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Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya
Background: Clinical outcomes data are a crucial component of efforts to improve health systems globally. Strengthening of these health systems is essential if the Sustainable Development Goals (SDG) are to be achieved. Target 3.2 of SDG Goal 3 is to end preventable deaths and reduce neonatal mortal...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611136/ https://www.ncbi.nlm.nih.gov/pubmed/31289756 http://dx.doi.org/10.12688/wellcomeopenres.15302.1 |
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author | Aluvaala, Jalemba Collins, Gary S. Maina, Beth Mutinda, Catherine Wayiego, Mary Berkley, James A. English, Mike |
author_facet | Aluvaala, Jalemba Collins, Gary S. Maina, Beth Mutinda, Catherine Wayiego, Mary Berkley, James A. English, Mike |
author_sort | Aluvaala, Jalemba |
collection | PubMed |
description | Background: Clinical outcomes data are a crucial component of efforts to improve health systems globally. Strengthening of these health systems is essential if the Sustainable Development Goals (SDG) are to be achieved. Target 3.2 of SDG Goal 3 is to end preventable deaths and reduce neonatal mortality to 12 per 1,000 or lower by 2030. There is a paucity of data on neonatal in-hospital mortality in Kenya that is poorly captured in the existing health information system. Better measurement of neonatal mortality in facilities may help promote improvements in the quality of health care that will be important to achieving SDG 3 in countries such as Kenya. Methods: This was a cohort study using routinely collected data from a large urban neonatal unit in Nairobi, Kenya. All the patients admitted to the unit between April 2014 to December 2015 were included. Clinical characteristics are summarised descriptively, while the competing risk method was used to estimate the probability of in-hospital mortality considering discharge alive as the competing risk. Results: A total of 9,115 patients were included. Most were males (966/9115, 55%) and the majority (6287/9115, 69%) had normal birthweight (2.5 to 4 kg). Median length of stay was 2 days (range, 0 to 98 days) while crude mortality was 9.2% (839/9115). The probability of in-hospital death was higher than discharge alive for birthweight less than 1.5 kg with the transition to higher probability of discharge alive observed after the first week in birthweight 1.5 to <2 kg. Conclusions: These prognostic data may inform decision making, e.g. in the organisation of neonatal in-patient service delivery to improve the quality of care. More of such data are therefore required from neonatal units in Kenya and other low resources settings especially as more advanced neonatal care is scaled up. |
format | Online Article Text |
id | pubmed-6611136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-66111362019-07-08 Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya Aluvaala, Jalemba Collins, Gary S. Maina, Beth Mutinda, Catherine Wayiego, Mary Berkley, James A. English, Mike Wellcome Open Res Research Article Background: Clinical outcomes data are a crucial component of efforts to improve health systems globally. Strengthening of these health systems is essential if the Sustainable Development Goals (SDG) are to be achieved. Target 3.2 of SDG Goal 3 is to end preventable deaths and reduce neonatal mortality to 12 per 1,000 or lower by 2030. There is a paucity of data on neonatal in-hospital mortality in Kenya that is poorly captured in the existing health information system. Better measurement of neonatal mortality in facilities may help promote improvements in the quality of health care that will be important to achieving SDG 3 in countries such as Kenya. Methods: This was a cohort study using routinely collected data from a large urban neonatal unit in Nairobi, Kenya. All the patients admitted to the unit between April 2014 to December 2015 were included. Clinical characteristics are summarised descriptively, while the competing risk method was used to estimate the probability of in-hospital mortality considering discharge alive as the competing risk. Results: A total of 9,115 patients were included. Most were males (966/9115, 55%) and the majority (6287/9115, 69%) had normal birthweight (2.5 to 4 kg). Median length of stay was 2 days (range, 0 to 98 days) while crude mortality was 9.2% (839/9115). The probability of in-hospital death was higher than discharge alive for birthweight less than 1.5 kg with the transition to higher probability of discharge alive observed after the first week in birthweight 1.5 to <2 kg. Conclusions: These prognostic data may inform decision making, e.g. in the organisation of neonatal in-patient service delivery to improve the quality of care. More of such data are therefore required from neonatal units in Kenya and other low resources settings especially as more advanced neonatal care is scaled up. F1000 Research Limited 2019-06-17 /pmc/articles/PMC6611136/ /pubmed/31289756 http://dx.doi.org/10.12688/wellcomeopenres.15302.1 Text en Copyright: © 2019 Aluvaala J et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Aluvaala, Jalemba Collins, Gary S. Maina, Beth Mutinda, Catherine Wayiego, Mary Berkley, James A. English, Mike Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya |
title | Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya |
title_full | Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya |
title_fullStr | Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya |
title_full_unstemmed | Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya |
title_short | Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya |
title_sort | competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in kenya |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611136/ https://www.ncbi.nlm.nih.gov/pubmed/31289756 http://dx.doi.org/10.12688/wellcomeopenres.15302.1 |
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