Correcting for measurement error in assessing gestational age in a low-resource setting: a regression calibration approach - Agogo, George O.; Verani, Jennifer R.; Otieno, Nancy A.; Nyawanda, Bryan O.; Widdowson, Marc-Alain; Chaves, Sandra S. (Frontiers in Medicine, 2023)
Introduction: Measurement error in gestational age (GA) may bias the association of GA with a health outcome. Ultrasound-based GA is considered the gold standard and is not readily available in low-resource settings. We corrected for measurement error in GA based on fundal height (FH) and date of last menstrual period (LMP) using ultrasound from the sub-cohort and adjusted for the bias in associating GA with neonatal mortality and low birth weight (< 2500 grams, LBW).We used data collected from 01/2015 through 09/2019 from pregnant women enrolled at two public hospitals in Siaya county, Kenya (N = 2750). We used regression calibration to correct for measurement error in FH and LMP-based GA accounting for maternal and child characteristics. We applied logistic regression to associate GA with neonatal mortality and low birth weight, with and without calibrating FH and LMP-based GA.Results: Calibration improved precision of LMP (correlation coefficient, 𝜌 from 0.48 to 0.57) and FH-based GA (𝜌 from 0.82 to 0.83). Calibrating FH/LMP-based GA eliminated the bias in mean GA estimates. The log odds ratio that quantifies the association of GA with neonatal mortality increased by 29% (from -0.159 to -0.205) by calibrating FH-based GA, and by more than twofold (from -0.158 to -0.471) by calibrating LMP-based GA.Calibrating FH/LMP-based GA improved the accuracy and precision of GA estimates and strengthened the association of GA with neonatal mortality/LBW. When assessing GA, neonatal public health and clinical interventions may benefit from calibration modeling in settings where ultrasound may not be fully available.