Diversity and inclusion for the All of Us research program: A scoping review

Brain Health Outcomes in Sexual and Gender Minority Groups: Results From the All of Us Research Program

BACKGROUND AND OBJECTIVES

Sexual and gender minority (SGM) groups have been historically underrepresented in neurologic research, and their brain health disparities are unknown. We aim to evaluate whether SGM persons are at higher risk of adverse brain health outcomes compared with cisgender straight (non-SGM) individuals.

METHODS

We conducted a cross-sectional study in the All of Us Research Program, a US population-based study, including all participants with information on gender identity and sexual orientation. We used baseline questionnaires to identify sexual minority (lesbian, gay, bisexual, diverse sexual orientation; nonstraight sexual orientation) and gender minority (gender diverse and transgender; gender identity different from sex assigned at birth) participants. The primary outcome was a composite of stroke, dementia, and late-life depression, assessed using electronic health record data and self-report. Secondarily, we evaluated each disease separately. Furthermore, we evaluated all subgroups of gender and sexual minorities stratified by sex assigned at birth. We used multivariable logistic regression (adjusted for age, sex assigned at birth, race/ethnicity, cardiovascular risk factors, other relevant comorbidities, and neighborhood deprivation index) to assess the relationship between SGM groups and the outcomes.

RESULTS

Of 413,457 US adults enrolled between May 31, 2017, and June 30, 2022, we included 393,041 participants with available information on sexual orientation and gender identity (mean age 51 [SD 17] years), of whom 39,632 (10%) belonged to SGM groups. Of them, 38,528 (97%) belonged to a sexual minority and 4,431 (11%) to a gender minority. Compared with non-SGM, SGM persons had 15% higher odds of the brain health composite outcome (odds ratio [OR] 1.15, 95% CI 1.08-1.22). In secondary analyses, these results persisted across sexual and gender minorities separately (all 95% CIs > 1). Assessing individual diseases, all SGM groups had higher odds of dementia (SGM vs non-SGM: OR 1.14, 95% CI 1.00-1.29) and late-life depression (SGM vs non-SGM: OR 1.27, 95% CI 1.17-1.38) and transgender women had higher odds of stroke (OR 1.68, 95% CI 1.04-2.70).

DISCUSSION

In a large US population study, SGM persons had higher odds of adverse brain health outcomes. Further research should explore structural causes of inequity to advance inclusive and diverse neurologic care.

Improving follow-up survey completion rates through pilot interventions in the All of Us Research Program: Results from a non-randomized intervention study

OBJECTIVE

Retention to complete follow-up surveys in extensive longitudinal epidemiological cohort studies is vital yet challenging. All of Us developed pilot interventions to improve response rates for follow-up surveys.

STUDY DESIGN AND SETTING

The pilot interventions occurred from April 27, 2020, to August 3, 2020. The three arms were: (1) telephone appointment [staff members calling participants offering appointments to complete surveys over phone] (2) postal [mail reminder to complete surveys through U.S. Postal Service], and (3) combination of telephone appointment and postal. Controls received digital-only reminders [program-level digital recontact via email or through the participant portal]. Study sites chose their study arm and participants were not randomized.

RESULTS

A total of 50 sites piloted interventions with 17,593 participants, while 47,832 participants comprised controls during the same period. Of all participants, 6,828 (10.4%) completed any follow-up surveys (1448: telephone; 522: postal; 486: combination; 4372: controls). Follow-up survey completions were 24% higher in the telephone appointment arm than in controls in bivariate analyses. When controlling for confounders, telephone appointment and combination arms increased rates of completion similarly compared to controls, while the postal arm had no significant effect (odds ratio [95% Confidence Interval], telephone appointment:2.01[1.81-2.23]; combination:1.91[1.66-2.20]; postal:0.92[0.79-1.07]). Although the effects of the telephone appointment and combination arms were similar, differential effects were observed across sub-populations.

CONCLUSION

Telephone appointments appeared to be the most successful intervention in our study. Lessons learned about retention interventions, and improvement in follow-up survey completion rates provide generalizable knowledge for similar cohort studies and demonstrate the potential value of precision reminders and engagement with sub-populations of a cohort.

Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education

Generative artificial intelligence and large language models are the continuation of a technological revolution in information processing that began with the invention of the transistor in 1947. These technologies, driven by transformer architectures for artificial neural networks, are poised to broadly influence society. It is already apparent that these technologies will be adapted to drive innovation in education. Medical education is a high-risk activity: Information that is incorrectly taught to a student may go unrecognized for years until a relevant clinical situation appears in which that error can lead to patient harm. In this article, I discuss the principal limitations to the use of generative artificial intelligence in medical education-hallucination, bias, cost, and security-and suggest some approaches to confronting these problems. Additionally, I identify the potential applications of generative artificial intelligence to medical education, including personalized instruction, simulation, feedback, evaluation, augmentation of qualitative research, and performance of critical assessment of the existing scientific literature.

Discrimination in Medical Settings across Populations: Evidence From the All of Us Research Program

INTRODUCTION

Discrimination in medical settings (DMS) contributes to healthcare disparities in the United States, but few studies have determined the extent of DMS in a large national sample and across different populations. This study estimated the national prevalence of DMS and described demographic and health-related characteristics associated with experiencing DMS in seven different situations.

METHODS

Survey data from 41,875 adults participating in the All of Us Research Program collected in 2021-2022 and logistic regression were used to examine the association between sociodemographic and health-related characteristics and self-reported DMS among adults engaged with a healthcare provider within the past 12 months. Statistical analysis was performed in 2023-2024.

RESULTS

About 36.89% of adults reported having experienced at least one DMS situation. Adults with relative social and medical disadvantages had higher prevalence of experiencing DMS. Compared to their counterparts, respondents with higher odds of experiencing DMS in at least one situation identified as female, non-Hispanic Black, having at least some college, living in the South, renter, having other living arrangement, being publicly insured, not having a usual source of care, having multiple chronic conditions, having any disability, and reporting fair or poor health, p<0.05.

CONCLUSIONS

The findings indicate a high prevalence of DMS, particularly among some population groups. Characterizing DMS may be a valuable tool for identifying populations at risk within the healthcare system and optimizing the overall patient care experience. Implementing relevant policies remains an essential strategy for mitigating the prevalence of DMS and reducing healthcare disparities.

The ADNI4 Digital Study: A novel approach to recruitment, screening, and assessment of participants for AD clinical research

INTRODUCTION

We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimer's Disease Neuroimaging Initiative (ADNI4).

METHODS

Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs.

RESULTS

Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level.

DISCUSSION

Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges.

HIGHLIGHTS

A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate > 50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

Using patient portals for large-scale recruitment of individuals underrepresented in biomedical research: an evaluation of engagement patterns throughout the patient portal recruitment process at a single site within the All of Us Research Program

OBJECTIVE

To evaluate the use of patient portal messaging to recruit individuals historically underrepresented in biomedical research (UBR) to the All of Us Research Program (AoURP) at a single recruitment site.

MATERIALS AND METHODS

Patient portal-based recruitment was implemented at Columbia University Irving Medical Center. Patient engagement was assessed using patient's electronic health record (EHR) at four recruitment stages: Consenting to be contacted, opening messages, responding to messages, and showing interest in participating. Demographic and socioeconomic data were also collected from patient's EHR and univariate logistic regression analyses were conducted to assess patient engagement.

RESULTS

Between October 2022 and November 2023, a total of 59 592 patients received patient portal messages inviting them to join the AoURP. Among them, 24 445 (41.0%) opened the message, 8983 (15.1%) responded, and 3765 (6.3%) showed interest in joining the program. Though we were unable to link enrollment data with EHR data, we estimate about 2% of patients contacted ultimately enrolled in the AoURP. Patients from underrepresented race and ethnicity communities had lower odds of consenting to be contacted and opening messages, but higher odds of showing interest after responding.

DISCUSSION

Patient portal messaging provided both patients and recruitment staff with a more efficient approach to outreach, but patterns of engagement varied across UBR groups.

