Applications of systems modelling in obesity research

Application of system dynamics approach in developing health interventions to strengthen health systems to combat obesity: a systematic literature review and critical analysis

BACKGROUND

Obesity is an escalating global public health challenge that is expected to impact a significant portion of the world's population in the coming decades. It leads to severe health conditions such as diabetes, cardiovascular diseases, and cancer, imposing significant economic burdens on health systems. Traditional intervention strategies, which emphasize individual lifestyle changes, fail to address the complex, systemic nature of obesity. This study aims to systematically review the application of system dynamics modelling (SDM) in obesity control, focusing on analyzing modelling methodologies and conducting a quality assessment of the included studies.

METHODS

Employing a comprehensive systematic literature retrieval, we explored terms pertinent to overweight/obesity and system dynamics across three databases, including PubMed, Web of Science, and Scopus. This search culminated in identifying peer-reviewed studies published from the inception of these databases until July 2024. Quality assessment was used to evaluate the SDM for obesity control. The protocol of this systematic review has been registered on PROSPERO (CRD42024554520).

RESULTS

Thirty studies were identified through a systematic review. These studies primarily focus on the effects of SD approaches, such as individual lifestyle changes, policy interventions within populations, and socio-economic and environmental improvements on obesity control. Among them, eleven studies completed the entire SDM process. Twenty-seven studies presented conceptual models, of which twenty-five developed casual loop diagrams (CLD). Seventeen studies conducted computational system dynamics modelling, with thirteen constructing stock-flow diagrams (SFD). Additionally, fourteen studies performed simulation analyses. These models facilitated multi-level strategies to reduce obesity prevalence.

CONCLUSIONS

Using SDM approaches has significant potential to enhance the effectiveness of obesity interventions and optimize resource allocation. Our study into the application of SDM in the design of obesity health interventions revealed its ability to promote multi-level, cross-sectoral cooperation and coordination, thereby enhancing the effectiveness of interventions. Further exploration and optimization of obesity health interventions can significantly advance health systems and welfare.

Insights from a codesigned dynamic modelling study of child and adolescent obesity in Australia

Introduction

Child and adolescent obesity is associated with a range of immediate health issues and influences obesity in adulthood. The complex nature of health determinants that contribute to obesity makes it challenging to deliver effective public health interventions. This research presents insights from a system dynamics model of childhood and adolescent obesity aimed at supporting evidence-based decision-making.

Methods

A system dynamics model was developed using the best available evidence and data, with input from research and industry experts to map the hypothetical causal structure of the factors contributing to childhood and adolescent obesity in Australia. The model was calibrated to fit the historical prevalence of obesity (R 2 =0.97, mean squared error (MSE)=4.94E-04). Intervention-based scenarios were simulated to examine how changes in environmental factors and health-related behaviours may affect the prevalence of obesity. The potential economic benefits of the scenarios were estimated from changes in population healthcare spending and quality of life compared with base model projections.

Results

A series of interventions were explored in the model, including changes in early childhood behaviours, changes to diet and physical activity in childcare and school settings, financial support for organised sports and sugar-sweetened beverage taxation. The most promising individually implemented intervention scenario for reducing the prevalence of childhood and adolescent obesity was a sugar-sweetened beverage tax (0.57 percentage points and 0.61 percentage points, respectively) and government funding of organised sports (0.42 percentage points and 0.63 percentage points, respectively). When all interventions were implemented in combination, childhood obesity was reduced by 1.43 percentage points and 1.81 percentage points in adolescents.

Conclusions

The findings highlight the challenges faced by policy-makers and public health practitioners working to reduce childhood and adolescent obesity. Insights from the model emphasise the value of public health programmes over the life course. Implementing initiatives with broad reach that support healthy choices may reduce obesity, resulting in a healthier Australian population.

The intersection of systems thinking and structural empowerment in the work of public health dietitians

BACKGROUND

Public health dietitians navigate complex professional landscapes amid dwindling resources, organisational disruptions and limited advancement opportunities. Cultivating systems thinking and structural empowerment competencies may enable this workforce to address multifaceted public health challenges more effectively. This study explored the extent to which public health dietitians apply systems thinking and perceive access to structural empowerment and the relationship between these constructs.

