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Preterm Birth: Analysis of Longitudinal Data on Siblings Based on Random-Effects Logit Models.
Silvia Bacci1, Francesco Bartolucci1, Liliana Minelli2
1Department of Economics, University of Perugia , Perugia , Italy.
Frontiers in Public Health
|January 10, 2017
Summary
Preterm birth risk is influenced by observable factors and unobservable traits. Identifying high-risk women requires considering both demographic/socioeconomic details and time-invariant characteristics.
Area of Science:
- Reproductive Health
- Biostatistics
- Epidemiology
Background:
- Preterm birth determinants remain debated in scientific literature.
- Distinguishing between observable and unobservable woman's characteristics is crucial for understanding birth outcomes.
- Unobservable characteristics are time-invariant and influence consecutive birth types.
Purpose of the Study:
- To analyze determinants of preterm birth.
- To differentiate between observable and unobservable factors affecting preterm delivery.
- To identify high-risk profiles for preterm birth.
Main Methods:
- Utilized a longitudinal dataset of 28,603 women in Italy (2005-2013).
- Analyzed singleton physiological pregnancies from natural conceptions.
- Estimated two logit models: a pooled model and a random-effects model accounting for unobservable characteristics.
Main Results:
- Preterm birth probability is linked to demographic, socioeconomic factors, miscarriage history, and baby's gender.
- The random-effects model significantly outperformed the pooled model.
- Concluded that repeated preterm deliveries show spurious state dependence.
Conclusions:
- The analysis effectively identifies women at high risk for preterm delivery.
- Risk profiling incorporates observable demographic/socioeconomic data and unobservable, time-constant traits.
- Unobservable factors may include genetic predispositions.
Keywords:
Italygeneralized linear mixed modelhealth carematernal characteristicsstandard certificate of live birthMore Related Videos
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