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Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360
BACKGROUND: Every Newborn Action Plan (ENAP) coverage target 4 necessitates national scale-up of Level-2 Small and Sick Newborn Care (SSNC) (with Continuous Positive Airway Pressure (CPAP)) in 80% of districts by 2025. Routine neonatal inpatient data is important for improving quality of care, targe...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10652643/ https://www.ncbi.nlm.nih.gov/pubmed/37968588 http://dx.doi.org/10.1186/s12887-023-04341-2 |
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author | Cross, James H. Bohne, Christine Ngwala, Samuel K. Shabani, Josephine Wainaina, John Dosunmu, Olabisi Kassim, Irabi Penzias, Rebecca E. Tillya, Robert Gathara, David Zimba, Evelyn Ezeaka, Veronica Chinyere Odedere, Opeyemi Chiume, Msandeni Salim, Nahya Kawaza, Kondwani Lufesi, Norman Irimu, Grace Tongo, Olukemi O. Malla, Lucas Paton, Chris Day, Louise T. Oden, Maria Richards-Kortum, Rebecca Molyneux, Elizabeth M. Ohuma, Eric O. Lawn, Joy E. |
author_facet | Cross, James H. Bohne, Christine Ngwala, Samuel K. Shabani, Josephine Wainaina, John Dosunmu, Olabisi Kassim, Irabi Penzias, Rebecca E. Tillya, Robert Gathara, David Zimba, Evelyn Ezeaka, Veronica Chinyere Odedere, Opeyemi Chiume, Msandeni Salim, Nahya Kawaza, Kondwani Lufesi, Norman Irimu, Grace Tongo, Olukemi O. Malla, Lucas Paton, Chris Day, Louise T. Oden, Maria Richards-Kortum, Rebecca Molyneux, Elizabeth M. Ohuma, Eric O. Lawn, Joy E. |
author_sort | Cross, James H. |
collection | PubMed |
description | BACKGROUND: Every Newborn Action Plan (ENAP) coverage target 4 necessitates national scale-up of Level-2 Small and Sick Newborn Care (SSNC) (with Continuous Positive Airway Pressure (CPAP)) in 80% of districts by 2025. Routine neonatal inpatient data is important for improving quality of care, targeting equity gaps, and enabling data-driven decision-making at individual, district, and national-levels. Existing neonatal inpatient datasets vary in purpose, size, definitions, and collection processes. We describe the co-design and operationalisation of a core inpatient dataset for use to track outcomes and improve quality of care for small and sick newborns in high-mortality settings. METHODS: A three-step systematic framework was used to review, co-design, and operationalise this novel neonatal inpatient dataset in four countries (Malawi, Kenya, Tanzania, and Nigeria) implementing with the Newborn Essential Solutions and Technologies (NEST360) Alliance. Existing global and national datasets were identified, and variables were mapped according to categories. A priori considerations for variable inclusion were determined by clinicians and policymakers from the four African governments by facilitated group discussions. These included prioritising clinical care and newborn outcomes data, a parsimonious variable list, and electronic data entry. The tool was designed and refined by > 40 implementers and policymakers during a multi-stakeholder workshop and online interactions. RESULTS: Identified national and international datasets (n = 6) contained a median of 89 (IQR:61–154) variables, with many relating to research-specific initiatives. Maternal antenatal/intrapartum history was the largest variable category (21, 23.3%). The Neonatal Inpatient Dataset (NID) includes 60 core variables organised in six categories: (1) birth details/maternal history; (2) admission details/identifiers; (3) clinical complications/observations; (4) interventions/investigations; (5) discharge outcomes; and (6) diagnosis/cause-of-death. Categories were informed through the mapping process. The NID has been implemented at 69 neonatal units in four African countries and links to a facility-level quality improvement (QI) dashboard used in real-time by facility staff. CONCLUSION: The NEST360 NID is a novel, parsimonious tool for use in routine information systems to inform inpatient SSNC quality. Available on the NEST360/United Nations Children's Fund (UNICEF) Implementation Toolkit for SSNC, this adaptable tool enables facility and country-level comparisons to accelerate progress toward ENAP targets. Additional linked modules could include neonatal at-risk follow-up, retinopathy of prematurity, and Level-3 intensive care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12887-023-04341-2. |
format | Online Article Text |