METHODS

A quantitative online survey incorporating the systems thinking scale (STS) and conditions for work effectiveness questionnaire-II (CWEQ-II) was conducted among US public health dietitians who worked in governmental public health. Data were collected from September 2022 to October 2022. Descriptive and inferential statistical analyses were conducted.

RESULTS

Among 216 respondents, 98% demonstrated moderate-to-high systems thinking competency (mean STS score = 60.3 ± 8.74, range 28-78). Over 88% reported moderate-to-high perceived structural empowerment (mean CWEQ-II score = 18.3 ± 0.96, range 8-29). Higher systems thinking scores were associated with greater decision-making authority (p = 0.01) but not budget oversight. Higher structural empowerment scores correlated with increased job responsibilities and decision-making authority (p < 0.001). A significant positive correlation existed between systems thinking and structural empowerment (r = 0.24, p < 0.001). Public health dietitians exhibited substantial systems thinking capabilities and perceived access to organisational power structures.

CONCLUSIONS

This study offers baseline understanding of systems thinking and structural empowerment among public health dietitians. The positive interplay between these constructs underscores their potential to drive systems-level change and influence population health outcomes. Integrating systems thinking and structural empowerment into dietetic education and professional development may enhance the workforce's preparedness for navigating complexities.

Understanding the obesity dynamics by socioeconomic status in Colombian and Mexican cities using a system dynamics model

Purpose

This paper aims to enrich understanding of the obesity transition among socioeconomic status (SES) strata by gender and age in cities of Colombia and Mexico. The study uses harmonized data from the Salud Urbana en América Latina (SALURBAL) study.

Methods

A population-level system dynamics model was developed using 2010 and 2015 data from Colombia and 2012 and 2016 data from Mexico (national health surveys). The model simulates the prevalence of different BMI categories (i.e., not overweight, overweight, obese) stratified by gender, age, and SES, in the SALURBAL cities (aggregated to the country level) of Colombia and Mexico from 2010 to 2050. Sample sizes for Colombia in 2010 and Mexico in 2012 were 7420 and 5785 children (<5 years), 21601 and 14413 children and adolescents (5-17 years), and 46597 and 20464 adults (18-64 years), respectively. Sample sizes for Colombia in 2015 and Mexico in 2016 were 4450 and 907 children, 12468 and 2350 children and adolescents, and 90430 and 3413 adults, respectively.

Results

For men in Colombia and Mexico, the burden of obesity is projected to increase among lower SES adults over time. Colombian women show similar patterns observed in men but the burden of obesity was already greater in the lower SES groups as early as 2012. In Mexican women, the burden of obesity in 2012 is higher in the lower SES population; however, the prevalence of obesity is projected to increase at a faster rate in the higher SES population. Patterns for children aged 0-14 years differed by gender and country.

Conclusions

The model suggests that the prevalence of obesity among SES strata by age and gender in SALURBAL cities of Colombia and Mexico are likely to change over time, and predicts their possible evolution through the different stages of the obesity transition.

The current state of complex systems research on socioeconomic inequalities in health and health behavior-a systematic scoping review

BACKGROUND

Interest in applying a complex systems approach to understanding socioeconomic inequalities in health is growing, but an overview of existing research on this topic is lacking. In this systematic scoping review, we summarize the current state of the literature, identify shared drivers of multiple health and health behavior outcomes, and highlight areas ripe for future research.

METHODS

SCOPUS, Web of Science, and PubMed databases were searched in April 2023 for peer-reviewed, English-language studies in high-income OECD countries containing a conceptual systems model or simulation model of socioeconomic inequalities in health or health behavior in the adult general population. Two independent reviewers screened abstracts and full texts. Data on study aim, type of model, all model elements, and all relationships were extracted. Model elements were categorized based on the Commission on Social Determinants of Health framework, and relationships between grouped elements were visualized in a summary conceptual systems map.

