Date of Award

Summer 8-8-2017

Degree Type


Degree Name

Master of Public Health (MPH)


Public Health

First Advisor

Katherine Masyn

Second Advisor

Louise Lawson


Clinicians use growth curves to assess infant health. Most children are measured on growth curves that contain percentiles for height, weight, and head circumference by sex. Preterm infants have their own growth curves. Infants who present with measurements below the 10th percentile are considered small-for-gestational age (SGA), and infants who present with measurements above the 90th percentile are considered large-for-gestational age (LGA). Growth curves and centiles can be generated using 3 and 4 parameter distribution models. To date, no studies have been published to investigate whether growth curves generated using a 3- or 4-parameter model differ significantly. Additionally, researchers have found mixed results when exploring the association between race and pregnancy/delivery. Black mothers may have greater risks and babies with lower weights than babies born to White mothers (Borrell, Rodriguez-Alvarez, Savitz, & Baquero, 2016), and growth curves that do not consider race may misclassify non-White babies (Buck-Louis et al., 2015). In this study, I had two specific aims: (1) to compare the preterm infant growth curves and centiles generated using 3 and 4 parameter methods (Lamba Mu Sigma [LMS] and Box-Cox Power Exponential [BCPE], respectively) and assess each model for adequate fit, and (2) to use percentile cut points from race-specific and non-race-specific LMS curves to classify babies in a validation dataset as SGA or LGA. Regarding the differences in curves generated from the LMS and BCPE distributions, the curves produced using the BCPE distribution had a lower GAIC in some cases but model fit criteria for the LMS curves were adequate. The simpler models generated by the LMS method were retained for birth length, head circumference, and weight by sex with an explanatory variable of gestational age. For aim 2, results indicated that race-specific curves classified babies within expected ranges. Non-race-specific curves overidentified Black babies as SGA and underidentified them as LGA. More research is required to test if this relationship persists for babies delivered at full term.