Center on Social Dynamics and Policy, Economics Studies Program, The Brookings Institution, Washington, DC.
Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts.
Obesity (Silver Spring). 2019 Sep;27(9):1494-1502. doi: 10.1002/oby.22553. Epub 2019 Jul 25.
Successful whole-of-community childhood obesity prevention interventions tend to involve community stakeholders in spreading knowledge about and engagement with obesity prevention efforts through the community. This process is referred to by the authors as stakeholder-driven community diffusion (SDCD). This study uses an agent-based model in conjunction with intervention data to increase understanding of how SDCD operates.
This agent-based model retrospectively simulated SDCD during Romp & Chomp, a 4-year whole-of-community childhood obesity prevention intervention in Victoria, Australia. Stakeholder survey data, intervention records, and expert estimates were used to parameterize the model. Model output was evaluated against criteria derived from empirical data and experts' estimates of the magnitude and timing of community knowledge and engagement change.
The model was able to produce outputs that met the evaluation criteria: increases in simulated community knowledge and engagement driven by SDCD closely matched expert estimates of magnitude and timing.
Strong suggestive evidence was found in support of a hypothesis that SDCD was a key driver of the success of the Romp & Chomp intervention. Model exploration also provided additional insights about these processes (including where additional data collection might prove most beneficial), as well as implications for the design and implementation of future interventions.
成功的全社区儿童肥胖预防干预措施往往涉及社区利益相关者,通过社区传播有关肥胖预防工作的知识并参与其中。作者将这一过程称为利益相关者驱动的社区扩散(SDCD)。本研究使用基于代理的模型结合干预数据,以增加对 SDCD 运作方式的理解。
本基于代理的模型对澳大利亚维多利亚州为期 4 年的全社区儿童肥胖预防干预措施 Romp & Chomp 期间的 SDCD 进行了回顾性模拟。利用利益相关者调查数据、干预记录和专家估计来为模型参数化。模型输出根据来自实证数据和专家对社区知识和参与度变化的幅度和时间的估计得出的标准进行评估。
该模型能够产生符合评估标准的输出:由 SDCD 驱动的模拟社区知识和参与度的增加与专家对幅度和时间的估计非常吻合。
有强有力的证据支持这样一个假设,即 SDCD 是 Romp & Chomp 干预成功的关键驱动因素。模型探索还提供了有关这些过程的其他见解(包括在哪里可以进行额外的数据收集以证明最有益),以及对未来干预措施的设计和实施的影响。