The Spread of Autism Diagnosis through Spatially Embedded Social Networks

通过空间嵌入的社交网络传播自闭症诊断

• 批准号: 8786111
• 负责人: Peter Shawn Bearman
• 金额: $ 23.66万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8786111
• 关键词: 9 year old; Accounting; Age; Autistic Disorder; Awareness; Birth Records; California; Censuses; Cereals; Child; Communities; Data; Diagnosis; Diagnostic; Diffusion; Disasters; Disease; Distal; Employee Strikes; Environmental Risk Factor; Epidemiology; Etiology; Family; Goals; Health; Incidence; Income; Individual; Knowledge; Lead; Life; Location; Methods; Modeling; Non-linear Models; Nonlinear Dynamics; Outcome; Parents; Pathway Analysis; Pathway interactions; Pattern; Persons; Play; Policies; Population; Positioning Attribute; Prevalence; Probability; Process; Public Health; Race; Research; Risk; Risk Factors; Role; Sampling; Schools; Science; Shapes; Social Interaction; Social Network; System; Target Populations; Thinking; Time; Validation; aged; base; high reward; high risk; interest; large scale simulation; models and simulation; simulation; social; socioeconomic disparity; socioeconomics; theories

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?

描述：该项目将通过建立一个大规模的、经过经验校准的父母社交和互动网络模拟模型，研究知识传播对自闭症日益流行的影响。尽管进行了数百项研究，但现有的解释无法解释过去三十年来自闭症患病率增加的大部分原因。尽管人们普遍认为自闭症意识和知识的提高是自闭症发病率上升的潜在显着因素，但它并不是许多实证研究的焦点。我们之前已经证明，通过空间上邻近的社会关系传播有关自闭症的知识在自闭症的增加中发挥了重要作用。通过网络互动的放大，自闭症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。它还可能有助于解释在接受自闭症诊断的概率和时间方面发现的社会经济差异。系统科学方法可以很好地模拟这种非线性、内源性扩散过程以及其他社会、制度和环境原因。该项目将使用模拟方法来模拟加利福尼亚州自闭症诊断的扩散。我们将根据三次联邦人口普查的区块级数据和 1989 年至 2007 年加州的所有出生记录，重建 1992 年至 2010 年该州 3 至 9 岁儿童的全部人口（每年约 300 万，约 5700 万儿童）。然后，我们将利用焦点（例如学校、商场、托儿中心和其他家长关注的地点）的位置数据，凭经验校准家长的社交网络。 相互影响）。 “假设”情景，包括远端环境灾害和发病率的初始分布，将被纳入模型中，所有情况也将被纳入模型中。 已知在个人层面发挥作用的传统风险因素、已知显着的社区层面因素以及随着时间的推移形成诊断制度的更大的制度流程。模拟结果将使用空间和时间数据进行严格的验证 观察 1992 年至 2010 年的自闭症发病率。我们的项目将证明，社交网络分析、基于代理的建模和日益可用的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。具体来说，我们预计该项目开发的建模方法将为那些有兴趣解释过去三十年自闭症患病率显着增加的人所面临的最重要问题提供答案：是什么解释了我们观察到的时间和空间模式？