Simulation models of obesity: a review of the literature and implications for research and policy

A Model of Individual BMI Trajectories

A risk factor model of body mass index (BMI) is an important building block of health simulations aimed at estimating government policy effects with regard to overweight and obesity. We created a model that generates representative population level distributions and that also mimics realistic BMI trajectories at an individual level so that policies aimed at individuals can be simulated. The model is constructed by combining several datasets. First, the population level distribution is extracted from a large, cross-sectional dataset. The trend in this distribution is estimated from historical data. In addition, longitudinal data are used to model how individuals move along typical trajectories over time. The model faithfully describes the population level distribution of BMI, stratified by sex, level of education and age. It is able to generate life course trajectories for individuals which seem plausible, but it does not capture extreme fluctuations, such as rapid weight loss.

Planning, implementing and governing systems-based co-creation: the DISCOVER framework

BACKGROUND

Increasingly, public health faces challenges requiring complex, multifaceted and multi-sectoral responses. This calls for systems-based approaches that facilitate the kind of collective and collaborative thinking and working required to address complexity. While the literature on systems thinking, system dynamics and the associated methodologies is extensive, there remains little clear guidance on how to plan, govern and implement participatory systems approaches within a co-creation process.

METHODS

We used a three-step process to develop DISCOVER, a framework for implementing, and governing systems-based co-creation: Stage 1: We conducted a literature analysis of key texts to identify well-documented methods and phases for co-creation using a systems approach, as well as areas where gaps existed. Stage 2: We looked for the most appropriate methods and approaches to fill the gaps in the knowledge production chain. Stage 3: We developed the framework, identifying how the different tools and approaches fit together end-to-end, from sampling and recruiting participants all the way through to responding with an action plan.

RESULTS

We devised DISCOVER to help guide researchers and stakeholders to collectively respond to complex social, health and wider problems. DISCOVER is a strategic research planning and governance framework that provides an actionable, systematic way to conceptualise complex problems and move from evidence to action, using systems approaches and co-creation. In this article, we introduce the eight-step framework and provide an illustrative case study showcasing its potential. The framework integrates complementary approaches and methods from social network analysis, systems thinking and co-creation literature. The eight steps are followed sequentially but can overlap.

CONCLUSIONS

DISCOVER increases rigour and transparency in system approaches to tackling complex issues going from planning to action. It is being piloted in environmental health research but may be suitable to address other complex challenges and could be incorporated into research proposals and protocols for future projects.

Health outcomes in Bulgaria: Simulated effects of obesogenic environmental changes in adulthood versus childhood

OBJECTIVE

Bulgarian government efforts to tackle obesity are focused mainly on guidelines affecting children. However, it is unclear whether targeting children for obesity-related health policies yields better long-term health outcomes as opposed to changing the risk of obesity in adulthood. This study aims to evaluate where policy efforts should be directed to alleviate the health burden associated with obesity.

METHODS

We compare the impact on population health of two simulated scenarios when (a) the prevalence of obesity upon entering adulthood is lowered; (b) the risk of getting an unhealthy weight as an adult is reduced. Additionally, we run (c) combinations of the two and (d) childhood obesity prevention on the one hand, and worsening (increasing) obesity incidence later in adulthood on the other.

RESULTS

Our findings show that obesogenic environmental changes throughout adulthood have a stronger effect on life expectancy (LE), diabetes-free life expectancy (DFLE) and type 2 diabetes prevalence outcomes compared to lowering the proportion of individuals with obesity during adolescence. Nevertheless, a sizable reduction in the number of young adults with unhealthy weight has the potential to recover years of LE/DFLE that would be lost if the risk of obesity in adulthood would continue to grow in time.

CONCLUSIONS

The two types of policies' (a-b) effects are not equivalent in strength and the best way forward is dependent on future obesity incidence trends.

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.

Ureteroscopy Is Equally Efficient and Safe in Obese and Morbidly Obese Patients: A Systematic Review and Meta-Analysis

Background

Ureteroscopy (URS) has been established as an effective treatment for stones in obese patients (OP). However, recent studies found that the efficacy of the procedure may be lower in patients with higher body mass index (BMI). In the current study, we aim to determine if obesity might influence the effectiveness and safety of URS.

Methods

In May 2021, a comprehensive search was conducted in the PubMed, EMBASE, Web of Science, Cochrane Library, and ClinicalTrials.gov to find eligible studies. Stone-free rate (SFR), operative time, length of stay, and complication rate were assessed utilizing RevMan 5.3.

Results

Thirteen studies involving 4,583 normal-weight patients (NWP), 2,465 OP, and 291 morbidly OP (MOP) were included. Pooled results showed that statistically similar SFR existed between OP and NWP [odds ratio (OR): 1.09; 95% CI: 0.79, 1.52; p = 0.59], and between MOP and NWP (OR: 1.03; 95% CI: 0.46, 2.31; p = 0.95). The operation time was similar between OP and NWP [mean difference (MD): −2.27; 95% CI: −8.98, 4.43; p = 0.51], and between MOP and NWP (MD: 4.85; 95% CI: −5.78, 15.47; p = 0.37). In addition, no significant difference regarding length of stay existed between OP and NWP (MD: −0.07; 95% CI: −0.20, 0.07; p = 0.33), and between MOP and NWP (MD: −0.06; 95% CI: −0.25, 0.14; p = 0.58). Furthermore, we observed similar minor complication rate between OP and NWP (OR: 1.04; 95% CI: 0.81, 1.32; p = 0.78), and between MOP and NWP (OR: 1.29; 95% CI: 0.80, 2.08; p = 0.30). The differences concerning major complication rate between OP and NWP (OR: 0.97; 95% CI: 0.39, 2.43; p = 0.95), and between MOP and NWP (OR: 2.01; 95% CI: 0.55, 7.30; p = 0.29) were also not significant.

