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Amrollahi F, Kennis BD, Shashikumar SP, Malhotra A, Taylor SP, Ford J, Rodriguez A, Weston J, Maheshwary R, Nemati S, Wardi G, Meier A. Prediction of Readmission Following Sepsis Using Social Determinants of Health. Crit Care Explor 2024; 6:e1099. [PMID: 38787299 PMCID: PMC11132367 DOI: 10.1097/cce.0000000000001099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024] Open
Abstract
OBJECTIVES To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables. DESIGN Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data. SETTINGS Thirty-five hospitals across the United States from 2017 to 2021. PATIENTS Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission. CONCLUSIONS In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.
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Affiliation(s)
- Fatemeh Amrollahi
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Brent D Kennis
- School of Medicine, University of California San Diego, La Jolla, CA
| | | | - Atul Malhotra
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California at San Diego, La Jolla, CA
| | | | - James Ford
- Department of Emergency Medicine, University of California, San Francisco, San Francisco, CA
| | - Arianna Rodriguez
- Department of Medicine, University of California San Diego, La Jolla, CA
| | - Julia Weston
- Department of Medicine, University of California San Diego, La Jolla, CA
| | - Romir Maheshwary
- Department of Medicine, University of California San Diego, La Jolla, CA
| | - Shamim Nemati
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Gabriel Wardi
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California at San Diego, La Jolla, CA
- Department of Emergency Medicine, University of California San Diego, San Diego, CA
| | - Angela Meier
- Department of Anesthesiology, Division of Critical Care, University of California, San Diego, La Jolla, CA
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Acharya N, Natarajan K. Development and Validation of an Individual Socioeconomic Deprivation Index (ISDI) in the NIH's All of Us Data Network. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:36-45. [PMID: 38827060 PMCID: PMC11141807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
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.
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Affiliation(s)
- Nripendra Acharya
- Columbia University Medical Center, Department of Biomedical Informatics, New York, New York
| | - Karthik Natarajan
- Columbia University Medical Center, Department of Biomedical Informatics, New York, New York
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Graves JM, Beese SR, Abshire DA, Bennett KJ. 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. J Rural Health 2024. [PMID: 38683037 DOI: 10.1111/jrh.12840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/10/2024] [Accepted: 04/12/2024] [Indexed: 05/01/2024]
Abstract
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.
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Affiliation(s)
- Janessa M Graves
- WWAMI Rural Health Research Center, Department of Family Medicine, School of Medicine, University of Washington, Seattle, Washington, USA
- College of Nursing, Washington State University, Spokane, Washington, USA
| | - Shawna R Beese
- College of Nursing, Washington State University, Spokane, Washington, USA
- College of Agricultural, Human, and Natural Resource Sciences, Extension, Washington State University, Pullman, Washington, USA
| | - Demetrius A Abshire
- College of Nursing, University of South Carolina, Columbia, South Carolina, USA
| | - Kevin J Bennett
- University of South Carolina School of Medicine-Columbia, Columbia, South Carolina, USA
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Tesfaye S, Cronin RM, Lopez-Class M, Chen Q, Foster CS, Gu CA, Guide A, Hiatt RA, Johnson AS, Joseph CLM, Khatri P, Lim S, Litwin TR, Munoz FA, Ramirez AH, Sansbury H, Schlundt DG, Viera EN, Dede-Yildirim E, Clark CR. Measuring social determinants of health in the All of Us Research Program. Sci Rep 2024; 14:8815. [PMID: 38627404 PMCID: PMC11021514 DOI: 10.1038/s41598-024-57410-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
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.
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Affiliation(s)
- Samantha Tesfaye
- Division of Medical and Scientific Research, All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Robert M Cronin
- Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - Maria Lopez-Class
- Division of Cohort Development (DCD), All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Qingxia Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christopher S Foster
- Division of Cohort Development (DCD), All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Callie A Gu
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Andrew Guide
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert A Hiatt
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Angelica S Johnson
- Division of Engagement and Outreach, All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | | | | | - Sokny Lim
- Office of Data and Analytics, All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Tamara R Litwin
- Division of Medical and Scientific Research, All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Fatima A Munoz
- Division of Health Support Services, San Ysidro Health, San Diego, CA, USA
| | - Andrea H Ramirez
- Office of Data and Analytics, All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Heather Sansbury
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
- Leidos, Inc., Reston, VA, USA
| | - David G Schlundt
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | | | - Elif Dede-Yildirim
- Office of Data and Analytics, All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Cheryl R Clark
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. CELL GENOMICS 2024; 4:100523. [PMID: 38508198 PMCID: PMC11019356 DOI: 10.1016/j.xgen.2024.100523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/15/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
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.
