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

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.

SUBMITTER: Cassidy R

PROVIDER: S-EPMC6862817 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Cassidy Rachel R Singh Neha S NS Schiratti Pierre-Raphaël PR Semwanga Agnes A Binyaruka Peter P Sachingongu Nkenda N Chama-Chiliba Chitalu Miriam CM Chalabi Zaid Z Borghi Josephine J Blanchet Karl K

BMC health services research 20191119 1

<h4>Background</h4>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 a ...[more]

PMID: 31739783

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Project description:BackgroundIn practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model.MethodologyIn a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions.ResultsIn this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM.ConclusionsThis research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions.

Project description:Because health systems are conceptualized as social systems, embedded in social contexts and shaped by human agency, values are a key factor in health system change. As such, health systems software-including values, norms, ideas and relationships-is considered a foundational focus of the field of health policy and systems research (HPSR). A substantive evidence-base exploring the influence of software factors on system functioning has developed but remains fragmented, with a lack of conceptual clarity and theoretical coherence. This is especially true for work on 'social values' within health systems-for which there is currently no substantive review available. This study reports on a systematic mixed-methods evidence mapping review on social values within HPSR. The study reaffirms the centrality of social values within HPSR and highlights significant evidence gaps. Research on social values in low- and middle-income country contexts is exceedingly rare (and mostly produced by authors in high-income countries), particularly within the limited body of empirical studies on the subject. In addition, few HPS researchers are drawing on available social science methodologies that would enable more in-depth empirical work on social values. This combination (over-representation of high-income country perspectives and little empirical work) suggests that the field of HPSR is at risk of developing theoretical foundations that are not supported by empirical evidence nor broadly generalizable. Strategies for future work on social values in HPSR are suggested, including: countering pervasive ideas about research hierarchies that prize positivist paradigms and systems hardware-focused studies as more rigorous and relevant to policy-makers; utilizing available social science theories and methodologies; conceptual development to build common framings of key concepts to guide future research, founded on quality empirical research from diverse contexts; and using empirical evidence to inform the development of operationalizable frameworks that will support rigorous future research on social values in health systems.

Project description:ObjectiveThe objective of this study is to conduct a systematic review of multi-scale HIV immunoepidemiological models to improve our understanding of the synergistic impact between the HIV viral-immune dynamics at the individual level and HIV transmission dynamics at the population level.BackgroundWhile within-host and between-host models of HIV dynamics have been well studied at a single scale, connecting the immunological and epidemiological scales through multi-scale models is an emerging method to infer the synergistic dynamics of HIV at the individual and population levels.MethodsWe reviewed nine articles using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework that focused on the synergistic dynamics of HIV immunoepidemiological models at the individual and population levels.ResultsHIV immunoepidemiological models simulate viral immune dynamics at the within-host scale and the epidemiological transmission dynamics at the between-host scale. They account for longitudinal changes in the immune viral dynamics of HIV+ individuals, and their corresponding impact on the transmission dynamics in the population. They are useful to analyze the dynamics of HIV super-infection, co-infection, drug resistance, evolution, and treatment in HIV+ individuals, and their impact on the epidemic pathways in the population. We illustrate the coupling mechanisms of the within-host and between-host scales, their mathematical implementation, and the clinical and public health problems that are appropriate for analysis using HIV immunoepidemiological models.ConclusionHIV immunoepidemiological models connect the within-host immune dynamics at the individual level and the epidemiological transmission dynamics at the population level. While multi-scale models add complexity over a single-scale model, they account for the time varying immune viral response of HIV+ individuals, and the corresponding impact on the time-varying risk of transmission of HIV+ individuals to other susceptibles in the population.

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

Publications

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

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