Conclusions

Our study demonstrated that URS performed in MOP and OP appears to have the same efficacy and safety as well as in NWP group.

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.

Factors influencing usage of urban blue spaces: A systems-based approach to identify leverage points

Urban blue spaces may have salutogenic health benefits. It is crucial to understand the factors that influence the use of urban blue spaces so that urban populations can benefit equitably. A system map of factors influencing usage was developed by qualitatively analysing 203 intercept interviews conducted with people actively using the towpath along the canal in North Glasgow, Scotland. Network analysis was used to analyse the system map's structure identifying Exercise & Health and Urban Nature as key leverage points and Cleanliness & Maintenance as the key area for improvement. Findings could be used to inform the management, governance and revitalisation of urban blue spaces with the ultimate aim of maximising their potential to be equitable, sustainable and salutogenic.

Developing an integrated microsimulation model for the impact of fiscal policies on child health in Europe: the example of childhood obesity in Italy

BACKGROUND

We developed an integrated model called Microsimulation for Income and Child Health (MICH) that provides a tool for analysing the prospective effects of fiscal policies on childhood health in European countries. The aim of this first MICH study is to evaluate the impact of alternative fiscal policies on childhood overweight and obesity in Italy.

METHODS

MICH model is composed of three integrated modules. Firstly, module 1 (M1) simulates the effects of fiscal policies on disposable household income using the tax-benefit microsimulation program EUROMOD fed with the Italian EU-SILC 2010 data. Secondly, module 2 (M2) exploits data provided by the Italian birth cohort called Nascita e Infanzia: gli Effetti dell'Ambiente (NINFEA), translated as Birth and Childhood: the Effects of the Environment study, and runs a series of concatenated regressions in order to estimate the prospective effects of income on child body mass index (BMI) at different ages. Finally, module 3 (M3) uses dynamic microsimulation techniques that combine the population structure and incomes obtained by M1, with regression model specifications and estimated effect sizes provided by M2, projecting BMI distributions according to the simulated policy scenarios.

RESULTS

Both universal benefits, such as universal basic income (BI), and targeted interventions, such as child benefit (CB) for poorer households, have a significant effect on childhood overweight, with a prevalence ratio (PR) in 10-year-old children-in comparison with the baseline fiscal system-of 0.88 (95%CI 0.82-0.93) and 0.89 (95%CI 0.83-0.94), respectively. The impact of the fiscal reforms was even larger for child obesity, reaching a PR of 0.67 (95%CI 0·50-0.83) for the simulated BI and 0.64 (95%CI 0.44-0.84) for CB at the same age. While both types of policies show similar effects, the estimated costs for a 1% prevalence reduction in overweight and obesity with respect to the baseline scenario is much lower with a more focalised benefit policy than with universal ones.

CONCLUSIONS

Our results show that fiscal policies can have a strong impact on childhood health conditions. Focalised interventions that increase family income, especially in the most vulnerable populations, can help to prevent child overweight and obesity. Robust microsimulation models to forecast the effects of fiscal policies on health should be considered as one of the instruments to reach the Health in All Policies (HiAP) goals.

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.

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.

Modeling Obesity in Norway (The MOON Study): A Decision-Analytic Approach-Prevalence, Costs, and Years of Life Lost

BACKGROUND

Limited knowledge exists on the expected long-term effects and cost-effectiveness of initiatives aiming to reduce the burden of obesity.

AIM

To develop a Norwegian obesity-focused disease-simulation model: the MOON model.

MATERIAL AND METHODS

We developed a Markov model and simulated a Norwegian birth cohort's movement between the health states "normal weight,""overweight,""obese 1,""obese 2," and "dead" using a lifetime perspective. Model input was estimated using longitudinal data from health surveys and real-world data (RWD) from local and national registers (N = 99,348). The model is deterministic and probabilistic and stratified by gender. Model validity was assessed by estimating the cohort's expected prevalence, health care costs, and mortality related to overweight and obesity.

RESULTS

Throughout the cohort's life, the prevalence of overweight increased steadily and stabilized at 45% at 45 y of age. The number of obese 1 and 2 individuals peaked at age 75 y, when 44% of women and 35% of men were obese. The incremental costs per person associated with obesity was highest in older ages and, when accumulated over the lifetime, higher among women (€12,118, €9,495-€15,047) than men (€6,646, €5,252-€10,900). On average, obesity shortened the life expectancy of women/men in the whole cohort by 1.31/1.08 y. The life expectancy for normal-weight women/men at age 30 was 83.31/80.31. The life expectancy was reduced by 1.05/0.65 y if the individual was overweight, obese (2.87/2.71 y), or obese 2 (4.06/4.83 y).

CONCLUSION

The high expected prevalence of obesity in the future will lead to substantial health care costs and large losses in life-years. This underscores the need to implement interventions to reduce the burden of obesity; the MOON model will enable economic evaluations for a wide range of interventions.

The opioid crisis: need for systems science research

The opioid epidemic in the United States has had a devastating impact on millions of people as well as on their families and communities. The increased prevalence of opioid misuse, use disorder and overdose in recent years has highlighted the need for improved public health approaches for reducing the tremendous harms of this illness. In this paper, we explain and call for the need for more systems science approaches, which can uncover the complexities of the opioid crisis, and help evaluate, analyse and forecast the effectiveness of ongoing and new policy interventions. Similar to how a stream of systems science research helped policy development in infectious diseases and obesity, more systems science research is needed in opioids.

Simulations of future cardiometabolic disease and life expectancy under counterfactual obesity reduction scenarios

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HIAs provide simulations of future disease levels related to an array of obesity scenarios.

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In a relatively lean population, obesity still contribute to a substantial reduction in life expectancy.

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Large reductions in diabetes and multi-morbidity is estimated as an effect of reducing obesity.

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Incremental increase in future stroke and IHD cases is expected.

