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      The Effectiveness of Patient-Centred Medical Home-Based Models of Care versus Standard Primary Care in Chronic Disease Management: A Systematic Review and Meta-Analysis of Randomised and Non-Randomised Controlled Trials

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          Abstract

          Patient-centred care by a coordinated primary care team may be more effective than standard care in chronic disease management. We synthesised evidence to determine whether patient-centred medical home (PCMH)-based care models are more effective than standard general practitioner (GP) care in improving biomedical, hospital, and economic outcomes. MEDLINE, CINAHL, Embase, Cochrane Library, and Scopus were searched to identify randomised (RCTs) and non-randomised controlled trials that evaluated two or more principles of PCMH among primary care patients with chronic diseases. Study selection, data extraction, quality assessment using Joanna Briggs Institute (JBI) appraisal tools, and grading of evidence using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach were conducted independently. A quantitative synthesis, where possible, was pooled using random effects models and the effect size estimates of standardised mean differences (SMDs) and odds ratios (ORs) with 95% confidence intervals were reported. Of the 13,820 citations, we identified 78 eligible RCTs and 7 quasi trials which included 60,617 patients. The findings suggested that PCMH-based care was associated with significant improvements in depression episodes (SMD −0.24; 95% CI −0.35, −0.14; I 2 = 76%) and increased odds of remission (OR 1.79; 95% CI 1.46, 2.21; I 2 = 0%). There were significant improvements in the health-related quality of life (SMD 0.10; 95% CI 0.04, 0.15; I 2 = 51%), self-management outcomes (SMD 0.24; 95% CI 0.03, 0.44; I 2 = 83%), and hospital admissions (OR 0.83; 95% CI 0.70, 0.98; I 2 = 0%). In terms of biomedical outcomes, with exception to total cholesterol, PCMH-based care led to significant improvements in blood pressure, glycated haemoglobin, and low-density lipoprotein cholesterol outcomes. The incremental cost of PCMH care was identified to be small and significantly higher than standard care (SMD 0.17; 95% CI 0.08, 0.26; I 2 = 82%). The quality of individual studies ranged from “fair” to “good” by meeting at least 60% of items on the quality appraisal checklist. Additionally, moderate to high heterogeneity across studies in outcomes resulted in downgrading the included studies as moderate or low grade of evidence. PCMH-based care has been found to be superior to standard GP care in chronic disease management. Results of the review have important implications that may inform patient, practice, and policy-level changes.

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          Bias in meta-analysis detected by a simple, graphical test

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            Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement

            Systematic reviews should build on a protocol that describes the rationale, hypothesis, and planned methods of the review; few reviews report whether a protocol exists. Detailed, well-described protocols can facilitate the understanding and appraisal of the review methods, as well as the detection of modifications to methods and selective reporting in completed reviews. We describe the development of a reporting guideline, the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Protocols 2015 (PRISMA-P 2015). PRISMA-P consists of a 17-item checklist intended to facilitate the preparation and reporting of a robust protocol for the systematic review. Funders and those commissioning reviews might consider mandating the use of the checklist to facilitate the submission of relevant protocol information in funding applications. Similarly, peer reviewers and editors can use the guidance to gauge the completeness and transparency of a systematic review protocol submitted for publication in a journal or other medium.
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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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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                21 September 2020
                September 2020
                : 17
                : 18
                : 6886
                Affiliations
                [1 ]Translational Health Research Institute, Western Sydney University, Sydney, NSW 2560, Australia; H.Jani@ 123456westernsydney.edu.au (H.J.); K.Agho@ 123456westernsydney.edu.au (K.A.); k.tannous@ 123456westernsydney.edu.au (W.K.T.)
                [2 ]Rozetta Institute, Level 4, 55 Harrington Street, Sydney, NSW 2000, Australia
                [3 ]School of Nursing and Midwifery, Western Sydney University, Sydney, NSW 2560, Australia; K.Peters@ 123456westernsydney.edu.au
                [4 ]School of Science and Health, Western Sydney University, Sydney, NSW 2560, Australia
                [5 ]School of Business, Western Sydney University, Sydney, NSW 2150, Australia
                Author notes
                Author information
                https://orcid.org/0000-0002-8720-8300
                https://orcid.org/0000-0002-3153-5652
                Article
                ijerph-17-06886
                10.3390/ijerph17186886
                7558011
                32967161
                bab7fd5f-44ee-4d5e-b3bc-91befc6cefc7
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 August 2020
                : 18 September 2020
                Categories
                Article

                Public health
                patient-centred medical home,enhanced primary care,chronic disease management,collaborative care,meta-analysis

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