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A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats

INTRODUCTION: Public health responses often lack the infrastructure to capture the impact of public health emergencies on pregnant women and infants, with limited mechanisms for linking pregnant women with their infants nationally to monitor long-term effects. In 2019, the Centers for Disease Contro...

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Autores principales: Woodworth, Kate R., Reynolds, Megan R., Burkel, Veronica, Gates, Cymone, Eckert, Valorie, McDermott, Catherine, Barton, Jerusha, Wilburn, Amanda, Halai, Umme-Aiman, Brown, Catherine M., Bocour, Angelica, Longcore, Nicole, Orkis, Lauren, Lopez, Camille Delgado, Sizemore, Lindsey, Ellis, Esther M., Schillie, Sarah, Gupta, Neil, Bowen, Virginia B., Torrone, Elizabeth, Ellington, Sascha R., Delaney, Augustina, Olson, Samantha M., Roth, Nicole M., Whitehill, Florence, Zambrano, Laura D., Meaney-Delman, Dana, Fehrenbach, S. Nicole, Honein, Margaret A., Tong, Van T., Gilboa, Suzanne M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780211/
https://www.ncbi.nlm.nih.gov/pubmed/33394275
http://dx.doi.org/10.1007/s10995-020-03106-y
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author Woodworth, Kate R.
Reynolds, Megan R.
Burkel, Veronica
Gates, Cymone
Eckert, Valorie
McDermott, Catherine
Barton, Jerusha
Wilburn, Amanda
Halai, Umme-Aiman
Brown, Catherine M.
Bocour, Angelica
Longcore, Nicole
Orkis, Lauren
Lopez, Camille Delgado
Sizemore, Lindsey
Ellis, Esther M.
Schillie, Sarah
Gupta, Neil
Bowen, Virginia B.
Torrone, Elizabeth
Ellington, Sascha R.
Delaney, Augustina
Olson, Samantha M.
Roth, Nicole M.
Whitehill, Florence
Zambrano, Laura D.
Meaney-Delman, Dana
Fehrenbach, S. Nicole
Honein, Margaret A.
Tong, Van T.
Gilboa, Suzanne M.
author_facet Woodworth, Kate R.
Reynolds, Megan R.
Burkel, Veronica
Gates, Cymone
Eckert, Valorie
McDermott, Catherine
Barton, Jerusha
Wilburn, Amanda
Halai, Umme-Aiman
Brown, Catherine M.
Bocour, Angelica
Longcore, Nicole
Orkis, Lauren
Lopez, Camille Delgado
Sizemore, Lindsey
Ellis, Esther M.
Schillie, Sarah
Gupta, Neil
Bowen, Virginia B.
Torrone, Elizabeth
Ellington, Sascha R.
Delaney, Augustina
Olson, Samantha M.
Roth, Nicole M.
Whitehill, Florence
Zambrano, Laura D.
Meaney-Delman, Dana
Fehrenbach, S. Nicole
Honein, Margaret A.
Tong, Van T.
Gilboa, Suzanne M.
author_sort Woodworth, Kate R.
collection PubMed
description INTRODUCTION: Public health responses often lack the infrastructure to capture the impact of public health emergencies on pregnant women and infants, with limited mechanisms for linking pregnant women with their infants nationally to monitor long-term effects. In 2019, the Centers for Disease Control and Prevention (CDC), in close collaboration with state, local, and territorial health departments, began a 5-year initiative to establish population-based mother–baby linked longitudinal surveillance, the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET). OBJECTIVES: The objective of this report is to describe an expanded surveillance approach that leverages and modernizes existing surveillance systems to address the impact of emerging health threats during pregnancy on pregnant women and their infants. METHODS: Mother–baby pairs are identified through prospective identification during pregnancy and/or identification of an infant with retrospective linking to maternal information. All data are obtained from existing data sources (e.g., electronic medical records, vital statistics, laboratory reports, and health department investigations and case reporting). RESULTS: Variables were selected for inclusion to address key surveillance questions proposed by CDC and health department subject matter experts. General variables include maternal demographics and health history, pregnancy and infant outcomes, maternal and infant laboratory results, and child health outcomes up to the second birthday. Exposure-specific modular variables are included for hepatitis C, syphilis, and Coronavirus Disease 2019 (COVID-19). The system is structured into four relational datasets (maternal, pregnancy outcomes and birth, infant/child follow-up, and laboratory testing). DISCUSSION: SET-NET provides a population-based mother–baby linked longitudinal surveillance approach and has already demonstrated rapid adaptation to COVID-19. This innovative approach leverages existing data sources and rapidly collects data and informs clinical guidance and practice. These data can help to reduce exposure risk and adverse outcomes among pregnant women and their infants, direct public health action, and strengthen public health systems.
