Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health

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 an...

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Bibliographic Details
Published in:American journal of public health (1971) 2022-10, Vol.112 (10), p.1436-1445
Main Authors: Thorpe, Lorna E, Chunara, Rumi, Roberts, Tim, Pantaleo, Nicholas, Irvine, Caleb, Conderino, Sarah, Li, Yuruo, Hsieh, Pei Yang, Gourevitch, Marc N, Levine, Shoshanna, Ofrane, Rebecca, Spoer, Benjamin
Format: Article
Language:English
Subjects:
Air pollution
Coronaviruses
COVID-19
COVID-19 - epidemiology
Data sources
Disease control
Economic models
Economics
Environmental conditions
Forecasting
Health care
Health surveillance
Humans
Literature reviews
Machine learning
Outdoor air quality
Pandemics
Public buildings
Public Health
Public Health Surveillance
Satellites
Social Conditions
Social conditions & trends
Systematic Reviews as Topic
United States - epidemiology
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Summary: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).
ISSN:0090-0036
1541-0048
1541-0048
DOI:10.2105/AJPH.2022.306917