The Spread of Autism Diagnosis through Spatially Embedded Social Networks

描述：这个项目将通过建立一个大规模的，经验性校准的父母的社会和互动网络的模拟模型，来研究知识的传播对自闭症日益流行的影响。尽管有数百项研究，但现有的解释不能解释过去30年自闭症患病率上升的主要原因。尽管人们普遍认为自闭症是自闭症上升的一个潜在的显著因素，但人们对自闭症的认识和认识的提高并不是许多实证研究的重点。我们先前已经证明，关于自闭症的知识通过空间上接近的社会关系的传播在自闭症的增加中发挥了重要作用。在网络互动的放大下，关于自闭症的知识的传播可能是自闭症发病率上升的时间和空间模式的关键驱动因素。这也可能有助于解释在接受自闭症诊断的概率和时间上发现的社会经济差异。系统科学方法可以很好地与其他社会、制度和环境原因一起模拟这种非线性的、内生的扩散过程。该项目将使用模拟方法对加州自闭症诊断的扩散进行建模。我们将根据三次联邦人口普查的区块水平数据和1989年至2007年加州的所有出生记录，重建该州从1992年到2010年的全部3至9岁儿童人口(每年约300万，约5700万儿童)。然后，我们将通过利用联络点(例如，学校、商场、托儿中心和其他父母互动的地点)的位置数据来经验性地校准父母的社交网络。模型将纳入“假设”情景，包括远距离环境灾害和发病率的初始分布，所有情况都将纳入。 已知在个人层面上起作用的传统风险因素、已知的突出社区层面因素，以及随着时间推移塑造诊断制度的更大体制进程。模拟结果将使用空间和时间数据进行严格的验证 观察1992-2010年间自闭症发病率。我们的项目将证明，社会网络分析、基于代理的建模以及日益可用的地理空间和组织数据可以有效地结合在一起，为非传染性疾病的流行病学提供信息。具体地说，我们预计，这个项目中开发的建模方法将为那些对解释过去30年自闭症患病率急剧上升感兴趣的人面临的最重要问题提供答案：我们观察到的时间和空间模式是如何解释的？

DESCRIPTION: This project will study the impact of diffusion of knowledge on the increasing prevalence of autism by building a large-scale, empirically calibrated simulation model of the social and interaction networks of parents. Despite hundreds of studies, existing explanations cannot account for the bulk of the increase in autism prevalence over the past three decades. Rising awareness and knowledge about autism has not been the focus of many empirical studies even though it has been widely acknowledged as a potential salient factor in the rise of autism. We have previously demonstrated that the diffusion of knowledge about autism through spatially proximate social relations has played an important role in autism's increase. Amplified by network interactions, the diffusion of knowledge about autism may be the key driver of the temporal and spatial patterns of rising autism incidence. It may also help explain the socio-economic disparity found in the probability and timing of receiving an autism diagnosis. A systems science approach is well positioned to model such a non-linear, endogenous diffusion process in tandem with other social, institutional and environmental causes. This project will use simulation methods to model the diffusion of autism diagnoses in California. We will reconstruct the state's entire population of 3 to 9 year old children from 1992 through 2010 (~3 million per year, ~57 million children) based on block level data from the three Federal censuses and all California birth records from 1989 to 2007. We will then empirically calibrate the parents' social networks by utilizing location data on focal points (e.g., schools, malls, childcare centers, and other points where parents interact). "What-if" scenarios, including distal environmental disasters and the initial distribution of incidence, will be incorporated in the model, as will all conventional risk factors known to operate at the individual level, community level factors known to be salient, and larger institutional processes that shape diagnostic regimes over time. The simulated results will be subjected to stringent validations using the spatial and temporal data of observed autism incidence from 1992 to 2010. Our project will demonstrate that social network analysis, agent-based modeling and increasingly available geospatial and organizational data can be effectively combined to inform the epidemiology of non- contagious diseases. Specifically, we anticipate that the modeling approach developed in this project will provide answers to the most important question confronting those interested in explaining the striking increase of autism prevalence over the past three decades: what accounts for the temporal and spatial patterns we observe?