Dataset Information

Correcting for measurement error in assessing gestational age in a low-resource setting: a regression calibration approach.

ABSTRACT:

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 (< 2,500 grams, LBW).

Methods

We used data collected from 01/2015 to 09/2019 from pregnant women enrolled at two public hospitals in Siaya county, Kenya (N = 2,750). 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 the 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 the 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.

Conclusion

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.

SUBMITTER: Agogo GO

PROVIDER: S-EPMC10613090 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Correcting for measurement error in assessing gestational age in a low-resource setting: a regression calibration approach.

Agogo George O GO Verani Jennifer R JR Otieno Nancy A NA Nyawanda Bryan O BO Widdowson Marc-Alain MA Chaves Sandra S SS

Frontiers in medicine 20231012

<h4>Introduction</h4>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 (< 2,500 grams, LBW).<h4>Methods</h4>We u ...[more]

PMID: 37901408

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Project description:ObjectivesFirst, to obtain measurement-error models for biometric measurements of fetal abdominal circumference (AC), head circumference (HC) and femur length (FL), and, second, to examine the impact of biometric measurement error on sonographic estimated fetal weight (EFW) and its effect on the prediction of small- (SGA) and large- (LGA) for-gestational-age fetuses with EFW < 10th and > 90th percentile, respectively.MethodsMeasurement error standard deviations for fetal AC, HC and FL were obtained from a previous large study on fetal biometry utilizing a standardized measurement protocol and both qualitative and quantitative quality-control monitoring. Typical combinations of AC, HC and FL that gave EFW on the 10th and 90th percentiles were determined. A Monte-Carlo simulation study was carried out to examine the effect of measurement error on the classification of fetuses as having EFW above or below the 10th and 90th percentiles.ResultsErrors were assumed to follow a Gaussian distribution with a mean of 0 mm and SDs, obtained from a previous well-conducted study, of 6.93 mm for AC, 5.15 mm for HC and 1.38 mm for FL. Assuming errors according to such distributions, when the 10th and 90th percentiles are used to screen for SGA and LGA fetuses, respectively, the detection rates would be 78.0% at false-positive rates of 4.7%. If the cut-offs were relaxed to the 30th and 70th percentiles, the detection rates would increase to 98.2%, but at false-positive rates of 24.2%. Assuming half of the spread in the error distribution, using the 10th and 90th percentiles to screen for SGA and LGA fetuses, respectively, the detection rates would be 86.6% at false-positive rates of 2.3%. If the cut-offs were relaxed to the 15th and 85th percentiles, respectively, the detection rates would increase to 97.0% and the false-positive rates would increase to 6.3%.ConclusionsMeasurement error in fetal biometry causes substantial error in EFW, resulting in misclassification of SGA and LGA fetuses. The extent to which improvement can be achieved through effective quality assurance remains to be seen but, as a first step, it is important for practitioners to understand how biometric measurement error impacts the prediction of SGA and LGA fetuses. © 2019 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Dataset Information

Correcting for measurement error in assessing gestational age in a low-resource setting: a regression calibration approach.

Introduction

Methods

Results

Conclusion

Publications

Correcting for measurement error in assessing gestational age in a low-resource setting: a regression calibration approach.

Similar Datasets

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