The aim of this study was to provide decision makers with an assessment of potential future health effects of interventions against overweight and obesity (OWOB). By means of the DYNAMO-HIA tool we conducted a health impact assessment simulating future prevented disease (ischemic heart disease (IHD), diabetes, stroke, and multi morbidity) incidence, prevalence and life expectancy (LE) related to a scenario where OWOB is reduced by 25% and a scenario where obesity is eliminated. The study covered projected number of persons living in Copenhagen, Denmark during year 2014–2040 (n 2040 = 742,129). Reducing the proportion of men/women with OWOB with 25% will increase population LE by 2.4/1.2 months and at the same time decrease LE with diabetes by 3.1/2.2 months. As a result of eliminating obesity, total LE will increase by 6.0/3.6 months and LE with diabetes will decrease with 9.8/10.3 months for men/women. We found no important effects on LE with IHD and stroke. This illustrates that the positive effects of lowering OWOB levels on IHD and stroke incidence is offset due to increasing total LE. Although the population of Copenhagen is relatively lean, reducing obesity levels will result in significant benefits for population cardiometabolic health status and LE. Future public health prevention programs may use the results as reference data for potential impact of reductions in OWOB.

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.

Forecasting the prevalence of overweight and obesity in India to 2040

Background

In India, the prevalence of overweight and obesity has increased rapidly in recent decades. Given the association between overweight and obesity with many non-communicable diseases, forecasts of the future prevalence of overweight and obesity can help inform policy in a country where around one sixth of the world’s population resides.

Methods

We used a system of multi-state life tables to forecast overweight and obesity prevalence among Indians aged 20–69 years by age, sex and urban/rural residence to 2040. We estimated the incidence and initial prevalence of overweight using nationally representative data from the National Family Health Surveys 3 and 4, and the Study on global AGEing and adult health, waves 0 and 1. We forecasted future mortality, using the Lee-Carter model fitted life tables reported by the Sample Registration System, and adjusted the mortality rates for Body Mass Index using relative risks from the literature.

Results

The prevalence of overweight will more than double among Indian adults aged 20–69 years between 2010 and 2040, while the prevalence of obesity will triple. Specifically, the prevalence of overweight and obesity will reach 30.5% (27.4%-34.4%) and 9.5% (5.4%-13.3%) among men, and 27.4% (24.5%-30.6%) and 13.9% (10.1%-16.9%) among women, respectively, by 2040. The largest increases in the prevalence of overweight and obesity between 2010 and 2040 is expected to be in older ages, and we found a larger relative increase in overweight and obesity in rural areas compared to urban areas. The largest relative increase in overweight and obesity prevalence was forecast to occur at older age groups.

Conclusion

The overall prevalence of overweight and obesity is expected to increase considerably in India by 2040, with substantial increases particularly among rural residents and older Indians. Detailed predictions of excess weight are crucial in estimating future non-communicable disease burdens and their economic impact.

Introduction to the Theme Issue on Dynamics of Health Behavior: Revisiting Systems Science for Population Health

Systems science can help public health professionals to better understand the complex dynamics between factors affecting health behaviors and outcomes and to identify intervention opportunities. Despite their demonstrated utility in addressing health topics such influenza, tobacco control, and obesity, the associated methods continue to be underutilized by researchers and practitioners addressing health behaviors. This article discusses the growth of systems science methods (e.g., system dynamics, social network analysis, and agent-based modeling) in health research, provides a frame for the articles included in this themed issue, and closes with recommendations for enhancing the future of systems science and health behavior research. We argue that integrating systems sciences methods into health behavior research and practice is essential for improved population health and look forward to supporting the evolution of the field.

Microsimulation model of child and adolescent overweight: making use of what we already know

BACKGROUND

New Zealand has high rates of child overweight and obesity when compared with other countries. Despite an abundance of research documenting the problem, it is unclear what the most effective policy changes or interventions are, and how policy changes might unfold over time within complex systems.

METHODS

We use estimates derived from meta-analyses to create a dynamic microsimulation model of child overweight (including obesity). Using census records we created a synthetic birth cohort of 10,000 children. Information on parental education, ethnicity and father's socio-economic position at birth were taken from census records. We used the New Zealand Health Survey to estimate population base rates for the prevalence of overweight and obesity. Information on other modifiers (such as maternal smoking, breastfeeding, preterm birth, regular breakfast consumption and so forth) were taken from three birth cohorts: Christchurch Health and Development Study, The Dunedin Multidisciplinary Health and Development Study and the Pacific Islands Families Study. Published intervention studies were used to derive plausible estimates for changes to modifiers.

RESULTS

Reducing the proportion of mothers classified as overweight and obesity (-3.31(95% CI -3.55; -3.07) percentage points), reducing the proportion of children watching two or more hours of TV (-3.78(95% CI -4.01; -3.54)), increasing the proportion of children eating breakfast regularly (-1.71(95% CI -1.96; -1.46)), and reducing the proportion of children born with high birth weights (-1.36(95% CI -1.61; -1.11)), lead to sizable decreases in the estimated prevalence of child overweight (including obesity). Reducing the proportion of mothers giving birth by caesarean (-0.23(95% CI -0.49; -0.23)) and increasing parental education (-0.07(95% CI -0.31; 0.18)) did not impact upon child overweight rates.

CONCLUSIONS

We created a working simulation model of New Zealand children that can be accessed by policy makers and researchers to determine known relationships between predictors and child overweight, as well as potential gains from targeting specific pathways.

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.

Socioeconomic inequalities in obesity: modelling future trends in Australia

OBJECTIVES

To develop a model to predict future socioeconomic inequalities in body mass index (BMI) and obesity.

DESIGN

Microsimulation modelling using BMI data from adult participants of Australian Health Surveys, and published data on the relative risk of mortality in relation to BMI and socioeconomic position (SEP), based on education.

SETTING

Australia.

