ABSTRACT
The COVID-19 pandemic restructured university learning environments while also underscoring the need for granular local health data. We describe how the University of Memphis School of Public Health used the City Health Dashboard, an online resource providing data at the city and neighborhood level for more than 35 measures of health outcomes, health drivers, and health equity for all US cities with populations >50 000, to enrich students' learning of applying data to community health policy. By facilitating students' engagement with population needs, assets, and capacities that affect communities' health-key components of the master of public health accreditation process-the Dashboard supports in-person and virtual learning at undergraduate and graduate levels and is recommended as a novel and rigorous data source for public health trainees.
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).