RESULTS

A total of 42 publications were included; 18 only contained a simulation model, 20 only contained a conceptual model, and 4 contained both types of models. General health outcomes (e.g., health status, well-being) were modeled more often than specific outcomes like obesity. Dietary behavior and physical activity were by far the most commonly modeled health behaviors. Intermediary determinants of health (e.g., material circumstances, social cohesion) were included in nearly all models, whereas structural determinants (e.g., policies, societal values) were included in about a third of models. Using the summary conceptual systems map, we identified 15 shared drivers of socioeconomic inequalities in multiple health and health behavior outcomes.

CONCLUSIONS

The interconnectedness of socioeconomic position, multiple health and health behavior outcomes, and determinants of socioeconomic inequalities in health is clear from this review. Factors central to the complex system as it is currently understood in the literature (e.g., financial strain) may be both efficient and effective policy levers, and factors less well represented in the literature (e.g., sleep, structural determinants) may warrant more research. Our systematic, comprehensive synthesis of the literature may serve as a basis for, among other things, a complex systems framework for socioeconomic inequalities in health.

The potential impact of policies and structural interventions in reducing cardiovascular disease and mortality: a systematic review of simulation-based studies

Background

The aim of the study was to investigate the potential effect of different structural interventions for preventing cardiovascular disease.

Methods

Medline and EMBASE were searched for peer-reviewed simulation-based studies of structural interventions for prevention of cardiovascular disease. We performed a systematic narrative synthesis.

Results

A total of 54 studies met the inclusion criteria. Diet, nutrition, tobacco and alcohol control and other programmes are among the policy simulation models explored. Food tax and subsidies, healthy food and lifestyles policies, palm oil tax, processed meat tax, reduction in ultra-processed foods, supplementary nutrition assistance programmes, stricter food policy and subsidised community-supported agriculture were among the diet and nutrition initiatives. Initiatives to reduce tobacco and alcohol use included a smoking ban, a national tobacco control initiative and a tax on alcohol. Others included the NHS Health Check, WHO 25 × 25 and air quality management policy.

Future work and limitations

There is significant heterogeneity in simulation models, making comparisons of output data impossible. While policy interventions typically include a variety of strategies, none of the models considered possible interrelationships between multiple policies or potential interactions. Research that investigates dose-response interactions between numerous modifications as well as longer-term clinical outcomes can help us better understand the potential impact of policy-level interventions.

Conclusions

The reviewed studies underscore the potential of structural interventions in addressing cardiovascular diseases. Notably, interventions in areas such as diet, tobacco, and alcohol control demonstrate a prospective decrease in cardiovascular incidents. However, to realize the full potential of such interventions, there is a pressing need for models that consider the interplay and cumulative impacts of multiple policies. Rigorous research into holistic and interconnected interventions will pave the way for more effective policy strategies in the future.

Study registration

The study is registered as PROSPERO CRD42019154836.

Funding

This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number 17/148/05.

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.

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.

How Could Different Obesity Scenarios Alter the Burden of Type 2 Diabetes and Liver Disease in Saudi Arabia?

INTRODUCTION

Obesity is a major risk factor for type 2 diabetes (T2DM) and liver disease, and obesity-attributable liver disease is a common indication for liver transplant. Obesity prevalence in Saudi Arabia (SA) has increased in recent decades. SA has committed to the WHO "halt obesity" target to shift prevalence to 2010 levels by 2025. We estimated the future benefits of reducing obesity in SA on incidence and costs of T2DM and liver disease under two policy scenarios: (1) SA meets the "halt obesity" target; (2) population body mass index (BMI) is reduced by 1% annually from 2020 to 2040.

METHODS

We developed a dynamic microsimulation of working-age people (20-59 years) in SA between 2010 and 2040. Model inputs included population demographic, disease and healthcare cost data, and relative risks of diseases associated with obesity. In our two policy scenarios, we manipulated population BMI and compared predicted disease incidence and associated healthcare costs to a baseline "no change" scenario.

RESULTS

Adults <35 years are expected to meet the "halt obesity" target, but those ≥35 years are not. Obesity is set to decline for females, but to increase amongst males 35-59 years. If SA's working-age population achieved either scenario, >1.15 million combined cases of T2DM, liver disease, and liver cancer could be avoided by 2040. Healthcare cost savings for the "halt obesity" and 1% reduction scenarios are 46.7 and 32.8 billion USD, respectively.

