2 datasets found
  1. o

    The U.S. COVID-19 County Policy Database

    • openicpsr.org
    delimited
    Updated Sep 22, 2022
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    Rita Hamad; Mark Pletcher; Thomas Carton (2022). The U.S. COVID-19 County Policy Database [Dataset]. http://doi.org/10.3886/E180482V2
    Explore at:
    delimitedAvailable download formats
    Dataset updated
    Sep 22, 2022
    Dataset provided by
    Harvard School of Public Health
    University of California San Francisco
    Louisiana Public Health Institute
    Authors
    Rita Hamad; Mark Pletcher; Thomas Carton
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    TX 117.Lee, NJ 72.Passaic, CA 19.San Diego, TX 139.Blanco, TX 135.Hood, MS 66.Harrison, TX 137.San Saba, NJ 78.Monmouth, UT 163.Wasatch, NJ 69.Union, United States
    Description

    It is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. Yet there has been limited work to systematically evaluate the substantial variation in local COVID-19-related policies in the U.S. The objective of the U.S. COVID-19 County Policy (UCCP) Database is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies. The current data upload represents the first wave of data collection, which includes data on over 20 policies gathered across 171 counties in 7 states during January-March 2021. These include county-level COVID-19-related policies within 3 policy domains that are likely to affect a variety of health outcomes: (1) containment/closure, (2) economic support, and (3) public health. In ongoing work, we are conducting retrospective longitudinal weekly data collection for the period 2020-2021 from a larger swath of 300+ U.S. counties in all 50 states and Washington D.C. The current database will be updated with new data as it becomes available, in late 2023 or early 2024.Researchers who use this database for their studies should acknowledge the funders below in all publications.

  2. o

    The U.S. COVID-19 County Policy Database

    • openicpsr.org
    delimited
    Updated Sep 22, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Rita Hamad; Mark Pletcher; Thomas Carton (2022). The U.S. COVID-19 County Policy Database [Dataset]. http://doi.org/10.3886/E180482V1
    Explore at:
    delimitedAvailable download formats
    Dataset updated
    Sep 22, 2022
    Dataset provided by
    University of California San Francisco
    Louisiana Public Health Institute
    Authors
    Rita Hamad; Mark Pletcher; Thomas Carton
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    CA 9.Del Norte, NY 91.Richmond, LA 45.Lincoln, LA 47.St. Tammany, CA 33.Sonoma, LA 43.De Soto, TX 132.Bosque, UT 162.Sanpete, UT 146.Morgan, LA 64.Hancock, United States
    Description

    It is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. Yet there has been limited work to systematically evaluate the substantial variation in local COVID-19-related policies in the U.S. The objective of the U.S. COVID-19 County Policy (UCCP) Database is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies. The current data upload represents the first wave of data collection, which includes data on over 20 policies gathered across 171 counties in 7 states during January-March 2021. These include county-level COVID-19-related policies within 3 policy domains that are likely to affect a variety of health outcomes: (1) containment/closure, (2) economic support, and (3) public health. In ongoing work, we are conducting retrospective longitudinal weekly data collection for the period 2020-2021 from a larger swath of 300+ U.S. counties in all 50 states and Washington D.C., and the current database will be updated with new data as it becomes available.

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Click to copy link
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Close
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Rita Hamad; Mark Pletcher; Thomas Carton (2022). The U.S. COVID-19 County Policy Database [Dataset]. http://doi.org/10.3886/E180482V2

The U.S. COVID-19 County Policy Database

Explore at:
22 scholarly articles cite this dataset (View in Google Scholar)
delimitedAvailable download formats
Dataset updated
Sep 22, 2022
Dataset provided by
Harvard School of Public Health
University of California San Francisco
Louisiana Public Health Institute
Authors
Rita Hamad; Mark Pletcher; Thomas Carton
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
TX 117.Lee, NJ 72.Passaic, CA 19.San Diego, TX 139.Blanco, TX 135.Hood, MS 66.Harrison, TX 137.San Saba, NJ 78.Monmouth, UT 163.Wasatch, NJ 69.Union, United States
Description

It is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. Yet there has been limited work to systematically evaluate the substantial variation in local COVID-19-related policies in the U.S. The objective of the U.S. COVID-19 County Policy (UCCP) Database is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies. The current data upload represents the first wave of data collection, which includes data on over 20 policies gathered across 171 counties in 7 states during January-March 2021. These include county-level COVID-19-related policies within 3 policy domains that are likely to affect a variety of health outcomes: (1) containment/closure, (2) economic support, and (3) public health. In ongoing work, we are conducting retrospective longitudinal weekly data collection for the period 2020-2021 from a larger swath of 300+ U.S. counties in all 50 states and Washington D.C. The current database will be updated with new data as it becomes available, in late 2023 or early 2024.Researchers who use this database for their studies should acknowledge the funders below in all publications.

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