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spelling pubmed-77802112021-01-04 A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats Woodworth, Kate R. Reynolds, Megan R. Burkel, Veronica Gates, Cymone Eckert, Valorie McDermott, Catherine Barton, Jerusha Wilburn, Amanda Halai, Umme-Aiman Brown, Catherine M. Bocour, Angelica Longcore, Nicole Orkis, Lauren Lopez, Camille Delgado Sizemore, Lindsey Ellis, Esther M. Schillie, Sarah Gupta, Neil Bowen, Virginia B. Torrone, Elizabeth Ellington, Sascha R. Delaney, Augustina Olson, Samantha M. Roth, Nicole M. Whitehill, Florence Zambrano, Laura D. Meaney-Delman, Dana Fehrenbach, S. Nicole Honein, Margaret A. Tong, Van T. Gilboa, Suzanne M. Matern Child Health J Methodological Notes INTRODUCTION: Public health responses often lack the infrastructure to capture the impact of public health emergencies on pregnant women and infants, with limited mechanisms for linking pregnant women with their infants nationally to monitor long-term effects. In 2019, the Centers for Disease Control and Prevention (CDC), in close collaboration with state, local, and territorial health departments, began a 5-year initiative to establish population-based mother–baby linked longitudinal surveillance, the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET). OBJECTIVES: The objective of this report is to describe an expanded surveillance approach that leverages and modernizes existing surveillance systems to address the impact of emerging health threats during pregnancy on pregnant women and their infants. METHODS: Mother–baby pairs are identified through prospective identification during pregnancy and/or identification of an infant with retrospective linking to maternal information. All data are obtained from existing data sources (e.g., electronic medical records, vital statistics, laboratory reports, and health department investigations and case reporting). RESULTS: Variables were selected for inclusion to address key surveillance questions proposed by CDC and health department subject matter experts. General variables include maternal demographics and health history, pregnancy and infant outcomes, maternal and infant laboratory results, and child health outcomes up to the second birthday. Exposure-specific modular variables are included for hepatitis C, syphilis, and Coronavirus Disease 2019 (COVID-19). The system is structured into four relational datasets (maternal, pregnancy outcomes and birth, infant/child follow-up, and laboratory testing). DISCUSSION: SET-NET provides a population-based mother–baby linked longitudinal surveillance approach and has already demonstrated rapid adaptation to COVID-19. This innovative approach leverages existing data sources and rapidly collects data and informs clinical guidance and practice. These data can help to reduce exposure risk and adverse outcomes among pregnant women and their infants, direct public health action, and strengthen public health systems. Springer US 2021-01-04 2021 /pmc/articles/PMC7780211/ /pubmed/33394275 http://dx.doi.org/10.1007/s10995-020-03106-y Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Methodological Notes
Woodworth, Kate R.
Reynolds, Megan R.
Burkel, Veronica
Gates, Cymone
Eckert, Valorie
McDermott, Catherine
Barton, Jerusha
Wilburn, Amanda
Halai, Umme-Aiman
Brown, Catherine M.
Bocour, Angelica
Longcore, Nicole
Orkis, Lauren
Lopez, Camille Delgado
Sizemore, Lindsey
Ellis, Esther M.
Schillie, Sarah
Gupta, Neil
Bowen, Virginia B.
Torrone, Elizabeth
Ellington, Sascha R.
Delaney, Augustina
Olson, Samantha M.
Roth, Nicole M.
Whitehill, Florence
Zambrano, Laura D.
Meaney-Delman, Dana
Fehrenbach, S. Nicole
Honein, Margaret A.
Tong, Van T.
Gilboa, Suzanne M.
A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats
title A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats
title_full A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats
title_fullStr A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats
title_full_unstemmed A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats
title_short A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats
title_sort preparedness model for mother–baby linked longitudinal surveillance for emerging threats
topic Methodological Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780211/
https://www.ncbi.nlm.nih.gov/pubmed/33394275
http://dx.doi.org/10.1007/s10995-020-03106-y
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