Using Mixture Models with Linear Predictors to Identify Incorrect Gestational Age in State Birth Records

Background: Birthweight distributions for early last‐menstrual‐period‐based gestational ages are bimodal, and some birthweights in the right‐side distribution are implausible for the specified gestational age. Mixture models can be used to identify births in the right‐side distribution. The objective of this study was to determine which maternal and infant factors to include in the mixture models to obtain the best fitting models for New Jersey state birth records. Methods: We included covariates in the models as linear predictors of the means of the component distributions and the proportion of births in each component. This allowed both the means and the proportions to vary across levels of the covariates. Results: The final model included maternal age and timing of entry into prenatal care. The proportion of births in the right‐side distribution was lowest for older mothers who entered prenatal care early, higher for teen mothers who entered prenatal care early, higher still for older mothers who entered prenatal care late, and highest for teens who entered prenatal care late. Over 44% of births were classified as incorrect reported gestational age. Conclusion: These results suggest that (1) including these two covariates as linear predictors of the means and mixing proportions gives the best model for identifying births with incorrect reported gestational age, (2) late entry into prenatal care is a mechanism by which erroneously short last‐menstrual‐period‐based gestational ages are generated, and (3) including linear predictors of the mixing proportions in the model increases the validity of the classification of incorrect reported gestational age.