Sounding out solutions: using SONAR to connect participants with relevant healthcare resources.

OBJECTIVE: This article outlines a scalable system developed by the All of Us Research Program's Genetic Counseling Resource to vet a large database of healthcare resources for supporting participants with health-related DNA results. MATERIALS AND METHODS: After a literature review of established evaluation frameworks for health resources, we created SONAR, a 10-item framework and grading scale for health-related participant-facing resources. SONAR was used to review clinical resources that could be shared with participants during genetic counseling. RESULTS: Application of SONAR shortened resource approval time from 7 days to 1 day. About 256 resources were approved and 8 rejected through SONAR review. Most approved resources were relevant to participants nationwide (60.0%). The most common resource types were related to support groups (20%), cancer care (30.6%), and general educational resources (12.4%). All of Us genetic counselors provided 1161 approved resources during 3005 (38.6%) consults, mainly to local genetic counselors (29.9%), support groups (21.9%), and educational resources (21.0%). DISCUSSION: SONAR's systematic method simplifies resource vetting for healthcare providers, easing the burden of identifying and evaluating credible resources. Compiling these resources into a user-friendly database allows providers to share these resources efficiently, better equipping participants to complete follow up actions from health-related DNA results. CONCLUSION: The All of Us Genetic Counseling Resource connects participants receiving health-related DNA results with relevant follow-up resources on a high-volume, national level. This has been made possible by the creation of a novel resource database and validation system.

Associations between Statin Use and Glaucoma in the All of Us Research Program.

PURPOSE: To investigate associations between statin use and glaucoma in the 2017-2022 All of Us (AoU) Research Program. DESIGN: Cross-sectional, population-based. PARTICIPANTS: 79,742 adult participants aged ≥ 40 years with hyperlipidemia and with electronic health record (EHR) data in the AoU database. METHODS: Hyperlipidemia, glaucoma status, and statin use were defined by diagnoses and medication information in EHR data collected by AoU. Logistic regression analysis was performed to evaluate the association between statin use and glaucoma likelihood. Logistic regression modeling was used to examine associations between glaucoma and all covariates included in adjusted analysis. Serum low-density lipoprotein cholesterol (LDL-C) was used to assess hyperlipidemia severity. Analyses stratified by LDL-C level and age were performed. MAIN OUTCOME MEASURES: Any glaucoma as defined by International Classification of Diseases (ICD) codes found in EHR data. RESULTS: Of 79,742 individuals with hyperlipidemia in AoU, there were 6,365 (8.0%) statin users. Statin use was associated with increased glaucoma prevalence when compared with statin non-use (adjusted odds ratio [aOR]: 1.13, 95% confidence interval [CI]: 1.01-1.26). Higher serum levels of LDL-C were associated with increased odds of glaucoma (aOR: 1.003, 95% CI: 1.003, 1.004). Statin users had significantly higher LDL-C levels compared to nonusers (144.9 mg/dL versus 136.3 mg/dL, p-value < 0.001). Analysis stratified by LDL-C identified positive associations between statin use and prevalence of glaucoma among those with optimal (aOR = 1.39, 95% CI = 1.05-1.82) and high (aOR = 1.37, 95% CI = 1.09-1.70) LDL-C levels. Age-stratified analysis showed a positive association between statin use and prevalence of glaucoma in individuals aged 60-69 years (aOR = 1.28, 95% CI = 1.05-1.56). CONCLUSIONS: Statin use was associated with increased glaucoma likelihood in the overall adult AoU population with hyperlipidemia, in individuals with optimal or high LDL-C levels, and in individuals 60-69 years old. Findings suggest that statin use may be an independent risk factor for glaucoma, which may furthermore be affected by one's lipid profile and age.

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.

The AORTA Gene score for detection and risk stratification of ascending aortic dilation.

BACKGROUND AND AIMS: This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone. METHODS: Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants. RESULTS: AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%-41.8%) vs. 29.3% (27.0%-31.5%) in UK Biobank, 36.5% (34.4%-38.5%) vs. 32.5% (30.4%-34.5%) in MGB, 41.8% (37.7%-45.9%) vs. 33.0% (28.9%-37.2%) in FHS, and 34.9% (28.8%-41.0%) vs. 28.9% (22.9%-35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049). CONCLUSIONS: A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%-41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors.

