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Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation

OBJECTIVE: To examine the value of health systems data as indicators of emerging COVID-19 activity. DESIGN: Observational study of health system indicators for the COVID Hotspotting Score (CHOTS) with prospective validation. SETTING AND PARTICIPANTS: An integrated healthcare delivery system in North...

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Autores principales: Liu, Vincent X, Thai, Khanh K, Galin, Jessica, Gerstley, Lawrence David, Myers, Laura C, Parodi, Stephen M, Chen, Yi-Fen Irene, Goler, Nancy, Escobar, Gabriel J, Kipnis, Patricia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316696/
https://www.ncbi.nlm.nih.gov/pubmed/34312202
http://dx.doi.org/10.1136/bmjopen-2020-048211
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author Liu, Vincent X
Thai, Khanh K
Galin, Jessica
Gerstley, Lawrence David
Myers, Laura C
Parodi, Stephen M
Chen, Yi-Fen Irene
Goler, Nancy
Escobar, Gabriel J
Kipnis, Patricia
author_facet Liu, Vincent X
Thai, Khanh K
Galin, Jessica
Gerstley, Lawrence David
Myers, Laura C
Parodi, Stephen M
Chen, Yi-Fen Irene
Goler, Nancy
Escobar, Gabriel J
Kipnis, Patricia
author_sort Liu, Vincent X
collection PubMed
description OBJECTIVE: To examine the value of health systems data as indicators of emerging COVID-19 activity. DESIGN: Observational study of health system indicators for the COVID Hotspotting Score (CHOTS) with prospective validation. SETTING AND PARTICIPANTS: An integrated healthcare delivery system in Northern California including 21 hospitals and 4.5 million members. MAIN OUTCOME MEASURES: The CHOTS incorporated 10 variables including four major (cough/cold calls, emails, new positive COVID-19 tests, COVID-19 hospital census) and six minor (COVID-19 calls, respiratory infection and COVID-19 routine and urgent visits, and respiratory viral testing) indicators assessed with change point detection and slope metrics. We quantified cross-correlations lagged by 7–42 days between CHOTS and standardised COVID-19 hospital census using observational data from 1 April to 31 May 2020 and two waves of prospective data through 21 March 2021. RESULTS: Through 30 September 2020, peak cross-correlation between CHOTS and COVID-19 hospital census occurred with a 28-day lag at 0.78; at 42 days, the correlation was 0.69. Lagged correlation between medical centre CHOTS and their COVID-19 census was highest at 42 days for one facility (0.63), at 35 days for nine facilities (0.52–0.73), at 28 days for eight facilities (0.28–0.74) and at 14 days for two facilities (0.73–0.78). The strongest correlation for individual indicators was 0.94 (COVID-19 census) and 0.90 (new positive COVID-19 tests) lagged 1–14 days and 0.83 for COVID-19 calls and urgent clinic visits lagged 14–28 days. Cross-correlation was similar (0.73) with a 35-day lag using prospective validation from 1 October 2020 to 21 March 2021. CONCLUSIONS: Passively collected health system indicators were strongly correlated with forthcoming COVID-19 hospital census up to 6 weeks before three successive COVID-19 waves. These tools could inform communities, health systems and public health officials to identify, prepare for and mitigate emerging COVID-19 activity.
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spelling pubmed-83166962021-07-30 Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation Liu, Vincent X Thai, Khanh K Galin, Jessica Gerstley, Lawrence David Myers, Laura C Parodi, Stephen M Chen, Yi-Fen Irene Goler, Nancy Escobar, Gabriel J Kipnis, Patricia BMJ Open Public Health OBJECTIVE: To examine the value of health systems data as indicators of emerging COVID-19 activity. DESIGN: Observational study of health system indicators for the COVID Hotspotting Score (CHOTS) with prospective validation. SETTING AND PARTICIPANTS: An integrated healthcare delivery system in Northern California including 21 hospitals and 4.5 million members. MAIN OUTCOME MEASURES: The CHOTS incorporated 10 variables including four major (cough/cold calls, emails, new positive COVID-19 tests, COVID-19 hospital census) and six minor (COVID-19 calls, respiratory infection and COVID-19 routine and urgent visits, and respiratory viral testing) indicators assessed with change point detection and slope metrics. We quantified cross-correlations lagged by 7–42 days between CHOTS and standardised COVID-19 hospital census using observational data from 1 April to 31 May 2020 and two waves of prospective data through 21 March 2021. RESULTS: Through 30 September 2020, peak cross-correlation between CHOTS and COVID-19 hospital census occurred with a 28-day lag at 0.78; at 42 days, the correlation was 0.69. Lagged correlation between medical centre CHOTS and their COVID-19 census was highest at 42 days for one facility (0.63), at 35 days for nine facilities (0.52–0.73), at 28 days for eight facilities (0.28–0.74) and at 14 days for two facilities (0.73–0.78). The strongest correlation for individual indicators was 0.94 (COVID-19 census) and 0.90 (new positive COVID-19 tests) lagged 1–14 days and 0.83 for COVID-19 calls and urgent clinic visits lagged 14–28 days. Cross-correlation was similar (0.73) with a 35-day lag using prospective validation from 1 October 2020 to 21 March 2021. CONCLUSIONS: Passively collected health system indicators were strongly correlated with forthcoming COVID-19 hospital census up to 6 weeks before three successive COVID-19 waves. These tools could inform communities, health systems and public health officials to identify, prepare for and mitigate emerging COVID-19 activity. BMJ Publishing Group 2021-07-26 /pmc/articles/PMC8316696/ /pubmed/34312202 http://dx.doi.org/10.1136/bmjopen-2020-048211 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Public Health
Liu, Vincent X
Thai, Khanh K
Galin, Jessica
Gerstley, Lawrence David
Myers, Laura C
Parodi, Stephen M
Chen, Yi-Fen Irene
Goler, Nancy
Escobar, Gabriel J
Kipnis, Patricia
Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation
title Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation
title_full Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation
title_fullStr Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation
title_full_unstemmed Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation
title_short Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation
title_sort development of a healthcare system covid hotspotting score in california: an observational study with prospective validation
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316696/
https://www.ncbi.nlm.nih.gov/pubmed/34312202
http://dx.doi.org/10.1136/bmjopen-2020-048211
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