CONCLUSION

Patient portal-based recruitment enables researchers to contact a substantial number of participants from diverse communities. However, more effort is needed to improve engagement from underrepresented racial and ethnic groups at the early stages of the recruitment process.

A Comparative Analysis of Oral Health and Self-Rated Health: 'All of Us Research Program' vs. 'Health and Retirement Study'

Poor oral health can impact overall health. This study assessed the association between dental factors (dentate status and dental utilization) and self-rated health (S-RH) among older adults in two cross-sectional datasets: (1) NIH "All of Us (AoU) Research Program" (May 2018-July 2022 release) and (2) U.S. nationally representative "Health and Retirement Study" (HRS) 2018 wave. Participants aged ≥ 51 years were included in these analyses if (1) from AoU, they had clinical dental and medical data from electronic health records (EHRs) and surveys (n = 5480), and (2) from HRS, they had dental and socio-demographic survey data (n = 14,358). S-RH was dichotomized (fair/poor vs. better) and analyzed with logistic regression. Sample survey weights for HRS and stratification and averaging AoU results used the weighted HRS race-ethnicity and age distribution standardized respective analyses to the U.S. population. Fair/poor S-RH was reported by 32.6% in AoU and 28.6% in HRS. Dentate status information was available from 7.7% of AoU EHRs. In population-standardized analyses, lack of dental service use increased odds of fair/poor S-RH in AoU, OR (95% CI) = 1.28 (1.11-1.48), and in HRS = 1.45 (1.09-1.94), as did having diabetes, less education, and ever being a smoker. Having no natural teeth was not statistically associated with fair/poor S-RH. Lack of dental service was positively associated with fair/poor S-RH in both datasets. More and better oral health information in AoU and HRS are needed.

Racial and ethnic differences in the receipt of continuous positive airway pressure treatment for obstructive sleep apnea

OBJECTIVE

To examine the pattern of health services access and utilization that may contribute to racial/ethnic disparities in receiving continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea (OSA).

METHODS

This cross-sectional study used a national sample from the All of Us Research Program, which included over 80 % of participants from underrepresented populations in biomedical research. Study participants included adults aged 18 years and older diagnosed with OSA (N = 8518). Diagnosis of OSA and CPAP treatment were ascertained by diagnostic and procedural codes from the electronic health records. Sociodemographic characteristics and health service utilization factors were identified using self-reported survey data.

RESULTS

With this national survey, the overall diagnosed prevalence of OSA was 8.8 %, with rates of 8.12 % in non-Hispanic (NH) Black adults, 5.99 % in Hispanic adults, and 10.35 % in NH White adults. When comparing to NH White adults, Hispanic adults were less likely to receive CPAP treatment for OSA after adjusting for socioeconomic and demographic characteristics, access to and utilization of health services, and comorbidities such as obesity and having multiple chronic conditions (OR = 0.73, 95 % CI = 0.59,0.90), p < 0.01.

CONCLUSIONS

The rates of CPAP treatment among OSA patients are not consistent across racial and ethnic groups. Unequal access to health services based on residence may contribute to these differences. Interventions that target disparities in OSA diagnosis, access to treatment, and barriers in insurance coverage could potentially help reduce racial and ethnic differences in OSA diagnosis and management.

Using Systems Engineering and Implementation Science to Design an Implementation Package for Preoperative Comprehensive Geriatric Assessment Among Older Adults Having Major Abdominal Surgery: Protocol for a 3-Phase Study

BACKGROUND

Older Americans, a growing segment of the population, have an increasing need for surgical services, and they experience a disproportionate burden of postoperative complications compared to their younger counterparts. A preoperative comprehensive geriatric assessment (pCGA) is recommended to reduce risk and improve surgical care delivery for this population, which has been identified as vulnerable. The pCGA optimizes multiple chronic conditions and factors commonly overlooked in routine preoperative planning, including physical function, polypharmacy, nutrition, cognition, mental health, and social and environmental support. The pCGA has been shown to decrease postoperative morbidity, mortality, and length of stay in a variety of surgical specialties. Although national guidelines recommend the use of the pCGA, a paucity of strategic guidance for implementation limits its uptake to a few academic medical centers. By applying implementation science and human factors engineering methods, this study will provide the necessary evidence to optimize the implementation of the pCGA in a variety of health care settings.

OBJECTIVE

The purpose of this paper is to describe the study protocol to design an adaptable, user-centered pCGA implementation package for use among older adults before major abdominal surgery.

METHODS

This protocol uses systems engineering methods to develop, tailor, and pilot-test a user-centered pCGA implementation package, which can be adapted to community-based hospitals in preparation for a multisite implementation trial. The protocol is based upon the National Institutes of Health Stage Model for Behavioral Intervention Development and aligns with the goal to develop behavioral interventions with an eye to real-world implementation. In phase 1, we will use observation and interviews to map the pCGA process and identify system-based barriers and facilitators to its use among older adults undergoing major abdominal surgery. In phase 2, we will apply user-centered design methods, engaging health care providers, patients, and caregivers to co-design a pCGA implementation package. This package will be applicable to a diverse population of older patients undergoing major abdominal surgery at a large academic hospital and an affiliate community site. In phase 3, we will pilot-test and refine the pCGA implementation package in preparation for a future randomized controlled implementation-effectiveness trial. We anticipate that this study will take approximately 60 months (April 2023-March 2028).

RESULTS

This study protocol will generate (1) a detailed process map of the pCGA; (2) an adaptable, user-centered pCGA implementation package ready for feasibility testing in a pilot trial; and (3) preliminary pilot data on the implementation and effectiveness of the package. We anticipate that these data will serve as the basis for future multisite hybrid implementation-effectiveness clinical trials of the pCGA in older adults undergoing major abdominal surgery.

CONCLUSIONS

The expected results of this study will contribute to improving perioperative care processes for older adults before major abdominal surgery.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/59428.

Hispanic individuals' cervical cancer screening disparities amidst the COVID-19 pandemic

OBJECTIVE

To examine the impact of the COVID-19 pandemic on cervical cancer screening rates of Hispanic individuals compared to non-Hispanic White (NHW) individuals in the United States, whether a responsive surge in catch-up screenings occurred as society adapted to pandemic changes, and to investigate the sociodemographic characteristics between the study populations.

METHODS

Using cross-sectional data from the All of Us Research Program, which incorporates electronic health record data and survey data from a demographically, geographically, and medically diverse participant group, we assessed the annual cervical cancer screening rates during 2019-2021 by race/ethnicity among eligible individuals ages 21-64.

RESULTS

Among 116,052 unique individuals (78,829 NHW and 37,223 Hispanic), Hispanic individuals had lower annual cervical cancer screening rates than NHWI across the three years studied. They experienced a more significant decrease in screening from 2019 to 2020 (39.27 %) compared to NHWIs (21.15 %) and less of a rebound increase in the following year, 2021 (10.33 % vs 13.83 %). Hispanic individuals aged 50-64 experienced the sharpest decline in screening rates (-43.01 % from 2019 to 2020). Hispanic individuals also experienced greater adverse social conditions, including lack of insurance or employment, lower educational attainment, and lower household income.

CONCLUSIONS

Hispanic individuals experienced a more significant decrease in cervical cancer screening rates with the onset of the COVID-19 pandemic compared with NHW individuals and did not experience a robust rebound in cervical cancer screening rates in 2021. As a result, the disparity in cervical cancer screening rates between NHW and Hispanic individuals considerably worsened with the COVID-19 pandemic.

How rural is All of Us? Comparing characteristics of rural participants in the National Institute of Health's All of Us Research Program to other national data sources

PURPOSE

The National Institute of Health's All of Us Research Program represents a national effort to develop a database to advance health research, especially among individuals historically underrepresented in research, including rural populations. The purpose of this study was to describe the rural populations identified in the All of Us Research Program using the only proxy measure currently available in the dataset.

METHODS

Currently, the All of Us Research Program provides a proxy measure of rurality that identifies participants who self-reported delaying care due to far travel distances associated with living in rural areas. Using the All of Us Controlled Tier Dataset v6, we compared sociodemographic and health characteristics of All of Us rural participants identified via this proxy to rural US residents from nationally representative data sources using chi-squared tests.