PARTICIPANTS

74 329 adults, aged 20 and over from Australian Health Surveys, 1995-2015.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcomes were BMI trajectories and obesity prevalence by SEP for four birth cohorts, born 10 years apart, centred on 1940, 1950, 1960 and 1970.

RESULTS

Simulations projected persistent or widening socioeconomic inequality in BMI and obesity over the adult life course, for all birth cohorts. Recent birth cohorts were predicted to have greater socioeconomic inequality by middle age, compared with earlier cohorts. For example, among men, there was no inequality in obesity prevalence at age 60 for the 1940 birth cohort (low SEP 25% (95% CI 17% to 34%); high SEP 26% (95% CI 19% to 34%)), yet for the 1970 birth cohort, obesity prevalence was projected to be 51% (95% CI 43% to 58%) and 41% (95% CI 36% to 46%) for the low and high SEP groups, respectively. Notably, for more recent birth cohorts, the model predicted the greatest socioeconomic inequality in severe obesity (BMI >35 kg/m²) at age 60.

CONCLUSIONS

Lower SEP groups and more recent birth cohorts are at higher risk of obesity and severe obesity, and its consequences in middle age. Prevention efforts should focus on these vulnerable population groups in order to avoid future disparities in health outcomes. The model provides a framework for further research to investigate which interventions will be most effective in narrowing the gap in socioeconomic disparities in obesity in adulthood.

A New Model for Evaluation of Interventions to Prevent Obesity in Early Childhood

Background: Childhood obesity is a serious public health issue. In Australia, 1 in 4 children is already affected by overweight or obesity at the time of school entry. Governments around the world have recognized this problem through investment in the prevention of pediatric obesity, yet few interventions in early childhood have been subjected to economic evaluation. Information on cost-effectiveness is vital to decisions about program implementation. A challenge in evaluating preventive interventions in early childhood is to capture long-term costs and outcomes beyond the duration of an intervention, as the benefits of early obesity prevention will not be realized until some years into the future. However, decisions need to be made in the present, and modeling is one way to inform such decisions. Objective: To describe the conceptual structure of a new health economic model (the Early Prevention of Obesity in CHildhood (EPOCH) model) for evaluating childhood obesity interventions; and to validate the epidemiologic predictions. Methods and Results: We use an individual-level (micro-simulation) method to model BMI trajectories and the progression of obesity from early childhood to adolescence. The equations predicting individual BMI change underpinning our model were derived from data from the population-representative study, the Longitudinal Study of Australian Children (LSAC). Our approach is novel because it will account for costs and benefits accrued throughout childhood and adolescence. As a first step to validate the epidemiological predictions of the model, we used input data representing over 250,000 children aged 4/5 years, and simulated BMI and obesity trajectories until adolescence. Simulated mean BMI and obesity prevalence for boys and girls were verified by nationally-representative data on children at 14/15 years of age. Discussion: The EPOCH model is epidemiologically sound in its prediction of both BMI trajectories and prevalence of obesity for boys and girls. Future developments of the model will include socio-economic position and will incorporate the impacts of obesity on healthcare costs. The EPOCH model will help answer: when is it best to intervene in childhood; what are the most cost-effective approaches and which population groups will benefit most from interventions.

Event simulation and external validation applied in published health economic models for obesity: a systematic review

INTRODUCTION

This study aims to determine methodological variations in the event simulation approaches of published health economic decision models, in the field of obesity, and to investigate whether their predictiveness and validity were investigated via external event validation techniques, which investigate how well the model reproduces reality.

AREAS COVERED

A systematic review identified a total of 87 relevant papers, of which 72 that simulated obesity-associated events were included. Most frequently simulated events were coronary heart disease (≈ 83%), type 2 diabetes (≈ 74%), and stroke (≈ 66%). Only for ten published model-based health economic assessments in obesity an external event validation was performed (14%; 10 of 72), and only for one the predictiveness and validity of the event simulation was investigated in a cohort of obese subjects.

EXPERT COMMENTARY

We identified a wide range of obesity related event simulation approaches. Published obesity models lack information on the predictive quality and validity of the applied event simulation approaches. Further work on comparing and validating these event simulation approaches is required to investigate their predictiveness and validity, which will offer guidance future modelling in the field of obesity.

Applications of systems modelling in obesity research

Obesity is a complex system problem involving a broad spectrum of policy, social, economic, cultural, environmental, behavioural, and biological factors and the complex interrelated, cross-sector, non-linear, dynamic relationships among them. Systems modelling is an innovative approach with the potential for advancing obesity research. This study examined the applications of systems modelling in obesity research published between 2000 and 2017, examined how the systems models were developed and used in obesity studies and discussed related gaps in current research. We focused on the applications of two main systems modelling approaches: system dynamics modelling and agent-based modelling. The past two decades have seen a growing body of systems modelling in obesity research. The research topics ranged from micro-level to macro-level energy-balance-related behaviours and policies (19 studies), population dynamics (five studies), policy effect simulations (eight studies), environmental (10 studies) and social influences (15 studies) and their effects on obesity rates. Overall, systems analysis in public health research is still in its early stages, with limitations linked to model validity, mixed findings and its actual use in guiding interventions. Challenges in theory and modelling practices need to be addressed to realize the full potential of systems modelling in future obesity research and interventions.

Obesity and kidney stone disease: a systematic review

INTRODUCTION

Currently, abdominal obesity has reached an epidemic stage and obesity represents an important challenge for worldwide health authorities. Epidemiologic studies have demonstrated that the stone risk incidence increases with Body Mass Index, through multiple pathways. Metabolic syndrome and diabetes are associated with an increased renal stones disease incidence. The aim of this systematic review was to investigate the prevalence, morbidity, risk factors involved in the association between obesity and urolithiasis.

EVIDENCE ACQUISITION

The search involved finding relevant studies from MEDLINE, EMBASE, Ovid, the Cochrane Central Register of Controlled Trials, CINAHL, Google Scholar, and individual urological journals between January 2001 and May 2017. The inclusion criteria were for studies written in the English language, reporting on the association between obesity and urinary stones.

