Detecting and eliminating erroneous gestational ages: a normal mixture model.
In perinatal research and clinical practice, gestational age is a crucial variable for measuring foetal 'growth' (birth weight for gestational age) and for estimating the risk of mortality and morbidity, yet reported gestational age values are affected by random and systematic errors due to the absence of a gold standard measure. Previous investigators have used birth weight (which is measured with greater validity and precision than is gestational age) to correct such errors, but existing methods are inadequate due to unreasonable assumptions about the distributions of birth weight and gestational age. We propose a new method for identifying and correcting implausible observations using the expectation-maximization (EM) algorithm. Using population-based data from U.S. birth certificates, we compare the resulting gestational ages, birth weight distributions at each gestational age, and gestational 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 age and the most realistic gestational-age-specific mortality rates, while each of the other methods has at least one significant flaw.
Platt
R W
RW
Department of Pediatrics, McGill University, Montreal, Canada. robert.platt@mcgill.ca
Abrahamowicz
M
M
Kramer
M S
MS
Joseph
K S
KS
Mery
L
L
Blondel
B
B
Bréart
G
G
Wen
S W
SW
D000465:Algorithms; D001724:Birth Weight; D015331:Cohort Studies; D005260:Female; D005865:Gestational Age; D006801:Humans; D007226:Infant Mortality; D007231:Infant, Newborn; D008297:Male; D008954:Models, Biological; D011247:Pregnancy; D014481:United States