5,017 research outputs found

    System Dynamics Modeling for Childhood Obesity

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    Effective strategies for prevention of obesity, particularly in youths, have been elusive since the recognition of obesity as a major public health issue two decades ago. In general, obesity is a result of chronic, quantitative imbalance between energy intake and energy expenditure, which is influenced by a combination of genetic, environmental, psychological and social factors. Therefore, a systems perspective is needed to examine effective obesity prevention strategies. In this study, a systems dynamics model was developed using the data from the Girls health Enrichment Multi-site Studies (GEMS). GEMS tested the efficacy of a 2-year family-based intervention to reduce excessive increase in body mass index (BMI) in 8-10 year old African American girls. First, an optimum model was built by systematically adding variables to fit the observed data by regression analysis for 50 randomly selected individuals from the cohort. The final model included nutrition, physical activity, and several environmental factors. Next, the model was used to compare two intervention strategies used in the GEMS study. Consistent with previous reports, we found that the two strategies did not affect the BMI increases observed in this cohort. Interestingly however, the model predicted that a 10 min increase in exercise would decrease BMI in the group receiving behavioral counseling. Our work suggests that system dynamics modeling may be useful for testing potential intervention strategies in complex disorders such as obesit

    School Policy, Food and Physical Activity Environment, and Childhood Obesity

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    The purpose of this study is to examine the effect of school food and physical activity environments on energy balance-related behaviors and Body Mass Index (BMI) and to simulate the effect of school-based nutrition and physical activity policies on childhood obesity prevention. Four models based on the Social Ecological Framework of childhood obesity were developed. Parameters of these models were calibrated and validated with empirical data derived from the Early Childhood Longitudinal Study – Kindergarten Class of 1998-99 and the 2003-2004 National Health and Nutrition Examination Study. The correlation between observed and simulated BMI was 0.85 for 5th grade children and 0.87 for 8th grade children, indicating the validity of the models. The results demonstrated (1) one occasion of sweet snack consumption in school each week may lead to a 0.027 unit increase in BMI among 5th grade children in 2 years and among 8th grade children in 3 years; (2) one occasion of salty snack consumption in school each week may lead to a 0.025 unit increase in BMI among 5th grade children in 2 years and among 8th grade children in 3 years; (3) one occasion of sugar-sweetened beverage consumption in school each week may lead to a 0.05 unit increase in BMI among 5th grade children in 2 years and a 0.06 unit increase in BMI among 8th grade children in 3 years; (4) one minute of physical activity in school each week may lead to a 0.0008 unit decrease in BMI among 5th grade children in 2 years and one physical education class each week may lead to a 0.05 unit decrease in BMI among 8th grade children in 3 years. Comparison of simulated and observed data revealed that school-based policies targeting competitive food availability and physical activity opportunity in school had the potential to prevent childhood obesity. Moreover, prevention and interventions should be taken as early as the first few years of children’s school life. A simulation modeling approach was useful in exploring the effect of environmental factors on childhood obesity and energy balance-related behaviors

    Combating obesity through healthy eating behavior: A call for system dynamics optimization

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    Poor eating behavior has been identified as one of the core contributory factors of the childhood obesity epidemic.The consequences of obesity on numerous aspects of life are thoroughly explored in the existing literature.For instance, evidence shows that obesity is linked to incidences of diseases such as heart disease, type-2 diabetes, and some cancers, as well as psycho social problems.To respond to the increasing trends in the UK, in 2008 the government set a target to reverse the prevalence of obesity (POB) back to 2000 levels by 2020. This paper will outline the application of system dynamics (SD) optimization to simulate the effect of changes in the eating behavior of British children (aged 2 to 15 years) on weight and obesity. This study also will identify how long it will take to achieve the government’s target. This paper proposed a simulation model called Intervention Childhood Obesity Dynamics (ICOD) by focusing the interrelations between various strands of knowledge in one complex human weight regulation system. The model offers distinct insights into the dynamics by capturing the complex inter dependencies from the causal loop and feedback structure, with the intention to better understand how eating behaviors influence children’s weight, body mass index (BMI), and POB measurement. This study proposed a set of equations that are revised from the original (baseline) equations. The new functions are constructed using a RAMP function of linear decrements in portion size and number of meal variables from 2013 until 2020 in order to achieve the 2020 desired target. Findings from the optimization analysis revealed that the 2020 target won’t be achieved until 2026 at the earliest, six years late.Thus, the model suggested that a longer period may be needed to significantly reduce obesity in this population