EVIDENCE SYNTHESIS

The underlying pathophysiology of stone formation in obese patients is thought to be related to insulin resistance, dietary factors, and a lithogenic urinary profile. Uric acid stones and calcium oxalate stones are observed frequently in these patients. Insulin resistance is thought to alter the renal acid-base metabolism, resulting in a lower urine pH, and increasing the risk of uric acid stone disease. Obesity is also associated with excess nutritional intake of lithogenic substances and with an increase in urinary tract infection incidence. Recent studies highlighted that renal stone disease increases the risk of myocardial infarction, progression of chronic kidney disease, and diabetes. Contemporary, bariatric surgery has been shown to be associated with hyperoxaluria and oxalate nephropathy. Certainly, the many health risks of obesity, including nephrolithiasis, will add more burden on urologists and nephrologists.

CONCLUSIONS

Obesity related nephrolithiasis seems to necessitate weight loss as primary treatment, but the recognition of the associated complications is necessary to prevent induction of new and equally severe medical problems. The optimal approach to obesity control that minimizes stone risk needs to be determined in order to manage obesity-induced renal stones disease.

Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT)

Background

Given the dramatic rise in the prevalence of obesity, greater focus on prevention is necessary. We sought to develop and validate a population risk tool for obesity to inform prevention efforts.

Methods

We developed the Obesity Population Risk Tool (OPoRT) using the longitudinal National Population Health Survey and sex-specific Generalized Estimating Equations to predict the 10-year risk of obesity among adults 18 and older. The model was validated using a bootstrap approach accounting for the survey design. Model performance was measured by the Brier statistic, discrimination was measured by the C-statistic, and calibration was assessed using the Hosmer-Lemeshow Goodness of Fit Chi Square (HL χ²).

Results

Predictive factors included baseline body mass index, age, time and their interactions, smoking status, living arrangements, education, alcohol consumption, physical activity, and ethnicity. OPoRT showed good performance for males and females (Brier 0.118 and 0.095, respectively), excellent discrimination (C statistic ≥ 0.89) and achieved calibration (HL χ² <20).

Conclusion

OPoRT is a valid and reliable algorithm that can be applied to routinely collected survey data to estimate the risk of obesity and identify groups at increased risk of obesity. These results can guide prevention efforts aimed at reducing the population burden of obesity.

Using systems science to understand the determinants of inequities in healthy eating

Introduction

Systems thinking has emerged in recent years as a promising approach to understanding and acting on the prevention and amelioration of non-communicable disease. However, the evidence on inequities in non-communicable diseases and their risks factors, particularly diet, has not been examined from a systems perspective. We report on an approach to developing a system oriented policy actor perspective on the multiple causes of inequities in healthy eating.

Methods

Collaborative conceptual modelling workshops were held in 2015 with an expert group of representatives from government, non-government health organisations and academia in Australia. The expert group built a systems model using a system dynamics theoretical perspective. The model developed from individual mind maps to pair blended maps, before being finalised as a causal loop diagram.

Results

The work of the expert stakeholders generated a comprehensive causal loop diagram of the determinants of inequity in healthy eating (the HE² Diagram). This complex dynamic system has seven sub-systems: (1) food supply and environment; (2) transport; (3) housing and the built environment; (4) employment; (5) social protection; (6) health literacy; and (7) food preferences.

Discussion

The HE² causal loop diagram illustrates the complexity of determinants of inequities in healthy eating. This approach, both the process of construction and the final visualisation, can provide the basis for planning the prevention and amelioration of inequities in healthy eating that engages with multiple levels of causes and existing policies and programs.

The development of scientific evidence for health policies for obesity: why and how?

Potential obesity-related policy approaches have recently been receiving more attention. Although some have been implemented and others only proposed, few have been formally evaluated. We discuss the relevance, and in some cases irrelevance, of some of the types of evidence that are often brought to bear in considering obesity-related policy decisions. We discuss major methods used to generate such evidence, emphasizing study design and the varying quality of the evidence obtained. Third, we consider what the standards of evidence should be in various contexts, who ought to set those standards, as well as the inherent subjectivity involved in making policy decisions. Finally, we suggest greater transparency from both academics and policymakers in the acknowledgment of subjectivities so they can distinguish and communicate the roles of empirical evidence and subjective values in the formulation of policy.

Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations

Most adults are overweight or obese in many western countries. Several population-level interventions on the physical, economical, political, or sociocultural environment have thus attempted to achieve a healthier weight. These interventions have involved different weight-related behaviours, such as food behaviours. Agent-based models (ABMs) have the potential to help policymakers evaluate food behaviour interventions from a systems perspective. However, fully realizing this potential involves a complex procedure starting with obtaining and analyzing data to populate the model and eventually identifying more efficient cross-sectoral policies. Current procedures for ABMs of food behaviours are mostly rooted in one technique, often ignore the food environment beyond home and work, and underutilize rich datasets. In this paper, we address some of these limitations to better support policymakers through two contributions. First, via a scoping review, we highlight readily available datasets and techniques to deal with these limitations independently. Second, we propose a three steps' process to tackle all limitations together and discuss its use to develop future models for food behaviours. We acknowledge that this integrated process is a leap forward in ABMs. However, this long-term objective is well-worth addressing as it can generate robust findings to effectively inform the design of food behaviour interventions.