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Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Leland E Hull
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia R Martin
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
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Zeng C, Schlueter DJ, Tran TC, Babbar A, Cassini T, Bastarache LA, Denny JC. Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank. J Am Med Inform Assoc 2024; 31:846-854. [PMID: 38263490 PMCID: PMC10990551 DOI: 10.1093/jamia/ocad260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/05/2023] [Accepted: 01/08/2024] [Indexed: 01/25/2024] Open
Abstract
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.
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Affiliation(s)
- Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - David J Schlueter
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
- Department of Health and Society, University of Toronto, Scarborough, Toronto, ON, Canada
| | - Tam C Tran
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Anav Babbar
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Thomas Cassini
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Lisa A Bastarache
- Center for Precision Medicine, Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Josh C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
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Peterson R, Hedden SL, Seo I, Palacios VY, Clark EC, Begale M, Sutherland S, Givens B, McQueen M, McClain JJ. Rethinking Data Collection Methods During the Pandemic: Development and Implementation of CATI for the All of Us Research Program. JOURNAL OF PUBLIC HEALTH MANAGEMENT AND PRACTICE 2024; 30:195-199. [PMID: 38271102 PMCID: PMC10827348 DOI: 10.1097/phh.0000000000001846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
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.
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Rasooly D, Moonesinghe R, Littrell K, Hull L, Khoury MJ. 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. J Am Heart Assoc 2023; 12:e030779. [PMID: 37947093 PMCID: PMC10727309 DOI: 10.1161/jaha.123.030779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 10/11/2023] [Indexed: 11/12/2023]
Abstract
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.
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Affiliation(s)
- Danielle Rasooly
- Division of Blood Disorders and Public Health GenomicsNational Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and PreventionAtlantaGAUSA
| | - Ramal Moonesinghe
- Division of Blood Disorders and Public Health GenomicsNational Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and PreventionAtlantaGAUSA
| | - Kevin Littrell
- Division of Blood Disorders and Public Health GenomicsNational Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and PreventionAtlantaGAUSA
| | - Leland Hull
- Division of General Internal Medicine, Massachusetts General HospitalBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - Muin J. Khoury
- Division of Blood Disorders and Public Health GenomicsNational Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and PreventionAtlantaGAUSA
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Rice C, Ayyala DN, Shi H, Madera-Acosta A, Bell S, Qureshi A, Carbone LD, Coughlin SS, Elam RE. Sex and Racial Differences in Systemic Lupus Erythematosus Among US Adults in the All of Us Research Program. Arthritis Care Res (Hoboken) 2023; 75:2096-2106. [PMID: 36705447 PMCID: PMC10372192 DOI: 10.1002/acr.25093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 11/28/2022] [Accepted: 01/24/2023] [Indexed: 01/28/2023]
Abstract
OBJECTIVE Men with systemic lupus erythematosus (SLE) are an understudied population. The present study characterized differences between men and women with SLE. METHODS We examined cross-sectionally participants with SLE in the All of Us Research Program, a US cohort with a participant survey at enrollment (May 2018 to June 2022) and linked electronic health record (EHR) data. We described and compared characteristics of men and women with SLE encompassing disease manifestations and prescribed medications from EHR data and socioeconomic factors, including health literacy and health care access and utilization, from surveys. We reported racial variations stratified by sex. RESULTS Of 1,462 participants with SLE, 126 (9%) were male. Men reported lower educational attainment and less fatigue than women. Myocardial infarction was significantly more common in men. Men had significantly less confidence in completing medical forms than women and exhibited a trend toward requiring more help in reading health-related materials. Barriers to health care access and utilization were common in both men and women (40% versus 47%, respectively, reporting some reason for delay in care; P = 0.35). Women of race other than Black or African American or White more often reported delaying care due to cultural differences between patient and provider. CONCLUSION Our study demonstrated major clinical and health literacy differences in men and women with SLE. Socioeconomic factors were significant barriers to health care in both sexes. Our study suggests men have disproportionately poorer health literacy, which may exacerbate preexisting disparities. Further large prospective studies, focusing on recruiting men, are needed to better characterize racial differences in men with SLE.
