Dataset Information

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

ABSTRACT: 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.

SUBMITTER: Bilinski AM

PROVIDER: S-EPMC10410764 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

Bilinski Alyssa M AM Salomon Joshua A JA Hatfield Laura A LA

Proceedings of the National Academy of Sciences of the United States of America 20230801 32

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 m ...[more]

PMID: 37527346

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Project 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 often lack 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 of risk metrics. 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 novel 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-week-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-week-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.Significance statementIn the rapidly-evolving COVID-19 pandemic, public health risk metrics often become less relevant over time. Risk metrics are designed to predict future severe disease and mortality based on currently-available surveillance data, such as cases and hospitalizations. However, the relationship between cases, hospitalizations, and mortality has varied considerably over the course of the pandemic, in the context of new variants and shifts in vaccine- and infection-induced immunity. We propose an adaptive approach that regularly updates metrics based on the relationship between surveillance inputs and future outcomes of policy interest. Our method captures changing pandemic dynamics, requires only hospitalization input data, and outperforms static risk metrics in predicting high-risk states and counties.

Project description:ObjectiveComparative evidence suggests that the impact of COVID-19 on life expectancy has been relatively milder in South Korea. This study aims to examine whether the pandemic has universal or unequal impacts on life expectancy across 250 districts with varying levels of deprivation.MethodsUsing mortality data from 2012 to 2021 obtained from the Microdata Integrated Service of Statistics Korea, we calculated life expectancy at birth and age 65 for both sexes, by deprivation quintiles, before and during the pandemic. We summarized life expectancy gaps using the slope of the inequality index (SII) and further decomposed the gaps by the contribution of age and cause of death using Arriaga's method.ResultsBoth men and women experienced consistent improvements in life expectancy from 2012 to 2019, but the trend was disrupted during 2020 and 2021, primarily driven by older people. While men in more deprived areas were initially hit harder by the pandemic, the life expectancy gap across deprivation quintiles remained relatively constant and persistent across the study period [SII: -2.48 (CI: -2.70 from -2.27) for 2019 and - 2.84 (CI: -3.06 from -2.63) for 2020]. Middle-aged men from the most deprived areas were the most significant contributors to the life expectancy gap, with liver disease, liver cancer, transport accidents, and intentional injuries being the leading causes, both in the pre and during the pandemic. While these contributors remained largely similar before and during the pandemic, the contribution of transport accidents and liver cancer to the male life expectancy gap slightly decreased during the pandemic, while that of ischemic heart disease and pneumonia slightly increased. A similar increase was also observed for the female life expectancy gap.ConclusionThis study found no clear evidence of an increased life expectancy gap during the pandemic in South Korea, unlike in other countries, although access to emergency healthcare services may have been slightly more disturbed in deprived areas. This achievement can provide lessons for other countries. However, the persistent regional gaps in life expectancy observed over the past decade indicate the need for more targeted public health policies to address this issue.

Dataset Information

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

Publications

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

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