id | pubmed-10652643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106526432023-11-15 Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360 Cross, James H. Bohne, Christine Ngwala, Samuel K. Shabani, Josephine Wainaina, John Dosunmu, Olabisi Kassim, Irabi Penzias, Rebecca E. Tillya, Robert Gathara, David Zimba, Evelyn Ezeaka, Veronica Chinyere Odedere, Opeyemi Chiume, Msandeni Salim, Nahya Kawaza, Kondwani Lufesi, Norman Irimu, Grace Tongo, Olukemi O. Malla, Lucas Paton, Chris Day, Louise T. Oden, Maria Richards-Kortum, Rebecca Molyneux, Elizabeth M. Ohuma, Eric O. Lawn, Joy E. BMC Pediatr Research BACKGROUND: Every Newborn Action Plan (ENAP) coverage target 4 necessitates national scale-up of Level-2 Small and Sick Newborn Care (SSNC) (with Continuous Positive Airway Pressure (CPAP)) in 80% of districts by 2025. Routine neonatal inpatient data is important for improving quality of care, targeting equity gaps, and enabling data-driven decision-making at individual, district, and national-levels. Existing neonatal inpatient datasets vary in purpose, size, definitions, and collection processes. We describe the co-design and operationalisation of a core inpatient dataset for use to track outcomes and improve quality of care for small and sick newborns in high-mortality settings. METHODS: A three-step systematic framework was used to review, co-design, and operationalise this novel neonatal inpatient dataset in four countries (Malawi, Kenya, Tanzania, and Nigeria) implementing with the Newborn Essential Solutions and Technologies (NEST360) Alliance. Existing global and national datasets were identified, and variables were mapped according to categories. A priori considerations for variable inclusion were determined by clinicians and policymakers from the four African governments by facilitated group discussions. These included prioritising clinical care and newborn outcomes data, a parsimonious variable list, and electronic data entry. The tool was designed and refined by > 40 implementers and policymakers during a multi-stakeholder workshop and online interactions. RESULTS: Identified national and international datasets (n = 6) contained a median of 89 (IQR:61–154) variables, with many relating to research-specific initiatives. Maternal antenatal/intrapartum history was the largest variable category (21, 23.3%). The Neonatal Inpatient Dataset (NID) includes 60 core variables organised in six categories: (1) birth details/maternal history; (2) admission details/identifiers; (3) clinical complications/observations; (4) interventions/investigations; (5) discharge outcomes; and (6) diagnosis/cause-of-death. Categories were informed through the mapping process. The NID has been implemented at 69 neonatal units in four African countries and links to a facility-level quality improvement (QI) dashboard used in real-time by facility staff. CONCLUSION: The NEST360 NID is a novel, parsimonious tool for use in routine information systems to inform inpatient SSNC quality. Available on the NEST360/United Nations Children's Fund (UNICEF) Implementation Toolkit for SSNC, this adaptable tool enables facility and country-level comparisons to accelerate progress toward ENAP targets. Additional linked modules could include neonatal at-risk follow-up, retinopathy of prematurity, and Level-3 intensive care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12887-023-04341-2. BioMed Central 2023-11-15 /pmc/articles/PMC10652643/ /pubmed/37968588 http://dx.doi.org/10.1186/s12887-023-04341-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . 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 in a credit line to the data. |
spellingShingle | Research Cross, James H. Bohne, Christine Ngwala, Samuel K. Shabani, Josephine Wainaina, John Dosunmu, Olabisi Kassim, Irabi Penzias, Rebecca E. Tillya, Robert Gathara, David Zimba, Evelyn Ezeaka, Veronica Chinyere Odedere, Opeyemi Chiume, Msandeni Salim, Nahya Kawaza, Kondwani Lufesi, Norman Irimu, Grace Tongo, Olukemi O. Malla, Lucas Paton, Chris Day, Louise T. Oden, Maria Richards-Kortum, Rebecca Molyneux, Elizabeth M. Ohuma, Eric O. Lawn, Joy E. Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360 |
title | Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360 |
title_full | Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360 |
title_fullStr | Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360 |
title_full_unstemmed | Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360 |
title_short | Neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with NEST360 |
title_sort | neonatal inpatient dataset for small and sick newborn care in low- and middle-income countries: systematic development and multi-country operationalisation with nest360 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10652643/ https://www.ncbi.nlm.nih.gov/pubmed/37968588 http://dx.doi.org/10.1186/s12887-023-04341-2 |
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