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Affiliation(s)
| | - Deepak Nag Ayyala
- Division of Biostatistics and Data Science, Department of Population Health Sciences, Augusta University, Augusta, GA, USA
| | - Hong Shi
- Division of Rheumatology, Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Adria Madera-Acosta
- Division of Rheumatology, Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA
- Charlie Norwood Veterans Affairs Medical Center, Augusta, GA, USA
| | - Stephen Bell
- Division of Rheumatology, Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA
- Charlie Norwood Veterans Affairs Medical Center, Augusta, GA, USA
| | - Anam Qureshi
- Division of Rheumatology, Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA
- Charlie Norwood Veterans Affairs Medical Center, Augusta, GA, USA
| | - Laura D. Carbone
- Charlie Norwood Veterans Affairs Medical Center, Augusta, GA, USA
- J. Harold Harrison, MD, Distinguished University Chair in Rheumatology, Division of Rheumatology, Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Steven S. Coughlin
- Division of Epidemiology, Department of Population Health Sciences, Augusta University, Augusta, GA, USA
| | - Rachel E. Elam
- Division of Rheumatology, Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA
- Charlie Norwood Veterans Affairs Medical Center, Augusta, GA, USA
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Yeh HH, Peltz-Rauchman C, Johnson CC, Pawloski PA, Chesla D, Waring SC, Stevens AB, Epstein M, Joseph C, Miller-Matero LR, Gui H, Tang A, Boerwinkle E, Cicek M, Clark CR, Cohn E, Gebo K, Loperena R, Mayo K, Mockrin S, Ohno-Machado L, Schully S, Ramirez AH, Qian J, Ahmedani BK. Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program. PLoS One 2023; 18:e0290416. [PMID: 37594966 PMCID: PMC10437856 DOI: 10.1371/journal.pone.0290416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/07/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND The All of Us Research Program enrolls diverse US participants which provide a unique opportunity to better understand the problem of opioid use. This study aims to estimate the prevalence of opioid use and its association with sociodemographic characteristics from survey data and electronic health record (EHR). METHODS A total of 214,206 participants were included in this study who competed survey modules and shared EHR data. Adjusted logistic regressions were used to explore the associations between sociodemographic characteristics and opioid use. RESULTS The lifetime prevalence of street opioids was 4%, and the nonmedical use of prescription opioids was 9%. Men had higher odds of lifetime opioid use (aOR: 1.4 to 3.1) but reduced odds of current nonmedical use of prescription opioids (aOR: 0.6). Participants from other racial and ethnic groups were at reduced odds of lifetime use (aOR: 0.2 to 0.9) but increased odds of current use (aOR: 1.9 to 9.9) compared with non-Hispanic White participants. Foreign-born participants were at reduced risks of opioid use and diagnosed with opioid use disorders (OUD) compared with US-born participants (aOR: 0.36 to 0.67). Men, Younger, White, and US-born participants are more likely to have OUD. CONCLUSIONS All of Us research data can be used as an indicator of national trends for monitoring the prevalence of receiving prescription opioids, diagnosis of OUD, and non-medical use of opioids in the US. The program employs a longitudinal design for routinely collecting health-related data including EHR data, that will contribute to the literature by providing important clinical information related to opioids over time. Additionally, this data will enhance the estimates of the prevalence of OUD among diverse populations, including groups that are underrepresented in the national survey data.
