Design and Implementation of the All of Us Research Program COVID-19 Participant Experience (COPE) Survey.

In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources.

Achieving a Representative Sample of Asian Americans in Biomedical Research Through Community-Based Approaches: Comparing Demographic Data in the All of Us Research Program With the American Community Survey.

BACKGROUND: Underrepresented persons are often not included in biomedical research. It is unknown if the general Asian American population is being represented in All of Us. The purpose of this study was to compare the Asian demographic data in the All of Us cohort with the Asian nationally representative data from the American Community Survey. METHOD: Demographic characteristics and health literacy of Asians in All of Us were examined. Findings were qualitatively compared with the Asian data in the 2019 American Community Survey 1-year estimate. RESULTS: Compared with the national composition of Asians, less All of Us participants were born outside the United States (64% vs 79%), were younger, and had higher levels of education (76% vs 52%). Over 60% of All of Us participants reported high levels of health literacy. CONCLUSION: This study had implications for the development of strategies that ensure diverse populations are represented in biomedical research.

Association of step counts over time with the risk of chronic disease in the All of Us Research Program.

The association between physical activity and human disease has not been examined using commercial devices linked to electronic health records. Using the electronic health records data from the All of Us Research Program, we show that step count volumes as captured by participants' own Fitbit devices were associated with risk of chronic disease across the entire human phenome. Of the 6,042 participants included in the study, 73% were female, 84% were white and 71% had a college degree, and participants had a median age of 56.7 (interquartile range 41.5-67.6) years and body mass index of 28.1 (24.3-32.9) kg m-2. Participants walked a median of 7,731.3 (5,866.8-9,826.8) steps per day over the median activity monitoring period of 4.0 (2.2-5.6) years with a total of 5.9 million person-days of monitoring. The relationship between steps per day and incident disease was inverse and linear for obesity (n = 368), sleep apnea (n = 348), gastroesophageal reflux disease (n = 432) and major depressive disorder (n = 467), with values above 8,200 daily steps associated with protection from incident disease. The relationships with incident diabetes (n = 156) and hypertension (n = 482) were nonlinear with no further risk reduction above 8,000-9,000 steps. Although validation in a more diverse sample is needed, these findings provide a real-world evidence-base for clinical guidance regarding activity levels that are necessary to reduce disease risk.

An Overview of Cancer in the First 315,000 All of Us Participants.

INTRODUCTION: The NIH All of Us Research Program will have the scale and scope to enable research for a wide range of diseases, including cancer. The program's focus on diversity and inclusion promises a better understanding of the unequal burden of cancer. Preliminary cancer ascertainment in the All of Us cohort from two data sources (self-reported versus electronic health records (EHR)) is considered. MATERIALS AND METHODS: This work was performed on data collected from the All of Us Research Program's 315,297 enrolled participants to date using the Researcher Workbench, where approved researchers can access and analyze All of Us data on cancer and other diseases. Cancer case ascertainment was performed using data from EHR and self-reported surveys across key factors. Distribution of cancer types and concordance of data sources by cancer site and demographics is analyzed. RESULTS AND DISCUSSION: Data collected from 315,297 participants resulted in 13,298 cancer cases detected in the survey (in 89,261 participants), 23,520 cancer cases detected in the EHR (in 203,813 participants), and 7,123 cancer cases detected across both sources (in 62,497 participants). Key differences in survey completion by race/ethnicity impacted the makeup of cohorts when compared to cancer in the EHR and national NCI SEER data. CONCLUSIONS: This study provides key insight into cancer detection in the All of Us Research Program and points to the existing strengths and limitations of All of Us as a platform for cancer research now and in the future.

Concordance of SARS-CoV-2 Antibody Results during a Period of Low Prevalence.

