Agent-based modeling of noncommunicable diseases: a systematic review

Scoping Literature Review of Disease Modeling of the Opioid Crisis

Opioid misuse continues to cause significant harm. To investigate current research, we conducted a scoping literature review of disease spread models of opioid misuse from January 2000 to December 2022. In total, 85 studies were identified and examined for the opioids modeled, model type, data sources used and model calibration and validation. Most of the studies (58%, 49) only modeled heroin; the next largest categories were prescription opioids and unspecified opioids which accounted for 9% (8) each. Most models were theoretical compartmental models (57) or applied compartmental models (21). Previously published research was the most used data source (38), and a majority of the model validation involved the researchers setting initial conditions to verify theoretical results (30). To represent typical opioid use more accurately, multiple opioids need to be incorporated into the disease spread models, and applying different modeling techniques may allow other insights into opioid misuse spread.

School Nutrition Stakeholders Find Utility in MealSim: An Agent-Based Model

OBJECTIVE

To obtain feedback from school nutrition stakeholders on an agent-based model simulating school lunch to inform model refinement and future applications.

DESIGN

Qualitative study using online discussion groups.

SETTING

School nutrition professional stakeholders across the US.

PARTICIPANTS

Twenty-eight school nutrition stakeholders.

PHENOMENON OF INTEREST

Perceptions and applicability of MealSim for school nutrition stakeholders to help reduce food waste.

ANALYSIS

Deductive approach followed by inductive analysis of discussion group transcripts.

RESULTS

Stakeholders appreciated the customizability of the cafeteria characteristics and suggested adding additional characteristics to best represent the school meal system, such as factors relating to school staff supervision of students during meals. The perceived utility of MealSim was high and included using it to train personnel and to advocate for policy and budgetary changes. However, they viewed MealSim as more representative of elementary than high schools. Stakeholders also provided suggestions for training school nutrition administrators on how to use MealSim and requested opportunities for technical assistance.

CONCLUSIONS AND IMPLICATIONS

Although agent-based models were new to the school nutrition stakeholders, MealSim was viewed as a useful tool. Application of these findings will allow the model to meet the intended audience's needs and better estimate the system.

Modelling HIV/AIDS epidemiological complexity: A scoping review of Agent-Based Models and their application

OBJECTIVE

To end the AIDS epidemic by 2030, despite the increasing poverty and inequalities, policies should be designed to deal with population heterogeneity and environmental changes. Bottom-up designs, such as the Agent-Based Model (ABM), can model these features, dealing with such complexity. HIV/AIDS has a complex dynamic of structural factors, risk behaviors, biomedical characteristics and interventions. All embedded in unequal, stigmatized and heterogeneous social structure. To understand how ABMs can model this complexity, we performed a scoping review of HIV applications, highlighting their potentialities.

METHODS

We searched on PubMed, Web of Science, and Scopus repositories following the PRISMA extension for scoping reviews. Our inclusion criteria were HIV/AIDS studies with an ABM application. We identified the main articles using a local co-citation analysis and categorized the overall literature aims, (sub)populations, regions, and if the papers declared the use of ODD protocol and limitations.

RESULTS

We found 154 articles. We identified eleven main papers, and discussed them using the overall category results. Most studies model Transmission Dynamics (37/154), about Men who have sex with Men (MSM) (41/154), or individuals living in the US or South Africa (84/154). Recent studies applied ABM to model PrEP interventions (17/154) and Racial Disparities (12/154). Only six papers declared the use of ODD Protocol (6/154), and 34/154 didn't mention the study limitations.

CONCLUSIONS

While ABM is among the most sophisticated techniques available to model HIV/AIDS complexity. Their applications are still restricted to some realities. However, researchers are challenged to think about social structure due model characteristics, the inclusion of these features is still restricted to case-specific. Data and computational power availability can enhance this feature providing insightful results.

The long-term effectiveness and cost-effectiveness of public health interventions; how can we model behavior? A review

The effectiveness and cost of a public health intervention is dependent on complex human behaviors, yet health economic models typically make simplified assumptions about behavior, based on little theory or evidence. This paper reviews existing methods across disciplines for incorporating behavior within simulation models, to explore what methods could be used within health economic models and to highlight areas for further research. This may lead to better-informed model predictions. The most promising methods identified which could be used to improve modeling of the causal pathways of behavior-change interventions include econometric analyses, structural equation models, data mining and agent-based modeling; the latter of which has the advantage of being able to incorporate the non-linear, dynamic influences on behavior, including social and spatial networks. Twenty-two studies were identified which quantify behavioral theories within simulation models. These studies highlight the importance of combining individual decision making and interactions with the environment and demonstrate the importance of social norms in determining behavior. However, there are many theoretical and practical limitations of quantifying behavioral theory. Further research is needed about the use of agent-based models for health economic modeling, and the potential use of behavior maintenance theories and data mining.

Active mobility and mental health: A scoping review towards a healthier world

Research has proven that engaging in active mobility (AM), namely walking and cycling for transportation, significantly enhances physical activity levels, leading to better physical health. It is still unclear whether AM could also offer any mental health benefits. This scoping review aims to provide a comprehensive understanding of the current knowledge on the relationship between AM and mental health, given its crucial role in public health. The authors searched online databases to isolate primary studies written in English involving an adult sample (16 or over). AM was the exposure factor. Many mental health elements were included as outcomes (depression, anxiety, self-esteem, self-efficacy, stress, psychological and subjective well-being, resilience, loneliness and social support, quality of life, mood, life satisfaction and sleep). The results were organised in a narrative summary per each outcome selected, graphical syntheses and an overview of gaps to be further examined. The authors identified a total of 55 papers as relevant. The results show inconsistency in study designs, definition and operationalisation of the variables, approach and methodologies used. A cross-sectional design was the dominant choice, primarily examining data from national public health surveys. Nonetheless, there has been improvement in outcomes of interests, initially mainly the quality of life and affect. Lately, authors have focused on a broader range of mental health-related factors (such as travel satisfaction). The experimental studies showed promising mental health improvements in those who used active modes more than those who used motorised vehicles. It creates a rationale for further research towards implementing a unified theoretical and methodological framework to study the link between AM and mental health. The ultimate goal is to generate solid conclusions that could support building societies and cities through public health promotion and sustainable strategies, like walking and cycling as a means of transport.

System dynamics simulation models on overweight and obesity in children and adolescents: A systematic review

It has increasingly been recognized that developing successful obesity prevention policies and interventions requires understanding of the complex mechanisms driving the obesity pandemic and that models could be useful tools for simulating policies. This paper reviews system dynamics simulation models of mechanisms driving childhood overweight and obesity and/or testing of preventive interventions. A systematic literature search was conducted in six databases from inception to January 2023 using terms related to overweight/obesity, children, and system dynamics. Study descriptives, mechanisms, and where to intervene (the leverage points), as well as quality assessments of the simulation models were extracted by two researchers into a predetermined template and narratively synthesized. Seventeen papers describing 15 models were included. Models describing the mechanisms ranged from only intrapersonal factors to models cutting across multiple levels of the ecological model, but mechanisms across levels were lacking. The majority of interventions tested in the simulation models were changes to existing model parameters with less emphasis on models that alter system structure. In conclusion, existing models included mechanisms driving youth obesity at multiple levels of the ecological model. This is useful for developing an integrated simulation model combining mechanisms at multiple levels and allowing for testing fundamental system changes.