RESULTS

3.1% of 160,880 All of Us participants were rural, compared to 15%-20% of US residents based on commonly accepted rural definitions. Proportionally more rural All of Us participants reported fair or poor health status, history of cancer, and history of heart disease (P<.01).

CONCLUSIONS

The All of Us measure may capture a subset of underserved participants who live in rural areas and experience health care access barriers due to distance. Researchers who use this proxy measure to characterize rurality should interpret their findings with caution due to differences in population and health characteristics using this proxy measure rural compared to other commonly used rural definitions.

Landscape of racial and ethnic health disparities in the All of Us Research Program

The All of Us Research Program ("All of Us") is an initiative led by the National Institutes of Health whose goal is to advance research on personalized medicine and health equity through the collection of genetic, environmental, demographic, and health data from volunteer participants who reside in the USA. The program's emphasis on recruiting a diverse participant cohort makes "All of Us" an effective platform for investigating health disparities. In this work, we analyzed participant electronic health record (EHR) data to identify the diseases and disease categories in the "All of Us" cohort for which racial and ethnic prevalence disparities can be observed. In conjunction with these analyses, we developed the US Health Disparities Browser as an interactive web application that enables users to visualize differences in race- and ethnic-group-specific prevalence estimates for 1755 different diseases: https://usdisparities.biosci.gatech.edu/. The web application features a catalog of all diseases represented in the browser, which can be sorted by overall prevalence as well as the variance in prevalence across racial and ethnic groups. The analyses outlined here provide details on the nature and extent of racial and ethnic health disparities in the "All of Us" participant cohort, and the accompanying browser can serve as a resource through which researchers can explore these disparities Database URL: https://usdisparities.biosci.gatech.edu.

Applying Engagement Marketing And Human-Centered Design to Cocreate a Digital Decision Support Tool for Research Participation with LGBTQIA+ Community Members

We used engagement marketing and human-centered design principles to cocreate a digital decision support tool for research participation with LGBTQIA+ community members to help them make an informed decision about joining the All of Us Research Program. Building on results from the research phase, we conducted eight problem validation and solutioning workshops with 48 LGBTQIA+ community members. Community members validated barriers to engagement with All of Us and brainstormed 47 potential digital solutions. We developed potential solutions into 27 concepts (descriptive text and visual storyboards) and assessed acceptability, appropriateness, feasibility, and engagement in a set of 10 concept testing workshops with 57 community members. We developed one of the highest rated concepts, the "Decide Later Tool," into a prototype and tested it with 45 LGBTQIA+ community members and 14 community advisory group members to assess acceptability, appropriateness, feasibility, usability, and engagement. Prototype testing participants indicated that the tool provides information to help with decision making, provides a clear value or benefit to them, was designed for someone like them, provides the right amount of information, and is easy to use; they also offered constructive feedback to improve it. Across the design and development phases, community members indicated that the process of engaging them demonstrated integrity, competence, dependability, trust, and collaboration; fostered a sense of connection to All of Us; and will enhance future engagement with All of Us. Our next steps are to develop the prototype into a fully functioning web tool and pilot test it in community and health care settings.

Burden of non-motor symptoms of Parkinson's disease: cost-of-illness and quality-of-life estimates through a scoping review

INTRODUCTION

Parkinson's Disease (PD) is a progressive, chronic neurodegenerative disease, representing significant economic and social burdens. It is typically defined by motor symptoms (MSs), however, this does not reflect the full patient burden. Non-motor symptoms (NMSs) are increasingly recognized as central characteristics of PD. However, they still lack recognition in research. Therefore, this study aims to identify relevant NMSs, their prevalence, and the effect they have on Quality-of-Life (QoL) and Cost-of-Illness (COI). Secondly, it aims to identify gaps in the current body of knowledge and propose possible ways future research could bridge those gaps.

METHODS

The study employed a scoping review, identifying 60 records for inclusion, using PubMed and Web of Science. It included studies from Spain or Italy, including data on People with Parkinson's Disease. A comparative analysis was performed using Microsoft Excel.

RESULTS

It showed that the body of evidence relevant to NMSs, their prevalence, QoL, and COI is limited, or that estimates vary to an extent where interpretation is difficult.

CONCLUSION

Most studies suffer from generalization, representation, and standardization issues, stemming from their designs and methodological decisions. Although the findings of this study should be interpreted with caution, several recommendations are made for future research.

Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales

IMPORTANCE

Scales often arise from multi-item questionnaires, yet commonly face item non-response. Traditional solutions use weighted mean (WMean) from available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us), and it is imperative to determine if the increased computational burden of employing MI to handle non-response is justifiable.

OBJECTIVES

Using the 5-item Physical Activity Neighborhood Environment Scale (PANES) in All of Us, this study assessed the tradeoff between efficacy and computational demands of WMean, MI, and inverse probability weighting (IPW) when dealing with item non-response.

MATERIALS AND METHODS

Synthetic missingness, allowing 1 or more item non-response, was introduced into PANES across 3 missing mechanisms and various missing percentages (10%-50%). Each scenario compared WMean of complete questions, MI, and IPW on bias, variability, coverage probability, and computation time.

RESULTS

All methods showed minimal biases (all <5.5%) for good internal consistency, with WMean suffered most with poor consistency. IPW showed considerable variability with increasing missing percentage. MI required significantly more computational resources, taking >8000 and >100 times longer than WMean and IPW in full data analysis, respectively.

DISCUSSION AND CONCLUSION

The marginal performance advantages of MI for item non-response in highly reliable scales do not warrant its escalated cloud computational burden in All of Us, particularly when coupled with computationally demanding post-imputation analyses. Researchers using survey scales with low missingness could utilize WMean to reduce computing burden.

Empowering the biomedical research community: Innovative SAS deployment on the All of Us Researcher Workbench

OBJECTIVES

The All of Us Research Program is a precision medicine initiative aimed at establishing a vast, diverse biomedical database accessible through a cloud-based data analysis platform, the Researcher Workbench (RW). Our goal was to empower the research community by co-designing the implementation of SAS in the RW alongside researchers to enable broader use of All of Us data.

MATERIALS AND METHODS

Researchers from various fields and with different SAS experience levels participated in co-designing the SAS implementation through user experience interviews.

RESULTS

Feedback and lessons learned from user testing informed the final design of the SAS application.

DISCUSSION

The co-design approach is critical for reducing technical barriers, broadening All of Us data use, and enhancing the user experience for data analysis on the RW.

CONCLUSION

Our co-design approach successfully tailored the implementation of the SAS application to researchers' needs. This approach may inform future software implementations on the RW.

Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial

BACKGROUND

Early identification of an acute respiratory infection is important for reducing transmission and enabling earlier therapeutic intervention. We aimed to prospectively evaluate the feasibility of home-based diagnostic self-testing of viral pathogens in individuals prompted to do so on the basis of self-reported symptoms or individual changes in physiological parameters detected via a wearable sensor.

METHODS

DETECT-AHEAD was a prospective, decentralised, randomised controlled trial carried out in a subpopulation of an existing cohort (DETECT) of individuals enrolled in a digital-only observational study in the USA. Participants aged 18 years or older were randomly assigned (1:1:1) with a block randomisation scheme stratified by under-represented in biomedical research status. All participants were offered a wearable sensor (Fitbit Sense smartwatch). Participants in groups 1 and 2 received an at-home self-test kit (Alveo be.well) for two acute respiratory viral pathogens: SARS-CoV-2 and respiratory syncytial virus. Participants in group 1 could be alerted through the DETECT study app to take the at-home test on the basis of changes in their physiological data (as detected by our algorithm) or due to self-reported symptoms; those in group 2 were prompted via the app to self-test only due to symptoms. Group 3 served as the control group, without alerts or home testing capability. The primary endpoints, assessed on an intention-to-treat basis, were the number of acute respiratory infections presented (self-reported) and diagnosed (electronic health record), and the number of participants using at-home testing in groups 1 and 2. This trial is registered with ClinicalTrials.gov, NCT04336020.

