Simulation modeling to assist with childhood obesity control: perceptions of Baltimore City policymakers

Abstract

Computational simulation models have potential to inform childhood obesity prevention efforts. To guide their future use in obesity prevention policies and programs, we assessed Baltimore City policymakers' perceptions of computational simulation models. Our research team conducted 15 in-depth interviews with stakeholders (policymakers in government and non-profit sectors), then transcribed and coded them for analysis. We learned that informants had limited understanding of computational simulation modeling. Although they did not understand how the model was developed, they perceived the tool to be useful when applying for grants, adding to the evidence base for decision-making, piloting programs and policies, and visualizing data. Their concerns included quality and relevance of data used to support the model. Key recommendations for model design included a visual display with explanations to facilitate understanding and a formal method for gathering feedback during model development.

Challenges and Lessons Learned from Multi-Level Multi-Component Interventions to Prevent and Reduce Childhood Obesity

“…For BHCK, these negotiations took place through initial community workshops and via a series of ongoing policy working group meetings [30,31]. This approach led to the identification of college and high school youth leaders, who both delivered the intervention and served as role models for younger youth.…”

“…All three MLMC trials have developed different strategies for making data available. BHCK is using project data to develop systems science simulation models in support of city policies and programs [30]. Specific analyses are made available to different city agencies at their request (including Recreation and Parks, Health departments).…”

Process Evaluation and Lessons Learned From Engaging Local Policymakers in the B’More Healthy Communities for Kids Trial

“…Suggesting parameters for the model was another avenue for engagement with policymakers. Stakeholders were identified for IDIs through purposive sampling and were conducted by the research team for about 60 minutes each, who asked questions related to how policies and programs on childhood obesity affected the physical activity of teens and the Baltimore food environment, and development of the simulation model (Seifu et al, 2018). The modeling team then determined and quantified how each parameter affected decision making (Lee et al, 2018).…”

“…The process evaluation indicators included the amount in which the intended audience was exposed to the intended intervention (dose-delivered), how well the intended audience was engaged in the Policy WG (reach), and how well the intervention was carried out as originally planned as a proxy for quality (fidelity; Steckler, Linnan, & Israel, 2002). Additional process indicators for simulation modeling development were created based on prior experience with program evaluations and current literature (Gittelsohn et al, 2014; Seifu et al, 2018). During Year 2 of the partnership, media use standards were added as the working group saw potential in the use of mass media to influence policymakers.…”

Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies

“…Agent-based dynamic simulation platforms to identify beneficial policies and interventions have been recently reported for cases such as childhood obesity control (16), the impact of sugar-sweetened beverage warning labels (17), the relation of urban crime with obesity (18), and reducing alcohol-related harms (19). The platforms take residential and sociodemographic data and, through experimental scenarios, estimate the probability and the evolution of various factors.…”