Understanding the Impact of Social Networks on the Spread of Obesity

Previous research has highlighted the significant role social networks play in the spread of non-communicable chronic diseases. In our research, we seek to explore the impact of these networks in more detail and gain insight into the mechanisms that drive this. We use obesity as a case study. To achieve this, we develop a generalisable hybrid simulation and optimisation approach aimed at gaining qualitative and quantitative insights into the effect of social networks on the spread of obesity. Our simulation model has two components. Firstly, an agent-based component mimics the dynamic structure of the social network within which individuals are situated. Secondly, a system dynamics component replicates the relevant behaviours of those individuals. The parameters from the combined model are refined and optimised using longitudinal data from the United Kingdom. The simulation produces projections of Body Mass Index broken down by different age groups and gender over a 10-year period. These projections are used to explore a range of scenarios in a computational study designed to address our research aims. The study reveals that, for the youngest population sub-groups, the network acts to magnify the impact of external and social factors on changes in obesity, whereas, for older sub-groups, the network mitigates the impact of these factors. The magnitude of that impact is inversely correlated with age. Our approach can be used by public health decision makers as well as managers in adult weight management services to enhance initiatives and strategies intended to reduce obesity. Our approach is generalisable to understand the impact of social networks on similar non-communicable diseases.

Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

Simulation Models for Suicide Prevention: A Survey of the State-of-the-Art

Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions.

A Systematic Review of Systems Science Approaches to Understand and Address Domestic and Gender-Based Violence

Purpose

We aimed to synthesize insights from systems science approaches applied to domestic and gender-based violence.

Methods

We conducted a systematic review of systems science studies (systems thinking, group model-building, agent-based modeling [ABM], system dynamics [SD] modeling, social network analysis [SNA], and network analysis [NA]) applied to domestic or gender-based violence, including victimization, perpetration, prevention, and community responses. We used blinded review to identify papers meeting our inclusion criteria (i.e., peer-reviewed journal article or published book chapter that described a systems science approach to domestic or gender-based violence, broadly defined) and assessed the quality and transparency of each study.

Results

Our search yielded 1,841 studies, and 74 studies met our inclusion criteria (45 SNA, 12 NA, 8 ABM, and 3 SD). Although research aims varied across study types, the included studies highlighted social network influences on risks for domestic violence, clustering of risk factors and violence experiences, and potential targets for intervention. We assessed the quality of the included studies as moderate, though only a minority adhered to best practices in model development and dissemination, including stakeholder engagement and sharing of model code.

Conclusions

Systems science approaches for the study of domestic and gender-based violence have shed light on the complex processes that characterize domestic violence and its broader context. Future research in this area should include greater dialogue between different types of systems science approaches, consideration of peer and family influences in the same models, and expanded use of best practices, including continued engagement of community stakeholders.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10896-023-00578-8.

A survey on agents applications in healthcare: Opportunities, challenges and trends

BACKGROUND AND OBJECTIVE

The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research.

METHODS

We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis.

RESULTS

Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics.

CONCLUSIONS

Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.

Estimating the impact of nutrition and physical activity policies with quasi-experimental methods and simulation modelling: an integrative review of methods, challenges and synergies

BACKGROUND

The promotion of healthy lifestyles has high priority on the global public health agenda. Evidence on the real-world (cost-)effectiveness of policies addressing nutrition and physical activity is needed. To estimate short-term policy impacts, quasi-experimental methods using observational data are useful, while simulation models can estimate long-term impacts. We review the methods, challenges and potential synergies of both approaches for the evaluation of nutrition and physical activity policies.

METHODS

We performed an integrative review applying purposive literature sampling techniques to synthesize original articles, systematic reviews and lessons learned from public international workshops conducted within the European Union Policy Evaluation Network.

RESULTS

We highlight data requirements for policy evaluations, discuss the distinct assumptions of instrumental variable, difference-in-difference, and regression discontinuity designs and describe the necessary robustness and falsification analyses to test them. Further, we summarize the specific assumptions of comparative risk assessment and Markov state-transition simulation models, including their extension to microsimulation. We describe the advantages and limitations of these modelling approaches and discuss future directions, such as the adequate consideration of heterogeneous policy responses. Finally, we highlight how quasi-experimental and simulation modelling methods can be integrated into an evidence cycle for policy evaluation.

CONCLUSIONS

Assumptions of quasi-experimental and simulation modelling methods in policy evaluations should be credible, rigorously tested and transparently communicated. Both approaches can be applied synergistically within a coherent framework to compare policy implementation scenarios and improve the estimation of nutrition and physical activity policy impacts, including their distribution across population sub-groups.

Evaluating the impact of community interventions on childhood obesity in populations living in low-income households in Los Angeles: A simulation study

BACKGROUND

The complex multifactorial nature of childhood obesity makes community interventions difficult to evaluate using traditional approaches; innovative methods are needed.

OBJECTIVE

To evaluate the impact of various interventions targeting childhood obesity-related behaviours, and classified as using a micro-level (e.g., home visitation programs) or macro-level (e.g., business practices) strategy, on obesity among children enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).

METHODS

We simulated a population of 1500 children enrolled in WIC, with specific diet, physical activity, breastfeeding behaviours and body mass index z-scores (BMIz), following them from age 2 to 5 years.

RESULTS

Combined interventions targeting breastfeeding appeared to be moderately effective, reducing BMIz by 0.03 (95% CI -005, -0.01). Two strategy-specific interventions, home visitation programs and business practices targeting obesity-related behaviours, appeared to be moderately effective at reducing BMIz by 0.04 (95% CI -0.06, -0.02) and 0.02 (95% CI -0.04, 0.00), respectively. Contrary to expectation, combining all micro and macro interventions appeared to have no impact or moderately increased the proportion of obesity/overweight among children.

CONCLUSION

Interventions targeting breastfeeding behaviour were most effective when both micro and macro strategies were implemented. Interventions targeting obesity-related behaviours in general were effective for two strategies, home visitation and business practices.

The need for a complex systems approach in rural health research

On a global scale, many major rural health issues have persisted for decades despite the introduction of new health interventions and public health policies. Although research efforts have generated valuable new knowledge about the aetiology of health, disease and health inequities in rural communities, rural health systems remain to be some of the most deprived and challenged in both the developing and developed world. While the reasons for this are many, a significant factor contributing to the current state of play is the pressing need for methodological innovation and relevant scientific approaches that have the capacity to support the translation of novel solutions into 'real world' rural contexts. Fortunately, complex systems approaches, which have seen an increase in popularity in the wider public health literature, could provide answers to some of the most resilient rural health problems in recent times. The purpose of this article is to promote the value and utility of a complex systems approach in rural health research. We explain the benefits of a complex systems approach and provide a background to the complexity sciences, including the main characteristics of complex systems. Two popular computational methods are described. The next step for rural health research involves exploring how a complex systems approach can help with the identification and evaluation of new and existing solutions to policy-resistant rural health issues. This includes generating awareness around the analytical trade-offs that occur between the use of traditional scientific methods and complex systems approaches.

