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Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations
Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10410764/ https://www.ncbi.nlm.nih.gov/pubmed/37527346 http://dx.doi.org/10.1073/pnas.2302528120 |
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author | Bilinski, Alyssa M. Salomon, Joshua A. Hatfield, Laura A. |
author_facet | Bilinski, Alyssa M. Salomon, Joshua A. Hatfield, Laura A. |
author_sort | Bilinski, Alyssa M. |
collection | PubMed |
description | Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus false-negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false-negative versus false-positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a method to update risk thresholds in real time. We show that this adaptive approach to designating areas as “high risk” improves performance over static metrics in predicting 3-wk-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-wk-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context. |
format | Online Article Text |
id | pubmed-10410764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-104107642023-08-10 Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations Bilinski, Alyssa M. Salomon, Joshua A. Hatfield, Laura A. Proc Natl Acad Sci U S A Biological Sciences Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus false-negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false-negative versus false-positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a method to update risk thresholds in real time. We show that this adaptive approach to designating areas as “high risk” improves performance over static metrics in predicting 3-wk-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-wk-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context. National Academy of Sciences 2023-08-01 2023-08-08 /pmc/articles/PMC10410764/ /pubmed/37527346 http://dx.doi.org/10.1073/pnas.2302528120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Bilinski, Alyssa M. Salomon, Joshua A. Hatfield, Laura A. Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations |
title | Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations |
title_full | Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations |
title_fullStr | Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations |
title_full_unstemmed | Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations |
title_short | Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations |
title_sort | adaptive metrics for an evolving pandemic: a dynamic approach to area-level covid-19 risk designations |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10410764/ https://www.ncbi.nlm.nih.gov/pubmed/37527346 http://dx.doi.org/10.1073/pnas.2302528120 |
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