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Sampling and weighting methods for COVID-19 end of pregnancy medical record abstraction
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October 2021
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Description:The Surveillance for Emerging Threats to Mothers and Babies Network, or SET-NET, is mother-baby linked longitudinal surveillance to understand the impact of emerging and reemerging threats on pregnant people and their infants. SET-NET is a preparedness network that can be further expanded if new threats emerge for mothers and babies. In early 2020 it was rapidly adapted for COVID-19 surveillance.1
Medical records abstraction (MRA) to capture key clinical information on mothers and infants is fundamental to the SET-NET surveillance approach. However, for COVID-19 surveillance, given high case counts in some parts of the United States and limited resources, jurisdictions requested assistance from CDC to prioritize surveillance resources by conducting MRA on a representative sample of included pregnancies as opposed to the entire cohort. Given the need to quickly report data for clinical decision-making and public health action and minimize the burden on already strained health departments, CDC developed a sampling approach to support health departments in the collection of population-based data. The sampling approach may be expanded to other SET- NET exposures of interest in the future.
SET-NET’s longitudinal design indicates that there are two main components for MRA: end-of-pregnancy abstraction, which captures maternal, pregnancy, and birth records, and infant follow-up (IFU) abstraction, which captures infants’ development over time. This document is intended for a technical audience and describes the methodology of the COVID-19 sampling approach for MRA of end of pregnancy, and calculations of sampling weights and population estimates. Additional documentation for the COVID-19 IFU sampling methodology and weight calculations, and the sampling methodology for additional SET-NET exposures, will be provided at a later date.
set-net-pregnancy-sampling-white-paper-508.pdf
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Pages in Document:11 numbered pages
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