A scoping review of systems approaches for increasing physical activity in populations

Introduction

The past decade has increasingly seen systems approaches as a featured theme in public health studies and policy documents. This trend is evident in the area of physical activity, which is a significant global health risk factor that is addressed in WHO’s Global Action Plan on Physical Activity. We undertook a comprehensive scoping review to characterize the application of systems approaches to physical activity, to develop a typology of the objectives, themes and methods of research papers that purported to apply systems thinking to this issue.

Methods

We searched electronic databases (PubMed, Web of Science, Scopus and PsycINFO) for studies published during the period 2010–2021 that explicitly applied systems approaches or methods to investigate and/or address population physical activity. A framework using systems-based methodological approaches was adapted to classify physical activity studies according to their predominant approach, covering basic descriptive, complex analytical and advanced forms of practice. We selected case studies from retained studies to depict the current “state of the art”.

Results

We included 155 articles in our narrative account. Literature reporting the application of systems approaches to physical activity is skewed towards basic methods and frameworks, with most attention devoted to conceptual framing and predictive modelling. There are few well-described examples of physical activity interventions which have been planned, implemented and evaluated using a systems perspective. There is some evidence of “retrofitted” complex system framing to describe programmes and interventions which were not designed as such.

Discussion

We propose a classification of systems-based approaches to physical activity promotion together with an explanation of the strategies encompassed. The classification is designed to stimulate debate amongst policy-makers, practitioners and researchers to inform the further implementation and evaluation of systems approaches to physical activity.

Conclusion

The use of systems approaches within the field of physical activity is at an early stage of development, with a preponderance of descriptive approaches and a dearth of more complex analyses. We need to see movement towards a more sophisticated research agenda spanning the development, implementation and evaluation of systems-level interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12961-022-00906-2.

Intersectoral collaboration in the management of non-communicable disease's risk factors in Iran: stakeholders and social network analysis

Introduction

As the major cause of premature death worldwide, noncommunicable diseases (NCDs) are complex and multidimensional, prevention and control of which need global, national, local, and multisectoral collaboration. Governmental stakeholder analysis and social network analysis (SNA) are among the recognized techniques to understand and improve collaboration. Through stakeholder analysis, social network analysis, and identifying the leverage points, we investigated the intersectoral collaboration (ISC) in preventing and controlling NCDs-related risk factors in Iran.

Methods

This is a mixed-methods study based on semi-structured interviews and reviewing of the legal documents and acts to identify and assess the interest, position, and power of collective decision-making centers on NCDs, followed by the social network analysis of related councils and the risk factors of NCDs. We used Gephi software version 0.9.2 to facilitate SNA. We determined the supreme councils' interest, position, power, and influence on NCDs and related risk factors. The Intervention Level Framework (ILF) and expert opinion were utilized to identify interventions to enhance inter-sectoral collaboration.

Results

We identified 113 national collective decision-making centers. Five councils had the highest evaluation score for the four criteria (Interest, Position, Power, and Influence), including the Supreme Council for Health and Food Security (SCHFS), Supreme Council for Standards (SCS), Supreme Council for Environmental Protection (SCIP), Supreme Council for Health Insurance (SCHI) and Supreme Council of the Centers of Excellence for Medical Sciences. We calculated degree, in degree, out-degree, weighted out-degree, closeness centrality, betweenness centrality, and Eigenvector centrality for all councils. Supreme Council for Standards and SCHFS have the highest betweenness centrality, showing Node's higher importance in information flow. Interventions to facilitate inter-sectoral collaboration were identified and reported based on Intervention Level Framework's five levels (ILF).

Conclusion

A variety of stakeholders influences the risk factors of non-communicable diseases. Through an investigation of stakeholders and their social networks, we determined the primary actors for each risk factor. Through the different (levels and types) of interventions identified in this study, the MoHME can leverage the ability of identified stakeholders to improve risk factors management. The proposed interventions for identified stakeholders could facilitate intersectoral collaboration, which is critical for more effective prevention and control of modifiable risk factors for NCDs in Iran. Supreme councils and their members could serve as key hubs for implementing targeted inter-sectoral approaches to address NCDs' risk factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14041-8.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Forecasting Obesity and Type 2 Diabetes Incidence and Burden: The ViLA-Obesity Simulation Model

Background

Obesity is a major public health problem affecting millions of Americans and is considered one of the most potent risk factors for type 2 diabetes. Assessing future disease burden is important for informing policy-decision making for population health and healthcare.

Objective

The aim of this study was to develop a computer model of a cohort of children born in Los Angeles County to study the life course incidence and trends of obesity and its effect on type 2 diabetes mellitus.

Methods

We built the Virtual Los Angeles cohort—ViLA, an agent-based model calibrated to the population of Los Angeles County. In particular, we developed the ViLA-Obesity model, a simulation suite within our ViLA platform that integrated trends in the causes and consequences of obesity, focusing on diabetes as a key obesity consequence during the life course. Each agent within the model exhibited obesity- and diabetes-related healthy and unhealthy behaviors such as sugar-sweetened beverage consumption, physical activity, fast-food consumption, fresh fruits, and vegetable consumption. In addition, agents could gain or lose weight and develop type 2 diabetes mellitus with a certain probability dependent on the agent's socio-demographics, past behaviors and past weight or type 2 diabetes status. We simulated 98,230 inhabitants from birth to age 65 years, living in 235 neighborhoods.

Results

The age-specific incidence of obesity generally increased from 10 to 30% across the life span with two notable peaks at age 6–12 and 30–39 years, while that of type 2 diabetes mellitus generally increased from <2% at age 18–24 to reach a peak of 25% at age 40–49. The 16-year risks of obesity were 32.1% (95% CI: 31.8%, 32.4%) for children aged 2–17 and 81% (95% CI: 80.8%, 81.3%) for adults aged 18–65. The 48-year risk of type 2 diabetes mellitus was 53.4% (95% CI: 53.1%, 53.7%) for adults aged 18–65.

Conclusion

This ViLA-Obesity model provides an insight into the future burden of obesity and type 2 diabetes mellitus in Los Angeles County, one of the most diverse places in the United States. It serves as a platform for conducting experiments for informing evidence-based policy-making.

Evaluating the impact of social determinants, conditional cash transfers and primary health care on HIV/AIDS: Study protocol of a retrospective and forecasting approach based on the data integration with a cohort of 100 million Brazilians

Background

Despite the great progress made over the last decades, stronger structural interventions are needed to end the HIV/AIDS pandemic in Low and Middle-Income Countries (LMIC). Brazil is one of the largest and data-richest LMIC, with rapidly changing socioeconomic characteristics and an important HIV/AIDS burden. Over the last two decades Brazil has also implemented the world’s largest Conditional Cash Transfer programs, the Bolsa Familia Program (BFP), and one of the most consolidated Primary Health Care (PHC) interventions, the Family Health Strategy (FHS).

