Cargando…

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

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-...

Descripción completa

Detalles Bibliográficos
Autores principales: Cassidy, Rachel, Singh, Neha S., Schiratti, Pierre-Raphaël, Semwanga, Agnes, Binyaruka, Peter, Sachingongu, Nkenda, Chama-Chiliba, Chitalu Miriam, Chalabi, Zaid, Borghi, Josephine, Blanchet, Karl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6862817/
https://www.ncbi.nlm.nih.gov/pubmed/31739783
http://dx.doi.org/10.1186/s12913-019-4627-7
_version_ 1783471639055302656
author Cassidy, Rachel
Singh, Neha S.
Schiratti, Pierre-Raphaël
Semwanga, Agnes
Binyaruka, Peter
Sachingongu, Nkenda
Chama-Chiliba, Chitalu Miriam
Chalabi, Zaid
Borghi, Josephine
Blanchet, Karl
author_facet Cassidy, Rachel
Singh, Neha S.
Schiratti, Pierre-Raphaël
Semwanga, Agnes
Binyaruka, Peter
Sachingongu, Nkenda
Chama-Chiliba, Chitalu Miriam
Chalabi, Zaid
Borghi, Josephine
Blanchet, Karl
author_sort Cassidy, Rachel
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6862817
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-68628172019-12-11 Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models Cassidy, Rachel Singh, Neha S. Schiratti, Pierre-Raphaël Semwanga, Agnes Binyaruka, Peter Sachingongu, Nkenda Chama-Chiliba, Chitalu Miriam Chalabi, Zaid Borghi, Josephine Blanchet, Karl BMC Health Serv Res Research Article 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. BioMed Central 2019-11-19 /pmc/articles/PMC6862817/ /pubmed/31739783 http://dx.doi.org/10.1186/s12913-019-4627-7 Text en © 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.
spellingShingle Research Article
Cassidy, Rachel
Singh, Neha S.
Schiratti, Pierre-Raphaël
Semwanga, Agnes
Binyaruka, Peter
Sachingongu, Nkenda
Chama-Chiliba, Chitalu Miriam
Chalabi, Zaid
Borghi, Josephine
Blanchet, Karl
Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models
title Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models
title_full Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models
title_fullStr Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models
title_full_unstemmed Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models
title_short Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models
title_sort mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6862817/
https://www.ncbi.nlm.nih.gov/pubmed/31739783
http://dx.doi.org/10.1186/s12913-019-4627-7
work_keys_str_mv AT cassidyrachel mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT singhnehas mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT schirattipierreraphael mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT semwangaagnes mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT binyarukapeter mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT sachingongunkenda mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT chamachilibachitalumiriam mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT chalabizaid mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT borghijosephine mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels
AT blanchetkarl mathematicalmodellingforhealthsystemsresearchasystematicreviewofsystemdynamicsandagentbasedmodels