Lorna E. Thorpe, Nicholas Pantaleo, Sarah Conderino, Yuruo Li, Marc N. Gourevitch, Shoshanna Levine, Rebecca Ofrane, and Benjamin Spoer are with the Department of Population Health, New York University (NYU) Grossman School of Medicine, New York, NY. Rumi Chunara is with the Department of Computer Science and Engineering, NYU Tandon School of Engineering, New York, NY. Tim Roberts is with the Medical Library, NYU Grossman School of Medicine. Caleb Irvine is with the Department of Medicine, NYU Grossman School of Medicine. Pei Yang Hsieh was with the Department of Population Health, NYU Grossman School of Medicine at the time of writing this article.
Am J Public Health. 2022 Oct;112(10):1436-1445. doi: 10.2105/AJPH.2022.306917. Epub 2022 Aug 4.
In response to rapidly changing societal conditions stemming from the COVID-19 pandemic, we summarize data sources with potential to produce timely and spatially granular measures of physical, economic, and social conditions relevant to public health surveillance, and we briefly describe emerging analytic methods to improve small-area estimation. To inform this article, we reviewed published systematic review articles set in the United States from 2015 to 2020 and conducted unstructured interviews with senior content experts in public heath practice, academia, and industry. We identified a modest number of data sources with high potential for generating timely and spatially granular measures of physical, economic, and social determinants of health. We also summarized modeling and machine-learning techniques useful to support development of time-sensitive surveillance measures that may be critical for responding to future major events such as the COVID-19 pandemic. (. 2022;112(10):1436-1445. https://doi.org/10.2105/AJPH.2022.306917).
针对源自 COVID-19 大流行的快速变化的社会状况，我们总结了具有产生与公共卫生监测相关的及时和空间粒度的物理、经济和社会条件衡量指标潜力的数据来源，并简要描述了改进小区域估计的新兴分析方法。为了撰写本文，我们回顾了 2015 年至 2020 年期间在美国进行的已发表的系统评价文章，并对公共卫生实践、学术界和行业的资深内容专家进行了非结构化访谈。我们确定了少量具有生成及时和空间粒度的健康物理、经济和社会决定因素衡量指标的高潜力数据来源。我们还总结了对支持开发对未来重大事件（如 COVID-19 大流行）做出响应可能至关重要的时间敏感监测措施有用的建模和机器学习技术。（2022；112（10）：1436-1445。https://doi.org/10.2105/AJPH.2022.306917）。
