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      Preventable medication harm across health care settings: a systematic review and meta-analysis

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          Abstract

          Background

          Mitigating or reducing the risk of medication harm is a global policy priority. But evidence reflecting preventable medication harm in medical care and the factors that derive this harm remain unknown. Therefore, we aimed to quantify the prevalence, severity and type of preventable medication harm across medical care settings.

          Methods

          We performed a systematic review and meta-analysis of observational studies to compare the prevalence of preventable medication harm. Searches were carried out in Medline, Cochrane library, CINAHL, Embase and PsycINFO from 2000 to 27 January 2020. Data extraction and critical appraisal was undertaken by two independent reviewers. Random-effects meta-analysis was employed followed by univariable and multivariable meta-regression. Heterogeneity was quantified using the I 2 statistic, and publication bias was evaluated. PROSPERO: CRD42020164156.

          Results

          Of the 7780 articles, 81 studies involving 285,687 patients were included. The pooled prevalence for preventable medication harm was 3% (95% confidence interval (CI) 2 to 4%, I 2 = 99%) and for overall medication harm was 9% (95% CI 7 to 11%, I 2 = 99.5%) of all patient incidence records. The highest rates of preventable medication harm were seen in elderly patient care settings (11%, 95% 7 to 15%, n = 7), intensive care (7%, 4 to 12%, n = 6), highly specialised or surgical care (6%, 3 to 11%, n = 13) and emergency medicine (5%, 2 to 12%, n = 12). The proportion of mild preventable medication harm was 39% (28 to 51%, n = 20, I 2 = 96.4%), moderate preventable harm 40% (31 to 49%, n = 22, I 2 = 93.6%) and clinically severe or life-threatening preventable harm 26% (15 to 37%, n = 28, I 2 = 97%). The source of the highest prevalence rates of preventable harm were at the prescribing (58%, 42 to 73%, n = 9, I 2 = 94%) and monitoring (47%, 21 to 73%, n = 8, I 2 = 99%) stages of medication use. Preventable harm was greatest in medicines affecting the ‘central nervous system’ and ‘cardiovascular system’.

          Conclusions

          This is the largest meta-analysis to assess preventable medication harm. We conclude that around one in 30 patients are exposed to preventable medication harm in medical care, and more than a quarter of this harm is considered severe or life-threatening. Our results support the World Health Organisation’s push for the detection and mitigation of medication-related harm as being a top priority, whilst highlighting other key potential targets for remedial intervention that should be a priority focus for future research.

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          Most cited references76

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            Quantifying heterogeneity in a meta-analysis.

            The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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              A basic introduction to fixed-effect and random-effects models for meta-analysis.

              There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                alexander.hodkinson@manchester.ac.uk
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                6 November 2020
                6 November 2020
                2020
                : 18
                : 313
                Affiliations
                [1 ]GRID grid.5379.8, ISNI 0000000121662407, National Institute for Health Research School for Primary Care Research, Centre for Primary Care and Health Services Research, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, , University of Manchester, Manchester Academic Health Science Centre, ; Williamson Building, Oxford Road, Manchester, M13 9PL UK
                [2 ]GRID grid.5379.8, ISNI 0000000121662407, National Institute for HealthResearch Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, , University of Manchester, ; Manchester, M13 9PL UK
                [3 ]GRID grid.5379.8, ISNI 0000000121662407, Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, , University of Manchester, ; Manchester, UK
                [4 ]GRID grid.5379.8, ISNI 0000000121662407, Pharmacy Department, , Greater Manchester Mental Health NHS Foundation Trust, University of Manchester, ; Manchester, M25 3BL UK
                [5 ]GRID grid.4563.4, ISNI 0000 0004 1936 8868, Division of Primary Care, School of Medicine, , University of Nottingham, ; Nottingham, NG7 2RD UK
                Article
                1774
                10.1186/s12916-020-01774-9
                7646069
                33153451
                50eae8b8-145b-41e1-adac-f66bf9973ad7
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 12 June 2020
                : 1 September 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100014559, General Medical Council;
                Award ID: RMS 113361
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100013235, NIHR Greater Manchester Patient Safety Translational Research Centre;
                Award ID: GMPSTRC-2012-1
                Funded by: NIHR Evidence Synthesis Working Group
                Award ID: project 390
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Medicine
                patient safety,preventable medication harm,prevalence,meta-analysis,medication error
                Medicine
                patient safety, preventable medication harm, prevalence, meta-analysis, medication error

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