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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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Detalles Bibliográficos
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
Descripción
Sumario: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).