Using the Area Under the Curve Method to Model Gestational Weight Gain in a Safety Net Population
Presenter Biography
Anna Booman is a first-year student in the PhD Epidemiology program. After growing up in the Portland area, Anna attended undergraduate and graduate programs on the east coast and moved back to Portland for this program in 2020. She is interested in maternal health disparities and how events during pregnancy can have lasting impacts on the health of both mother and child.
Institution
OHSU
Program/Major
Epidemiology
Degree
PhD
Presentation Type
Presentation
Start Date
4-8-2021 12:02 PM
End Date
4-8-2021 12:13 PM
Rights
© Copyright the author(s)
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Persistent Identifier
https://archives.pdx.edu/ds/psu/35585
Subjects
gestational weight gain; area under the curve; trajectory modeling; methodology
Abstract
Introduction: Gestational weight gain (GWG) is a predictor of maternal and perinatal health outcomes, but standard techniques used to assess GWG do not provide an estimation of timing of weight gain. The area under the curve (AUC) is an uncommonly used method to model GWG that provides, in a single statistic, an understanding of both total GWG and the GWG trajectory.
Objective: To (a) describe feasibility and interpretation of the AUC method applied to EMR data in a safety net population and (b) compare results to commonly used GWG assessment techniques.
Methods: We applied the AUC method to EMR data from 40,085 low-income (up to 250% FPL) pregnant patients who received care at community health centers throughout the U.S, totaling 19,905,167 person-weeks. We compared interpretations with standard methods used to assess GWG.
Results: Mean AUC (pound-days) was 2064.9 with a range from 2702 among women with an underweight pre-pregnancy BMI status to 1131.8 among women with an obesity class III pre-pregnancy BMI status. Mean total GWG was 25.1 pounds with a range from 31.1 among women with an underweight pre-pregnancy BMI status to 14.3 among women with an obesity class II pre-pregnancy BMI status. While interpretation of results from the AUC method is difficult and comparability across studies is not yet possible, the characterization of GWG trajectories is a defining feature.
Significance: This project will provide a tutorial of applying the AUC method to pregnancy data, providing future researchers with the information needed to use this method and understand results.
Using the Area Under the Curve Method to Model Gestational Weight Gain in a Safety Net Population
Introduction: Gestational weight gain (GWG) is a predictor of maternal and perinatal health outcomes, but standard techniques used to assess GWG do not provide an estimation of timing of weight gain. The area under the curve (AUC) is an uncommonly used method to model GWG that provides, in a single statistic, an understanding of both total GWG and the GWG trajectory.
Objective: To (a) describe feasibility and interpretation of the AUC method applied to EMR data in a safety net population and (b) compare results to commonly used GWG assessment techniques.
Methods: We applied the AUC method to EMR data from 40,085 low-income (up to 250% FPL) pregnant patients who received care at community health centers throughout the U.S, totaling 19,905,167 person-weeks. We compared interpretations with standard methods used to assess GWG.
Results: Mean AUC (pound-days) was 2064.9 with a range from 2702 among women with an underweight pre-pregnancy BMI status to 1131.8 among women with an obesity class III pre-pregnancy BMI status. Mean total GWG was 25.1 pounds with a range from 31.1 among women with an underweight pre-pregnancy BMI status to 14.3 among women with an obesity class II pre-pregnancy BMI status. While interpretation of results from the AUC method is difficult and comparability across studies is not yet possible, the characterization of GWG trajectories is a defining feature.
Significance: This project will provide a tutorial of applying the AUC method to pregnancy data, providing future researchers with the information needed to use this method and understand results.