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Affiliation(s)
- Hsueh-Han Yeh
- Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, Michigan, United States of America
| | - Cathryn Peltz-Rauchman
- Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, United States of America
| | - Christine C. Johnson
- Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, United States of America
| | - Pamala A. Pawloski
- HealthPartners Institute, Bloomington, Minnesota, United States of America
| | - David Chesla
- Office of Research and Education, Spectrum Health, Grand Rapids, Michigan, United States of America
| | - Stephen C. Waring
- Essentia Health, Essentia Institute of Rural Health, Duluth, Minnesota, United States of America
| | - Alan B. Stevens
- Center for Applied Health Research, Baylor Scott & White Health, Temple, Texas, United States of America
| | - Mara Epstein
- Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Christine Joseph
- Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, United States of America
| | - Lisa R. Miller-Matero
- Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, Michigan, United States of America
- Behavioral Health Services, Henry Ford Health, Detroit, Michigan, United States of America
| | - Hongsheng Gui
- Behavioral Health Services, Henry Ford Health, Detroit, Michigan, United States of America
| | - Amy Tang
- Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, United States of America
| | - Eric Boerwinkle
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Mine Cicek
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Cheryl R. Clark
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Elizabeth Cohn
- Hunter-Bellevue School of Nursing, Hunter College, City University of New York, New York, New York, United States of America
| | - Kelly Gebo
- Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Roxana Loperena
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Kelsey Mayo
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Stephen Mockrin
- All of Us Research Program, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Lucila Ohno-Machado
- Department of Biomedical Informatics, UCSD Health, La Jolla, California, United States of America
| | - Sheri Schully
- All of Us Research Program, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Andrea H. Ramirez
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jun Qian
- Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Brian K. Ahmedani
- Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, Michigan, United States of America
- Behavioral Health Services, Henry Ford Health, Detroit, Michigan, United States of America
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Mayo KR, Basford MA, Carroll RJ, Dillon M, Fullen H, Leung J, Master H, Rura S, Sulieman L, Kennedy N, Banks E, Bernick D, Gauchan A, Lichtenstein L, Mapes BM, Marginean K, Nyemba SL, Ramirez A, Rotundo C, Wolfe K, Xia W, Azuine RE, Cronin RM, Denny JC, Kho A, Lunt C, Malin B, Natarajan K, Wilkins CH, Xu H, Hripcsak G, Roden DM, Philippakis AA, Glazer D, Harris PA. The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research. Annu Rev Biomed Data Sci 2023; 6:443-464. [PMID: 37561600 PMCID: PMC11157478 DOI: 10.1146/annurev-biodatasci-122120-104825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.
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Affiliation(s)
- Kelsey R Mayo
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Melissa A Basford
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert J Carroll
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Moira Dillon
- Verily Life Sciences, South San Francisco, California, USA
| | - Heather Fullen
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jesse Leung
- Verily Life Sciences, South San Francisco, California, USA
| | - Hiral Master
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shimon Rura
- Verily Life Sciences, South San Francisco, California, USA
| | - Lina Sulieman
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Eric Banks
- Data Sciences Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - David Bernick
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Asmita Gauchan
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lee Lichtenstein
- Data Sciences Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Brandy M Mapes
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kayla Marginean
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Steve L Nyemba
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Andrea Ramirez
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Charissa Rotundo
- Vanderbilt University Medical Center Enterprise Cybersecurity, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Keri Wolfe
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Weiyi Xia
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Romuladus E Azuine
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Robert M Cronin
- Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Joshua C Denny
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Abel Kho
- Department of Medicine and Institute for Augmented Intelligence in Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Christopher Lunt
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Bradley Malin
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Consuelo H Wilkins
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Hua Xu
- Section of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, Connecticut, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Dan M Roden
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - David Glazer
- Verily Life Sciences, South San Francisco, California, USA
| | - Paul A Harris
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
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12
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Mayer CS, Huser V. Learning important common data elements from shared study data: The All of Us program analysis. PLoS One 2023; 18:e0283601. [PMID: 37418391 PMCID: PMC10328251 DOI: 10.1371/journal.pone.0283601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/13/2023] [Indexed: 07/09/2023] Open
Abstract
There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.
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Affiliation(s)
- Craig S. Mayer
- Lister Hill National Center for Biomedical Communication, National Library of Medicine, NIH, Bethesda, Maryland, United States of America
| | - Vojtech Huser
- Lister Hill National Center for Biomedical Communication, National Library of Medicine, NIH, Bethesda, Maryland, United States of America
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13
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Schulkey CE, Litwin TR, Ellsworth G, Sansbury H, Ahmedani BK, Choi KW, Cronin RM, Kloth Y, Ashbeck AW, Sutherland S, Mapes BM, Begale M, Bhat G, King P, Marginean K, Wolfe KA, Kouame A, Raquel C, Ratsimbazafy F, Bornemeier Z, Neumeier K, Baskir R, Gebo KA, Denny J, Smoller JW, Garriock HA. Design and Implementation of the All of Us Research Program COVID-19 Participant Experience (COPE) Survey. Am J Epidemiol 2023; 192:972-986. [PMID: 36799620 PMCID: PMC10505411 DOI: 10.1093/aje/kwad035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 11/16/2022] [Accepted: 02/08/2023] [Indexed: 02/18/2023] Open
Abstract
In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources.