Objective

We will evaluate the effects of socioeconomic determinants, BFP exposure and FHS coverage on HIV/AIDS incidence, treatment adherence, hospitalizations, case fatality, and mortality using unprecedently large aggregate and individual-level longitudinal data. Moreover, we will integrate the retrospective datasets and estimated parameters with comprehensive forecasting models to project HIV/AIDS incidence, prevalence and mortality scenarios up to 2030 according to future socioeconomic conditions and alternative policy implementations.

Methods and analysis

We will combine individual-level data from all national HIV/AIDS registries with large-scale databases, including the “100 Million Brazilian Cohort”, over a 19-year period (2000–2018). Several approaches will be used for the retrospective quasi-experimental impact evaluations, such as Regression Discontinuity Design (RDD), Random Administrative Delays (RAD) and Propensity Score Matching (PSM), combined with multivariable Poisson regressions for cohort analyses. Moreover, we will explore in depth lagged and long-term effects of changes in living conditions and in exposures to BFP and FHS. We will also investigate the effects of the interventions in a wide range of subpopulations. Finally, we will integrate such retrospective analyses with microsimulation, compartmental and agent-based models to forecast future HIV/AIDS scenarios.

Conclusion

The unprecedented datasets, analyzed through state-of-the-art quasi-experimental methods and innovative mathematical models will provide essential evidences to the understanding and control of HIV/AIDS epidemic in LMICs such as Brazil.

Evaluating community-driven cardiovascular health policy changes in the United States using agent-based modeling

Comprehensive smoke-free policy is a strategy to prevent cardiovascular disease (CVD) at a population-level; however, evaluating their long-term outcomes is difficult. This study used an agent-based model to estimate long-term impacts of a comprehensive smoke-free policy, as it was implemented in two communities, Arlington and Mesquite, Texas. The model predicted the percentage of myocardial infarction (MI), stroke, and diabetes in the population 10 and 20 years following policy adoption. In Arlington, the percentage of the population with these conditions each decreased by approximately 0.5% over 20 years; in Mesquite, the percentage of the population with diabetes, myocardial infarction (MI), and stroke decreased by 1.1%, 0.6%, and 0.3%, respectively, after 20 years. The results were statistically significant (p < 0.001). As an evaluation strategy, agent-based modeling can help researchers and practitioners estimate the potential long-term effects of policies and garner intervention support for implementation.

Parallel cognition: hybrid intelligence for human-machine interaction and management

As an interdisciplinary research approach, traditional cognitive science adopts mainly the experiment, induction, modeling, and validation paradigm. Such models are sometimes not applicable in cyber-physical-social-systems (CPSSs), where the large number of human users involves severe heterogeneity and dynamics. To reduce the decision-making conflicts between people and machines in human-centered systems, we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages: descriptive cognition based on artificial cognitive systems (ACSs), predictive cognition with computational deliberation experiments, and prescriptive cognition via parallel behavioral prescription. To make iteration of these stages constantly on-line, a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive knowledge. Preliminary experiments on two representative scenarios, urban travel behavioral prescription and cognitive visual reasoning, indicate that our parallel cognition learning is effective and feasible for human behavioral prescription, and can thus facilitate human-machine cooperation in both complex engineering and social systems.

The Impact of Expanding Telehealth-Delivered Dietary Interventions on Long-Term Cardiometabolic Health

A healthy diet is an important protective factor to prevent cardiometabolic disease. Traditional face-to-face dietary interventions are often episodic, expensive, and may have limited effectiveness, particularly among older adults and people living in rural areas. Telehealth-delivered dietary interventions have proven to be a low-cost and effective alternative approach to improve dietary behaviors among adults with chronic health conditions. In this study, we developed a validated agent-based model of cardiometabolic health conditions to project the impact of expanding telehealth-delivered dietary interventions among older adults in the state of Georgia, a state with a large rural population. We projected the incidence of major cardiometabolic health conditions (type 2 diabetes, hypertension, and high cholesterol) with the implementation of telehealth-delivered dietary interventions versus no intervention among all older adults and 3 subpopulations (older adults with diabetes, hypertension, and high cholesterol, separately). The results showed that expanding telehealth-delivered dietary interventions could avert 22,774 (95% confidence interval [CI]: 22,091-23,457) cases of type 2 diabetes, 19,732 (19,145-20,329) cases of hypertension, and 18,219 (17,672-18,766) cases of high cholesterol for 5 years among older adults in Georgia. The intervention would have a similar effect in preventing cardiometabolic health conditions among the 3 selected subpopulations. Therefore, expanding telehealth-delivered dietary interventions could substantially reduce the burden of cardiometabolic health conditions in the long term among older adults and those with chronic health conditions.

Applications of Complex Systems Models to Improve Retail Food Environments for Population Health: A Scoping Review

Retail food environments (RFEs) are complex systems with important implications for population health. Studying the complexity within RFEs comes with challenges. Complex systems models are computational tools that can help. We performed a systematic scoping review of studies that used complex systems models to study RFEs for population health. We examined the purpose for using the model, RFE features represented, extent to which the complex systems approach was maximized, and quality and transparency of methods employed. The PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines were followed. Studies using agent-based modeling, system dynamics, discrete event simulations, networks, hybrid, or microsimulation models were identified from 7 multidisciplinary databases. Fifty-six studies met the inclusion criteria, including 23 microsimulation, 13 agent-based, 10 hybrid, 4 system dynamics, 4 network, and 2 discrete event simulation models. Most studies (n = 45) used models for experimental purposes and evaluated effects of simulated RFE policies and interventions. RFE characteristics simulated in models were diverse, and included the features (e.g., prices) customers encounter when shopping (n = 55), the settings (e.g., restaurants, supermarkets) where customers purchase food and beverages (n = 30), and the actors (e.g., store managers, suppliers) who make decisions that influence RFEs (n = 25). All models incorporated characteristics of complexity (e.g., feedbacks, conceptual representation of multiple levels), but these were captured to varying degrees across model types. The quality of methods was adequate overall; however, few studies engaged stakeholders (n = 10) or provided sufficient transparency to verify the model (n = 12). Complex systems models are increasingly utilized to study RFEs and their contributions to public health. Opportunities to advance the use of these approaches remain, and areas to improve future research are discussed. This comprehensive review provides the first marker of the utility of leveraging these approaches to address RFEs for population health.

Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention

CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical "doses" of the HealtheRx shared their information with others ("social doses"). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population ("agents") using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.

