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MODELLING THE RISK FACTORS FOR BIRTH WEIGHT IN TWIN GESTATIONS: A QUANTILE REGRESSION APPROACH

A John Michael1, Belavendra Antonisamy1, S Mahasampath Gowri1

  • 1Department of Biostatistics,Christian Medical College,Vellore,India.

Journal of Biosocial Science
|February 28, 2017
PubMed

Insights

Maternal education significantly impacts low birth weight in twins, particularly at lower birth weight quantiles. Quantile regression reveals differential risk factor effects, outperforming ordinary least squares (OLS) estimates.

Area of Science:

  • Obstetrics and Gynecology
  • Perinatology
  • Biostatistics

Background:

  • Birth weight is a key indicator of newborn health, with low birth weight (LBW) associated with adverse short- and long-term child growth outcomes.
  • LBW is more prevalent in twin gestations, necessitating specialized risk factor analysis.
  • Traditional statistical methods may not fully capture the complex interplay of factors influencing birth weight distribution.

Purpose of the Study:

  • To investigate the influence of maternal and socio-demographic factors on various quantiles of birth weight in twin gestations.
  • To compare the efficacy of quantile regression against ordinary least squares (OLS) for identifying risk factors in twin pregnancies.
  • To understand how different risk factors affect the entire spectrum of birth weight, not just the average.

Main Methods:

  • Retrospective analysis of 1304 twin pregnancy records from 1991-2005 at Christian Medical College, Vellore, India.
  • Application of quantile regression to assess risk factor effects across different birth weight percentiles.
  • Comparison of quantile regression results with OLS estimates.

Main Results:

  • Gestational age, chroniocity, gravida, and child's sex demonstrated significant effects across all birth weight quantiles.
  • Maternal age showed no significant association with birth weight at any quantile.
  • Maternal and paternal education exhibited substantial impact on lower birth weight quantiles (10th and 25th), effects underestimated by OLS.

Conclusions:

  • Quantile regression is a valuable tool for analyzing risk factors in twin gestations, providing a more nuanced understanding than OLS.
  • Socio-demographic factors, especially parental education, play a critical role in determining birth weight at the lower end of the distribution.
  • The study highlights the importance of considering the entire birth weight distribution when assessing risk factors in twin pregnancies.

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