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Affiliation(s)
- Claire E Schulkey
- Correspondence to Dr. Claire E. Schulkey, BG 6710 Rockledge Dr. Wing B Room 4320-03, 6710b Rockledge Drive, Bethesda, MD 20817 (e-mail: )
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14
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Keloth VK, Banda JM, Gurley M, Heider PM, Kennedy G, Liu H, Liu F, Miller T, Natarajan K, V Patterson O, Peng Y, Raja K, Reeves RM, Rouhizadeh M, Shi J, Wang X, Wang Y, Wei WQ, Williams AE, Zhang R, Belenkaya R, Reich C, Blacketer C, Ryan P, Hripcsak G, Elhadad N, Xu H. Representing and utilizing clinical textual data for real world studies: An OHDSI approach. J Biomed Inform 2023; 142:104343. [PMID: 36935011 PMCID: PMC10428170 DOI: 10.1016/j.jbi.2023.104343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 01/21/2023] [Accepted: 03/13/2023] [Indexed: 03/19/2023]
Abstract
Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.
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Affiliation(s)
- Vipina K Keloth
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Michael Gurley
- Lurie Cancer Center, Northwestern University, Chicago, Illinois, USA
| | - Paul M Heider
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA
| | - Georgina Kennedy
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Feifan Liu
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Timothy Miller
- Computational Health Informatics Program, Boston Children's Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Olga V Patterson
- VA Informatics and Computing Infrastructure, Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA; Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA; Verily Life Sciences, Mountain View, CA, USA
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Kalpana Raja
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Ruth M Reeves
- TN Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Masoud Rouhizadeh
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA; Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
| | - Jianlin Shi
- VA Informatics and Computing Infrastructure, Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA; Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA; Department of Biomedical Informatics, University of Utah, Salt Lake City, USA
| | - Xiaoyan Wang
- Sema4 Mount Sinai Genomics Incorporation, Stamford, CT, USA
| | - Yanshan Wang
- Department of Health Information Management, Department of Biomedical Informatics, and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Rui Zhang
- Institute for Health Informatics, and Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA
| | | | | | - Clair Blacketer
- Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Patrick Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Noémie Elhadad
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
| | - Hua Xu
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
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15
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Hoyt MA, Darabos K, Llave K. Disparities in health-related quality of life among lesbian, gay, and bisexual cancer survivors. J Psychosoc Oncol 2023; 41:661-672. [PMID: 37183953 DOI: 10.1080/07347332.2023.2210548] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
OBJECTIVE This study compared health-related quality of life (HRQOL) among lesbian, gay, and bisexual (LGB) cancer survivors and their heterosexual counterparts in a US population-based sample of cancer survivors. METHODS The study utilized data from the All of Us research program. LGB survivors (n = 885) were matched for age, gender identity, marital status, income, education, and cancer site with heterosexual survivors (n = 885) using 1:1 propensity matching. Physical, mental, and social HRQOL were assessed with items from the Patient-Reported Outcomes Measurement Information System (PROMIS). RESULTS Relative to heterosexuals, LGB cancer survivors reported lower HRQOL in mental and social domains, but not in physical HRQOL. Older age was associated with higher HRQOL across domains. LGB survivors identifying as Black/African American were more likely to experience lower social HRQOL than White survivors. CONCLUSIONS This study highlights several disparities in HRQOL that exist between LGB and heterosexual cancer survivors.
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Affiliation(s)
- Michael A Hoyt
- Department of Population Health and Disease Prevention, University of California, Irvine, California, USA
| | - Katie Darabos
- Department of Health Behavior, Society, and Policy, Rutgers School of Public Health, Piscataway, New Jersey, USA
| | - Karen Llave
- Department of Population Health and Disease Prevention, University of California, Irvine, California, USA
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16
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Yhdego HH, Nayebnazar A, Amrollahi F, Boussina A, Shashikumar S, Wardi G, Nemati S. Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.10.23288371. [PMID: 37090626 PMCID: PMC10120790 DOI: 10.1101/2023.04.10.23288371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Predictive models have been suggested as potential tools for identifying highest risk patients for hospital readmissions, in order to improve care coordination and ultimately long-term patient outcomes. However, the accuracy of current predictive models for readmission prediction is still moderate and further data enrichment is needed to identify at risk patients. This paper describes models to predict 90-day readmission, focusing on testing the predictive performance of wearable sensor features generated using multiscale entropy techniques and clinical features. Our study explores ways to incorporate pre-discharge and post-discharge wearable sensor features to make robust patient predictions. Data were used from participants enrolled in the AllofUs Research program. We extracted the inpatient cohort of patients and integrated clinical data from the electronic health records (EHR) and Fitbit sensor measurements. Entropy features were calculated from the longitudinal wearable sensor data, such as heart rate and mobility-related measurements, in order to characterize time series variability and complexity. Our best performing model acheived an AUC of 83%, and at 80% sensitivity acheived 75% specificity and 57% positive predictive value. Our results indicate that it would be possible to improve the ability to predict unplanned hospital readmissions by considering pre-discharge and post-discharge wearable features.
