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

INTRODUCTION: 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...

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Detalles Bibliográficos
Autores principales: Bacci, Silvia, Bartolucci, Francesco, Minelli, Liliana, Chiavarini, Manuela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5179574/
https://www.ncbi.nlm.nih.gov/pubmed/28066757
http://dx.doi.org/10.3389/fpubh.2016.00278
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author Bacci, Silvia
Bartolucci, Francesco
Minelli, Liliana
Chiavarini, Manuela
author_facet Bacci, Silvia
Bartolucci, Francesco
Minelli, Liliana
Chiavarini, Manuela
author_sort Bacci, Silvia
collection PubMed
description INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. TRIAL REGISTRATION: Not applicable.
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spelling pubmed-51795742017-01-06 Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models Bacci, Silvia Bartolucci, Francesco Minelli, Liliana Chiavarini, Manuela Front Public Health Public Health INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. TRIAL REGISTRATION: Not applicable. Frontiers Media S.A. 2016-12-23 /pmc/articles/PMC5179574/ /pubmed/28066757 http://dx.doi.org/10.3389/fpubh.2016.00278 Text en Copyright © 2016 Bacci, Bartolucci, Minelli and Chiavarini. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Bacci, Silvia
Bartolucci, Francesco
Minelli, Liliana
Chiavarini, Manuela
Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models
title Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models
title_full Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models
title_fullStr Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models
title_full_unstemmed Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models
title_short Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models
title_sort preterm birth: analysis of longitudinal data on siblings based on random-effects logit models
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5179574/
https://www.ncbi.nlm.nih.gov/pubmed/28066757
http://dx.doi.org/10.3389/fpubh.2016.00278
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