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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Published on: July 3, 2020

Using mixture models with linear predictors to identify incorrect gestational age in state birth records.

Jack K Leiss¹, C M Suchindran, Lakota Kruse

¹Epidemiology Research Program, Cedar Grove Institute for Sustainable Communities, Mebane, NC 27302, USA. jackl@mcmoss.org

Paediatric and Perinatal Epidemiology

|August 14, 2012

Summary

Maternal age and prenatal care timing significantly impact gestational age accuracy. Late prenatal care increases the likelihood of incorrectly reported gestational ages, especially for teen mothers.

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Area of Science:

Perinatal epidemiology
Biostatistics
Public Health

Background:

Birthweight distributions show bimodality at early gestational ages.
Some birthweights are implausible for the stated gestational age.
Mixture models can identify these implausible birthweights.

Purpose of the Study:

Identify maternal and infant factors for optimal mixture models.
Improve accuracy of gestational age reporting in New Jersey birth records.

Main Methods:

Utilized mixture models with covariates as linear predictors.
Modeled means and proportions of component distributions.
Allowed means and proportions to vary with covariates.

Main Results:

Maternal age and prenatal care entry timing were key covariates.
Older mothers with early care had fewer implausible births.
Teen mothers with late care had the highest proportion of implausible births.
Over 44% of births were classified with incorrect gestational age.

Conclusions:

Maternal age and care timing improve models for identifying incorrect gestational age.
Late prenatal care contributes to erroneously short gestational ages.
Including linear predictors enhances classification validity.