Evolution and Reproducibility of Simulation Modeling in Epidemiology and Health Policy Over Half a Century

Simulation models are increasingly being used to inform epidemiologic studies and health policy, yet there is great variation in their transparency and reproducibility. In this review, we provide an overview of applications of simulation models in health policy and epidemiology, analyze the use of best reporting practices, and assess the reproducibility of the models using predefined, categorical criteria. We identified and analyzed 1,613 applicable articles and found exponential growth in the number of studies over the past half century, with the highest growth in dynamic modeling approaches. The largest subset of studies focused on disease policy models (70%), within which pathological conditions, viral diseases, neoplasms, and cardiovascular diseases account for one-third of the articles. Model details were not reported in almost half of the studies. We also provide in-depth analysis of modeling best practices, reporting quality and reproducibility of models for a subset of 100 articles (50 highly cited and 50 randomly selected from the remaining articles). Only 7 of 26 in-depth evaluation criteria were satisfied by more than 80% of samples. We identify areas for increased application of simulation modeling and opportunities to enhance the rigor and documentation in the conduct and reporting of simulation modeling in epidemiology and health policy.

The Application of Systems Science in Nutrition-Related Behaviors and Outcomes Implementation Research: A Scoping Review

Use of systems science can improve the dissemination and implementation (D&I) process. However, little is known about use of systems science in nutrition D&I research. The purpose of this article is to synthesize the ways in which systems science methodology is applied in nutrition D&I research. Scoping review methodology involved searching 6 academic databases for full-text, peer-reviewed, English articles published between 1970 and 2020 that employed systems science within nutrition D&I research. Data extraction included intervention type, population, study aim, methods, theoretical approach, outcomes, and results. Descriptive statistics and qualitative thematic analysis followed. Thirty-four retained articles qualitatively identified benefits (successful planning and organization of complex interventions) and challenges (limited resources, trainings, and lack of knowledge) to utilizing systems science in nutrition D&I research. Future research should work toward building knowledge capacity among nutrition practitioners by increasing available trainings and resources to enhance the utilization of systems science in nutrition D&I research.

Situating agent-based modelling in population health research

Today's most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method's conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the 'wicked' problems in population health, and could make significant contributions to theory and intervention development in these areas.

Matrix and agent-based modeling of threats to a diamond-backed terrapin population

Population models are important tools for evaluating human impacts and potential management approaches on declining species. However, often studies are limited by constraints of the specific modeling approach. In this study we considered the persistence of a diamond-backed terrapin (Malaclemys terrapin) population using two distinct modeling approaches. Two of the models were deterministic matrix models. Analysis of the discrete non-spatial models showed that female adult survival rate had the largest positive impact on population growth while delaying sexual maturity decreased population growth. The matrix models also demonstrated that an increase in crab traps skewed the sex ratio of the population in favor of females. The third model was a stochastic agent-based formulation that evaluated how increases in the number of crab traps and frequency of nest disturbances affected the long-term viability of diamond-backed terrapins. The spatial agent-based model revealed how terrapin mortality was highly sensitive to the proximity of traps to the primary terrapin habitat. Results from this project improve our understanding of threats to diamond-backed terrapins and can be used to guide conservation efforts.

Dietary Policies and Programs: Moving Beyond Efficacy and Into "Real-World" Settings

Purpose: Dietary behaviors are key modifiable risk factors in averting cardiovascular disease (CVD), the leading cause of morbidity, mortality, and disability in the United States. Before investing in adoption and implementation, community-based organizations, public health practitioners, and policymakers—often working with limited resources—need to compare the population health impacts of different food policies and programs to determine priorities, build capacity, and maximize resources. Numerous reports, reviews, and policy briefs have synthesized across evidence-based policies and programs to make recommendations, but few have made a deep acknowledgment that dietary policies and programs are not implemented in a vacuum, and that “real-world” settings are complex, multifaceted and dynamic.

Methods: A narrative review was conducted of currently recommended evidence-based approaches to improving dietary behaviors, to describe and characterize applied and practical factors for consideration when adopting and implementing these dietary policies and programs across diverse settings.

Results: From the narrative review, six key considerations emerged to guide community-based organizations, public health practitioners, and policymakers on moving from the evidence base, toward implementation in local and community settings.

Conclusions: Considerations of “real-world” contextual factors are necessary and important when adopting and selecting evidence-based policies and programs to improve dietary behaviors and ultimately improve CVD outcomes. Promising approaches include those that apply community-partnered research and systems science to examine the equitable implementation of evidence-based dietary policies and programs.

The role of alcohol use in the aetiology and progression of liver disease: A narrative review and a quantification

Issues.

Alcohol use has been shown to impact on various forms of liver disease, not restricted to alcoholic liver disease.

Approach.

We developed a conceptual framework based on a narrative review of the literature to identify causal associations between alcohol use and various forms of liver disease including the complex interactions of alcohol with other major risk factors. Based on this framework, we estimate the identified relations for 2017 for the USA.

Key Findings.

The following pathways were identified and modelled for the USA for the year 2017. Alcohol use caused 35 200 (95% uncertainty interval 32 800–37 800) incident cases of alcoholic liver cirrhosis. There were 1700 (uncertainty interval 1100–2500) acute hepatitis B and C virus (HBV and HCV) infections attributable to heavy-drinking occasions, and 14 000 (uncertainty interval 5900–19 500) chronic HBV and 1700 (uncertainty interval 700–2400) chronic HCV infections due to heavy alcohol use interfering with spontaneous clearance. Alcohol use and its interactions with other risk factors (HBV, HCV, obesity) led to 54 500 (uncertainty interval 50 900–58 400) new cases of liver cirrhosis. In addition, alcohol use caused 6600 (uncertainty interval 4200–9300) liver cancer deaths and 40 700 (uncertainty interval 36 600–44 600) liver cirrhosis deaths.

Implications.

Alcohol use causes a substantial number of incident cases and deaths from chronic liver disease, often in interaction with other risk factors.

Conclusion.

This additional disease burden is not reflected in the current alcoholic liver disease categories. Clinical work and prevention policies need to take this into consideration.

The dynamics of food shopping behavior: Exploring travel patterns in low-income Detroit neighborhoods experiencing extreme disinvestment using agent-based modeling

Only a handful of studies have leveraged agent-based models (ABMs) to examine public health outcomes and policy interventions associated with uneven urban food environments. While providing keen insights about the role of ABMs in studying urban food environments, these studies underutilize real-world data on individual behavior in their models. This study provides a unique contribution to the ABM and food access literature by utilizing survey data to develop an empirically-rich spatially-explicit ABM of food access. This model is used to simulate and scrutinize individual travel behavior associated with accessing food in low-income neighborhoods experiencing disinvestment in Detroit (Michigan), U.S. In particular, the relationship between trip frequencies, mode of travel, store choice, and distances traveled among individuals grouped into strata based on selected sociodemographic characteristics, including household income and age, is examined. Results reveal a diversified picture of not only how income and age shape food shopping travel but also the different thresholds of tolerance for non-motorized travel to stores. Younger and poorer population subgroups have a higher propensity to utilize non-motorized travel for shopping than older and wealthier subgroups. While all groups tend to travel considerable distances outside their immediate local food environment, different sociodemographic groups maintain unique spatial patterns of grocery-shopping behavior throughout the city and the suburbs. Overall, these results challenge foundational tenets in urban planning and design, regarding the specific characteristics necessary in the built environment to facilitate accessibility to urban amenities, such as grocery stores. In neighborhoods experiencing disinvestment, sociodemographic conditions play a more important role than the built environment in shaping food accessibility and ultimately travel behavior.

