Agent-based Simulation Models of the College Sorting Process

We explore how dynamic processes related to socioeconomic inequality operate to sort students into, and create stratification among, colleges. We use an agent-based model to simulate a stylized version of this sorting processes in order to explore how factors related to family resources might influence college application choices and college enrollment. We include two types of “agents”—students and colleges—to simulate a two-way matching process that iterates through three stages: application, admission, and enrollment. Within this model, we examine how five mechanisms linking students’ socioeconomic background to college sorting might influence socioeconomic stratification between colleges including relationships between student resources and: achievement; the quality of information used in the college selection process; the number of applications students submit; how students value college quality; and the students’ ability to enhance their apparent caliber. We find that the resources-achievement relationship explains much of the student sorting by resources but that other factors also have non-trivial influences

Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density

To identify the behavioral mechanisms and effects of tobacco control policies designed to reduce tobacco retailer density

Understanding misimplementation in U.S. state health departments: An agent-based model

INTRODUCTION: The research goal of this study is to explore why misimplementation occurs in public health agencies and how it can be reduced. Misimplementation is ending effective activities prematurely or continuing ineffective ones, which contributes to wasted resources and suboptimal health outcomes. METHODS: The study team created an agent-based model that represents how information flow, filtered through organizational structure, capacity, culture, and leadership priorities, shapes continuation decisions. This agent-based model used survey data and interviews with state health department personnel across the U.S. between 2014 and 2020; model design and analyses were conducted with substantial input from stakeholders between 2019 and 2021. The model was used experimentally to identify potential approaches for reducing misimplementation. RESULTS: Simulations showed that increasing either organizational evidence-based decision-making capacity or information sharing could reduce misimplementation. Shifting leadership priorities to emphasize effectiveness resulted in the largest reduction, whereas organizational restructuring did not reduce misimplementation. CONCLUSIONS: The model identifies for the first time a specific set of factors and dynamic pathways most likely driving misimplementation and suggests a number of actionable strategies for reducing it. Priorities for training the public health workforce include evidence-based decision making and effective communication. Organizations will also benefit from an intentional shift in leadership decision-making processes. On the basis of this initial, successful application of agent-based model to misimplementation, this work provides a framework for further analyses

Toward optimal implementation of cancer prevention and control programs in public health: A study protocol on mis-implementation

Abstract Background Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. Methods This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. Discussion This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas

Development and testing of a novel survey to assess Stakeholder-driven Community Diffusion of childhood obesity prevention efforts

Background: Involving groups of community stakeholders (e.g., steering committees) to lead community-wide health interventions appears to support multiple outcomes ranging from policy and systems change to individual biology. While numerous tools are available to measure stakeholder characteristics, many lack detail on reliability and validity, are not context specific, and may not be sensitive enough to capture change over time. This study describes the development and reliability of a novel survey to measure Stakeholder-driven Community Diffusion via assessment of stakeholders’ social networks, knowledge, and engagement about childhood obesity prevention. Methods: This study was completed in three phases. Phase 1 included conceptualization and online survey development through literature reviews and expert input. Phase 2 included a retrospective study with stakeholders from two completed whole-of-community interventions. Between May–October 2015, 21 stakeholders from the Shape Up Somerville and Romp & Chomp interventions recalled their social networks, knowledge, and engagement pre-post intervention. We also assessed one-week test-retest reliability of knowledge and engagement survey modules among Shape Up Somerville respondents. Phase 3 included survey modifications and a second prospective reliability assessment. Test-retest reliability was assessed in May 2016 among 13 stakeholders involved in ongoing interventions in Victoria, Australia. Results: In Phase 1, we developed a survey with 7, 20 and 50 items for the social networks, knowledge, and engagement survey modules, respectively. In the Phase 2 retrospective study, Shape Up Somerville and Romp & Chomp networks included 99 and 54 individuals. Pre-post Shape Up Somerville and Romp & Chomp mean knowledge scores increased by 3.5 points (95% CI: 0.35–6.72) and (− 0.42–7.42). Engagement scores did not change significantly (Shape Up Somerville: 1.1 points (− 0.55–2.73); Romp & Chomp: 0.7 points (− 0.43–1.73)). Intraclass correlation coefficients (ICCs) for knowledge and engagement were 0.88 (0.67–0.97) and 0.97 (0.89–0.99). In Phase 3, the modified knowledge and engagement survey modules included 18 and 25 items, respectively. Knowledge and engagement ICCs were 0.84 (0.62–0.95) and 0.58 (0.23–0.86). Conclusions: The survey measures upstream stakeholder properties—social networks, knowledge, and engagement—with good test-retest reliability. Future research related to Stakeholder-driven Community Diffusion should focus on prospective change and survey validation for intervention effectiveness. Electronic supplementary material The online version of this article (10.1186/s12889-018-5588-1) contains supplementary material, which is available to authorized users