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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.
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The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey - over 20 million responses in its first year of operation - allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems. Significance statementThe U.S. COVID-19 Trends and Impact Survey (CTIS) has operated continuously since April 6, 2020, collecting over 20 million responses. The largest public health survey ever conducted in the United States, CTIS was designed to facilitate detailed demographic and geographic analyses, track trends over time, and accommodate rapid response to emerging priorities. Using examples of CTIS results illuminating trends in symptoms, risks, mitigating behaviors, testing and vaccination in relation to evolving high-priority policy questions over 12 months of the pandemic, we illustrate the value of online surveys for tracking patterns and trends in COVID outcomes as an adjunct to official reporting, and showcase unique insights that would not be visible through traditional public health reporting.
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ImportanceCOVID-19 vaccine hesitancy has become a leading barrier to increasing the US vaccination rate. ObjectiveTo evaluate time trends in COVID-19 vaccine intent during the US vaccine rollout, and identify key factors related to and self-reported reasons for COVID-19 vaccine hesitancy in May 2021. Design, participants and settingA COVID-19 survey was offered to US adult Facebook users in several languages yielding 5,088,772 qualifying responses from January 6 to May 31, 2021. Data was aggregated by month. Survey weights matched the sample to the age, gender, and state profile of the US population. ExposureDemographics, geographic factors, political/COVID-19 environment, health status, beliefs, and behaviors. Main outcome measures"If a vaccine to prevent COVID-19 were offered to you today, would you choose to get vaccinated." Hesitant was defined as responding probably or definitely would not choose to get vaccinated (versus probably or definitely would, or already vaccinated). ResultsCOVID-19 vaccine hesitancy decreased by one-third from 25.4% (95%CI, 25.3, 25.5) in January to 16.6% (95% CI, 16.4, 16.7) in May, with relatively large decreases among participants with Black, Pacific Islander or Hispanic race/ethnicity and [≤]high school education. Independent risk factors for vaccine hesitancy in May (N=525,644) included younger age, non-Asian race, < 4 year college degree, living in a more rural county, living in a county with higher Trump vote share in the 2020 election, lack of worry about COVID-19, working outside the home, never intentionally avoiding contact with others, and no past-year flu vaccine. Differences in hesitancy by race/ethnicity varied by age (e.g., Black adults more hesitant than White adults <35 years old, but less hesitant among adults [≥]45 years old). Differences in hesitancy by age varied by race/ethnicity. Almost half of vaccine hesitant respondents reported fear of side effects (49.2% [95%CI, 48.7, 49.7]) and not trusting the COVID-19 vaccine (48.4% [95%CI, 48.0, 48.9]); over one-third reported not trusting the government, not needing the vaccine, and waiting to see if safe. Reasons differed by degree of vaccine intent and by race/ethnicity. ConclusionCOVID-19 vaccine hesitancy varied by demographics, geography, beliefs, and behaviors, indicating a need for a range of messaging and policy options to target high-hesitancy groups.
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The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data is available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.
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COVID-19 vaccine hesitancy threatens pandemic control efforts. We evaluated vaccine hesitancy in the US by employment status and occupation category during the COVID-19 vaccine rollout. US adults 18-64 years completed an online COVID-19 survey 3,179,174 times from January 6-May 19, 2021. Data was aggregated by month. Survey weights matched the sample to the US population age, gender, and state profile. Weighted percentages and 95% confidence intervals (95%CI) were calculated. Changes in vaccine hesitancy from January-May varied widely by employment status (e.g., -7.8% [95%CI, -8.2--7.5] among those working outside the home, a 26.6% decrease; -13.3% [95%CI, -13.7--13.0] among those not working for pay, a 44.9% decrease), and occupation category (e.g., -15.9% [95%CI, -17.7--14.2] in production, a 39.3% decrease; -1.4% [95%CI, -3.8--1.0] in construction/extraction, a 3.0% decrease). April 20-May 19, 2021, vaccine hesitancy ranged from 7.3% (95%CI, 6.7-7.8) in computer/mathematical professions to 45.2% (95%CI, 43.2-46.8) in construction/extraction. Hesitancy was 9.0% (95%CI, 8.6-9.3) among educators and 14.5% (95%CI, 14.0-15.0) among healthcare practitioners/technicians. While the prevalence of reasons for hesitancy differed by occupation, over half of employed hesitant participants reported concern about side effects (51.7%) and not trusting COVID-19 vaccines (51.3%), whereas only 15.0% didnt like vaccines in general. Over a third didnt believe they needed the vaccine, didnt trust the government, and/or were waiting to see if it was safe. In this massive national survey of adults 18-64 years, vaccine hesitancy varied widely by occupation. Reasons for hesitancy indicate messaging about safety and addressing trust are paramount.