FINDINGS

Between Sept 28 and Dec 30, 2021, 450 participants were recruited and randomly assigned to group 1 (n=149), group 2 (n=151), or group 3 (n=150). 179 (40%) participants were male, 264 (59%) were female, and seven (2%) identified as other. 232 (52%) were from populations historically under-represented in biomedical research. 118 (39%) of the 300 participants in groups 1 and 2 were prompted to self-test, with 61 (52%) successfully completing self-testing. Participants were prompted to home-test more frequently due to symptoms (41 [28%] in group 1 and 51 [34%] in group 2) than due to detected physiological changes (26 [17%] in group 1). Significantly more participants in group 1 received alerts to test than did those in group 2 (67 [45%] vs 51 [34%]; p=0·047). Of the 61 individuals who were prompted to test and successfully did so, 19 (31%) tested positive for a viral pathogen-all for SARS-CoV-2. The individuals diagnosed as positive for SARS-CoV-2 in the electronic health record were eight (5%) in group 1, four (3%) in group 2, and two (1%) in group 3, but it was difficult to confirm if they were tied to symptomatic episodes documented in the trial. There were no adverse events.

INTERPRETATION

In this direct-to-participant trial, we showed early feasibility of a decentralised programme to prompt individuals to use a viral pathogen diagnostic test based on symptoms tracked in the study app or physiological changes detected using a wearable sensor. Barriers to adequate participation and performance were also identified, which would need to be addressed before large-scale implementation.

FUNDING

Janssen Pharmaceuticals.

Racial and Ethnic Disparities in Antihypertensive Medication Prescribing Patterns and Effectiveness

Variability in drug effectiveness and provider prescribing patterns have been reported in different racial and ethnic populations. We sought to evaluate antihypertensive drug effectiveness and prescribing patterns among self-identified Hispanic/Latino (Hispanic), Non-Hispanic Black (Black), and Non-Hispanic White (White) populations that enrolled in the NIH All of Us Research Program, a US longitudinal cohort. We employed a self-controlled case study method using electronic health record and survey data from 17,718 White, Hispanic, and Black participants who were diagnosed with essential hypertension and prescribed at least one of 19 commonly used antihypertensive medications. Effectiveness was determined by calculating the reduction in systolic blood pressure measurements after 28 or more days of drug exposure. Starting systolic blood pressure and effectiveness for each medication were compared for self-reported Black, Hispanic, and White participants using adjusted linear regressions. Black and Hispanic participants were started on antihypertensive medications at significantly higher SBP than White participants in 13 and 7 out of 19 medications, respectively. More Black participants were prescribed multiple antihypertensive medications (58.46%) than White (52.35%) or Hispanic (49.9%) participants. First-line HTN medications differed by race and ethnicity. Following the 2017 American College of Cardiology and the American Heart Association High Blood Pressure Guideline release, around 64% of Black participants were prescribed a recommended first-line antihypertensive drug compared with 76% of White and 82% of Hispanic participants. Effect sizes suggested that most antihypertensive drugs were less effective in Hispanic and Black, compared with White, participants, and statistical significance was reached in 6 out of 19 drugs. These results indicate that Black and Hispanic populations may benefit from earlier intervention and screening and highlight the potential benefits of personalizing first-line medications.

Use of calibration to improve the precision of estimates obtained from All of Us data

OBJECTIVES

To highlight the use of calibration weighting to improve the precision of estimates obtained from All of Us data and increase the return of value to communities from the All of Us Research Program.

MATERIALS AND METHODS

We used All of Us (2017-2022) data and raking to obtain prevalence estimates in two examples: discrimination in medical settings (N = 41 875) and food insecurity (N = 82 266). Weights were constructed using known population proportions (age, sex, race/ethnicity, region of residence, annual household income, and home ownership) from the 2020 National Health Interview Survey.

RESULTS

About 37% of adults experienced discrimination in a medical setting. About 20% of adults who had not seen a doctor reported being food insecure compared with 14% of adults who regularly saw a doctor.

CONCLUSIONS

Calibration using raking is cost-effective and may lead to more precise estimates when analyzing All of Us data.

Research to classrooms: a co-designed curriculum brings All of Us data to secondary schools

OBJECTIVES

We describe new curriculum materials for engaging secondary school students in exploring the "big data" in the NIH All of Us Research Program's Public Data Browser and the co-design processes used to collaboratively develop the materials. We also describe the methods used to develop and validate assessment items for studying the efficacy of the materials for student learning as well as preliminary findings from these studies.

MATERIALS AND METHODS

Secondary-level biology teachers from across the United States participated in a 2.5-day Co-design Summer Institute. After learning about the All of Us Research Program and its Data Browser, they collaboratively developed learning objectives and initial ideas for learning experiences related to exploring the Data Browser and big data. The Genetic Science Learning Center team at the University of Utah further developed the educators' ideas. Additional teachers and their students participated in classroom pilot studies to validate a 22-item instrument that assesses students' knowledge. Educators completed surveys about the materials and their experiences.

RESULTS

The "Exploring Big Data with the All of Us Data Browser" curriculum module includes 3 data exploration guides that engage students in using the Data Browser, 3 related multimedia pieces, and teacher support materials. Pilot testing showed substantial growth in students' understanding of key big data concepts and research applications.

DISCUSSION AND CONCLUSION

Our co-design process provides a model for educator engagement. The new curriculum module serves as a model for introducing secondary students to big data and precision medicine research by exploring diverse real-world datasets.

Weighting the United States All of Us Research Program data to known population estimates using raking

Background

The All of Us Research Program aims to collect longitudinal health-related data from a million individuals in the United States. An inherent challenge of a non-probability sampling strategy through voluntary participation in All of Us is that findings may not be nationally representative for addressing health and health care at the population level. We generated survey weights for the All of Us data that can be used to address the challenge.

Research design

We developed raked weights using demographic, health, and socioeconomic variables available in both the 2020 National Health Interview Survey (NHIS) and All of Us. We then compared the unweighted and weighted prevalence of a set of health-related variables (health behaviors, health conditions, and health insurance coverage) estimated from All of Us data with the weighted prevalence estimates obtained from NHIS data.

Subjects

The sample included 100,391 All of Us participants 18 years of age and older with complete data collected between May 2017 and January 2022 across the United States.

Results

Final variables in the raking procedure included age, sex, race/ethnicity, region of residence, annual household income, and home ownership. The mean percentage difference between known proportions obtained from the NHIS and All of Us was reduced by 18.89% for health-related variables after applying the raked weights.

Conclusions

Raking improved the comparability of prevalence estimates obtained from All of Us to known national prevalence estimates. Refining the process of variable selection for raking may further improve the comparability between All of Us and nationally representative data.

Medical, Environmental, and Social Determinants Associated With Periocular Cutaneous Malignancies in the United States Using the All of Us National Database

OBJECTIVE

To identify common factors associated with periocular cutaneous malignancies using the National Institutes of Health (NIH) All of Us database.

METHODOLOGY

In this case-control study, we extracted electronic health records and sociodemographic data for 385 cases of periocular cutaneous malignancies from the All of Us nationwide database. Controls (N = 1540) were matched to the demographic characteristics of the 2020 United States Census. Bivariate analyses and multivariable logistic regression determined variables significantly associated with increased odds of periocular cutaneous malignancies. We analyzed medical, environmental, and social determinants to evaluate which factors were associated with increased odds of periocular cutaneous malignancies.

RESULTS

Among the cases, the mean (standard deviation) age was 66.8 (11.2) years at the time of diagnosis. The majority were male (207, 54%) and white (361, 94%). Periocular cutaneous malignancy was significantly more likely among individuals with high sun exposure (odds ratio [OR] 14.79, 95% confidence interval [CI] 3.35-85.73, P = 0.001), those identifying as white race (OR 3.88, 95% CI 1.06-25.33, P = 0.079), and those with higher socioeconomic status, including higher annual income (OR 1.35, 95% CI 1.25-1.46, P < 0.001).

