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Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India

INTRODUCTION: Improving the quality of care during childbirth is essential for reducing neonatal and maternal mortality. One barrier to improving quality of care is understanding the appropriate level to target interventions. We examine quality of care data during labour and delivery from multiple c...

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Autores principales: Helfinstein, Sarah, Jain, Mokshada, Ramesh, Banadakoppa Manjappa, Blanchard, James, Kemp, Hannah, Gothalwal, Vikas, Namasivayam, Vasanthakumar, Kumar, Pankaj, Sgaier, Sema K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7440830/
https://www.ncbi.nlm.nih.gov/pubmed/32816803
http://dx.doi.org/10.1136/bmjgh-2020-002437
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author Helfinstein, Sarah
Jain, Mokshada
Ramesh, Banadakoppa Manjappa
Blanchard, James
Kemp, Hannah
Gothalwal, Vikas
Namasivayam, Vasanthakumar
Kumar, Pankaj
Sgaier, Sema K
author_facet Helfinstein, Sarah
Jain, Mokshada
Ramesh, Banadakoppa Manjappa
Blanchard, James
Kemp, Hannah
Gothalwal, Vikas
Namasivayam, Vasanthakumar
Kumar, Pankaj
Sgaier, Sema K
author_sort Helfinstein, Sarah
collection PubMed
description INTRODUCTION: Improving the quality of care during childbirth is essential for reducing neonatal and maternal mortality. One barrier to improving quality of care is understanding the appropriate level to target interventions. We examine quality of care data during labour and delivery from multiple countries to assess whether quality varies primarily from nurse to nurse within the same facility, or primarily between facilities. METHODS: To assess the relative contributions of nurses and facilities to variance in quality of care, we performed a variance decomposition analysis using a linear mixed effect model on two data sources: (1) the number of vital signs assessed for women in labour from a study of nurse practices in Uttar Pradesh, India; 2) broad-scale indices of respectful and competent care generated from Service Provision Assessments in Kenya and Malawi. We used unsupervised clustering, a data mining technique that groups objects together based on similar characteristics, to identify groups of facilities that displayed distinct patterns of vital signs assessment behaviour. RESULTS: We found 3–10 times more variance in quality of care was explained by the facility where a patient received care than by the nurse who provided it. The unsupervised clustering analysis revealed groups of facilities with highly distinct patterns of vital signs assessment, even when overall rates of vital signs assessments were similar (eg, some facilities consistently test fetal heart rate, but not other vitals, others only blood pressure). CONCLUSION: Facilities within a region can vary substantially in the quality of care they provide to women in labour, but within a facility, nurses tend to provide similar care. This holds true both for care that can be influenced by equipment availability and technical training (eg, vital signs assessment), as well as cultural aspects (eg, respectful care).
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spelling pubmed-74408302020-08-28 Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India Helfinstein, Sarah Jain, Mokshada Ramesh, Banadakoppa Manjappa Blanchard, James Kemp, Hannah Gothalwal, Vikas Namasivayam, Vasanthakumar Kumar, Pankaj Sgaier, Sema K BMJ Glob Health Original Research INTRODUCTION: Improving the quality of care during childbirth is essential for reducing neonatal and maternal mortality. One barrier to improving quality of care is understanding the appropriate level to target interventions. We examine quality of care data during labour and delivery from multiple countries to assess whether quality varies primarily from nurse to nurse within the same facility, or primarily between facilities. METHODS: To assess the relative contributions of nurses and facilities to variance in quality of care, we performed a variance decomposition analysis using a linear mixed effect model on two data sources: (1) the number of vital signs assessed for women in labour from a study of nurse practices in Uttar Pradesh, India; 2) broad-scale indices of respectful and competent care generated from Service Provision Assessments in Kenya and Malawi. We used unsupervised clustering, a data mining technique that groups objects together based on similar characteristics, to identify groups of facilities that displayed distinct patterns of vital signs assessment behaviour. RESULTS: We found 3–10 times more variance in quality of care was explained by the facility where a patient received care than by the nurse who provided it. The unsupervised clustering analysis revealed groups of facilities with highly distinct patterns of vital signs assessment, even when overall rates of vital signs assessments were similar (eg, some facilities consistently test fetal heart rate, but not other vitals, others only blood pressure). CONCLUSION: Facilities within a region can vary substantially in the quality of care they provide to women in labour, but within a facility, nurses tend to provide similar care. This holds true both for care that can be influenced by equipment availability and technical training (eg, vital signs assessment), as well as cultural aspects (eg, respectful care). BMJ Publishing Group 2020-08-19 /pmc/articles/PMC7440830/ /pubmed/32816803 http://dx.doi.org/10.1136/bmjgh-2020-002437 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Original Research
Helfinstein, Sarah
Jain, Mokshada
Ramesh, Banadakoppa Manjappa
Blanchard, James
Kemp, Hannah
Gothalwal, Vikas
Namasivayam, Vasanthakumar
Kumar, Pankaj
Sgaier, Sema K
Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India
title Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India
title_full Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India
title_fullStr Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India
title_full_unstemmed Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India
title_short Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India
title_sort facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in kenya, malawi and india
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7440830/
https://www.ncbi.nlm.nih.gov/pubmed/32816803
http://dx.doi.org/10.1136/bmjgh-2020-002437
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