CONCLUSION

SA's younger working-age population is set to meet the "halt obesity" target, but those aged 35-59 are off track. Even a modest annual 1% BMI reduction could result in substantial future health and economic benefits. Our findings strongly support universal initiatives to reduce population-level obesity, with targeted initiatives for working-age people ≥35 years of age.

Guidance on the use of complex systems models for economic evaluations of public health interventions

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA

The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10109-023-00415-y.

Development of the Systems Thinking for Health Actions framework: a literature review and a case study

BACKGROUND

Systems thinking is an approach that views systems with a holistic lens, focusing on how components of systems are interconnected. Specifically, the application of systems thinking has proven to be beneficial when applied to health systems. Although there is plenty of theory surrounding systems thinking, there is a gap between the theoretical use of systems thinking and its actual application to tackle health challenges. This study aimed to create a framework to expose systems thinking characteristics in the design and implementation of actions to improve health.

METHODS

A systematised literature review was conducted and a Taxonomy of Systems Thinking Objectives was adapted to develop the new 'Systems Thinking for Health Actions' (STHA) framework. The applicability of the framework was tested using the COVID-19 response in Pakistan as a case study.

RESULTS

The framework identifies six key characteristics of systems thinking: (1) recognising and understanding interconnections and system structure, (2) identifying and understanding feedback, (3) identifying leverage points, (4) understanding dynamic behaviour, (5) using mental models to suggest possible solutions to a problem and (6) creating simulation models to test policies. The STHA framework proved beneficial in identifying systems thinking characteristics in the COVID-19 national health response in Pakistan.

CONCLUSION

The proposed framework can provide support for those aiming to applying systems thinking while developing and implementing health actions. We also envision this framework as a retrospective tool that can help assess if systems thinking was applied in health actions.

Modeling obesity in complex food systems: Systematic review

Obesity-related data derived from multiple complex systems spanning media, social, economic, food activity, health records, and infrastructure (sensors, smartphones, etc.) can assist us in understanding the relationship between obesity drivers for more efficient prevention and treatment. Reviewed literature shows a growing adaptation of the machine-learning model in recent years dealing with mechanisms and interventions in social influence, nutritional diet, eating behavior, physical activity, built environment, obesity prevalence prediction, distribution, and healthcare cost-related outcomes of obesity. Most models are designed to reflect through time and space at the individual level in a population, which indicates the need for a macro-level generalized population model. The model should consider all interconnected multi-system drivers to address obesity prevalence and intervention. This paper reviews existing computational models and datasets used to compute obesity outcomes to design a conceptual framework for establishing a macro-level generalized obesity model.

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.

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.

National childhood obesity-related intervention systems and intervention programs in China in 1949 to 2020: A narrative review

OBJECTIVE

This review examines main government and nongovernmental institutions for childhood obesity prevention and control in China, as well as major national interventions for childhood obesity.

METHODS

PubMed, China National Knowledge Infrastructure (CNKI), Wanfang, official websites of national governments and professional institutions/associations, Baidu.com, and Google.com were systematically searched in March 2020 to April 2020. A total of 20 international and national experts on childhood obesity were surveyed.

RESULTS

"Government-led multisector cooperation" obesity-related intervention systems have been formed. National-level interventions were mainly implemented by the Chinese State Council and its administrative departments, along with national professional institutions/associations. Provincial, municipal, and county governments and their subordinate departments coordinated local works. Actions taken by these sectors to fight childhood obesity included developing and implementing regulations and laws, health standards and practice guidelines, surveillance for obesity and related determinants, governmental budget and research funds, and interventions. A total of 14 major national childhood obesity-related interventions were found: comprehensive interventions (e.g., "Healthy Lifestyle for All campaign," 2007), diet and nutrition (e.g., "Chinese Rural Compulsory Education Student Nutrition Improvement Program," since 2011), and physical activity (e.g., "Happy 10 Minutes Program," 2006).

