Development of an imputation model to recalibrate birth weights measured in the early neonatal period to time at delivery and assessment of its impact on size-for-gestational age and low birthweight prevalence estimates: a secondary analysis of a pregnancy cohort in rural Nepal

Authors

Elizabeth A. Hazel, International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA ehazel1@jhu.edu.
Luke C. Mullany, International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Scott L. Zeger, International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Diwakar Mohan, International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Seema Subedi, International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
James M. Tielsch, Global Health, George Washington University Milken Institute School of Public Health, Washington, District of Columbia, USA.
Subarna K. Khatry, Nepal Nutrition Intervention Project Sarlahi, Kathmandu, Nepal.
Joanne Katz, International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.

Document Type

Journal Article

Publication Date

7-12-2022

Journal

BMJ open

Volume

12

Issue

7

DOI

10.1136/bmjopen-2021-060105

Keywords

maternal medicine; neonatology; public health

Abstract

OBJECTIVES: In low-income countries, birth weights for home deliveries are often measured at the nadir when babies may lose up of 10% of their birth weight, biasing estimates of small-for-gestational age (SGA) and low birth weight (LBW). We aimed to develop an imputation model that predicts the 'true' birth weight at time of delivery. DESIGN: We developed and applied a model that recalibrates weights measured in the early neonatal period to time=0 at delivery and uses those recalibrated birth weights to impute missing birth weights. SETTING: This is a secondary analysis of pregnancy cohort data from two studies in Sarlahi district, Nepal. PARTICIPANTS: The participants are 457 babies with daily weights measured in the first 10 days of life from a subsample of a larger clinical trial on chlorhexidine (CHX) neonatal skin cleansing and 31 116 babies followed through the neonatal period to test the impact of neonatal massage oil type (Nepal Oil Massage Study (NOMS)). OUTCOME MEASURES: We developed an empirical Bayes model of early neonatal weight change using CHX trial longitudinal data and applied it to the NOMS dataset to recalibrate and then impute birth weight at delivery. The outcomes are size-for-gestational age and LBW. RESULTS: When using the imputed birth weights, the proportion of SGA is reduced from 49% (95% CI: 48% to 49%) to 44% (95% CI: 43% to 44%). Low birth weight is reduced from 30% (95% CI: 30% to 31%) to 27% (95% CI: 26% to 27%). The proportion of babies born large-for-gestational age increased from 4% (95% CI: 4% to 4%) to 5% (95% CI: 5% to 5%). CONCLUSIONS: Using weights measured around the nadir overestimates the prevalence of SGA and LBW. Studies in low-income settings with high levels of home births should consider a similar recalibration and imputation model to generate more accurate population estimates of small and vulnerable newborns.

APA Citation

Hazel, Elizabeth A.; Mullany, Luke C.; Zeger, Scott L.; Mohan, Diwakar; Subedi, Seema; Tielsch, James M.; Khatry, Subarna K.; and Katz, Joanne, "Development of an imputation model to recalibrate birth weights measured in the early neonatal period to time at delivery and assessment of its impact on size-for-gestational age and low birthweight prevalence estimates: a secondary analysis of a pregnancy cohort in rural Nepal" (2022). GW Authored Works. Paper 1315.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/1315

Department

Global Health