Childhood obesity is epidemic in the United States, and a leading cause of diabetes, cardiovascular heart disease, cancer, and other chronic conditions. Disparities in childhood obesity prevalence are found across US states and by age, gender, socioeconomic status, and race/ethnicity. Interventions with effectiveness to reduce childhood obesity have been developed, but little is known about their overall impact on obesity and subsequent chronic disease, their cost-effectiveness, and their potential to reduce health disparities. Our team?s preliminary work in developing cost-effectiveness simulation models of leading preventive and treatment interventions for childhood obesity (e.g. excise taxes on sugar sweetened beverages) has documented dramatic differences in population health impact and cost-effectiveness. But what is not known is the impact of these preventive health interventions on reducing health disparities and cost-effectiveness of the interventions in the context of intersectional health disparities. The planned research will build on our team?s prior work in developing accurate simulation models of the growth of BMI by gender and race/ethnicity over the life course.
We aim to develop a microsimulation model to project the 10- and 30-year national impact of six widely studied and effective childhood obesity preventive and treatment interventions, evaluating cases of obesity prevented, cost-effectiveness metrics, chronic disease, and disparities metrics in outcome by race/ethnicity. Secondarily, we will test impacts at the intersection of race/ethnicity and place among six states: West Virginia, New York, Mississippi, Washington, Alaska, and Hawaii, with the goal of identifying interventions that can both cost-effectively improve population health and reduce racial/ethnic disparities in obesity and future chronic disease.
Our specific aims : 1) Systematically evaluate evidence for effect, reach, obesity risk factors, costs and implementation ? and differences in these characteristics ? of six effective interventions for reducing obesity in children. 2) Develop a microsimulation model to accurately project nationally, over 10 and 30 years, the impact on obesity, chronic disease, cost-effectiveness and racial/ethnic disparities of the six interventions, accounting for effectiveness, reach, obesity risks, implementation and uncertainty. 3) Adapt the microsimulation model to six states and project the cost-effectiveness, impact on obesity, chronic disease, and impact on racial/ethnic disparities of the six interventions, accounting for local characteristics including effectiveness, reach, obesity risks, implementation, and accounting for uncertainty. Decision makers have limited resources to use in reducing obesity prevalence. While there is evidence for effective preventive and treatment interventions, there is limited cost-effectiveness analysis. If successful, this project will create a new paradigm for evaluating obesity interventions, emphasizing cost-effectiveness, population health impact, and health disparities metrics, providing decision makers with the tools to identify cost-effective interventions that reduce health disparities while improving overall population health.
Simulation Modeling and Disparities in Obesity and Chronic Disease Project Narrative We aim to develop a microsimulation model to project the 10- and 30-year national impact of six widely studied and effective childhood obesity preventive and treatment interventions, evaluating cases of obesity prevented, cost-effectiveness metrics, chronic disease, and disparities metrics in outcome by race/ethnicity. Secondarily, we will test impacts at the intersection of race/ethnicity among six states: West Virginia, New York, Mississippi, Washington, Alaska, and Hawaii, with the goal of identifying interventions that can both cost-effectively improve population health and reduce racial/ethnic disparities in obesity and future chronic disease. If successful, this project will create a new paradigm for evaluating obesity interventions, emphasizing cost- effectiveness, population health impact, and health disparities metrics, providing decision makers with the tools to identify cost-effective interventions that reduce health disparities while improving population health.
