UCSF Data Science Training to Advance Behavioral and Social Science Expertise for Health Research (DaTABASE) Program

总结 行为和社会科学研究人员对于推进减少健康差距的研究至关重要。 下一代这样的研究人员必须配备最强大的，当代分析 工具.新的可用数据和方法有可能改变所提出的问题， 使用的设计和统计方法，以加深我们对基本驱动因素的理解， 我们的目标是促进健康和提高我们改善人口健康和缩小差距的能力。以利用 这些机会，我们需要研究人员与跨学科的培训，使他们能够连接 传统的行为和社会科学专业知识与快速发展的技术剧目， 计算健康科学家UCSF数据科学培训，以推进行为和社会科学 健康研究专业知识（DaTABASE）计划编织培训股以前运作 在UCSF独立工作。DaTABASE的特殊优势包括：1）以严格的， 跨学科定量培训计划，为研究设计和统计分析奠定基础， 提供关于健康的可操作证据; 2）多个数据流的集成，包括：组学，临床（例如， 加州大学范围内，地理定位和纵向医疗记录，点击流数据从加州大学旧金山分校的独特的临床 信息学基础设施），数字（基于设备的数据流，社交媒体数据，技术支持的队列）， 人口（例如，旧金山弗朗西斯科公共卫生部提供的健康和社会服务数据， 无家可归的个人或其他弱势群体）和政策数据集（例如，地方政策调控 加州社区的大麻零售点）; 3）围绕 消除健康差距。DaTABASE受训人员将完成严格的方法培训， 流行病学和转化科学博士。额外的内容培训，由UCSF的教师监督 健康与社区中心，将提供理论框架和学科背景的社会 和行为科学。其他定量方法培训将通过巴卡尔计算 健康科学研究所（BCHSI）。BCHSI强调机器学习工具来挖掘数据源， 洞察力和发现，包括精准医学和人口健康。本次培训将为 将这些分析工具应用于行为和社会过程研究的新数据集 潜在的健康差距。DaTABASE计划将从内容和 方法专家。DaTABASE校友将准备领导研究解决当前和新兴的公共 健康挑战与创新的计算和数据科学分析方法。

Summary Behavioral and social science researchers are essential for advancing research on reducing health disparities. The next generation of such researchers must be equipped with the most powerful, contemporary analytic tools. Newly available data and methods have the potential to transform the questions asked, the research designs used, and the statistical approaches applied to deepen our understanding of fundamental drivers of health and increase our capacity to improve population health and reduce disparities. To take advantage of these opportunities, we need researchers with interdisciplinary training that allows them to connect the traditional behavioral and social science expertise with the rapidly evolving technical repertoire of computational health scientists. The UCSF Data Science Training to Advance Behavioral and Social Science Expertise for Health Research (DaTABASE) program braids training strands previously operating independently within UCSF. Particular strengths of DaTABASE include: 1) Grounding in a rigorous, interdisciplinary quantitative training program for a foundation in study designs and statistical analyses to deliver actionable evidence on health; 2) Integration of multiple data streams, including: ‘omics, clinical (e.g., UC-wide, geolocated and longitudinal medical records, clickstream data from UCSF’s unique clinical informatics infrastructure), digital (device-based data flows, social media data, technology enabled cohorts), population (e.g., San Francisco Department of Public Health data linking health and social services provided to homeless individuals or other vulnerable populations) and policy data sets (e.g., local policies regulating cannabis retail outlets in California communities); 3) training and research activities organized around the elimination of health disparities. DaTABASE trainees will complete the rigorous methodological training for a PhD in Epidemiology and Translational Science. Additional content training, overseen by faculty in the UCSF Center for Health and Community, will provide theoretical frameworks and disciplinary background for social and behavioral sciences. Additional quantitative methods training will be delivered via the Bakar Computational Health Sciences Institute (BCHSI). BCHSI emphasizes machine learning tools to mine data sources for novel insights and discovery, including precision medicine and population health. This training initiative will prepare graduates to apply those analytic tools to novel data sets for research on behavioral and social processes underlying health disparities. The DaTABASE program will provide intensive mentoring from both content and methods experts. DaTABASE alumni will be prepared to lead research addressing current and emerging public health challenges with innovative computational and data science analytic approaches.