Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey.

BACKGROUND: The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey. DESIGN: We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up. RESULTS: Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds. LIMITATIONS: We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape. CONCLUSIONS: 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. IMPLICATIONS: Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs. HIGHLIGHTS: The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness.The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement.We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey.Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making.

Dynamic spinal reflex adaptation during locomotor adaptation.

The dynamics and interaction of spinal and supraspinal centers during locomotor adaptation remain vaguely understood. In this work, we use Hoffmann reflex measurements to investigate changes in spinal reflex gains during split-belt locomotor adaptation. We show that spinal reflex gains are dynamically modulated during split-belt locomotor adaptation. During first exposure to split-belt transitions, modulation occurs mostly on the leg ipsilateral to the speed change and constitutes rapid suppression or facilitation of the reflex gains, followed by slow recovery to baseline. Over repeated exposure, the modulation pattern washes out. We further show that reflex gain modulation strongly correlates with correction of leg asymmetry, and cannot be explained by speed modulation solely. We argue that reflex modulation is likely of supraspinal origins and constitutes an integral part of the neural substrate underlying split-belt locomotor adaptation.NEW & NOTEWORTHY This work presents direct evidence for spinal reflex modulation during locomotor adaptation. In particular, we show that reflexes can be modulated on-demand unilaterally during split-belt locomotor adaptation and speculate about reflex modulation as an underlying mechanism for adaptation of gait asymmetry in healthy adults.

A Randomized, Open-Label, Crossover Phase 1 Safety and Pharmacokinetic Study of Oral Maraviroc and Maraviroc 1% Gel (the CHARM-03 Study).

The Combination HIV Antiretroviral Rectal Microbicide-3 (CHARM-03) study was a randomized, open-label, crossover Phase 1 safety and pharmacokinetic (PK) study of oral maraviroc (MVC) and MVC 1% gel. At a single site, healthy HIV-uninfected men and women were enrolled and randomized to an open label crossover sequence of eight consecutive daily exposures to MVC 300 mg dosed orally, MCV 1% gel dosed rectally, and MVC 1% gel dosed vaginally. Male participants received oral and rectal dosing and female participants received oral, rectal, and vaginal dosing. Assessments were undertaken at baseline and following each 8-day period and included collection of plasma, rectal/cervical tissue (CT), and rectal/endocervical/vaginal fluids. Eleven men and nine women were enrolled. Two participants withdrew from the study before receiving study product. There were 25 adverse events, of which 24 were Grade 1 (G1) and one was G2 (unrelated). After eight doses, MVC was quantifiable in all samples following oral, rectal, or vaginal product administration. The highest drug concentrations in plasma, rectal tissue (RT), and CT were associated with oral, rectal, and vaginal drug delivery, respectively. There were significant reductions in tissue drug concentrations when rectal and cervical biopsies were incubated in media before tissue processing for PK (p < .0001). Only oral MVC was associated with limited protection in the rectal explant HIV challenge model (p < .05). There were no immunological changes in RT, and all products were acceptable to participants. In conclusion, all products were found to be safe and acceptable and did not induce local inflammation. The lack of ex vivo efficacy demonstrated in study samples may be due to rapid disassociation of MVC from the explant tissue. ClinicalTrials.gov Identifier: NCT02346084.

The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination.

The US 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.

An open repository of real-time COVID-19 indicators.

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 deidentified 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 are 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.

Time trends, factors associated with, and reasons for COVID-19 vaccine hesitancy: A massive online survey of US adults from January-May 2021.

IMPORTANCE: COVID-19 vaccine hesitancy has become a leading barrier to increasing the US vaccination rate. OBJECTIVE: To 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 SETTING: A 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. EXPOSURE: Demographics, 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). RESULTS: COVID-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. CONCLUSION: COVID-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.

COVID-19 vaccine hesitancy January-May 2021 among 18-64 year old US adults by employment and occupation.

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 (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% didn't like vaccines in general. Over a third didn't believe they needed the vaccine, didn't 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.