TY - JOUR
T1 - Building Public Health Surveillance 3.0
T2 - Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health
AU - Thorpe, Lorna E.
AU - Chunara, Rumi
AU - Roberts, Tim
AU - Pantaleo, Nicholas
AU - Irvine, Caleb
AU - Conderino, Sarah
AU - Li, Yuruo
AU - Hsieh, Pei Yang
AU - Gourevitch, Marc N.
AU - Levine, Shoshanna
AU - Ofrane, Rebecca
AU - Spoer, Benjamin
N1 - Publisher Copyright:
© 2022 American Public Health Association Inc.. All rights reserved.
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
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U2 - 10.2105/AJPH.2022.306917
DO - 10.2105/AJPH.2022.306917
M3 - Article
C2 - 35926162
AN - SCOPUS:85138448992
SN - 0090-0036
VL - 112
SP - 1436
EP - 1445
JO - American journal of public health
JF - American journal of public health
IS - 10
ER -