Investigating the Role of Childhood Adiposity in the Development of Adult Type 2 Diabetes in a 64-year Follow-up Cohort: An Application of the Parametric G-formula Within an Agent-based Simulation Study

BACKGROUND

The contribution of childhood obesity to adult type 2 diabetes (T2DM), not through adult adiposity, as well as the causal pathways through which childhood obesity increases adult T2DM risk are not well understood. This study investigated the contribution of childhood obesity to incident T2DM including pathways not through adult adiposity, and explored whether race modified this contribution.

METHODS

We used data from the Virtual Los Angeles Cohort, an agent-based longitudinal birth cohort composed of 98,230 simulated individuals born in 2009 and followed until age 65 years. We applied the parametric mediational g-formula to the causal mediation analysis investigating the impact of childhood obesity on the development of adult T2DM.

RESULTS

The marginal adjusted odds ratio (aOR) for the total effect of childhood obesity on adult T2DM was 1.37 (95% CI = 1.32, 1.46). Nearly all the effect of childhood obesity on adult T2DM was mostly attributable to pathways other than through adult obesity; the aOR for the pure direct effect was 1.36 (95% CI = 1.31, 1.41). In all racial subpopulations, a similar 3% of the total effect of childhood obesity on adult T2DM was attributable to its effect on adult obesity.

CONCLUSIONS

Childhood obesity remains a risk factor for adult T2DM separate from its effects on adult obesity. This study emphasizes the potential benefits of early interventions and illustrates that agent-based simulation models could serve as virtual laboratories for exploring mechanisms in obesity research.

Simulation Modeling as a Novel and Promising Strategy for Improving Success Rates With Research Funding Applications: A Constructive Thought Experiment

Writing a successful grant or other funding applications is a requirement for continued employment, promotion, and tenure among nursing faculty and researchers. Writing successful applications is a challenging task, with often uncertain results. The inability to secure funding not only threatens the ability of nurse researchers to conduct relevant health care research but may also negatively impact their career trajectories. Many individuals and organizations have offered advice for improving success with funding applications. While helpful, those recommendations are common knowledge and simply form the basis of any well-considered, well-formulated, and well-written application. For nurse researchers interested in taking advantage of innovative computational methods and leading-edge analytical techniques, we propose adding the results from computer-based simulation modeling experiments to funding applications. By first conducting a research study in a virtual space, nurse researchers can refine their study design, test various assumptions, conduct experiments, and better determine which elements, variables, and parameters are necessary to answer their research question. In short, simulation modeling is a learning tool, and the modeling process helps nurse researchers gain additional insights that can be applied in their real-world research and used to strengthen funding applications. Simulation modeling is well-suited for answering quantitative research questions. Still, the design of these models can benefit significantly from the addition of qualitative data and can be helpful when simulating the results of mixed methods studies. We believe this is a promising strategy for improving success rates with funding applications, especially among nurse researchers interested in contributing new knowledge supporting the paradigm shift in nursing resulting from advances in computational science and information technology.

A systematic review of empirical and simulation studies evaluating the health impact of transportation interventions

Urban transportation is an important determinant of health and environmental outcomes, and therefore essential to achieving the United Nation's Sustainable Development Goals. To better understand the health impacts of transportation initiatives, we conducted a systematic review of longitudinal health evaluations involving: a) bus rapid transit (BRT); b) bicycle lanes; c) Open Streets programs; and d) aerial trams/cable cars. We also synthesized systems-based simulation studies of the health-related consequences of walking, bicycling, aerial tram, bus and BRT use. Two reviewers screened 3302 unique titles and abstracts identified through a systematic search of MEDLINE (Ovid), Scopus, TRID and LILACS databases. We included 39 studies: 29 longitudinal evaluations and 10 simulation studies. Five studies focused on low- and middle-income contexts. Of the 29 evaluation studies, 19 focused on single component bicycle lane interventions; the rest evaluated multi-component interventions involving: bicycle lanes (n = 5), aerial trams (n = 1), and combined bicycle lane/BRT systems (n = 4). Bicycle lanes and BRT systems appeared effective at increasing bicycle and BRT mode share, active transport duration, and number of trips using these modes. Of the 10 simulation studies, there were 9 agent-based models and one system dynamics model. Five studies focused on bus/BRT expansions and incentives, three on interventions for active travel, and the rest investigated combinations of public transport and active travel policies. Synergistic effects were observed when multiple policies were implemented, with several studies showing that sizable interventions are required to significantly shift travel mode choices. Our review indicates that bicycle lanes and BRT systems represent promising initiatives for promoting population health. There is also evidence to suggest that synergistic effects might be achieved through the combined implementation of multiple transportation policies. However, more rigorous evaluation and simulation studies focusing on low- and middle-income countries, aerial trams and Open Streets programs, and a more diverse set of health and health equity outcomes is required.

Charting the life course: Emerging opportunities to advance scientific approaches using life course research

Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.

Public transit and depression among older adults: using agent-based models to examine plausible impacts of a free bus policy

BACKGROUND

Daily transport is associated with mental health. A free bus policy (FBP) may be effective in promoting the use of public transit in older adults and be associated with reductions in depressive symptoms.

METHODS

We developed an agent-based model and grounded it using empirical data from England to examine the impact of an FBP on public transit use and depression among older adults. We also used the model to explore whether the impact of the FBP bus use and depression is modified by the type of income segregation or by simultaneous efforts to improve attitudes towards the bus, to reduce waiting times or to increase the cost of driving via parking fees or fuel price.

RESULTS

Our model suggests that improving attitudes towards the bus (eg, campaigns that promote bus use) could enhance the effect of the FBP, especially for those in proximity to public transit. Reducing wait times could also significantly magnify FPB impacts, especially in those who live in proximity to public transit. Contrary to expectation, neither fuel costs nor parking fees significantly enhanced the impact of the FBP. The impact of improving attitudes towards the bus and increasing bus frequency was more pronounced in the lower-income groups in an income segregation scenario in which destination and public transit are denser in the city centre.

CONCLUSION

Our results suggest that the beneficial mental health effects of an FBP for older adults can be magnified when combined with initiatives that reduce bus waiting times and increased spatial access to transit.

Bringing new tools, a regional focus, resource-sensitivity, local engagement and necessary discipline to mental health policy and planning

Background

While reducing the burden of mental and substance use disorders is a global challenge, it is played out locally. Mental disorders have early ages of onset, syndromal complexity and high individual variability in course and response to treatment. As most locally-delivered health systems do not account for this complexity in their design, implementation, scale or evaluation they often result in disappointing impacts.

Discussion

In this viewpoint, we contend that the absence of an appropriate predictive planning framework is one critical reason that countries fail to make substantial progress in mental health outcomes. Addressing this missing infrastructure is vital to guide and coordinate national and regional (local) investments, to ensure limited mental health resources are put to best use, and to strengthen health systems to achieve the mental health targets of the 2015 Sustainable Development Goals.

