利用代用模式-混合模型解释两次大型调查中 COVID-19 疫苗接种率估计值的偏差

Rebecca R Andridge
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摘要

摘要 最近,两项基于互联网的大型概率调查未能正确估计 2021 年初美国 COVID-19 疫苗的接种率,这引起了人们的关注。德尔菲-Facebook COVID-19 趋势与影响调查(CTIS)和人口普查家庭脉搏调查(HPS)均大幅高估了2021年5月的接种率,分别高估了17个百分点和14个百分点。这些调查的受访者人数众多,但回复率非常低(&lt;10%),因此,不可忽略的非回复可能会产生重大影响。具体来说,鉴于调查主题(大流行病对日常生活的影响),"反疫苗 "者参与调查的可能性较低。在本文中,我们使用代理模式-混合模型 (PPMM),利用 CTIS 和 HPS 的数据,在不可忽略的非响应假设下,估算了至少接种过一剂 COVID-19 疫苗的成年人(18 岁以上)的比例。美国社区调查数据为 PPMMs 提供了必要的人口数据。我们将这些估计值与真实的基准接种人数进行了比较,结果表明 PPMM 可以检测出偏差的方向,并提供有意义的偏差界限。我们还使用 PPMM 估算了疫苗接种犹豫率(我们没有基准真实值),并将其与直接调查估算值进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using proxy pattern-mixture models to explain bias in estimates of COVID-19 vaccine uptake from two large surveys
Abstract Recently, attention was drawn to the failure of two very large internet-based probability surveys to correctly estimate COVID-19 vaccine uptake in the U.S. in early 2021. Both the Delphi-Facebook COVID-19 Trends and Impact Survey (CTIS) and Census Household Pulse Survey (HPS) overestimated uptake substantially, by 17 and 14 percentage points in May 2021, respectively. These surveys had large numbers of respondents but very low response rates (&lt;10%), thus, nonignorable nonresponse could have had substantial impact. Specifically, it is plausible that ‘anti-vaccine’ individuals were less likely to participate given the topic (impact of the pandemic on daily life). In this article, we use proxy pattern-mixture models (PPMMs) to estimate the proportion of adults (18 +) who received at least one dose of a COVID-19 vaccine, using data from the CTIS and HPS, under a nonignorable nonresponse assumption. Data from the American Community Survey provide the necessary population data for the PPMMs. We compare these estimates to the true benchmark uptake numbers and show that the PPMM could have detected the direction of the bias and provide meaningful bias bounds. We also use the PPMM to estimate vaccine hesitancy, a measure for which we do not have a benchmark truth, and compare to the direct survey estimates.
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