    School Design to Promote Physical Activity

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    Increasing children’s physical activity (PA) at school is a national focus to address childhood obesity. Research has demonstrated associations between school built environments and students’ PA, but has lacked a comprehensive synthesis of evidence. Chapter 1 presents new evidence-, theory-, and practice-informed school design guidelines, including evidence substantiality ratings, to promote PA in school communities. These guidelines delineate strategies for school designers, planners, and educators to create K-12 school environments conducive to PA. They also engage public health scientists in needed transdisciplinary perspectives. There have been few longitudinal studies to verify causal relationships between the school built environment and PA. Chapter 2 presents results from a natural experiment with objective PA-related measures before and after a move to a new K-5 school designed based on the Chapter 1 guidelines. The study hypothesized that the school would have desirable impacts on students’ sedentary behaviors and PA. The intervention school group was compared longitudinally with a demographically-similar group at 2 control schools. School-time analyses showed that the intervention school design had positive impact on accumulation of sedentary time, and time in light PA, likely due to movement-promoting classroom design. Studies of built environment impacts on human behaviors and health have presented challenges in control of confounding effects. Chapter 3 presents results from experiments using an agent based model (ABM) to simulate population samples of children and to quantify the impact of a single design intervention, dynamic furniture in school, on obesity and overweight prevalence over time. Results of computational experiments showed that there could be some desirable population impact among girls with low PA profiles. Chapter 4 places the work presented in Chapters 1-3 in a larger context. Via exploration of theories of space as a social phenomenon, of design as a discipline with human purpose, and of limitations of current public health built environment studies, the investigator proposes key strategies toward achieving substantial unrealized potential to design our built environments to achieve health

    The Obesity Epidemic in Turkey: A System Dynamics and Behavioral Economics Approach in the context of an Obesogenic System

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    Obesity is an increasing problem across the world, and it has risen dramatically in the last decades. It is a major risk factor for noncommunicable diseases which are the world’s leading cause of death. In Turkey, the obesity epidemic is becoming a growing concern. Policies against obesity have had minimal success thus far. Given this issue, the aim of this study is to analyze the underlying structure of the obesity problem from a system’s perspective since the obesogenic system is a complex adaptive system. Therefore, this study uncovers the dynamic interactions within this system and resulting behavior patterns by developing a system dynamics simulation model. Furthermore, behavioral economics and reinforcement pathology frameworks are integrated into the model to provide policymakers with more robust insights. This thesis employs a system dynamics methodology to analyze aggregated level interactions between system components to understand complex systems. Combining system dynamics with behavioral economics and reinforcement pathology frameworks provides a guide to this complex adaptive system to understand how the obesogenic environment shapes individual decision-making. A theoretical model developed to show how reinforcement pathology occurs within the obesogenic environment, as well as the feedback loop analysis to identify important feedbacks within the system. Thereafter, the theoretical model quantified into a system dynamics simulation model that generates the behavior pattern and trend from endogenous interactions for further analysis of the system. According to the findings, the obesogenic environment is a complex adaptive system where ingestive behavior is shaped by the environment as well as the environment is influenced by the ingestive behavior. It was found that this system is dominated by many uncontrolled powerful reinforcing feedback loops at various levels interacting with each other. In addition, the study found that reinforcement pathology framework integrated to system dynamics methodology shows how environmental factors are making food consumption more valuable, more reinforcing within this adaptive system, hence affecting individual behavior. Additionally, the study also identified several leverage points to intervene obesogenic system namely intervening reinforcement pathology feedback loop by creating substitutes for food, the weak balancing feedback loop that fails to balance the relative reinforcing value of food and lack of rules within the system especially mechanisms that reward individuals with healthier lifestyle. In conclusion, the study showed that without a clear understanding feedback mechanisms working within an obesogenic environment and interventions that aim to address those feedback processes may result in less effective policies. This research sheds some light into understanding the obesity problem as a complex adaptive system and how the system can be leveraged to help reduce obesity rates.Master's Thesis in System DynamicsGEO-SD351INTL-KMDINTL-JUSINTL-MEDINTL-MNMASV-SYSDYINTL-SVINTL-HFINTL-PSY