Accurate, highly specific immunoassays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to evaluate seroprevalence. This study investigated the concordance of results across four immunoassays targeting different antigens for sera collected at the beginning of the SARS-CoV-2 pandemic in the United States. Specimens from All of Us participants contributed between January and March 2020 were tested using the Abbott Architect SARS-CoV-2 IgG (immunoglobulin G) assay (Abbott) and the EuroImmun SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA) (EI). Participants with discordant results, participants with concordant positive results, and a subset of concordant negative results by Abbott and EI were also tested using the Roche Elecsys anti-SARS-CoV-2 (IgG) test (Roche) and the Ortho-Clinical Diagnostics Vitros anti-SARS-CoV-2 IgG test (Ortho). The agreement and 95% confidence intervals were estimated for paired assay combinations. SARS-CoV-2 antibody concentrations were quantified for specimens with at least two positive results across four immunoassays. Among the 24,079 participants, the percent agreement for the Abbott and EI assays was 98.8% (95% confidence interval, 98.7%, 99%). Of the 490 participants who were also tested by Ortho and Roche, the probability-weighted percentage of agreement (95% confidence interval) between Ortho and Roche was 98.4% (97.9%, 98.9%), that between EI and Ortho was 98.5% (92.9%, 99.9%), that between Abbott and Roche was 98.9% (90.3%, 100.0%), that between EI and Roche was 98.9% (98.6%, 100.0%), and that between Abbott and Ortho was 98.4% (91.2%, 100.0%). Among the 32 participants who were positive by at least 2 immunoassays, 21 had quantifiable anti-SARS-CoV-2 antibody concentrations by research assays. The results across immunoassays revealed concordance during a period of low prevalence. However, the frequency of false positivity during a period of low prevalence supports the use of two sequentially performed tests for unvaccinated individuals who are seropositive by the first test. IMPORTANCE What is the agreement of commercial SARS-CoV-2 immunoglobulin G (IgG) assays during a time of low coronavirus disease 2019 (COVID-19) prevalence and no vaccine availability? Serological tests produced concordant results in a time of low SARS-CoV-2 prevalence and no vaccine availability, driven largely by the proportion of samples that were negative by two immunoassays. The CDC recommends two sequential tests for positivity for future pandemic preparedness. In a subset analysis, quantified antinucleocapsid and antispike SARS-CoV-2 IgG antibodies do not suggest the need to specify the antigen targets of the sequential assays in the CDC's recommendation because false positivity varied as much between assays targeting the same antigen as it did between assays targeting different antigens.

The All of Us Research Program: Data quality, utility, and diversity.

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

Hypertension prevalence in the All of Us Research Program among groups traditionally underrepresented in medical research.

The All of Us Research Program was designed to enable broad-based precision medicine research in a cohort of unprecedented scale and diversity. Hypertension (HTN) is a major public health concern. The validity of HTN data and definition of hypertension cases in the All of Us (AoU) Research Program for use in rule-based algorithms is unknown. In this cross-sectional, population-based study, we compare HTN prevalence in the AoU Research Program to HTN prevalence in the 2015-2016 National Health and Nutrition Examination Survey (NHANES). We used AoU baseline data from patient (age ≥ 18) measurements (PM), surveys, and electronic health record (EHR) blood pressure measurements. We retrospectively examined the prevalence of HTN in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED) codes and blood pressure medications recorded in the EHR. We defined HTN as the participant having at least 2 HTN diagnosis/billing codes on separate dates in the EHR data AND at least one HTN medication. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, and ≥ 60). Among the 185,770 participants enrolled in the AoU Cohort (mean age at enrollment = 51.2 years) available in a Researcher Workbench as of October 2019, EHR data was available for at least one SNOMED code from 112,805 participants, medications for 104,230 participants, and 103,490 participants had both medication and SNOMED data. The total number of persons with SNOMED codes on at least two distinct dates and at least one antihypertensive medication was 33,310 for a crude prevalence of HTN of 32.2%. AoU age-adjusted HTN prevalence was 27.9% using 3 groups compared to 29.6% in NHANES. The AoU cohort is a growing source of diverse longitudinal data to study hypertension nationwide and develop precision rule-based algorithms for use in hypertension treatment and prevention research. The prevalence of hypertension in this cohort is similar to that in prior population-based surveys.

Predictive Analytics for Glaucoma Using Data From the All of Us Research Program.

PURPOSE: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research. DESIGN: Development and evaluation of machine learning models. METHODS: Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall. RESULTS: The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests). CONCLUSIONS: Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.