How to influence the obesity landscape using health policies

There is widespread acceptance that a significant and sustained impact on the growing global obesity burden requires implementation of a range of health policies to influence the obesity landscape. This acceptance is underpinned by the understanding that the obesity landscape is a complex interaction between the many factors that influence an individual's dietary intake and physical activity levels. Over the past decade we have seen increasing convergence in national and international recommendations on how to best improve this obesity landscape. In the past few years this has led to a noticeable increase in the implementation of these recommended national, state and local government policies. Here, we argue that to maximise the impact of population-level policies intended to improve diet and activity environments we need to see progress in a number of key areas, namely: broadening the range of environments that can be empowered to implement policy; improving our understanding of how best to combine multiple policies and interventions; and improving our understanding of the equity impact of these policies. We also argue that a key goal moving forward should be better capture and communication of the existing activities in order to more rapidly spread the uptake of these policies globally and at scale.

Modelling obesity trends in Australia: unravelling the past and predicting the future

BACKGROUND/OBJECTIVES

Modelling is increasingly being used to predict the epidemiology of obesity progression and its consequences. The aims of this study were: (a) to present and validate a model for prediction of obesity among Australian adults and (b) to use the model to project the prevalence of obesity and severe obesity by 2025.

SUBJECTS/METHODS

Individual level simulation combined with survey estimation techniques to model changing population body mass index (BMI) distribution over time. The model input population was derived from a nationally representative survey in 1995, representing over 12 million adults. Simulations were run for 30 years. The model was validated retrospectively and then used to predict obesity and severe obesity by 2025 among different aged cohorts and at a whole population level.

RESULTS

The changing BMI distribution over time was well predicted by the model and projected prevalence of weight status groups agreed with population level data in 2008, 2012 and 2014.The model predicts more growth in obesity among younger than older adult cohorts. Projections at a whole population level, were that healthy weight will decline, overweight will remain steady, but obesity and severe obesity prevalence will continue to increase beyond 2016. Adult obesity prevalence was projected to increase from 19% in 1995 to 35% by 2025. Severe obesity (BMI>35), which was only around 5% in 1995, was projected to be 13% by 2025, two to three times the 1995 levels.

CONCLUSIONS

The projected rise in obesity severe obesity will have more substantial cost and healthcare system implications than in previous decades. Having a robust epidemiological model is key to predicting these long-term costs and health outcomes into the future.

Parental Predictions and Perceptions Regarding Long-Term Childhood Obesity-Related Health Risks

OBJECTIVE

To assess how parents perceive long-term risks for developing obesity-related chronic health conditions.

METHODS

A Web-based nationally representative survey was administered to 502 US parents with a 5- to 12-year-old child. Parents reported whether their child was most likely to be at a healthy weight or overweight, and the probability that their child would develop hypertension, heart disease, depression, or type 2 diabetes in adulthood. Responses of parents of children with overweight and obesity were compared to those of healthy-weight children using multivariate models.

RESULTS

The survey had an overall response rate of 39.2%. The mean (SD) unadjusted parent predicted health risks were 15.4% (17.7%), 11.2% (14.7%), 12.5% (16.2%), and 12.1% (16.1%) for hypertension, heart disease, depression, and diabetes, respectively. Despite underperceiving their child's current body mass index class, parents of children with obesity estimate their children to be at greater risk for obesity-related health conditions than parents of healthy-weight children by 5 to 6 percentage points. Having a family history of a chronic disease, higher quality of care, and older parent age were also significant predictors of estimating higher risk probabilities.

CONCLUSIONS

Despite evidence that parents of children who are overweight may not perceive these children as being overweight, parents unexpectedly estimate greater future risk of weight-related health conditions for these children. Focusing communication about weight on screening for and reducing the risk of weight-related diseases may prove useful in engaging parents and children in weight management.

Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions

The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.

Analyzing heterogeneity in the effects of physical activity in children on social network structure and peer selection dynamics

BACKGROUND

Social networks influence children and adolescents' physical activity. The focus of this paper is to examine the differences in the effects of physical activity on friendship selection, with eye to the implications on physical activity interventions for young children. Network interventions to increase physical activity are warranted but have not been conducted. Prior to implementing a network intervention in the field, it is important to understand potential heterogeneities in the effects that activity level have on network structure. In this study, the associations between activity level and cross sectional network structure, and activity level and change in network structure are assessed.

METHODS

We studied a real-world friendship network among 81 children (average age 7.96 years) who lived in low SES neighborhoods, attended public schools, and attended one of two structured aftercare programs, of which one has existed and the other was new. We used the exponential random graph model (ERGMs) and its longitudinal extension to evaluate the association between activity level and various demographic factors in having, forming, and dissolving friendship. Due to heterogeneity between the friendship networks within the aftercare programs, separate analyses were conducted for each network.

RESULTS

There was heterogeneity in the effect of physical activity on both cross sectional network structure and the formation and dissolution processes, both across time and between networks.

CONCLUSIONS

Network analysis could be used to assess the unique structure and dynamics of a social network before an intervention is implemented, so as to optimize the effects of the network intervention for increasing childhood physical activity. Additionally, if peer selection processes are changing within a network, a static network intervention strategy for childhood physical activity could become inefficient as the network evolves.

Overcoming Barriers in Unhealthy Settings: A Phenomenological Study of Healthy Truck Drivers

We investigated the phenomenon of sustained health-supportive behaviors among long-haul commercial truck drivers, who belong to an occupational segment with extreme health disparities. With a focus on setting-level factors, this study sought to discover ways in which individuals exhibit resiliency while immersed in endemically obesogenic environments, as well as understand setting-level barriers to engaging in health-supportive behaviors. Using a transcendental phenomenological research design, 12 long-haul truck drivers who met screening criteria were selected using purposeful maximum sampling. Seven broad themes were identified: access to health resources, barriers to health behaviors, recommended alternative settings, constituents of health behavior, motivation for health behaviors, attitude toward health behaviors, and trucking culture. We suggest applying ecological theories of health behavior and settings approaches to improve driver health. We also propose the Integrative and Dynamic Healthy Commercial Driving (IDHCD) paradigm, grounded in complexity science, as a new theoretical framework for improving driver health outcomes.