    Applied Computational Modeling Approaches in Cigarette Smoking Epidemiology: Expanding Statistical Associations to Convey Theoretical Pathways

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    50 years since the landmark 1964 Surgeon General’s report on smoking and health, cigarette smoking remains the leading preventable cause of death and disability in the United States. The success of epidemiology and public health in the study of cigarette smoking, both as an exposure as well as a health outcome, has offered rich datasets and mechanistic discoveries that provide opportunities for the evolution of epidemiologic methods. Specifically, advancing computational science approaches allow for novel applications of methodologies, such as agent-based modeling or networks theory, in the epidemiological sciences to expand on existing knowledge. In this dissertation, we utilize approaches from epidemiology, statistics, computer science, and the philosophy of science to explore a range of hypothesized dynamics of smoking behavior that could contribute to changes in population-level smoking prevalence. We begin with a computational model that weighs the magnitude of the potential harms and benefits of electronic cigarette (e-cigarette or vaping) use from an adult smoking prevalence standpoint. We find that e-cigarettes can exert a much larger influence on smoking prevalence through routes of smoking cessation, as opposed to smoking initiation, if e-cigarette use remains primarily concentrated among current smokers. Conversely, e-cigarettes would need to behave as extremely effective gateways for smoking initiation, and never smokers would need to become e-cigarette users at substantially higher levels than currently observed, for these products to independently generate increases in population-level smoking prevalence. Next, we explore how contextual and individual network factors and demographic covariates change the effect of peer influence on smoking behavior in the National Longitudinal Study of Adolescent to Adult Health (Add Health). Using stratified mixed effects models, we find that the magnitude of friendship influence on smoking initiation differs by school social network density. We additionally find that the contextual factors, rather than peer influence, may be stronger predictors of smoking cessation. The effect estimates of these factors on smoking cessation of also differ by network density. Extending these results, we conclude with an abstract simulation of the hypothesized mechanisms that contribute to the outcomes of the stratified mixed effects model described previously. We find that network structure and peer influence are sufficient in combination to generate substantial differences in smoking prevalence by urbanicity, sex, and race, among US adolescents. These results provide evidence that support the potential for effect modification by network density on the hypothesized pathway between friendship influence and smoking behavior. While the field of tobacco control has been traditionally amenable to computational modeling approaches, few studies use computational modeling within an epidemiologic framework to provide support for hypothesized causal pathways that contribute to smoking behavior outcomes. Such perspectives are critical as the tobacco landscape continues to change with the introduction of new products, and as we gain a better understanding of the role that social networks play in the propagation of health behaviors. Through the integration of statistics, computational modeling, and epidemiologic methods, this dissertation seeks to provide insights into the potential causal pathways between various risk factors and smoking behavior outcomes. The results and discussions of this dissertation present potential avenues through which computational modeling can contribute added value to epidemiologic methods, in addition to our understanding of smoking behavior, beyond those of projection and evaluation.PHDEpidemiological ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137162/1/scherng_1.pd

