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Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models

Overview of attention for article published in Frontiers in Public Health, December 2016
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Title
Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models
Published in
Frontiers in Public Health, December 2016
DOI 10.3389/fpubh.2016.00278
Pubmed ID
Authors

Silvia Bacci, Francesco Bartolucci, Liliana Minelli, Manuela Chiavarini

Abstract

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 23%
Researcher 2 15%
Student > Master 2 15%
Professor 1 8%
Student > Ph. D. Student 1 8%
Other 0 0%
Unknown 4 31%
Readers by discipline Count As %
Nursing and Health Professions 3 23%
Medicine and Dentistry 2 15%
Economics, Econometrics and Finance 1 8%
Mathematics 1 8%
Psychology 1 8%
Other 1 8%
Unknown 4 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 December 2016.
All research outputs
#18,498,050
of 22,919,505 outputs
Outputs from Frontiers in Public Health
#5,785
of 10,069 outputs
Outputs of similar age
#309,680
of 420,056 outputs
Outputs of similar age from Frontiers in Public Health
#46
of 60 outputs
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