CONCLUSIONS

This study demonstrates similar risk factors for periocular cutaneous malignancies, echoing prior research that showed increased associations with lighter-pigmented skin and higher socioeconomic status. It also sheds light on the positive impact of physician surveillance and health utilization factors in the early detection and treatment of these malignancies, aspects less explored in prior analyses.

Comparing the frequency of variants of uncertain significance (VUS) between ancestry groups in a paediatric epilepsy cohort

BACKGROUND

Studies indicate that variants of uncertain significance are more common in non-European populations due to lack of a diversity in population databases. This difference has not been explored in epilepsy, which is increasingly found to be genetic in paediatric populations, and has precision medicine applications. This study examines the differences in the frequency of uncertain next-generation sequencing (NGS) results among a paediatric epilepsy cohort between ancestral groups historically under-represented in biomedical research (UBR) and represented in biomedical research (RBR).

METHODS

A retrospective chart review of patients with epilepsy seen at Columbia University Irving Medical Center (CUIMC). One hundred seventy-eight cases met the following criteria: (1) visited any provider within the Pediatric Neurology Clinic at CUIMC, (2) had an ICD code indicating a diagnosis of epilepsy, (3) underwent NGS testing after March 2015 and (4) had self-reported ancestry that fit into a single dichotomous category of either historically represented or under-represented in biomedical research.

RESULTS

UBR cases had significantly higher rates of uncertain results when compared with RBR cases (79.2% UBR, 20.8% RBR; p value=0.002). This finding remained true after controlling for potential confounding factors, including sex, intellectual disability or developmental delay, epilepsy type, age of onset, number of genes tested and year of testing.

CONCLUSION

Our results add to the literature that individuals who are of ancestries historically under-represented in genetics research are more likely to receive uncertain genetic results than those of represented majority ancestral groups and establishes this finding in an epilepsy cohort.

Egypt Genome: Towards an African new genomic era

BACKGROUND

Studying the human genome is crucial to embrace precision medicine through tailoring treatment and prevention strategies to the unique genetic makeup and molecular information of individuals. After human genome project (1990-2003) generated the first full sequence of a human genome, there have been concerns towards Northern bias due to underrepresentation of other populations. Multiple countries have now established national genome projects aiming at the genomic knowledge that can be harnessed from their populations, which in turn can serve as a basis for their health care policies in the near future.

AIM OF REVIEW

The intention is to introduce the recently established Egypt Genome (EG) to delineate the genomics and genetics of both the modern and Ancient Egyptian populations. Leveraging genomic medicine to improve precision medicine strategies while building a solid foundation for large-scale genomic research capacity is the fundamental focus of EG.

KEY SCIENTIFIC CONCEPTS

EG generated genomic knowledge is predicted to enrich the existing human genome and to expand its diversity by studying the underrepresented African/Middle Eastern populations. The insightful impact of EG goes beyond Egypt and Africa as it fills the knowledge gaps in health and disease genomics towards improved and sustainable genomic-driven healthcare systems for better outcomes. Promoting the integration of genomics into clinical practice and spearheading the implementation of genomic-driven healthcare and precision medicine is therefore a key focus of EG. Mining into the wealth of Ancient Egyptian Genomics to delineate the genetic bridge between the contemporary and Ancient Egyptian populations is another excitingly unique area of EG to realize the global vision of human genome.

Validity of European-centric cardiometabolic polygenic scores in multi-ancestry populations

Polygenic scores (PGSs) provide an individual level estimate of genetic risk for any given disease. Since most PGSs have been derived from genome wide association studies (GWASs) conducted in populations of White European ancestry, their validity in other ancestry groups remains unconfirmed. This is especially relevant for cardiometabolic diseases which are known to disproportionately affect people of non-European ancestry. Thus, we aimed to evaluate the performance of PGSs for glycaemic traits (glycated haemoglobin, and type 1 and type 2 diabetes mellitus), cardiometabolic risk factors (body mass index, hypertension, high- and low-density lipoproteins, and total cholesterol and triglycerides) and cardiovascular diseases (including stroke and coronary artery disease) in people of White European, South Asian, and African Caribbean ethnicity in the UK Biobank. Whilst PGSs incorporated some GWAS data from multi-ethnic populations, the vast majority originated from White Europeans. For most outcomes, PGSs derived mostly from European populations had an overall better performance in White Europeans compared to South Asians and African Caribbeans. Thus, multi-ancestry GWAS data are needed to derive ancestry stratified PGSs to tackle health inequalities.

Trends in Patient Representation in Low Back Pain Pharmacological Randomized Clinical Trials, 2011 to 2020: A Systematic Review

Low back pain (LBP) significantly affects global health, with associated detrimental outcomes such as physical impairment, emotional distress, and exacerbated mental health symptoms. This study evaluated the representation of marginalized groups, including racialized, gender minority, pregnant/lactating, and elderly individuals in randomized controlled trials for pharmacological interventions treating LBP from 2011 to 2020. We searched Embase, MEDLINE, and CINAHL in December 2021, and 139 studies were eligible. Most trials (n = 113, 81%) reported participant sex; however, no study collected data on sexual and gender minorities, and the majority (n = 99, 71%) excluded pregnant/lactating individuals. Most trials (n = 105, 76%) reported no data on participant race or ethnicity. We limited within-country analyses of race and ethnicity to US-based trials because US-based trials were more likely to report race and/or ethnicity (48%) compared to non-US-based trials (8%). Black participants were the only racialized group whose composition was comparable to US Census estimates. About half (n = 73, 53%) of all trials had an upper age limit for eligibility (range: 40-85 years old) and 24% (n = 33) excluded adults aged >65 years. Our findings confirm that trials for pharmacological LBP interventions underreport demographic data, and the trials that include this data have unrepresentative samples. There is an urgent need for more inclusive and representative patient samples to ensure generalizability and equitable benefits. Standardizing demographic data reporting and integrating community-based participatory research methods can help foster inclusive research practices. This review was registered with prospective register of systematic reviews (PROSPERO), ID 296017. PERSPECTIVE: This systematic review investigates patient representation in pharmacological-based clinical trials for low back pain, LBP, the most prevalent pain condition worldwide. Improvements in reporting demographic data and recruiting diverse participant populations-across different racialized, gender and sexual minority, and age groups-will help clinical research generalizability and provide equitable benefits.

Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation

Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. In this work, we study the problem of imputation of missing step count data, one of the most ubiquitous forms of wearable sensor data. We construct a novel and large scale data set consisting of a training set with over 3 million hourly step count observations and a test set with over 2.5 million hourly step count observations. We propose a domain knowledge-informed sparse self-attention model for this task that captures the temporal multi-scale nature of step-count data. We assess the performance of the model relative to baselines and conduct ablation studies to verify our specific model designs.

Development and Validation of an Individual Socioeconomic Deprivation Index (ISDI) in the NIH's All of Us Data Network

Many of the existing composite social determinant of health indices, such as Area Deprivation Index, are constrained by their reliance on geographic approximations and American Community Survey data. This study builds on the body of literature around deprivation indices to construct an individual socioeconomic deprivation index (ISDI) within the NIH's All of Us Data Network by using weighted multiple correspondence analysis on SDOH data elements collected at the participant level. In this study, the correlation between ISDI and another area-approximated index is assessed to the extent possible, along with the changes in an AI models performance due to stratified sampling based on ISDI quintiles. Individual level deprivation indices may have a wide range of utility particularly in the context of precision medicine in both centralized and distributed data networks.

Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data

BACKGROUND

 Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.

OBJECTIVES

 This study explored using preoperative electronic health record (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use.

METHODS

 The study cohort consisted of approximately 347 All of Us Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use.

RESULTS

 The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve score for severe pain versus other pain was highest (0.88) when predicting acute postoperative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes.

CONCLUSION

 SHAP detection of individual risk factors for severe pain can help health care providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.

