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Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings
High stillbirth and neonatal mortality are major public health problems, particularly in low-resource settings in low- and middle-income countries (LMIC). Despite sustained efforts by national and international organizations over the last several decades, quality intrapartum and neonatal care is not...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762612/ https://www.ncbi.nlm.nih.gov/pubmed/36540873 http://dx.doi.org/10.5005/jp-journals-11002-0034 |
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author | Shukla, Vivek V Carlo, Waldemar A |
author_facet | Shukla, Vivek V Carlo, Waldemar A |
author_sort | Shukla, Vivek V |
collection | PubMed |
description | High stillbirth and neonatal mortality are major public health problems, particularly in low-resource settings in low- and middle-income countries (LMIC). Despite sustained efforts by national and international organizations over the last several decades, quality intrapartum and neonatal care is not universally available, especially in these low-resource settings. A few studies identify risk factors for adverse perinatal outcomes in low-resource settings in LMICs. This review highlights the evidence of risk prediction for stillbirth and neonatal death. Evidence using advanced machine-learning statistical models built on data from low-resource settings in LMICs suggests that the predictive accuracy for intrapartum stillbirth and neonatal mortality using prenatal and pre-delivery data is low. Models with delivery and post-delivery data have good predictive accuracy of the risk for neonatal mortality. Birth weight is the most important predictor of neonatal mortality. Further validation and testing of the models in other low-resource settings and subsequent development and testing of possible interventions could advance the field. |
format | Online Article Text |
id | pubmed-9762612 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-97626122022-12-19 Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings Shukla, Vivek V Carlo, Waldemar A Newborn (Clarksville) Article High stillbirth and neonatal mortality are major public health problems, particularly in low-resource settings in low- and middle-income countries (LMIC). Despite sustained efforts by national and international organizations over the last several decades, quality intrapartum and neonatal care is not universally available, especially in these low-resource settings. A few studies identify risk factors for adverse perinatal outcomes in low-resource settings in LMICs. This review highlights the evidence of risk prediction for stillbirth and neonatal death. Evidence using advanced machine-learning statistical models built on data from low-resource settings in LMICs suggests that the predictive accuracy for intrapartum stillbirth and neonatal mortality using prenatal and pre-delivery data is low. Models with delivery and post-delivery data have good predictive accuracy of the risk for neonatal mortality. Birth weight is the most important predictor of neonatal mortality. Further validation and testing of the models in other low-resource settings and subsequent development and testing of possible interventions could advance the field. 2022 2022-07-05 /pmc/articles/PMC9762612/ /pubmed/36540873 http://dx.doi.org/10.5005/jp-journals-11002-0034 Text en https://creativecommons.org/licenses/by-nc/4.0/Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and non-commercial reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Article Shukla, Vivek V Carlo, Waldemar A Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings |
title | Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings |
title_full | Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings |
title_fullStr | Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings |
title_full_unstemmed | Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings |
title_short | Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings |
title_sort | risk prediction for stillbirth and neonatal mortality in low-resource settings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762612/ https://www.ncbi.nlm.nih.gov/pubmed/36540873 http://dx.doi.org/10.5005/jp-journals-11002-0034 |
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