Daniels Jena, Schwartz Jessey, Albert Nikhila, Du Michael, Wall Dennis P
Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA USA.
Mol Autism. 2017 Oct 23;8:55. doi: 10.1186/s13229-017-0163-7. eCollection 2017.
Although the number of autism diagnoses is on the rise, we have no evidence-based tracking of size and severity of gaps in access to autism-related resources, nor do we have methods to geographically triangulate the locations of the widest gaps in either the US or elsewhere across the globe. To combat these related issues of (1) mapping diagnosed cases of autism and (2) quantifying gaps in access to key intervention services, we have constructed a crowd-based mobile platform called "GapMap" (http://gapmap.stanford.edu) for real-time tracking of autism prevalence and autism-related resources that can be accessed from any mobile device with cellular or wireless connectivity. Now in beta, our aim is for this Android/iOS compatible mobile tool to simultaneously crowd-enroll the massive and growing community of families with autism to capture geographic, diagnostic, and resource usage information while automatically computing prevalence at granular geographical scales to yield a more complete and dynamic understanding of autism resource epidemiology.
尽管自闭症的诊断数量在上升,但我们没有基于证据的对获取自闭症相关资源差距的规模和严重程度的跟踪,也没有方法在地理上确定美国或全球其他地方差距最大的位置。为了解决与(1)绘制自闭症确诊病例地图和(2)量化关键干预服务获取差距相关的这些问题,我们构建了一个名为“差距地图”(http://gapmap.stanford.edu)的基于众包的移动平台,用于实时跟踪自闭症患病率和自闭症相关资源,任何具有蜂窝或无线连接的移动设备都可以访问这些资源。目前处于测试阶段,我们的目标是让这个兼容安卓/苹果系统的移动工具同时众包招募庞大且不断增长的自闭症家庭群体,以获取地理、诊断和资源使用信息,同时在精细的地理尺度上自动计算患病率,从而对自闭症资源流行病学有更完整、动态的了解。