Measuring social determinants of health in the All of Us Research Program

To accelerate medical breakthroughs, the All of Us Research Program aims to collect data from over one million participants. This report outlines processes used to construct the All of Us Social Determinants of Health (SDOH) survey and presents the psychometric characteristics of SDOH survey measures in All of Us. A consensus process was used to select SDOH measures, prioritizing concepts validated in diverse populations and other national cohort surveys. Survey item non-response was calculated, and Cronbach's alpha was used to analyze psychometric properties of scales. Multivariable logistic regression models were used to examine associations between demographic categories and item non-response. Twenty-nine percent (N = 117,783) of eligible All of Us participants submitted SDOH survey data for these analyses. Most scales had less than 5% incalculable scores due to item non-response. Patterns of item non-response were seen by racial identity, educational attainment, income level, survey language, and age. Internal consistency reliability was greater than 0.80 for almost all scales and most demographic groups. The SDOH survey demonstrated good to excellent reliability across several measures and within multiple populations underrepresented in biomedical research. Bias due to survey non-response and item non-response will be monitored and addressed as the survey is fielded more completely.

Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program

OBJECTIVE

With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment.

MATERIALS AND METHODS

We matched All of Us participants with available trials on ClinicalTrials.gov based on medical conditions, age, sex, and geographic location. Based on the number of matched trials, we (1) developed the Trial Opportunities Compass (TOC) to help sponsors assess trial investment portfolios, (2) characterized the landscape of trial opportunities in a phenome-wide association study (PheWAS), and (3) assessed the relationship between trial opportunities and social determinants of health (SDoH) to identify potential barriers to trial participation.

RESULTS

Our study included 181 529 All of Us participants and 18 634 trials. The TOC identified opportunities for portfolio investment and gaps in currently available trials across federal, industrial, and academic sponsors. PheWAS results revealed an emphasis on mental disorder-related trials, with anxiety disorder having the highest adjusted increase in the number of matched trials (59% [95% CI, 57-62]; P < 1e-300). Participants from certain communities underrepresented in biomedical research, including self-reported racial and ethnic minorities, had more matched trials after adjusting for other factors. Living in a nonmetropolitan area was associated with up to 13.1 times fewer matched trials.

DISCUSSION AND CONCLUSION

All of Us data are a valuable resource for identifying trial opportunities to inform trial portfolio planning. Characterizing these opportunities with consideration for SDoH can provide guidance on prioritizing the most pressing barriers to trial participation.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Engaging Sexual and Gender Minority (SGM) Communities for Health Research: Building and Sustaining PRIDEnet

Lesbian, gay, bisexual, transgender, queer, intersex, asexual, aromantic, and other sexual and/or gender minority (LGBTQIA+) communities are underrepresented in health research and subject to documented health disparities. In addition, LGBTQIA+ communities have experienced mistreatment, discrimination, and stigma in health care and health research settings. Effectively engaging LGBTQIA+ communities and individuals in health research is critical to developing representative data sets, improving health care provision and policy, and reducing disparities. However, little is known about what engagement approaches work well with LGBTQIA+ people. This paper describes the development of PRIDEnet (pridenet.org), a national network dedicated to catalyzing LGBTQIA+ community involvement in health research and built upon well-established community-engaged research (CEnR) principles. PRIDEnet's relationship building and digital communications activities engage thousands of LGBTQIA+-identified people across the country and offer multiple low-threshold ways to participate in specific studies and shape research. These activities comprise a CEnR infrastructure that engages LGBTQIA+ people on behalf of other projects, primarily The PRIDE Study (pridestudy.org) and the National Institutes of Health's All of Us Research Program (joinallofus.org/lgbtqia). Our impact, results, and lessons learned apply to those engaging communities underserved in biomedical research and include: the importance of building adaptable infrastructure that sustains transformational relationships long-term; implementing high-touch activities to establish trust and broad-reach activities to build large data sets; nurturing a team of diverse professionals with lived experiences that reflect those of the communities to be engaged; and maintaining CEnR mechanisms that exceed advice-giving and result in substantive research contributions from beginning to end.

Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank

IMPORTANCE

Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort.

OBJECTIVES

More than 280 000 participants have shared their electronic health records (EHRs) in the All of Us Research Program. We aim to understand the phenomic profiles of this cohort through comparisons with those in the US general population and a well-established nation-wide cohort, UK Biobank, and to test whether association results of selected commonly studied diseases in the All of Us cohort were comparable to those in UK Biobank.

MATERIALS AND METHODS

We included participants with EHRs in All of Us and participants with health records from UK Biobank. The estimates of prevalence of diseases in the US general population were obtained from the Global Burden of Diseases (GBD) study. We conducted phenome-wide association studies (PheWAS) of 9 commonly studied diseases in both cohorts.

RESULTS

This study included 287 012 participants from the All of Us EHR cohort and 502 477 participants from the UK Biobank. A total of 314 diseases curated by the GBD were evaluated in All of Us, 80.9% (N = 254) of which were more common in All of Us than in the US general population [prevalence ratio (PR) >1.1, P < 2 × 10-5]. Among 2515 diseases and phenotypes evaluated in both All of Us and UK Biobank, 85.6% (N = 2152) were more common in All of Us (PR >1.1, P < 2 × 10-5). The Pearson correlation coefficients of effect sizes from PheWAS between All of Us and UK Biobank were 0.61, 0.50, 0.60, 0.57, 0.40, 0.53, 0.46, 0.47, and 0.24 for ischemic heart diseases, lung cancer, chronic obstructive pulmonary disease, dementia, colorectal cancer, lower back pain, multiple sclerosis, lupus, and cystic fibrosis, respectively.

DISCUSSION

Despite the differences in prevalence of diseases in All of Us compared to the US general population or the UK Biobank, our study supports that All of Us can facilitate rapid investigation of a broad range of diseases.

CONCLUSION

Most diseases were more common in All of Us than in the general US population or the UK Biobank. Results of disease-disease association tests from All of Us are comparable to those estimated in another well-studied national cohort.

Genetic Researchers' Use of and Interest in Research With Diverse Ancestral Groups

Importance

Genetic researchers must have access to databases populated with data from diverse ancestral groups to ensure research is generalizable or targeted for historically excluded communities.

Objective

To determine genetic researchers' interest in doing research with diverse ancestral populations, which database stewards offer adequate samples, and additional facilitators for use of diverse ancestral data.

Design, Setting, and Participants

This survey study was conducted from June to December 2022 and was part of an exploratory sequential mixed-methods project in which previous qualitative results informed survey design. Eligible participants included genetic researchers who held US academic affiliations and conducted research using human genetic databases.

Exposure

Internet-administered survey to genetic research professionals.

Main Outcomes and Measures

The survey assessed respondents' experience and interest in research with diverse ancestral data, perceptions of adequacy of diverse data across database stewards (ie, private, government, or consortia), and identified facilitators for encouraging use of diverse ancestral data. Descriptive statistics, χ2 tests, and z tests were used to describe respondents' perspectives and experiences.

Results

A total of 294 researchers (171 men [58.5%]; 121 women [41.2%]) were included in the study, resulting in a response rate of 20.4%. Across seniority level, 109 respondents (37.1%) were senior researchers, 85 (28.9%) were mid-level researchers, 71 (24.1%) were junior researchers, and 27 (9.2%) were trainees. Significantly more respondents worked with data from European ancestral populations (261 respondents [88.8%]) compared with any other ancestral population. Respondents who had not done research with Indigenous ancestral groups (210 respondents [71.4%]) were significantly more likely to report interest in doing so than not (121 respondents [41.2%] vs 89 respondents [30.3%]; P < .001). Respondents reported discrepancies in the adequacy of ancestral populations with significantly more reporting European samples as adequate across consortium (203 respondents [90.6%]), government (200 respondents [89.7%]), and private (42 respondents [80.8%]) databases, compared with any other ancestral population. There were no significant differences in reported adequacy of ancestral populations across database stewards. A majority of respondents without access to adequate diverse samples reported that increasing the ancestral diversity of existing databases (201 respondents [68.4%]) and increasing access to databases that are already diverse (166 respondents [56.5%]) would increase the likelihood of them using a more diverse sample.