CONCLUSIONS

Although obesity-related intervention systems and national interventions were implemented, more efforts and stronger government leadership and commitment are needed.

Adolescent Obesity Modeling: A Framework of Socio-Economic Analysis on Public Health

Bayesian Structural Equation Modeling (SEM-Bayesian) was applied across different research areas to model the correlation between manifest and latent variables. The primary purpose of this study is to introduce a new framework of complexity to adolescent obesity modeling based on adolescent lifestyle through the application of SEM-Bayesian. The introduced model was designed based on the relationships among several factors: household socioeconomic status, healthy food intake, unhealthy food intake, lifestyle, body mass index (BMI) and body fat. One of the main contributions of this study is from considering both BMI and body fat as dependent variables. To demonstrate the reliability of the model, especially in terms of its fitting and accuracy, real-time data were extracted and analyzed across 881 adolescents from secondary schools in Tehran, Iran. The output of this study may be helpful for researchers who are interested in adolescent obesity modeling based on the lifestyle and household socioeconomic status of adolescents.

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.

Simulation modeling validity and utility in colorectal cancer screening delivery: A systematic review

OBJECTIVE

This study sought to assess the impact and validity of simulation modeling in informing decision making in a complex area of healthcare delivery: colorectal cancer (CRC) screening.

MATERIALS AND METHODS

We searched 10 electronic databases for English-language articles published between January 1, 2008, and March 1, 2019, that described the development of a simulation model with a focus on average-risk CRC screening delivery. Included articles were reviewed for evidence that the model was validated, and provided real or potential contribution to informed decision making using the GRADE EtD (Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision) framework.

RESULTS

A total of 43 studies met criteria. The majority used Markov modeling (n = 31 [72%]) and sought to determine cost-effectiveness, compare screening modalities, or assess effectiveness of screening. No study reported full model validation and only (58%) reported conducting any validation. Majority of models were developed to address a specific health systems or policy question; few articles report the model's impact on this decision (n = 39 [91%] vs. n = 5 [12%]). Overall, models provided evidence relevant to every element important to decision makers as outlined in the GRADE EtD framework.

DISCUSSION AND CONCLUSION

Simulation modeling contributes evidence that is considered valuable to decision making in CRC screening delivery, particularly in assessing cost-effectiveness and comparing screening modalities. However, the actual impact on decisions and validity of models is lacking in the literature. Greater validity testing, impact assessment, and standardized reporting of both is needed to understand and demonstrate the reliability and utility of simulation modeling.

Use of Simulation Modeling to Inform Decision Making for Health Care Systems and Policy in Colorectal Cancer Screening: Protocol for a Systematic Review

Background

Simulation modeling has frequently been used to assess interventions in complex aspects of health care, such as colorectal cancer (CRC) screening, where clinical trials are not feasible. Simulation models provide estimates of outcomes, unintended consequences, and costs of an intervention; thus offering an invaluable decision aid for policy makers and health care leaders. However, the contribution that simulation models have made to policy and health system decisions is unknown.

Objective

This study aims to assess if simulation modeling has supported evidence-informed decision making in CRC screening.

Methods

A preliminary literature search and pilot screening of 100 references were conducted by three independent reviewers to define and refine the inclusion criteria of this systematic review. Using the developed inclusion criteria, a search of the academic and gray literature published between January 1, 2008, and March 1, 2019, will be conducted to identify studies that developed a simulation model focusing on the delivery of CRC screening of average-risk individuals. The three independent reviewers will assess the validation process and the extent to which the study contributed evidence toward informed decision making (both reported and potential). Validation will be assessed based on adherence to the best practice recommendations described by the International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making (ISPOR-SMDM). Criteria for potential contribution to decision making will be defined as outlined in the internationally recognized Grading of Recommendations Assessment, Development and Evaluation Evidence to Decision (GRADE EtD) framework. These criteria outline information that the health system and policy decision makers should consider when making an evidence-informed decision including an intervention’s resource utilization, cost-effectiveness, impact on health equity, and feasibility. Subgroup analysis of articles based on their GRADE EtD criteria will be conducted to identify methods associated with decision support capacity (ie, participatory, quantitative, or mixed methods).