Economic Impact of Obesity

Parallel to rising obesity rates is an increase in costs associated with excess weight. Estimates of future direct (medical) and indirect (nonmedical) costs related to obesity suggest rising expenditures that will impose a significant economic burden to individuals and society as a whole. This article reviews research on direct and indirect medical costs and future economic trends associated with obesity and associated comorbidities. Cost disparities associated with subsets of the population experiencing higher than average rates of obesity are explored. Finally, potential solutions with the highest estimated impact are offered, and future directions are proposed.

What Happens to Patterns of Food Consumption when Food Prices Change? Evidence from A Systematic Review and Meta-Analysis of Food Price Elasticities Globally

Recent years have seen considerable interest in examining the impact of food prices on food consumption and subsequent health consequences. Fiscal policies targeting the relative price of unhealthy foods are frequently put forward as ways to address the obesity epidemic. Conversely, various food subsidy interventions are used in attempts to reduce levels of under-nutrition. Information on price elasticities is essential for understanding how such changes in food prices affect food consumption. It is crucial to know not only own-price elasticities but also cross-price elasticities, as food substitution patterns may have significant implications for policy recommendations. While own-price elasticities are common in analyses of the impact of food price changes on health, cross-price effects, even though generally acknowledged, are much less frequently included in analyses, especially in the public health literature. This article systematically reviews the global evidence on cross-price elasticities and provides combined estimates for seven food groups in low-income, middle-income and high-income countries alongside previously estimated own-price elasticities. Changes in food prices had the largest own-price effects in low-income countries. Cross-price effects were more varied and depending on country income level were found to be reinforcing, undermining or alleviating own-price effects.

Separating boys and girls and increasing weight? Assessing the impacts of single-sex schools through random assignment in Seoul

A growing body of research reports associations of school contexts with adolescents' weight and weight-related behaviors. One interesting, but under-researched, dimension of school context that potentially matters for adolescents' weight is the gender composition. If boys and girls are separated into single-sex schools, they might be less concerned about physical appearance, which may result in increased weight. Utilizing a unique setting in Seoul, Korea where students are randomly assigned to single-sex and coeducational schools within school districts, we estimate causal effects of single-sex schools on weight and weight-related behaviors. Our results show that students attending single-sex schools are more likely to be overweight, and that the effects are more pronounced for girls. We also find that girls in single-sex schools are less likely to engage in strenuous activities than their coeducational counterparts.

Health and Wellness Programs for Commercial Motor-Vehicle Drivers

The workplace is an invaluable venue for health protection and promotion interventions, particularly for truck drivers due to their overreliance on their work environments, a plethora of work-related stressors, and their morbidity rates. Extant efforts of trucking companies to address driver health through worksite health and wellness programs have been inadequate, producing unsustainable results. The Driver Health and Wellness Program Survey was designed for and disseminated to 46 trucking companies to assess the current state of health and wellness programs in the trucking industry, including program participation rates and longevity, program evaluation procedures, and program activities and resources. Findings indicate that programmatic efforts in trucking companies continue to fall short, and health and wellness programs are insufficient to improve health outcomes in a sustainably positive direction. A new integrated, systems-based paradigm is proposed as a conceptual and methodological framework with the potential to meaningfully advance interventions in blue-collar work settings.

Rising trends of obesity and abdominal obesity in 10 years of follow-up among Tehranian adults: Tehran Lipid and Glucose Study (TLGS)

OBJECTIVE

Some recent studies have shown stablity or declining trends in obesity while others still report increasing trends. The present study aimed to investigate the trends of obesity and abdominal obesity in Tehranian adults during a median follow-up of 10 years.

DESIGN

Prospective cohort study.

SETTING

Community-based data collection from the Tehran Lipid and Glucose Study (TLGS).

SUBJECTS

Participants from four phases of the TLGS from 1999 to 2011 (n 10,368), aged ≥20 years.

RESULTS

The crude prevalence of obesity and abdominal obesity increased from 23·1% and 47·9% at baseline to 34·1% and 71·1% at the end of follow-up, respectively. Generalized estimating equation (GEE) models were used to analyse the correlated data and calculate the relative risks (RR). Risks of obesity and abdominal obesity increased over the whole study period for men (RR=1·62; 95% CI 1·49, 1·76 and RR=1·46; 95% CI 1·41, 1·52, respectively) and women (RR=1·24; 95% CI 1·19, 1·29 and RR=1·22; 95% CI 1·18, 1·27, respectively). These rising trends were observed in all subgroups regardless of age, marital status and educational level.

CONCLUSIONS

Trends of obesity and abdominal obesity are increasing in Tehranian adults during a decade of follow-up in both genders and in all study subgroups. These results underscore the still growing obesity epidemic in the capital of Iran, calling for urgent action to educate people in lifestyle modifications and the need for effective preventive and educational strategies on obesity.

Network interventions on physical activity in an afterschool program: an agent-based social network study

OBJECTIVES

We studied simulated interventions that leveraged social networks to increase physical activity in children.

METHODS

We studied a real-world social network of 81 children (average age = 7.96 years) who lived in low socioeconomic status neighborhoods, and attended public schools and 1 of 2 structured afterschool programs. The sample was ethnically diverse, and 44% were overweight or obese. We used social network analysis and agent-based modeling simulations to test whether implementing a network intervention would increase children's physical activity. We tested 3 intervention strategies.

RESULTS

The intervention that targeted opinion leaders was effective in increasing the average level of physical activity across the entire network. However, the intervention that targeted the most sedentary children was the best at increasing their physical activity levels.

CONCLUSIONS

Which network intervention to implement depends on whether the goal is to shift the entire distribution of physical activity or to influence those most adversely affected by low physical activity. Agent-based modeling could be an important complement to traditional project planning tools, analogous to sample size and power analyses, to help researchers design more effective interventions for increasing children's physical activity.