Conclusions and Relevance

In this survey study of US genetic researchers, respondents reported existing databases only provide adequate ancestral samples for European populations, despite their interest in other ancestral populations. These findings suggest there are specific gaps in access to and composition of genetic databases, highlighting the urgent need to boost diversity in research samples to improve inclusivity in genetic research practices.

Post stroke pain: Is there under-diagnosis in Black versus White patients?

Stroke incidence is higher and stroke outcomes are poorer in Black patients compared to White patients. Poststroke pain, however, is not a well understood stroke outcome. Using the National Institutes of Health All of Us Research Program database, we hypothesized that the dataset would demonstrate proportionately higher relative risk of poststroke pain in the Black poststroke patient population compared to the White poststroke patient population. However, our analysis showed that Black stroke patients were diagnosed with poststroke pain at a similar rate as White stroke patients. As our results are not consistent with other poststroke outcomes in the literature, this study identifies a potentially underdiagnosed patient population, highlighting the need for further research.

Key Contextual Factors Involved with Participation in Medical and Genomic Screening and Research for African American and Caucasian Americans: A Qualitative Inquiry American Journal of Community Genetics

Tremendous progress has been made promoting diversity in recruitment for genomic research, yet challenges remain for several racial demographics. Research has cited intertwined fears of racial discrimination and medical mistrust as contributing factors. This study aimed to identify key factors to establishing trust in medical and genomic screening and research among African Americans and White Americans. Participants completed online focus groups and resulting transcripts were analyzed using a qualitative descriptive approach, with content analysis methods based on recommendations by Schreier. Fifteen African Americans and 23 Caucasian Americans participated in the study, 63% of which were female. The mean age of participants was 38.53 (SD = 16.6). The Overarching Theme of Trust is Context Dependent was identified, along with the following five themes describing elements influencing trustworthiness for our participants: 1) Professional Experience, Education, and Training Bolster Trust; 2) Trust Depends on Relationships; 3) Cross-checking Provided Information is Influential in Establishing Trust; 4) Trust is Undermined by Lack of Objectivity and Bias; and 5) Racism is an Embedded Concern and a Medical Trust Limiting Component for African Americans. To effectively address mistrust and promote recruitment of diverse participants, genomic research initiatives must be communicated in a manner that resonates with the specific diverse communities targeted. Our results suggest key factors influencing trust that should be attended to if we are to promote equity appropriately and respectfully by engaging diverse populations in genomic research.

Rethinking Data Collection Methods During the Pandemic: Development and Implementation of CATI for the All of Us Research Program

The All of Us Research Program is a longitudinal cohort study aiming to build a diverse database to advance precision medicine. The COVID-19 pandemic hindered the ability of participants to receive in-person assistance at enrollment sites to complete digital surveys. Therefore, the program implemented Computer-Assisted Telephone Interviewing (CATI) to facilitate survey completion remotely to combat the disrupted data collection procedures. In January 2021, All of Us implemented a 1-year CATI Pilot supporting 9399 participants and resulting in 16 337 submitted surveys. The pilot showed that CATI was successful in increasing survey completion and retention activities for the All of Us Research Program, given the additional remote support offered to participants. Given the success of the CATI Pilot, multimodal survey administration will continue.

Genomic data in the All of Us Research Program

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Defining the Dimensions of Diversity to Promote Inclusion in the Digital Era of Health Care: A Lexicon

The pandemic provided a stark reminder of the inequities faced by populations historically marginalized by the health care system and accelerated the adoption of digital health technologies to drive innovation. Digital health technologies' purported promises to reduce inefficiencies and costs, improve access and health outcomes, and empower patients add a new level of urgency to health equity. As conventional medicine shifts toward digital medicine, we have the opportunity to intentionally develop and deploy digital health technologies with an inclusion focus. The first step is ensuring that the multiple dimensions of diversity are captured. We propose a lexicon that encompasses elements critical for implementing an inclusive approach to advancing health care quality and health services research in the digital era.

Glucagon-like Peptide-1 Receptor Agonists Associated Gastrointestinal Adverse Events: A Cross-Sectional Analysis of the National Institutes of Health All of Us Cohort

BACKGROUND

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are commonly used diabetes and obesity medications but have been associated with gastrointestinal (GI) adverse events. However, real-world evidence on comparative GI adverse reaction profiles is limited.

OBJECTIVES

This study aimed to evaluate GI adverse events among GLP-1 RA users and compare semaglutide, dulaglutide, liraglutide, and exenatide safety regarding the GI adverse reaction profile.

METHODS

This retrospective cross-sectional analysis utilized real-world data on 10,328 adults with diabetes/obesity in the National Institutes of Health All of Us cohort. New GLP-1 RA users were identified, and GI adverse events were examined. Logistic regression determined factors associated with GI adverse events.

RESULTS

The mean age of the study population was 61.4 ± 12.6 years, 65.7% were female, 51.3% were White, and they had a high comorbidity burden. Abdominal pain (57.6%) was the most common GI adverse event, followed by constipation (30.4%), diarrhea (32.7%), nausea and vomiting (23.4%), GI bleeding (15.9%), gastroparesis (5.1%), and pancreatitis (3.4%). Dulaglutide and liraglutide had higher rates of abdominal pain, constipation, diarrhea, and nausea and vomiting than semaglutide and exenatide. Liraglutide and exenatide had the highest pancreatitis (4.0% and 3.8%, respectively). Compared to semaglutide, dulaglutide and liraglutide had higher odds of abdominal pain, and nausea and vomiting. They also had higher odds of gastroparesis than semaglutide. No significant differences existed in GI bleeding or pancreatitis risks between the GLP-1 RAs.

CONCLUSIONS

In this real-world cohort, GI adverse events were common with GLP-1 RAs. Differences in GI safety profiles existed between agents, with exenatide appearing safer than other GLP-1 RAs, except for gastroparesis. These findings can inform GLP-1 RA selection considering GI risk factors. Further studies are needed to evaluate the causal relationship and GLP-1 RA safety with concomitant medication use.

Inclusive Approaches for Measuring Demographics of Underrepresented Populations in STEM and Biomedical Research Training Programs

As federal strategic plans prioritize increasing diversity within the biomedical workforce, and STEM training and outreach programs seek to recruit and retain students from historically underrepresented populations, there is a need for interrogation of traditional demographic descriptors and careful consideration of best practices for obtaining demographic data. To accelerate this work, equity-focused researchers and leaders from STEM programs convened to examine approaches for measuring demographic variables. Gender, race/ethnicity, disability, and disadvantaged background were prioritized given their focus by federal funding agencies. Categories of sex minority, sexual (orientation) minority, and gender minority (SSGM) should be included in demographic measures collected by STEM programs, consistent with recommendations from White House Executive Orders and federal reports. Our manuscript offers operationalized phrasing for demographic questions and recommendations for use across student-serving programs. Inclusive demographics permit the identification of individuals who are being excluded, marginalized, or improperly aggregated, increasing capacity to address inequities in biomedical research training. As trainees do not enter training programs with equal access, accommodations, or preparation, inclusive demographic measures can welcome trainees and inform a nuanced set of program outcomes that facilitate research on intersectionality to support the recruitment and retention of underrepresented students in biomedical research.

Broadening the Environmental Lens to Include Social and Structural Determinants of Women's Health Disparities

BACKGROUND

Due to the physical, metabolic, and hormonal changes before, during, and after pregnancy, women-defined here as people assigned female at birth-are particularly susceptible to environmental insults. Racism, a driving force of social determinants of health, exacerbates this susceptibility by affecting exposure to both chemical and nonchemical stressors to create women's health disparities.

OBJECTIVES

To better understand and address social and structural determinants of women's health disparities, the National Institute of Environmental Health Sciences (NIEHS) hosted a workshop focused on the environmental impacts on women's health disparities and reproductive health in April 2022. This commentary summarizes foundational research and unique insights shared by workshop participants, who emphasized the need to broaden the definition of the environment to include upstream social and structural determinants of health. We also summarize current challenges and recommendations, as discussed by workshop participants, to address women's environmental and reproductive health disparities.

