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      Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models

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          Abstract

          Background

          Mathematical modelling has been a vital research tool for exploring complex systems, most recently to aid understanding of health system functioning and optimisation. System dynamics models (SDM) and agent-based models (ABM) are two popular complementary methods, used to simulate macro- and micro-level health system behaviour. This systematic review aims to collate, compare and summarise the application of both methods in this field and to identify common healthcare settings and problems that have been modelled using SDM and ABM.

          Methods

          We searched MEDLINE, EMBASE, Cochrane Library, MathSciNet, ACM Digital Library, HMIC, Econlit and Global Health databases to identify literature for this review. We described papers meeting the inclusion criteria using descriptive statistics and narrative synthesis, and made comparisons between the identified SDM and ABM literature.

          Results

          We identified 28 papers using SDM methods and 11 papers using ABM methods, one of which used hybrid SDM-ABM to simulate health system behaviour. The majority of SDM, ABM and hybrid modelling papers simulated health systems based in high income countries. Emergency and acute care, and elderly care and long-term care services were the most frequently simulated health system settings, modelling the impact of health policies and interventions such as those targeting stretched and under resourced healthcare services, patient length of stay in healthcare facilities and undesirable patient outcomes.

          Conclusions

          Future work should now turn to modelling health systems in low- and middle-income countries to aid our understanding of health system functioning in these settings and allow stakeholders and researchers to assess the impact of policies or interventions before implementation. Hybrid modelling of health systems is still relatively novel but with increasing software developments and a growing demand to account for both complex system feedback and heterogeneous behaviour exhibited by those who access or deliver healthcare, we expect a boost in their use to model health systems.

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          Most cited references56

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          Systems thinking for strengthening health systems in LMICs: need for a paradigm shift.

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            Discrete event simulation for performance modelling in health care: a review of the literature

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              Agent Based Modelling and Simulation tools: A review of the state-of-art software

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                Author and article information

                Contributors
                0044 2079272818 , rachel.cassidy@lshtm.ac.uk
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                19 November 2019
                19 November 2019
                2019
                : 19
                : 845
                Affiliations
                [1 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Department of Global Health and Development, , London School of Hygiene and Tropical Medicine, ; 15-17 Tavistock Place, London, WC1H 9SH UK
                [2 ]ISNI 0000000121901201, GRID grid.83440.3b, Department of Mathematics, , University College London, ; London, UK
                [3 ]Sia Partners UK, London, UK
                [4 ]ISNI 0000 0004 0620 0548, GRID grid.11194.3c, Information Systems Department, College of Computing and Information Sciences, , Makerere University, ; P.O. Box 7062, Kampala, Uganda
                [5 ]ISNI 0000 0000 9144 642X, GRID grid.414543.3, Ifakara Health Institute, ; PO Box 78373, Dar es Salaam, Tanzania
                [6 ]ISNI 0000 0000 8914 5257, GRID grid.12984.36, Department of Gender Studies, School of Humanities and Social Sciences, , University of Zambia, ; 10101 Lusaka, Zambia
                [7 ]ISNI 0000 0000 8914 5257, GRID grid.12984.36, Economic and Business Research Programme, , University of Zambia, Institute of Economic and Social Research, ; P O Box 30900, 10101 Lusaka, Zambia
                [8 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Department of Public Health, Environments and Society, , London School of Hygiene and Tropical, ; London, UK
                Author information
                http://orcid.org/0000-0002-4824-0260
                Article
                4627
                10.1186/s12913-019-4627-7
                6862817
                31739783
                2251b2fa-a485-49d9-be8e-39e6adcc4527
                © The Author(s). 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 14 June 2019
                : 11 October 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/R013454/1
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Health & Social care
                system dynamics,agent-based,hybrid,health systems,systematic review,modelling
                Health & Social care
                system dynamics, agent-based, hybrid, health systems, systematic review, modelling

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