The prevalence and trends in overweight and obesity in Irish adults between 1990 and 2011

OBJECTIVE

Obesity is a serious public health issue, the prevalence of which is increasing globally. The present study aimed to investigate trends in overweight and obesity in Irish adults between 1990 and 2011.

DESIGN

Anthropometric data from three Irish national food consumption surveys were used to calculate trends in BMI, waist circumference and waist:hip ratio.

SETTING

Three cross-sectional food consumption surveys: the Irish National Nutrition Survey (1990), the North/South Ireland Food Consumption Survey (2001) and the National Adult Nutrition Survey (2011).

SUBJECTS

A collective sample of free-living Irish adults (n 3125), aged 18-64 years.

RESULTS

There were significant increases in mean weight, height and BMI from 1990 to 2011. Significant increments were also reported in waist and hip circumferences and waist:hip ratio between 2001 and 2011, with concurrent increases in the proportion of individuals at risk of developing CVD, particularly females aged 18-35 years. In 2011, 23·4 % of the Irish population was classified as obese; with the mean BMI increasing by 1·1 kg/m2 between 1990 and 2001 and by 0·6 kg/m2 between 2001 and 2011.

CONCLUSIONS

The present paper characterises obesity levels in Irish adults from 1990 to 2011. Absolute levels of overweight and obesity have increased between these time points. Of concern is the increase in the proportion of young women classified as at risk of CVD, using waist circumference and waist:hip ratio. Effective prevention strategies are needed to avoid further increases.

A metabolic-epidemiological microsimulation model to estimate the changes in energy intake and physical activity necessary to meet the Healthy People 2020 obesity objective

OBJECTIVES

We combined a metabolic and an epidemiological model of obesity to estimate changes in calorie intake and physical activity necessary to achieve the Healthy People 2020 objective of reducing adult obesity prevalence from 33.9% to 30.5%.

METHODS

We used the National Health and Nutrition Examination Survey (1999-2010) to construct and validate a microsimulation model of the US population aged 10 years and older, for 2010 to 2020.

RESULTS

Obesity prevalence is expected to shift toward older adults, and disparities are expected to widen between White, higher-income groups and minority, lower-income groups if recent calorie consumption and expenditure trends continue into the future. Although a less than 10% reduction in daily calorie intake or increase in physical activity would in theory achieve the Healthy People 2020 objective, no single population-level intervention is likely to achieve the target alone, and individual weight-loss attempts are even more unlikely to achieve the target.

CONCLUSIONS

Changes in calorie intake and physical activity portend rising inequalities in obesity prevalence. These changes require multiple simultaneous population interventions.

Mainstreaming modeling and simulation to accelerate public health innovation

Dynamic modeling and simulation are systems science tools that examine behaviors and outcomes resulting from interactions among multiple system components over time. Although there are excellent examples of their application, they have not been adopted as mainstream tools in population health planning and policymaking. Impediments to their use include the legacy and ease of use of statistical approaches that produce estimates with confidence intervals, the difficulty of multidisciplinary collaboration for modeling and simulation, systems scientists' inability to communicate effectively the added value of the tools, and low funding for population health systems science. Proposed remedies include aggregation of diverse data sets, systems science training for public health and other health professionals, changing research incentives toward collaboration, and increased funding for population health systems science projects.

Systems science: a good investment for the public's health

This supplement of Health Education & Behavior showcases the current state of the field of systems science applications in health promotion and public health. Behind this work lies a steady stream of public dollars at the federal level. This perspective details nearly a decade of investment by the National Institutes of Health's Office of Behavioral and Social Sciences Research. These investments have included funding opportunity announcements, training programs, developing resources for researchers, cross-disciplinary fertilization, and publication. While much progress has been made, continuing investment is needed in the future to ensure the viability and sustainability of this young but increasingly important field.

Evaluation of bias in estimates of early childhood obesity from parent-reported heights and weights

OBJECTIVES

We evaluated bias in estimated obesity prevalence owing to error in parental reporting. We also evaluated bias mitigation through application of Centers for Disease Control and Prevention's biologically implausible value (BIV) cutoffs.

METHODS

We simulated obesity prevalence of children aged 2 to 5 years in 2 panel surveys after counterfactually substituting parameters estimated from 1999-2008 National Health and Nutrition Examination Survey data for prevalence of extreme height and weight and for proportions obese in extreme height or weight categories.

RESULTS

Heights reported below the first and fifth height-for-age percentiles explained between one half and two thirds, respectively, of total bias in obesity prevalence. Bias was reduced by one tenth when excluding cases with height-for-age and weight-for-age BIVs and by one fifth when excluding cases with body mass-index-for-age BIVs. Applying BIVs, however, resulted in incorrect exclusion of nonnegligible proportions of obese children.

CONCLUSIONS

Correcting the reporting of children's heights in the first percentile alone may reduce overestimation of early childhood obesity prevalence in surveys with parental reporting by one half to two thirds. Excluding BIVs has limited effectiveness in mitigating this bias.

Reducing social inequalities in health: the role of simulation modelling in chronic disease epidemiology to evaluate the impact of population health interventions

Reducing health inequalities has become a major public health priority internationally. However, how best to achieve this goal is not well understood. Population health intervention research has the potential to address some of this knowledge gap. This review argues that simulation studies can produce unique evidence to build the population health intervention research evidence base on reducing social inequalities in health. To this effect, the advantages of using simulation models over other population health intervention research methods are discussed. Key questions regarding the potential challenges of developing simulation models to investigate population health intervention research on reducing social inequalities in health and the types of population health intervention research questions that can be answered using this methodology are reviewed. We use the example of social inequalities in coronary heart disease to illustrate how simulation models can elucidate the effectiveness of a number of ‘what-if’ counterfactual population health interventions on reducing social inequalities in coronary heart disease. Simulation models are a flexible, cost-effective, evidence-based research method with the capacity to inform public health policy-makers regarding the implementation of population health interventions to reduce social inequalities in health.