Results

A database search of the literature yielded 484 references to screen for inclusion in the systematic review. We anticipate that this systematic review will provide an insight into the contribution of simulation modeling methods to informed decision making in CRC screening delivery and discuss methods that may be associated with a stronger impact on decision making. The project was funded in May 2019. Data collection took place from January 2008 to March 2019. Data analysis was completed in November 2019, and are expected to be published in spring 2020.

Conclusions

Our findings will help guide researchers and health care leaders to mobilize the potential for simulation modeling to inform evidence-informed decisions in CRC screening delivery. The methods of this study may also be replicated to assess the utility of simulation modeling in other areas of complex health care decision making.

International Registered Report Identifier (IRRID)

DERR1-10.2196/16103

Trial Registration

PROSPERO no. 130823; https://www.crd.york.ac.uk/PROSPERO

Transforming Research and Innovation for Sustainable Food Systems—A Coupled-Systems Perspective

Current research and innovation (R&I) systems are not equipped to fully serve as catalysts for the urgently needed transformation of food systems. Though research on food systems transformation (first order: ‘what?’) and transformative research (second order: ‘how to’) are rapidly gaining traction in academic and policy environments, current efforts fail to explicitly recognize the systemic nature of the challenges associated with performing transformative second-order research. To recognize these manifold and interlinked challenges embedded in R&I systems, there is a need for a coupled-systems perspective. Transformations are needed in food systems as well as R&I systems (‘how to do the “how to”’). We set out to conceptualize an approach that aims to trigger double transformations by nurturing innovations at the boundaries of R&I systems and food systems that act upon systemic leverage points, so that their multisystem interactions can better support food system transformations. We exemplify this coupled-systems approach by introducing the FIT4FOOD2030 project with its 25 living labs as a promising multilevel boundary innovation at the cross-section of R&I and food systems. We illustrate how this approach paves the way for double systems transformations, and therefore for an R&I system that is fit for future-proofing food systems.

Is weight regain after bariatric surgery associated with psychiatric comorbidity? A systematic review and meta-analysis

Bariatric surgery has been recognized as the gold standard treatment for severe obesity. Although postbariatric surgery patients usually achieve and maintain substantial weight loss, a group of individuals may exhibit weight regain. Several factors are proposed to weight regain, including psychiatric comorbidity. The objective of the study is to conduct a systematic review and meta-analysis of studies investigating the relationship between psychiatric comorbidity and weight regain. A systematic review through PubMed, Web of Science, Cochrane Library, Scopus, and PsycINFO was performed, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). After a stepwise selection, 13 articles were included in the qualitative analysis and 5 were included for a meta-analysis. Women was majority in most of the studies (87.6%), and a bypass procedure was the bariatric intervention most evaluated (66.8%), followed by gastric banding (32.1%) and sleeve (1.1%). Higher rates of postbariatric surgery eating psychopathology were reported in patients with weight regain. However, the association between general psychopathology and weight regain was not consistent across the studies. In the meta-analysis, the odds of eating psychopathology in the weight regain group was higher compared with the nonweight regain group (OR = 2.2, 95% CI 1.54-3.15). Postbariatric surgery eating psychopathology seems to play an important role in weight regain.

Multi-etiological Perspective on Child Obesity Prevention

PURPOSE OF REVIEW

The simple energy balance model of obesity is inconsistent with the available findings on obesity etiology, prevention, and treatment. Yet, the most commonly stated causes of pediatric obesity are predicated on this model. A more comprehensive biological model is needed upon which to base behavioral interventions aimed at obesity prevention. In this light, alternative etiologies are little investigated and thereby poorly understood.

RECENT FINDINGS

Three candidate alternate etiologies are briefly presented: infectobesity, the gut microbiome, and circadian rhythms. Behavioral child obesity preventive investigators need to collaborate with biological colleagues to more intensively analyze the behavioral aspects of these etiologies and to generate innovative procedures for preventing a multi-etiological problem, e.g., group risk analysis, triaging for likely causes of obesity.