Bias-adjusted predictions of county-level vaccination coverage from the COVID-19 Trends and Impact Survey

This repository contains analytic code for our manuscript.

Abstract: The potential for bias in non-representative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision-making. We developed a multi-step regression framework to bias-adjust vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey that included post-stratification to the American Community Survey and secondary normalization to an unbiased reference indicator. As a case study, we applied this framework to generate county-level predictions of long-run vaccination coverage among children ages 5 to 11 years. Our vaccination coverage predictions suggest a low ceiling on long-term national coverage (46%), detect substantial geographic heterogeneity (ranging from 11% to 91% across counties in the US), and highlight widespread disparities in the pace of scale-up in the three months following Emergency Use Authorization of COVID-19 vaccination for 5 to 11 year-olds. Generally, our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time-scale and geographic-scale necessary for proactive decision-making. The utility of large-scale, low-cost survey data for improving population health measurement is amplified when these data are combined with other representative sources of data.

Contact: Marissa Reitsma (mreitsma@stanford.edu)

Data Availability: Survey microdata are not publicly available because survey participants only consented to public disclosure of aggregate data, and because the legal agreement with Facebook governing operation of the survey prohibits disclosure of microdata without confidentiality protections for respondents. Deidentified microdata are available to researchers under a Data Use Agreement that protects the confidentiality of respondents. Access can be requested online. Requests are reviewed by the Carnegie Mellon University Office of Sponsored Programs and Facebook Data for Good.