Suppr超能文献

一种评估 COVID-19 检测效果的新综合指标:国家间比较及其与地理位置、政府和政策应对的关联。

A novel comprehensive metric to assess effectiveness of COVID-19 testing: Inter-country comparison and association with geography, government, and policy response.

机构信息

Faculty of Public Health, Khon Kaen University, Khon Kaen, Thailand.

Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.

出版信息

PLoS One. 2021 Mar 5;16(3):e0248176. doi: 10.1371/journal.pone.0248176. eCollection 2021.

Abstract

Testing and case identification are key strategies in controlling the COVID-19 pandemic. Contact tracing and isolation are only possible if cases have been identified. The effectiveness of testing should be assessed, but a single comprehensive metric is not available to assess testing effectiveness, and no timely estimates of case detection rate are available globally, making inter-country comparisons difficult. The purpose of this paper was to propose a single, comprehensive metric, called the COVID-19 Testing Index (CovTI) scaled from 0 to 100, derived from epidemiological indicators of testing, and to identify factors associated with this outcome. The index was based on case-fatality rate, test positivity rate, active cases, and an estimate of the detection rate. It used parsimonious modeling to estimate the true total number of COVID-19 cases based on deaths, testing, health system capacity, and government transparency. Publicly reported data from 165 countries and territories that had reported at least 100 confirmed cases by June 3, 2020 were included in the index. Estimates of detection rates aligned satisfactorily with previous estimates in literature (R2 = 0.44). As of June 3, 2020, the states with the highest CovTI included Hong Kong (93.7), Australia (93.5), Iceland (91.8), Cambodia (91.3), New Zealand (90.6), Vietnam (90.2), and Taiwan (89.9). Bivariate analyses showed the mean CovTI in countries with open public testing policies (66.9, 95% CI 61.0-72.8) was significantly higher than in countries with no testing policy (29.7, 95% CI 17.6-41.9) (p<0.0001). A multiple linear regression model assessed the association of independent grouping variables with CovTI. Open public testing and extensive contact tracing were shown to significantly increase CovTI, after adjusting for extrinsic factors, including geographic isolation and centralized forms of government. The correlation of testing and contact tracing policies with improved outcomes demonstrates the validity of this model to assess testing effectiveness and also suggests these policies were effective at improving health outcomes. This tool can be combined with other databases to identify other factors or may be useful as a standalone tool to help inform policymakers.

摘要

检测和病例识别是控制 COVID-19 大流行的关键策略。如果没有识别出病例,就不可能进行接触者追踪和隔离。应该评估检测的有效性,但目前没有单一的综合指标来评估检测效果,也没有及时的全球病例检出率估计值,使得国家间的比较变得困难。本文的目的是提出一个单一的、综合的指标,称为 COVID-19 检测指数(CovTI),范围从 0 到 100,由检测的流行病学指标推导而来,并确定与该结果相关的因素。该指数基于病死率、检测阳性率、活跃病例数以及检出率的估计值。它使用简约建模来根据死亡、检测、卫生系统能力和政府透明度来估计 COVID-19 病例的真实总数。该指数纳入了截至 2020 年 6 月 3 日报告至少 100 例确诊病例的 165 个国家和地区的公开报告数据。检出率的估计值与文献中的先前估计值吻合良好(R2=0.44)。截至 2020 年 6 月 3 日,CovTI 最高的州包括香港(93.7)、澳大利亚(93.5)、冰岛(91.8)、柬埔寨(91.3)、新西兰(90.6)、越南(90.2)和台湾(89.9)。 二元分析显示,公开公共检测政策国家的平均 CovTI(66.9,95%置信区间 61.0-72.8)明显高于没有检测政策的国家(29.7,95%置信区间 17.6-41.9)(p<0.0001)。 多元线性回归模型评估了独立分组变量与 CovTI 的关联。在调整了地理隔离和集中式政府形式等外在因素后,公开公共检测和广泛的接触者追踪被证明可以显著提高 CovTI。 检测和接触者追踪政策与改善结果的相关性表明,该模型可用于评估检测效果的有效性,也表明这些政策在改善健康结果方面是有效的。该工具可与其他数据库结合使用以识别其他因素,也可以作为独立工具帮助决策者做出决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a29/7935311/42fc79ab25a8/pone.0248176.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验