Most broad national policies over-emphasize provision of single elements of care (e.g. medicines, individual psychological therapies) and assess their population-level impact through static, linear and program logic-based evaluation. More sophisticated decision analytic approaches that can account for complexity have long been successfully used in non-health sectors and are now emerging in mental health research and practice. We argue that utilization of advanced decision support tools such as systems modelling and simulation, is now required to bring a necessary discipline to new national and local investments in transforming mental health systems.

Conclusion

Systems modelling and simulation delivers an interactive decision analytic tool to test mental health reform and service planning scenarios in a safe environment before implementing them in the real world. The approach drives better decision-making and can inform the scale up of effective and contextually relevant strategies to reduce the burden of mental disorder and enhance the mental wealth of nations.

Complex Systems Approaches to Understand Drivers of Mental Health and Inform Mental Health Policy: A Systematic Review

We conducted a systematic review of studies employing complex systems approaches (i.e., agent based and system dynamics models) to understand drivers of mental health and inform mental health policy. We extracted key data (e.g., purpose, design, data) for each study and provide a narrative synthesis of insights generated across studies. The studies investigated drivers and policy intervention strategies across a diversity of mental health outcomes. Based on these studies and the extant literature, we propose a typology of mental health research and policy areas that may benefit from complex systems approaches.

Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal

A society's social structure and the interactions of its members determine when key drivers of health occur, for how long they last, and how they operate. Yet, it has been unclear whether causal inference methods can help us find meaningful interventions on these fundamental social drivers of health. Galea and Hernán propose we place hypothetical interventions on a spectrum and estimate their effects by emulating trials, either through individual-level data analysis or systems science modeling (Am J Epidemiol. 2020;189(3):167-170). In this commentary, by way of example in health disparities research, we probe this "closer engagement of social epidemiology with formal causal inference approaches." The formidable, but not insurmountable, tensions call for causal reasoning and effect estimation in social epidemiology that should always be enveloped by a thorough understanding of how systems and the social exposome shape risk factor and health distributions. We argue that one way toward progress is a true partnership of social epidemiology and causal inference with bilateral feedback aimed at integrating social epidemiologic theory, causal identification and modeling methods, systems thinking, and improved study design and data. To produce consequential work, we must make social epidemiology more causal and causal inference more social.

Systems approaches to population health in Canada: how have they been applied, and what are the insights and future implications for practice?

OBJECTIVES

Systems approaches are promising yet under-utilized methods for tackling complex public health problems. This paper explores how systems approaches are understood in the public health literature, how they have been applied in Canada, the insights, and implications for future practice.

METHODS

A rapid review of the literature, including a content analysis and cross-case comparison, was conducted. It was used to distinguish concepts of systems approaches and identify case examples of the application of systems approaches in Canada. Seven cases with a population health perspective (non-health care related) were prioritized for analysis.

RESULTS

Systems approaches are a variety of qualitative and quantitative methods that aim to understand a system of interest. Most case examples demonstrated systems thinking methods. Systems science methods were applied predominantly in health care. Only one case of systems science for the social determinants of health was found. Findings indicate that systems approaches were utilized because traditional methods were proving ineffective. These approaches can introduce new ways of thinking, enable collaboration across diverse stakeholders, identify where best to focus action and with what intensity, and provide more robust evidence for decision-making.

CONCLUSION

There is a need to build capacity among practitioners for more widespread adoption and use of systems approaches. Population health professionals need to move beyond reductionist approaches, generate more case examples, and use an iterative evaluation approach that prioritizes the application of processes. This will provide further insight into the usefulness of systems approaches as effective methods to address complex health problems.

A narrative review of the use of agent-based modeling in health behavior and behavior intervention

Studies of health behaviors and behavior intervention have begun to explore the potential of agent-based modeling (ABM). A review of how ABMs have been used in health behavior, behavior intervention, and corresponding insights is warranted. The goal of this study was to provide a narrative review of the applications of ABMs in health behavior change and intervention. I will focus on two perspectives: (a) the mechanism of behavior and behavior change and (b) ABMs' use for behavior intervention. I identified and reviewed 17 ABMs applied to behaviors including physical activity, diet, alcoholic drinking, smoking, and drug use. Among these ABMs, I grouped their mechanisms of behavior change into four categories and evaluated the advantages and disadvantages of each mechanism. For behavior intervention, I evaluated the use of ABMs on levels of individual, interpersonal, and neighborhood environment. Various behavior change mechanisms and simplifications existed because of our limited knowledge of behaviors at the individual level. Utility maximization was the most frequently used mechanism. ABMs offered insights for behavior intervention including the benefits of upstream interventions and multilevel intervention, as well as balances among various factors, outcomes, and populations. ABMs have been used to model a diversity of behaviors, populations, and interventions. The use of ABMs in health behavior is at an early stage, and a major challenge is our limited knowledge of behaviors at the individual level.

Agent-Based Modeling: A Method for Investigating Challenging Research Problems

BACKGROUND

For all our successes, many urgent health problems persist, and although some of these problems may be explored with established research methods, others remain uniquely challenging to investigate-maybe even impossible to study in the real world because of practical and pragmatic obstacles inherent to the nature of the research question.

OBJECTIVES

The purpose of this review article is to introduce agent-based modeling (ABM) and simulation and demonstrate its value and potential as a novel research method applied in nursing science.

METHODS

An introduction to ABM and simulation is described. Examples of current research literature on the subject are provided. A case study example of community nursing and opioid dependence is presented.

RESULTS

The use of ABM and simulation in human health research has increased dramatically over the past decade, and meaningful research is now commonly found published widely in respected, peer-reviewed journals. Absent from this list is innovative ABM and simulation research published by nurse researchers in nursing-specific journals.

DISCUSSION

ABM and simulation is a powerful method with tremendous potential in nursing research. It is vital that nursing embrace and adopt innovative and advanced research methods if we are to remain a progressive voice in health research, practice, and policy.

The SIPHER Consortium: Introducing the new UK hub for systems science in public health and health economic research

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER ( Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium’s vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing.

A systematic review of system dynamics and agent-based obesity models: Evaluating obesity as part of the global syndemic

The problem of obesity has recently been reframed as part of the global syndemic-the co-occurring, interacting pandemics of obesity, undernutrition, and climate change that are driven by common underlying societal drivers. System science modeling approaches may help clarify how these shared drivers operate and the best ways to address them. The objective of this paper was to determine to what extent existing agent-based and system dynamics computational models of obesity provide insights into the shared drivers of the global syndemic. Peer-reviewed studies published until July 2018 were identified from Scopus, Web of Science, and PubMed databases. Thirty-eight studies representing 30 computational models were included. They show a growing use of system dynamics and agent-based modeling in the past decade. They most often examined mechanisms and interventions in the areas of social network-based influences on obesity, physiology and disease state mechanics, and the role of food and physical activity environments. Usefulness for identifying common drivers of the global syndemic was mixed; most models represented Western settings and focused on obesity determinants close to the person (eg, social circles, school settings, and neighborhood environments), with a relative paucity in models at mesolevel and macrolevel and in developing country contexts.