Characterizing apparent treatment resistant hypertension in the United States: insights from the All of Us Research Program.

BACKGROUND: Hypertension (HTN) remains a significant public health concern and the primary modifiable risk factor for cardiovascular disease, which is the leading cause of death in the United States. We applied our validated HTN computable phenotypes within the All of Us Research Program to uncover prevalence and characteristics of HTN and apparent treatment-resistant hypertension (aTRH) in United States. METHODS: Within the All of Us Researcher Workbench, we built a retrospective cohort (January 1, 2008-July 1, 2023), identifying all adults with available age data, at least one blood pressure (BP) measurement, prescribed at least one antihypertensive medication, and with at least one SNOMED "Essential hypertension" diagnosis code. RESULTS: We identified 99 461 participants with HTN who met the eligibility criteria. Following the application of our computable phenotypes, an overall population of 81 462 were further categorized to aTRH (14.4%), stable-controlled HTN (SCH) (39.5%), and Other HTN (46.1%). Compared to participants with SCH, participants with aTRH were older, more likely to be of Black or African American race, had higher levels of social deprivation, and a heightened prevalence of comorbidities such as hyperlipidemia and diabetes. Heart failure, chronic kidney disease, and diabetes were the comorbidities most strongly associated with aTRH. ß-blockers were the most prescribed antihypertensive medication. At index date, the overall BP control rate was 62%. DISCUSSION AND CONCLUSION: All of Us provides a unique opportunity to characterize HTN in the United States. Consistent findings from this study with our prior research highlight the interoperability of our computable phenotypes.

Communicating research findings as a return of value to All of Us Research Program participants: insights from staff at Federally Qualified Health Centers.

OBJECTIVES: Research participants value learning how their data contributions are advancing health research (ie, data stories). The All of Us Research Program gathered insights from program staff to learn what research topics they think are of interest to participants, what support staff need to communicate data stories, and how staff use data story dissemination tools. MATERIALS AND METHODS: Using an online 25-item assessment, we collected information from All of Us staff at 7 Federally Qualified Health Centers. RESULTS: Topics of greatest interest or relevance included income insecurity (83%), diabetes (78%), and mental health (78%). Respondents prioritized in-person outreach in the community (70%) as a preferred setting to share data stories. Familiarity with available dissemination tools varied. DISCUSSION: Responses support prioritizing materials for in-person outreach and training staff how to use dissemination tools. CONCLUSION: The findings will inform All of Us communication strategy, content, materials, and staff training resources to effectively deliver data stories as return of value to participants.

Model-based estimation of individual-level social determinants of health and its applications in All of Us.

OBJECTIVES: We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program. MATERIALS AND METHODS: Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes. RESULTS: Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900. DISCUSSION AND CONCLUSION: The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.

Factors associated with pityriasis versicolor in a large national database.

BACKGROUND: Pityriasis versicolor (PV), a cutaneous fungal infection, most commonly affects adolescents and young adults and is associated with hyperhidrosis and humid weather. Understanding other factors associated with PV might help improve diagnostic and treatment practices. OBJECTIVES: PV's associations with patient demographics, comorbidities and medication exposures were assessed using the All of Us Database, a large, diverse, national database from the United States. METHODS: A case-control study with multivariable analysis was performed. RESULTS: We identified 456 PV case-patients and 1368 control-patients. PV case-patients (vs. control-patients) were younger (median age [years] (standard deviation): 48.7 (15.4) vs. 61.9 (15.5); OR: 0.95, CI: 0.94-0.96) and more likely to be men versus women (42.8% vs. 33.9%, OR: 1.45, CI: 1.16-1.79) and Black (19.5% vs. 15.8%, OR: 1.35, 95% CI: 1.02-1.80) or Asian (4.6% vs. 2.7%, OR: 1.86, CI: 1.07-3.24) versus White. PV case-patients more frequently had acne (5.3% vs. ≤1.5%, OR: 5.37, CI: 2.76-10.48) and less frequently had type 2 diabetes mellitus (T2DM) (14.7% vs. 24.7%, OR: 0.52, CI: 0.39-0.70) and hypothyroidism (OR: 10.3% vs. 16.4%, OR: 0.59, CI: 0.42-0.82). In multivariable analysis, PV odds were significantly higher in those with acne and lower in those with T2DM, older age and female sex. CONCLUSIONS: Our results may be used as a basis for future studies evaluating whether acne treatment may decrease PV risk. Physicians could educate patients with acne about PV, including strategies to control modifiable PV risk factors, such as avoidance of hot and humid environments and avoidance of use of topical skin oils.

Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

PURPOSE: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain. DESIGN: This study was a retrospective, observational study. METHODS: We used demographic, diagnosis, and social survey data from the NIH 'All of Us' program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model. RESULTS: The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance. CONCLUSION: Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes. CLINICAL RELEVANCE: Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.

allofus: an R package to facilitate use of the All of Us Researcher Workbench.

OBJECTIVES: Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time dependencies. TARGET AUDIENCE: All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the package. SCOPE: We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.

Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program.

OBJECTIVE: This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables. MATERIALS AND METHODS: We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period. RESULTS: The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors. DISCUSSION: The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness. CONCLUSION: This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.

An evaluation of the All of Us Research Program database to examine cumulative stress.

OBJECTIVES: To evaluate the NIH All of Us Research Program database as a potential data source for studying allostatic load and stress among adults in the United States (US). MATERIALS AND METHODS: We evaluated the All of Us database to determine sample size significance for original-10 allostatic load biomarkers, Allostatic Load Index-5 (ALI-5), Allostatic Load Five, and Cohen's Perceived Stress Scale (PSS). We conducted a priori, post hoc, and sensitivity power analyses to determine sample sizes for conducting null hypothesis significance tests. RESULTS: The maximum number of responses available for each measure is 21 participants for the original-10 allostatic load biomarkers, 150 for the ALI-5, 22 476 for Allostatic Load Five, and n = 90 583 for the PSS. DISCUSSION: The NIH All of Us Research Program is well-suited for studying allostatic load using the Allostatic Load Five and psychological stress using PSS. CONCLUSION: Improving biomarker data collection in All of Us will facilitate more nuanced examinations of allostatic load among US adults.

Sex-based disparities with cost-related medication adherence issues in patients with hypertension, ischemic heart disease, and heart failure.

IMPORTANCE AND OBJECTIVE: Identifying sources of sex-based disparities is the first step in improving clinical outcomes for female patients. Using All of Us data, we examined the association of biological sex with cost-related medication adherence (CRMA) issues in patients with cardiovascular comorbidities. MATERIALS AND METHODS: Retrospective data collection identified the following patients: 18 and older, completing personal medical history surveys, having hypertension (HTN), ischemic heart disease (IHD), or heart failure (HF) with medication use history consistent with these diagnoses. Implementing univariable and adjusted logistic regression, we assessed the influence of biological sex on 7 different patient-reported CRMA outcomes within HTN, IHD, and HF patients. RESULTS: Our study created cohorts of HTN (n = 3891), IHD (n = 5373), and HF (n = 2151) patients having CRMA outcomes data. Within each cohort, females were significantly more likely to report various cost-related medication issues: being unable to afford medications (HTN hazards ratio [HR]: 1.68, confidence interval [CI]: 1.33-2.13; IHD HR: 2.33, CI: 1.72-3.16; HF HR: 1.82, CI: 1.22-2.71), skipping doses (HTN HR: 1.76, CI: 1.30-2.39; IHD HR: 2.37, CI: 1.69-3.64; HF HR: 3.15, CI: 1.87-5.31), taking less medication (HTN HR: 1.86, CI: 1.37-2.45; IHD HR: 2.22, CI: 1.53-3.22; HF HR: 2.99, CI: 1.78-5.02), delaying filling prescriptions (HTN HR: 1.83, CI: 1.43-2.39; IHD HR: 2.02, CI: 1.48-2.77; HF HR: 2.99, CI: 1.79-5.03), and asking for lower cost medications (HTN HR: 1.41, CI: 1.16-1.72; IHD HR: 1.75, CI: 1.37-2.22; HF HR: 1.61, CI: 1.14-2.27). DISCUSSION AND CONCLUSION: Our results clearly demonstrate CRMA issues disproportionately affect female patients with cardiovascular comorbidities, which may contribute to the larger sex-based disparities in cardiovascular care. These findings call for targeted interventions and strategies to address these disparities and ensure equitable access to cardiovascular medications and care for all patients.

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.

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.