Evidence map›Paper›PMID 32816803›Full record

ArticleBMJ global health2020

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.

Sarah Helfinstein, Mokshada Jain, Banadakoppa Manjappa Ramesh, James Blanchard, Hannah Kemp, Vikas Gothalwal, Vasanthakumar Namasivayam, Pankaj Kumar, Sema K Sgaier

Abstract read
In one paragraph

Article in BMJ global health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sarah HelfinsteinSurgo Foundation, Washington, DC, USA.
Mokshada JainSurgo Foundation, Washington, DC, USA.
Banadakoppa Manjappa RameshIndia Health Action Trust, Lucknow, Uttar Pradesh, India.
James BlanchardCentre for Global Public Health, University of Manitoba, Winnipeg, Manitoba, Canada.
Hannah KempSurgo Foundation, Washington, DC, USA.
Vikas GothalwalIndia Health Action Trust, Lucknow, Uttar Pradesh, India.
Vasanthakumar NamasivayamIndia Health Action Trust, Lucknow, Uttar Pradesh, India.
Pankaj KumarNational Health Mission, Government of Uttar Pradesh, Lucknow, Uttar Pradesh, India.
Sema K SgaierSurgo Foundation, Washington, DC, USA semasgaier@surgofoundation.org.ORCID 0000-0002-8311-2686

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionImproving 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.

methodsTo 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.

resultsWe 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

conclusionFacilities 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).

Indexed as

Cluster AnalysisFemaleHumansIndiaInfant, NewbornKenyaMalawiPregnancyhealth services researchmaternal health

Identifiers

PMID32816803
PMCPMC7440830

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.