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
Summary
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.
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