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Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

Ramazan Fallah1, Anoshirvan Kazemnejad, Farid Zayeri

  • 1. r.fallahvalamdehi@modares.ac.ir.

Global Journal of Health Science
|March 2, 2016
PubMed

Insights

Quantile regression offers a more comprehensive analysis of birthweight factors than traditional methods. This study in Zanjan province highlights its value for understanding infant health indicators.

Area of Science:

  • Medical Statistics
  • Perinatal Health
  • Epidemiology

Background:

  • Birthweight is a critical predictor of adult health and a key infant health indicator.
  • Maintaining balanced birthweight is a global health system priority.
  • This study focuses on assessing neonatal birthweight in Zanjan province.

Purpose of the Study:

  • To evaluate neonatal birthweight in Zanjan province.
  • To compare the efficacy of quantile regression versus multiple linear regression for birthweight analysis.

Main Methods:

  • Analytical descriptive study utilizing pre-registered data (March 2010 - March 2012).
  • Employed multiple-stage cluster sampling in Zanjan province's urban and rural health centers.
  • Analyzed data using multiple linear regression and quantile regression with SAS 9.2 software.

Main Results:

  • Analyzed 8456 neonates; 49% were female, with a mean maternal age of 27.1 years.
  • Mean neonatal birthweight was 3104 ± 431 grams; 6.8% were low birthweight (<2500g).
  • Gestational age, maternal weight, and maternal education significantly correlated with birthweight across quantiles; other factors showed no consistent significance.

Conclusions:

  • Multiple linear regression and quantile regression yielded different results.
  • Quantile regression is recommended for analyzing asymmetric response variables or data with outliers in birthweight studies.
Abstract

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