Depression and alcohol misuse among older adults: exploring mechanisms and policy impacts using agent-based modelling

PURPOSE

To: (1) explore how multi-level factors impact the longitudinal prevalence of depression and alcohol misuse among urban older adults (≥ 65 years), and (2) simulate the impact of alcohol taxation policies and targeted interventions that increase social connectedness among excessive drinkers, socially isolated and depressed older adults; both alone and in combination.

METHODS

An agent-based model was developed to explore the temporal co-evolution of depression and alcohol misuse prevalence among older adults nested in a spatial network. The model was based on Los Angeles and calibrated longitudinally using data from the Multi-Ethnic Study of Atherosclerosis.

RESULTS

Interventions with a social component targeting depressed and socially isolated older adults appeared more effective in curbing depression prevalence than those focused on excessive drinkers. Targeting had similar impacts on alcohol misuse, though the effects were marginal compared to those on depression. Alcohol taxation alone had little impact on either depression or alcohol misuse trajectories.

CONCLUSIONS

Interventions that improve social connectedness may reduce the prevalence of depression among older adults. Targeting considerations could play an important role in determining the success of such efforts.

Leveraging system sciences methods in clinical trial evaluation: An example concerning African American women diagnosed with breast cancer via the Patient Navigation in Medically Underserved Areas study

BACKGROUND

Systems science methodologies offer a promising assessment approach for clinical trials by: 1) providing an in-silico laboratory to conduct investigations where purely empirical research may be infeasible or unethical; and, 2) offering a more precise measurement of intervention benefits across individual, network, and population levels. We propose to assess the potential of systems sciences methodologies by quantifying the spillover effects of randomized controlled trial via empirical social network analysis and agent-based models (ABM).

DESIGN/METHODS

We will evaluate the effects of the Patient Navigation in Medically Underserved Areas (PNMUA) study on adult African American participants diagnosed with breast cancer and their networks through social network analysis and agent-based modeling. First, we will survey 100 original trial participants (50 navigated, 50 non-navigated) and 150 of members of their social networks (75 from navigated, 75 non-navigated) to assess if navigation results in: 1) greater dissemination of breast health information and breast healthcare utilization throughout the trial participants' networks; and, 2) lower incremental costs, when incorporating navigation effects on trial participants and network members. Second, we will compare cost-effectiveness models, using a provider perspective, incorporating effects on trial participants versus trial participants and network members. Third, we will develop an ABM platform, parameterized using published data sources and PNMUA data, to examine if navigation increases the proportion of early stage breast cancer diagnoses.

DISCUSSION

Our study results will provide promising venues for leveraging systems science methodologies in clinical trial evaluation.

Complex Systems Approaches to Diet: A Systematic Review

CONTEXT

Complex systems approaches can help to elucidate mechanisms that shape population-level patterns in diet and inform policy approaches. This study reports results of a structured review of key design elements and methods used by existing complex systems models of diet.

EVIDENCE ACQUISITION

The authors conducted systematic searches of the PubMed, Web of Science, and LILACS databases between May and September 2018 to identify peer-reviewed manuscripts that used agent-based models or system dynamics models to explore diet. Searches occurred between November 2017 and May 2018. The authors extracted relevant data regarding each study's diet and nutrition outcomes; use of data for parameterization, calibration, and validation; results; and generated insights. The literature search adhered to PRISMA guidelines.

EVIDENCE SYNTHESIS

Twenty-two agent-based model studies and five system dynamics model studies met the inclusion criteria. Mechanistic studies explored neighborhood- (e.g., residential segregation), interpersonal- (e.g., social influence) and individual-level (e.g., heuristics that guide food purchasing decisions) mechanisms that influence diet. Policy-oriented studies examined policies related to food pricing, the food environment, advertising, nutrition labels, and social norms. Most studies used empirical data to inform values of key parameters; studies varied in their approaches to calibration and validation.

CONCLUSIONS

Opportunities remain to advance the state of the science of complex systems approaches to diet and nutrition. These include using models to better understand mechanisms driving population-level diet, increasing use of models for policy decision support, and leveraging the wide availability of epidemiologic and policy evaluation data to improve model validation.

Examining the possible impact of daily transport on depression among older adults using an agent-based model

OBJECTIVES

Daily transport may impact depression risk among older adults through several pathways including facilitating the ability to meet basic needs, enabling and promoting contact with other people and nature, and promoting physical activity (e.g. through active transportation such as walking or walking to public transit). Both daily transport and depression are influenced by the neighborhood environment. To provide insights into how transport interventions may affect depression in older adults, we developed a pilot agent-based model to explore the contribution of daily transport and neighborhood environment to older adults' depression in urban areas.

METHOD

The model includes about 18,500 older adults (i.e. agents) between the ages of 65 and 85 years old, living in a hypothetical city. The city has a grid space with a number of neighborhoods and locations. Key dynamic processes in the model include aging, daily transport use and feedbacks, and the development of depression. Key parameters were derived from US data sources. The model was validated using empirical studies.

RESULTS

An intervention that combines a decrease in bus fares, shorter bus waiting times, and more bus lines and stations is most effective at reducing depression. Lower income groups are likely to be more sensitive to the public transit-oriented intervention.

CONCLUSION

Preliminary results suggest that promoting public transit use may be a promising strategy to increase daily transport and decrease depression. Our results may have implications for transportation policies and interventions to prevent depression in older adults.

Assessing the Impact of Lifestyle Interventions on Diabetes Prevention in China: A Modeling Approach

China's diabetes epidemic is getting worse. People with diabetes in China usually have a lower body weight and a different lifestyle profile compared to their counterparts in the United States (US). More and more evidence show that certain lifestyles can possibly be spread from person to person, leading some to propose considering social influence when establishing preventive policies. This study developed an innovative agent-based model of the diabetes epidemic for the Chinese population. Based on the risk factors and related complications of diabetes, the model captured individual health progression, quantitatively described the peer influence of certain lifestyles, and projected population health outcomes over a specific time period. We simulated several hypothetical interventions (i.e., improving diet, controlling smoking, improving physical activity) and assessed their impact on diabetes rates. We validated the model by comparing simulation results with external datasets. Our results showed that improving physical activity could result in the most significant decrease in diabetes prevalence compared to improving diet and controlling smoking. Our model can be used to inform policymakers on how the diabetes epidemic develops and help them compare different diabetes prevention programs in practice.

Systems Modeling to Advance the Promise of Data Science in Epidemiology

Systems science models use computer-based algorithms to model dynamic interactions between study units within and across levels and are characterized by nonlinear and feedback processes. They are particularly valuable approaches that complement the traditional epidemiologic toolbox in cases in which real data are not available and in cases in which traditional epidemiologic methods are limited by issues such as interference, spatial dependence, and dynamic feedback processes. In this commentary, we propose 2 key contributions that systems models can make to epidemiology: 1) the ability to test assumptions about underlying mechanisms that give rise to population distributions of disease; and 2) help in identifying the types of interventions that have the greatest potential to reduce population rates of disease in the future or in new sites where they have not yet been implemented. We discuss central challenges in the application of systems science approaches in epidemiology, propose potential solutions, and predict future developments in the role that systems science can play in epidemiology.