通过贝叶斯插补法对被抑制的临时 COVID-19 死亡人数进行推断，为决策提供更好的数据。

Better data for decision-making through Bayesian imputation of suppressed provisional COVID-19 death counts.

机构信息

Division of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.

Alan Shawn Feinstein College of Education, University of Rhode Island, Kingston, Rhode Island, United States of America.

出版信息

PLoS One. 2023 Aug 3;18(8):e0288961. doi: 10.1371/journal.pone.0288961. eCollection 2023.

DOI:10.1371/journal.pone.0288961

PMID:37535647

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10399909/

Abstract

PURPOSE

To facilitate use of timely, granular, and publicly available data on COVID-19 mortality, we provide a method for imputing suppressed COVID-19 death counts in the National Center for Health Statistic's 2020 provisional mortality data by quarter, county, and age.

METHODS

We used a Bayesian approach to impute suppressed COVID-19 death counts by quarter, county, and age in provisional data for 3,138 US counties. Our model accounts for multilevel data structures; numerous zero death counts among persons aged <50 years, rural counties, early quarters in 2020; highly right-skewed distributions; and different levels of data granularity (county, state or locality, and national levels). We compared three models with different prior assumptions of suppressed COVID-19 deaths, including noninformative priors (M1), the same weakly informative priors for all age groups (M2), and weakly informative priors that differ by age (M3) to impute the suppressed death counts. After the imputed suppressed counts were available, we assessed three prior assumptions at the national, state/locality, and county level, respectively. Finally, we compared US counties by two types of COVID-19 death rates, crude (CDR) and age-standardized death rates (ASDR), which can be estimated only through imputing suppressed death counts.

RESULTS

Without imputation, the total COVID-19 death counts estimated from the raw data underestimated the reported national COVID-19 deaths by 18.60%. Using imputed data, we overestimated the national COVID-19 deaths by 3.57% (95% CI: 3.37%-3.80%) in model M1, 2.23% (95% CI: 2.04%-2.43%) in model M2, and 2.96% (95% CI: 2.76%-3.16%) in model M3 compared with the national report. The top 20 counties that were most affected by COVID-19 mortality were different between CDR and ASDR.

CONCLUSIONS

Bayesian imputation of suppressed county-level, age-specific COVID-19 deaths in US provisional data can improve county ASDR estimates and aid public health officials in identifying disparities in deaths from COVID-19.

摘要

目的

为了方便使用关于 COVID-19 死亡率的及时、粒度细化且公开可得的数据，我们提供了一种方法，通过季度、县和年龄对国家卫生统计中心 2020 年临时死亡率数据中被抑制的 COVID-19 死亡人数进行估算。

方法

我们使用贝叶斯方法，通过季度、县和年龄对美国 3138 个县的临时数据中被抑制的 COVID-19 死亡人数进行估算。我们的模型考虑了多层次的数据结构；50 岁以下人群、农村县、2020 年早期季度中大量的零死亡人数；高度右偏分布；以及不同的数据粒度级别（县、州/地方和国家级别）。我们比较了三种具有不同被抑制 COVID-19 死亡先验假设的模型，包括非信息先验（M1）、所有年龄组相同的弱信息先验（M2）和按年龄划分的弱信息先验（M3），以估算被抑制的死亡人数。在获得估算的被抑制死亡人数后，我们分别在国家、州/地方和县一级评估了三种先验假设。最后，我们通过两种类型的 COVID-19 死亡率（粗死亡率（CDR）和年龄标准化死亡率（ASDR））比较了美国各县，这两种死亡率只能通过估算被抑制的死亡人数来估计。

结果

如果不进行估算，从原始数据中估计的 COVID-19 总死亡人数将比全国报告的 COVID-19 死亡人数低 18.60%。使用估算数据，与全国报告相比，模型 M1 中全国 COVID-19 死亡人数高估了 3.57%（95%CI：3.37%-3.80%），模型 M2 中高估了 2.23%（95%CI：2.04%-2.43%），模型 M3 中高估了 2.96%（95%CI：2.76%-3.16%）。在 CDR 和 ASDR 中，受 COVID-19 死亡率影响最大的前 20 个县是不同的。