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Affiliation(s)
- Haben H Yhdego
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Arshia Nayebnazar
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Fatemeh Amrollahi
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Aaron Boussina
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Supreeth Shashikumar
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Gabriel Wardi
- Division of Pulmonary, Critical Care and Sleep Medicine, UC San Diego, La Jolla, CA
| | - Shamim Nemati
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
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17
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Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.21.23286110. [PMID: 36865265 PMCID: PMC9980241 DOI: 10.1101/2023.02.21.23286110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and 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. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after 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.
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Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Leland E. Hull
- Division of General Internal Medicine, 100 Cambridge Street,
Massachusetts General Hospital, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - David A. van Heel
- Blizard Institute, Barts and the London School of Medicine and
Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - Alicia R. Martin
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - S. Hong Lee
- Australian Centre for Precision Health, University of South
Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000,
Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
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18
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Desine S, Master H, Annis J, Hughes A, Roden DM, Harris PA, Brittain EL. Daily Step Counts Before and After the COVID-19 Pandemic Among All of Us Research Participants. JAMA Netw Open 2023; 6:e233526. [PMID: 36939705 PMCID: PMC10028484 DOI: 10.1001/jamanetworkopen.2023.3526] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/31/2023] [Indexed: 03/21/2023] Open
Abstract
This cohort study of US adults examines changes in physical activity following the onset of the COVID-19 pandemic.
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Affiliation(s)
- Stacy Desine
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hiral Master
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jeffrey Annis
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Andrew Hughes
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dan M. Roden
- Department of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Paul A. Harris
- Departments of Biomedical Informatics, Biomedical Engineering and Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Evan L. Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
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19
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Hedden SL, McClain J, Mandich A, Baskir R, Caulder MS, Denny JC, Hamlet MRJ, Prabhu Das I, McNeil Ford N, Lopez-Class M, Elmi A, Wallace R, Linkie A, Garriock HA. The Impact of COVID-19 on the All of Us Research Program. Am J Epidemiol 2023; 192:11-24. [PMID: 36205043 PMCID: PMC10144611 DOI: 10.1093/aje/kwac169] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 08/08/2022] [Accepted: 09/29/2022] [Indexed: 01/11/2023] Open
Abstract
The All of Us Research Program, a health and genetics epidemiologic data collection program, has been substantially affected by the coronavirus disease 2019 (COVID-19) pandemic. Although the program is highly digital in nature, certain aspects of the data collection require in-person interaction between staff and participants. Before the pandemic, the program was enrolling approximately 12,500 participants per month at more than 400 clinical sites. In March 2020, because of the pandemic, all in-person activity at program sites and by engagement partners was paused to develop processes and procedures for in-person activities that incorporated strict safety protocols. In addition, the program adopted new data collection methodologies to reduce the need for in-person activities. Through February 2022, a total of 224 clinical sites had reactivated in-person activity, and all enrollment and engagement partners have adopted new data collection methods that can be used remotely. As the COVID-19 pandemic persists, the program continues to require safety procedures for in-person activity and continues to generate and pilot methodologies that reduce risk and make it easier for participants to provide information.
