The Spread of Autism Diagnosis through Spatially Embedded Social Networks

项目概要/摘要 该项目将研究知识传播对自闭症日益流行的影响， 建立一个大规模的，经验校准的社会和互动网络的模拟模型， 父母尽管有数百项研究，现有的解释不能解释大部分的增加， 自闭症患病率在过去的三十年里。提高对自闭症的认识和知识并没有 尽管它被广泛认为是一种潜在的， 自闭症发病率上升的一个重要因素。我们以前已经证明，知识的传播， 自闭症通过空间上接近的社会关系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的关键驱动力。 孤独症发病率上升的时间和空间模式。这也可能有助于解释社会经济问题。 在接受自闭症诊断的概率和时间上发现的差异。系统科学方法 是很好地定位，以模拟这样一个非线性，内源性的扩散过程，与其他社会， 体制和环境原因。本项目将使用模拟方法模拟 加州的自闭症诊断。我们将重建该州3至9岁儿童的全部人口， 从1992年到2010年（每年约300万，约5700万儿童），基于三个地区的块级数据， 联邦人口普查和1989年至2007年所有加州出生记录。然后，我们将根据经验校准 父母的社交网络通过利用关于焦点的位置数据（例如，学校，商场，托儿所， 以及父母互动的其他点）。“假设”情景，包括远端环境灾害和 发病率的初始分布，以及所有传统的风险因素， 已知在个人层面运作，社区层面的因素已知是突出的，更大的机构 随着时间的推移形成诊断机制的过程。模拟结果将受到严格的 使用1992年至2010年观察到的自闭症发病率的空间和时间数据进行验证。我们 该项目将证明，社会网络分析，基于代理的建模和越来越多的可用 地理空间和组织数据可以有效地结合起来，为非传染性疾病的流行病学提供信息， 传染病具体来说，我们预计，在这个项目中开发的建模方法将 为那些有兴趣解释罢工的人所面临的最重要的问题提供答案。 在过去的三十年里，自闭症患病率的增加：是什么造成了时间和空间的变化？ 我们观察到的模式？ !

Project summary/Abstract 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? !