Decision letter for "Association between nurse work environment and severe maternal morbidity in high‐income countries: A systematic review and call to action"

2020 ◽  
Author(s):  
Kyrah K Brown ◽  
Jessica G. Smith ◽  
RaeAnna L Jeffers ◽  
Claudy Jean Pierre

Abstract Background The United States has the highest maternal mortality and morbidity rates compared to its high-income peer nations. In high-income nations, a considerable amount of maternal morbidity cases are preventable and linked to provider-related factors. Better nurse work environments are associated with positive patient outcomes, but little is known about its impact on maternal morbidity. In this systematic review, we aim to identify the association between nurse work environment and maternal morbidity, specifically in high-income countries. Methods This systematic review will include original articles on the association between nurse work environment and maternal morbidity. CINAHL, PubMed/Medline and the Cochrane Central Register of Controlled Trials will be searched to retrieve potential original articles that are published between 1990 and 2019 in English language. Citations will be screened by two reviewers, in two rounds, for inclusion based on a priori inclusion and exclusion criteria. Data extraction templates will be populated with data to evaluate the methodological and reporting quality of each study. A combination of structured narrative synthesis and quantitative summaries in tabular format will allow for discussion and recommendations for future research. Discussion Results from this systematic review will provide evidence to elucidate the association between nurse work environment and maternal morbidity. While there is strong evidence demonstration the relation between nurse work environment and general patient outcomes, less is known about its influence on maternal morbidity. Findings from this review will help to guide research in the field and nursing professional in the development of targeted practices and policies aimed at reducing the rates of maternal morbidity.


BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e027100 ◽  
Author(s):  
Tesfaye S Mengistu ◽  
Jessica Turner ◽  
Christopher Flatley ◽  
Jane Fox ◽  
Sailesh Kumar

IntroductionSevere maternal morbidity (SMM) includes conditions that are on a continuum of maternal morbidity to maternal death. Rates of SMM are increasing both in high-income countries (HICs) as well as in low/middle-income countries (LMICs). There is evidence that analysis of SMM trends and detailed investigation of factors implicated in these cases may reflect the standard of maternal healthcare both in HICs and LMICs. SMM is also associated with poorer perinatal outcomes. The aim of this protocol is to describe the proposed methodology for the synthesis and analyses of the data describing the relationship between SMM and adverse perinatal outcomes in a systematic review and meta-analysis.MethodsThis systematic review and meta-analysis will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and will be registered with the International Prospective Register of Systematic Reviews (PROSPERO). Original peer-reviewed epidemiologic/clinical studies of observational (cross-sectional, cohort, case-control) and randomised controlled trial studies conducted in high-income countries will be included. An electronic search of PubMed, Embase, CINAHL and Scopus databases will be performed without restricting publication date/year. Two authors will independently screen the titles, review abstracts and perform data extraction. Where possible, meta-analyses will be done to calculate pooled estimates.Ethics and disseminationAs this is a protocol for systematic review and meta-analysis of published data, ethics review and approval are not required. The findings will be published in peer-reviewed journals and disseminated at scientific conferences.PROSPERO registration numberCRD42019130933.


2019 ◽  
Author(s):  
Natalie England ◽  
Julia Madill ◽  
Amy Metcalfe ◽  
Laura Magee ◽  
Stephanie Cooper ◽  
...  

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