人口统计学和社会经济特征，而非流产史和婴儿性别。此外，由于随机效应模型的拟合效果明显优于带有滞后响应的混合模型，我们得出重复早产分娩之间存在虚假状态依赖的结论。
所提出的分析是检测早产高危女性特征的有用工具。在检测这些特征时，考虑了女性可观察到的人口统计学和社会经济特征以及可能与女性基因构成相关的不可观察到的和时间不变的特征。
不适用。