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Affiliation(s)
- Sarra L Hedden
- Correspondence to Dr. Sarra Hedden, Division of Scientific Programs, All of Us Research Program, National Institutes of Health, 6710B Rockledge Drive, 4th Floor, Bethesda, MD 20817 (e-mail: )
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20
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Cronin RM, Feng X, Sulieman L, Mapes B, Garbett S, Able A, Hale R, Couper MP, Sansbury H, Ahmedani BK, Chen Q. Importance of missingness in baseline variables: A case study of the All of Us Research Program. PLoS One 2023; 18:e0285848. [PMID: 37200348 DOI: 10.1371/journal.pone.0285848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 05/02/2023] [Indexed: 05/20/2023] Open
Abstract
OBJECTIVE The All of Us Research Program collects data from multiple information sources, including health surveys, to build a national longitudinal research repository that researchers can use to advance precision medicine. Missing survey responses pose challenges to study conclusions. We describe missingness in All of Us baseline surveys. STUDY DESIGN AND SETTING We extracted survey responses between May 31, 2017, to September 30, 2020. Missing percentages for groups historically underrepresented in biomedical research were compared to represented groups. Associations of missing percentages with age, health literacy score, and survey completion date were evaluated. We used negative binomial regression to evaluate participant characteristics on the number of missed questions out of the total eligible questions for each participant. RESULTS The dataset analyzed contained data for 334,183 participants who submitted at least one baseline survey. Almost all (97.0%) of the participants completed all baseline surveys, and only 541 (0.2%) participants skipped all questions in at least one of the baseline surveys. The median skip rate was 5.0% of the questions, with an interquartile range (IQR) of 2.5% to 7.9%. Historically underrepresented groups were associated with higher missingness (incidence rate ratio (IRR) [95% CI]: 1.26 [1.25, 1.27] for Black/African American compared to White). Missing percentages were similar by survey completion date, participant age, and health literacy score. Skipping specific questions were associated with higher missingness (IRRs [95% CI]: 1.39 [1.38, 1.40] for skipping income, 1.92 [1.89, 1.95] for skipping education, 2.19 [2.09-2.30] for skipping sexual and gender questions). CONCLUSION Surveys in the All of Us Research Program will form an essential component of the data researchers can use to perform their analyses. Missingness was low in All of Us baseline surveys, but group differences exist. Additional statistical methods and careful analysis of surveys could help mitigate challenges to the validity of conclusions.
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Affiliation(s)
- Robert M Cronin
- Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Xiaoke Feng
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Lina Sulieman
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Brandy Mapes
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Shawn Garbett
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Ashley Able
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Ryan Hale
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Mick P Couper
- Survey Research Center, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Heather Sansbury
- National Institutes of Health, Bethesda, Maryland, United States of America
| | - Brian K Ahmedani
- Center for Health Policy & Health Services Research, Henry Ford Health, Detroit, Michigan, United States of America
| | - Qingxia Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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21
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Ng DQ, Jia S, Wisseh C, Cadiz C, Nguyen M, Lee J, McBane S, Nguyen L, Chan A, Hurley-Kim K. Sociodemographic characteristics differ across routine adult vaccine cohorts: An All of Us descriptive study. J Am Pharm Assoc (2003) 2022; 63:582-591.e20. [PMID: 36549934 DOI: 10.1016/j.japh.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND The National Institutes of Health All of Us (AoU) Research Program is currently building a database of 1million+ adult subjects. With it, we describe the characteristics of those with documented vaccinations. OBJECTIVES To describe the sociodemographic, health status, and lifestyle factors associated with vaccinations. METHODS This is a retrospective study involving data from the AoU program (R2020Q4R2, N = 315,297). Five vaccine cohorts [influenza, hepatitis B (HBV), pneumococcal <65 years old, pneumococcal ≥65 years old, and human papillomavirus (HPV)] were generated based on vaccination history. The influenza cohort comprised participants with documented influenza vaccinations in electronic health records (EHRs) from September 2017 to May 2018. Other vaccine cohorts comprised participants with ≥1 lifetime record(s) of vaccination documented in the EHR by December 2018. The vaccine cohorts were compared to the overall AoU cohort. Descriptive statistics were generated using EHR- and survey-based sociodemographic, health, and lifestyle information. The SAMBA (0.9.0) R package was utilized to adjust for EHR selection and outcome misclassification biases to infer sources of disparity for pneumococcal vaccinations in older adults. RESULTS Cohort counts were as follows: influenza (n = 15,346), HBV (n = 6323), pneumococcal <65 (n = 15,217), pneumococcal ≥65 (n = 15,100), and HPV (n = 2125). All vaccine cohorts had higher proportions of White and non-Hispanic/Latino participants compared to the overall AoU cohort. The largest differences were found in pneumococcal age ≥65, with 80.2% White participants compared to 52.9% in the overall study population. Multivariable analysis revealed that race/ethnic disparities in pneumococcal vaccination among older adults were explained by biological sex, income, health insurance, and education-related variables. CONCLUSION Racial, ethnic, education, and income characteristics differ across the vaccine cohorts among AoU participants. These findings inform future utilization of large health databases in vaccine epidemiology research and emphasize the need for more targeted interventions that address differences in vaccine uptake.