    System Dynamics and Agent-Based Models Applied to Public Health Problems

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    During the past decades, there has been a growing body of research on the development of new methodologies in system sciences in public health. While systems thinking is prevalent in the practice of public health, there is a need for tools to quantify the multidimensional and multidisciplinary aspects of such thinking. In this thesis, we focus on two system science methods: Agent-Based Modeling (ABM) and System Dynamics (SD). We begin with an ABM to simulate the effects of an urban food desert environment on school-aged children. The data that was used to inform this model is based on children in low-income neighborhoods of Baltimore City. The baseline model was used to predict changes in body mass index due to eating behaviors of simulated children interacting with their food environment. The model was then used as a virtual social laboratory by introducing interventions into the environment and assessing their effects on child behaviors and weight gains. For our second application of systems science, we developed an SD model to study the stability of community functioning (CF) after a natural disaster. We define CF as a measure of a broad range of community activities in providing services to its residents. We studied the dynamic response of CF post-disaster from two different aspects: resilience, which indicates the speed of recovery after event, and resistance, which measures the degree to which a community can continue to function during the event. Key components that support or reduce CF were identified and were quantified as variables in a system of ordinary differential equations. The data for the model was obtained at the county level for 3143 United States counties, and the model results for resilience and resistance ratings were presented in a series of maps so that the regional patterns of our findings could be visualized. Finally, our last application was an SD model applied in a different public health context: an analysis of the mechanisms underlying the health system in Afghanistan between 2010 and 2012. We were interested in the Pay-for-Performance (P4P) intervention, in which relatively small health facilities were given bonus payments as a reward for year-to-year improvements in quality and quantity of health services. A recently published data analysis of the P4P intervention showed no improvement in health services. By working with some of the researchers who participated in this intervention, we were able to develop causal loops in the system associated with some of the key interactions that were generated within the health facilities. We then synthesized these loops into a model of differential equations with delays. We were able to generate several scenarios that indicate that the failure of P4P may be caused by poor implementation processes and gaming within the system. In summary, we demonstrate how ABM and SD can naturally embody systems thinking into a quantitative form, and can produce a wide array of numerical and visual results that capture the complex processes that characterize public health

    Implementation and evaluation of the VA DPP clinical demonstration: protocol for a multi-site non-randomized hybrid effectiveness-implementation type III trial.

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    BackgroundThe Diabetes Prevention Program (DPP) study showed that lifestyle intervention resulted in a 58% reduction in incidence of type 2 diabetes among individuals with prediabetes. Additional large randomized controlled trials have confirmed these results, and long-term follow-up has shown sustained benefit 10-20 years after the interventions ended. Diabetes is a common and costly disease, especially among Veterans, and despite strong evidence supporting the feasibility of type 2 diabetes prevention, the DPP has not been widely implemented. The first aim of this study will evaluate implementation of the Veterans Affairs (VA) DPP in three VA medical centers. The second aim will assess weight and hemoglobin A1c (A1c) outcomes, and the third aim will determine the cost-effectiveness and budget impact of implementation of the VA DPP from a health system perspective.Methods/designThis partnered multi-site non-randomized systematic assignment study will use a highly pragmatic hybrid effectiveness-implementation type III mixed methods study design. The implementation and administration of the VA DPP will be funded by clinical operations while the evaluation of the VA DPP will be funded by research grants. Seven hundred twenty eligible Veterans will be systematically assigned to the VA DPP clinical demonstration or the usual care VA MOVE!® weight management program. A multi-phase formative evaluation of the VA DPP implementation will be conducted. A theoretical program change model will be used to guide the implementation process and assess applicability and feasibility of the DPP for VA. The Consolidated Framework for Implementation Research (CFIR) will be used to guide qualitative data collection, analysis, and interpretation of barriers and facilitators to implementation. The RE-AIM framework will be used to assess Reach, Effectiveness, Adoption, Implementation, and Maintenance of the VA DPP. Twelve-month weight and A1c change will be evaluated for the VA DPP compared to the VA MOVE!ProgramMediation analyses will be conducted to identify whether program design differences impact outcomes.DiscussionFindings from this pragmatic evaluation will be highly applicable to practitioners who are tasked with implementing the DPP in clinical settings. In addition, findings will determine the effectiveness and cost-effectiveness of the VA DPP in the Veteran population
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