Unrepresentative big surveys significantly overestimated US vaccine uptake.

Surveys are a crucial tool for understanding public opinion and behaviour, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the effect of survey bias: an instance of the Big Data Paradox1. Here we demonstrate this paradox in estimates of first-dose COVID-19 vaccine uptake in US adults from 9 January to 19 May 2021 from two large surveys: Delphi-Facebook2,3 (about 250,000 responses per week) and Census Household Pulse4 (about 75,000 every two weeks). In May 2021, Delphi-Facebook overestimated uptake by 17 percentage points (14-20 percentage points with 5% benchmark imprecision) and Census Household Pulse by 14 (11-17 percentage points with 5% benchmark imprecision), compared to a retroactively updated benchmark the Centers for Disease Control and Prevention published on 26 May 2021. Moreover, their large sample sizes led to miniscule margins of error on the incorrect estimates. By contrast, an Axios-Ipsos online panel5 with about 1,000 responses per week following survey research best practices6 provided reliable estimates and uncertainty quantification. We decompose observed error using a recent analytic framework1 to explain the inaccuracy in the three surveys. We then analyse the implications for vaccine hesitancy and willingness. We show how a survey of 250,000 respondents can produce an estimate of the population mean that is no more accurate than an estimate from a simple random sample of size 10. Our central message is that data quality matters more than data quantity, and that compensating the former with the latter is a mathematically provable losing proposition.

COVID-19 Pandemic Impact on the National Health Care Surveys.

While underscoring the need for timely, nationally representative data in ambulatory, hospital, and long-term-care settings, the COVID-19 pandemic posed many challenges to traditional methods and mechanisms of data collection. To continue generating data from health care and long-term-care providers and establishments in the midst of the COVID-19 pandemic, the National Center for Health Statistics had to modify survey operations for several of its provider-based National Health Care Surveys, including quickly adding survey questions that captured the experiences of providing care during the pandemic. With the aim of providing information that may be useful to other health care data collection systems, this article presents some key challenges that affected data collection activities for these national provider surveys, as well as the measures taken to minimize the disruption in data collection and to optimize the likelihood of disseminating quality data in a timely manner. (Am J Public Health. 2021;111(12):2141-2148. https://doi.org/10.2105/AJPH.2021.306514).