title: Classification of births by birth weight and gestational age: an application of multivariate mixture models.
abstract: BACKGROUND
  Classifications such as low birth weight, premature, and small for gestational age. i.e. compromised births, have been criticized because they depend upon arbitrary standards that may not be appropriate for all populations.
  
  
  OBJECTIVE
  This study applies multivariate Gaussian mixture models with covariates to characterize birth weight by gestational age distributions.
  
  
  METHODS
  The data consist of Asian, African, Hispanic and European American births in New York State in 1988. The analysis employs maximum likelihood methods.
  
  
  RESULTS
  Birth cohorts appear heterogeneous and composed of at least two sub-populations. One sub-population accounts for the majority of births, has a higher mean birth weight and gestational age but small variances. The other sub-population has a lower mean birthweight and gestational age but very large variances. As a result of the large variances this sub-population accounts for compromised births. The model also suggests that a number of compromised births occur within the normal birth weight and gestational age range. Among normal births, birth weight increases and gestational age declines with maternal age. The effects on compromised births vary among populations.
  
  
  CONCLUSIONS
  Multivariate Gaussian mixture models provide a method of identifying compromised births that is not dependent upon arbitrary standards.
authors.affiliation: University at Albany-SUNY, Albany, New York, USA. tbg97@albany.edu
authors.forename: T B
authors.initials: TB
authors.lastname: Gage
mesh_terms: D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data; D001213:Asian / Q000706:statistics & numerical data; D001724:Birth Weight / Q000502:physiology*; D016009:Chi-Square Distribution; D005260:Female; D005865:Gestational Age; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D007231:Infant, Newborn; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D009518:New York / Q000453:epidemiology; D016011:Normal Distribution; D044465:White People / Q000706:statistics & numerical data