English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, attention was drawn to the failure of two very large internet-basedprobability surveys to correctly estimate COVID-19 vaccine uptake in the UnitedStates in early 2021. Both the Delphi-Facebook CTIS and Census Household PulseSurvey (HPS) overestimated uptake substantially, by 17 and 14 percentage pointsin May 2021, respectively. These surveys had large numbers of respondents butvery low response rates (<10%), thus, non-ignorable nonresponse could have hadsubstantial impact. Specifically, it is plausible that "anti-vaccine"individuals were less likely to participate given the topic (impact of thepandemic on daily life). In this paper we use proxy pattern-mixture models(PPMMs) to estimate the proportion of adults (18+) who received at least onedose of a COVID-19 vaccine, using data from the CTIS and HPS, under anon-ignorable nonresponse assumption. Data from the American Community Surveyprovide the necessary population data for the PPMMs. We compare these estimatesto the true benchmark uptake numbers and show that the PPMM could have detectedthe direction of the bias and provide meaningful bias bounds. We also use thePPMM to estimate vaccine hesitancy, a measure for which we do not have abenchmark truth, and compare to the direct survey estimates.

Using Proxy Pattern-Mixture Models to Explain Bias in Estimates of COVID-19 Vaccine Uptake from Two Large Surveys