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Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental
Lorna E Thorpe1, Rumi Chunara1, Tim Roberts1
1Lorna 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.
Abstract:
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. (Am J Public Health. 2022;112(10):1436-1445. https://doi.org/10.2105/AJPH.2022.306917).
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