DISCUSSION

The challenges related to women's health equity, as identified by workshop attendees, included developing research approaches to better capture the social and structural environment in both human and animal studies, integrating environmental health principles into clinical care, and implementing more inclusive publishing and funding approaches. Workshop participants discussed recommendations in each of these areas that encourage interdisciplinary collaboration among researchers, clinicians, funders, publishers, and community members. https://doi.org/10.1289/EHP12996.

Do digital health interventions hold promise for stroke prevention and care in Black and Latinx populations in the United States? A scoping review

BACKGROUND

Black and Latinx populations are disproportionately affected by stroke and are likely to experience gaps in health care. Within fragmented care systems, remote digital solutions hold promise in reversing this pattern. However, there is a digital divide that follows historical disparities in health. Without deliberate attempts to address this digital divide, rapid advances in digital health will only perpetuate systemic biases. This study aimed to characterize the range of digital health interventions for stroke care, summarize their efficacy, and examine the inclusion of Black and Latinx populations in the evidence base.

METHODS

We searched PubMed, the Web of Science, and EMBASE for publications between 2015 and 2021. Inclusion criteria include peer-reviewed systematic reviews or meta-analyses of experimental studies focusing on the impact of digital health interventions on stroke risk factors and outcomes in adults. Detailed information was extracted on intervention modality and functionality, clinical/behavioral outcome, study location, sample demographics, and intervention results.

RESULTS

Thirty-eight systematic reviews met inclusion criteria and yielded 519 individual studies. We identified six functional categories and eight digital health modalities. Case management (63%) and health monitoring (50%) were the most common intervention functionalities. Mobile apps and web-based interventions were the two most commonly studied modalities. Evidence of efficacy was strongest for web-based, text-messaging, and phone-based approaches. Although mobile applications have been widely studied, the evidence on efficacy is mixed. Blood pressure and medication adherence were the most commonly studied outcomes. However, evidence on the efficacy of the various intervention modalities on these outcomes was variable. Among all individual studies, only 38.0% were conducted in the United States (n = 197). Of these U.S. studies, 54.8% adequately reported racial or ethnic group distribution. On average, samples were 27.0% Black, 17.1% Latinx, and 63.4% White.

CONCLUSION

While evidence of the efficacy of selected digital health interventions, particularly those designed to improve blood pressure management and medication adherence, show promise, evidence of how these interventions can be generalized to historically underrepresented groups is insufficient. Including these underrepresented populations in both digital health experimental and feasibility studies is critical to advancing digital health science and achieving health equity.

Data-driven science and diversity in the All of Us Research Program

The National Institutes of Health's All of Us Research Program is an accessible platform that hosts genomic and phenotypic data to be collected from 1 million participants in the United States. Its mission is to accelerate medical research and clinical breakthroughs with a special emphasis on diversity.

Social Determinants of Health and Perceived Barriers to Care in Diabetic Retinopathy Screening

Importance

Regular screening for diabetic retinopathy often is crucial for the health of patients with diabetes. However, many factors may be barriers to regular screening and associated with disparities in screening rates.

Objective

To evaluate the associations between visiting an eye care practitioner for diabetic retinopathy screening and factors related to overall health and social determinants of health, including socioeconomic status and health care access and utilization.

Design, Setting, and Participants

This retrospective cross-sectional study included adults aged 18 years or older with type 2 diabetes who answered survey questions in the All of Us Research Program, a national multicenter cohort of patients contributing electronic health records and survey data, who were enrolled from May 1, 2018, to July 1, 2022.

Exposures

The associations between visiting an eye care practitioner and (1) demographic and socioeconomic factors and (2) responses to the Health Care Access and Utilization, Social Determinants of Health, and Overall Health surveys were investigated using univariable and multivariable logistic regressions.

Main Outcome and Measures

The primary outcome was whether patients self-reported visiting an eye care practitioner in the past 12 months. The associations between visiting an eye care practitioner and demographic and socioeconomic factors and responses to the Health Care Access and Utilization, Social Determinants of Health, and Overall Health surveys in All of Us were investigated using univariable and multivariable logistic regression.

Results

Of the 11 551 included participants (54.55% cisgender women; mean [SD] age, 64.71 [11.82] years), 7983 (69.11%) self-reported visiting an eye care practitioner in the past year. Individuals who thought practitioner concordance was somewhat or very important were less likely to have seen an eye care practitioner (somewhat important: adjusted odds ratio [AOR], 0.83 [95% CI, 0.74-0.93]; very important: AOR, 0.85 [95% CI, 0.76-0.95]). Compared with financially stable participants, individuals with food or housing insecurity were less likely to visit an eye care practitioner (food insecurity: AOR, 0.75 [95% CI, 0.61-0.91]; housing insecurity: AOR, 0.86 [95% CI, 0.75-0.98]). Individuals who reported fair mental health were less likely to visit an eye care practitioner than were those who reported good mental health (AOR, 0.84; 95% CI, 0.74-0.96).

Conclusions and Relevance

This study found that food insecurity, housing insecurity, mental health concerns, and the perceived importance of practitioner concordance were associated with a lower likelihood of receiving eye care. Such findings highlight the self-reported barriers to seeking care and the importance of taking steps to promote health equity.

Association Between a First-Degree Family History and Self-Reported Personal History of Obesity, Diabetes, and Heart and Blood Conditions: Results From the All of Us Research Program

BACKGROUND

Family history reflects the complex interplay of genetic susceptibility and shared environmental exposures and is an important risk factor for obesity, diabetes, and heart and blood conditions (ODHB). However, the overlap in family history associations between various ODHBs has not been quantified.

METHODS AND RESULTS

We assessed the association between a self-reported family history of ODHBs and their risk in the adult population (age ≥20 years) of the AoU (All of Us) Research Program, a longitudinal cohort study of diverse participants across the United States. We conducted a family history-wide association study to systematically assess the association of a first-degree family history of 15 ODHBs in AoU. We performed stratified analyses based on racial and ethnic categories, education, household income and gender minority status, and quantified associations by type of affected relatives. Of 125 430 participants, 76.8% reported a first-degree family history of any ODHB, most commonly hypertension (n=64 982, 51.8%), high cholesterol (49 753, 39.7%), and heart attack (29 618, 23.6%). We use the FamWAS method to estimate 225 familial associations among 15 ODHBs. The results include overlapping associations between family history of different types of cardiometabolic conditions (such as type 2 diabetes and coronary artery disease), and their risk factors (obesity, hypertension), where adults with a family history of 1 ODHB exhibited 1.1 to 5.6 times (1.5, on average) the odds of having a different ODHB.

CONCLUSIONS

Our findings inform the utility of family history data as a risk assessment and screening tool for the prevention of ODHBs and to provide additional insights into shared risk factors and pathogenic mechanisms.

Precision medicine for cardiometabolic disease: a framework for clinical translation

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.

The need for an intersectionality framework in precision medicine research

Precision medicine research has seen growing efforts to increase participation of communities that have been historically underrepresented in biomedical research. Marginalized racial and ethnic communities have received particular attention, toward the goal of improving the generalizability of scientific knowledge and promoting health equity. Against this backdrop, research has highlighted three key issues that could impede the promise of precision medicine research: issues surrounding (dis)trust and representation, challenges in translational efforts to improve health outcomes, and the need for responsive community engagement. Existing efforts to address these challenges have predominantly centered on single-dimensional demographic criteria such as race, ethnicity, or sex, while overlooking how these and additional variables, such as disability, gender identity, and socioeconomic factors, can confound and jointly impact research participation. We argue that increasing cohort diversity and the responsiveness of precision medicine research studies to community needs requires an approach that transcends conventional boundaries and embraces a more nuanced, multi-layered, and intersectional framework for data collection, analyses, and implementation. We draw attention to gaps in existing work, highlight how overlapping layers of marginalization might shape and substantiate one another and affect the precision-medicine research cycle, and put forth strategies to facilitate equitable advantages from precision-medicine research to diverse participants and internally heterogeneous communities.