Detecting and eliminating erroneous gestational ages: a normal mixture model

In perinatal research and clinical practice, gestational age is a crucial v ariable for measuring foetal 'growth' (birth weight for gestational age) an d for estimating the risk of mortality and morbidity, yet reported gestatio nal age values are affected by random and systematic errors due to the abse nce of a gold standard measure. Previous investigators have used birth weig ht (which is measured with greater validity and precision than is gestation al age) to correct such errors, but existing methods are inadequate due to unreasonable assumptions about the distributions of birth weight and gestat ional age. We propose a new method for identifying and correcting implausib le observations using the expectation-maximization (EM) algorithm. Using po pulation-based data from U.S. birth certificates, we compare the resulting gestational ages, birth weight distributions at each gestational age, and g estational age-specific infant mortality based on the new method with those on the same population produced by previous published correction methods. The new method gives the best birth weight distributions for gestational ag e and the most realistic gestational-age-specific mortality rates, while ea ch of the other methods has at least one significant flaw. Copyright (C) 20 01 John Wiley & Sons, Ltd.