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22
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Ramirez AH, Sulieman L, Schlueter DJ, Halvorson A, Qian J, Ratsimbazafy F, Loperena R, Mayo K, Basford M, Deflaux N, Muthuraman KN, Natarajan K, Kho A, Xu H, Wilkins C, Anton-Culver H, Boerwinkle E, Cicek M, Clark CR, Cohn E, Ohno-Machado L, Schully SD, Ahmedani BK, Argos M, Cronin RM, O’Donnell C, Fouad M, Goldstein DB, Greenland P, Hebbring SJ, Karlson EW, Khatri P, Korf B, Smoller JW, Sodeke S, Wilbanks J, Hentges J, Mockrin S, Lunt C, Devaney SA, Gebo K, Denny JC, Carroll RJ, Glazer D, Harris PA, Hripcsak G, Philippakis A, Roden DM, Ahmedani B, Cole Johnson CD, Ahsan H, Antoine-LaVigne D, Singleton G, Anton-Culver H, Topol E, Baca-Motes K, Steinhubl S, Wade J, Begale M, Jain P, Sutherland S, Lewis B, Korf B, Behringer M, Gharavi AG, Goldstein DB, Hripcsak G, Bier L, Boerwinkle E, Brilliant MH, Murali N, Hebbring SJ, Farrar-Edwards D, Burnside E, Drezner MK, Taylor A, Channamsetty V, Montalvo W, Sharma Y, Chinea C, Jenks N, Cicek M, Thibodeau S, Holmes BW, Schlueter E, Collier E, Winkler J, Corcoran J, D’Addezio N, Daviglus M, Winn R, Wilkins C, Roden D, Denny J, Doheny K, Nickerson D, Eichler E, Jarvik G, Funk G, Philippakis A, Rehm H, Lennon N, Kathiresan S, Gabriel S, Gibbs R, Gil Rico EM, Glazer D, Grand J, Greenland P, Harris P, Shenkman E, Hogan WR, Igho-Pemu P, Pollan C, Jorge M, Okun S, Karlson EW, Smoller J, Murphy SN, Ross ME, Kaushal R, Winford E, Wallace F, Khatri P, Kheterpal V, Ojo A, Moreno FA, Kron I, Peterson R, Menon U, Lattimore PW, Leviner N, Obedin-Maliver J, Lunn M, Malik-Gagnon L, Mangravite L, Marallo A, Marroquin O, Visweswaran S, Reis S, Marshall G, McGovern P, Mignucci D, Moore J, Munoz F, Talavera G, O'Connor GT, O'Donnell C, Ohno-Machado L, Orr G, Randal F, Theodorou AA, Reiman E, Roxas-Murray M, Stark L, Tepp R, Zhou A, Topper S, Trousdale R, Tsao P, Weidman L, Weiss ST, Wellis D, Whittle J, Wilson A, Zuchner S, Zwick ME. The All of Us Research Program: Data quality, utility, and diversity. PATTERNS 2022; 3:100570. [PMID: 36033590 PMCID: PMC9403360 DOI: 10.1016/j.patter.2022.100570] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 03/30/2022] [Accepted: 07/14/2022] [Indexed: 11/05/2022]
Abstract
The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools. The All of Us Research Program has released data for over 315,000 participants Demonstration projects support the utility and validity of the All of Us dataset The cloud-based Researcher Workbench provides secure, low-cost compute power
The engagement of participants in the research process and broad availability of data to diverse researchers are essential elements in building precision medicine equitably available for all. The NIH has established the ambitious All of Us Research Program to build one of the most diverse health databases in history with tools to support research to improve human health. Here, we present the initial launch of the Researcher Workbench with data types including surveys, physical measurements, and electronic health record data with validation studies to support researcher use of this novel platform. Broad access for researchers to data like these is a critical step in returning value to participants seeking to support the advancement of precision medicine and improved health for all.
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23
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Fulkerson PC, Lussier SJ, Bendixsen CG, Castina SM, Gebretsadik T, Marlin JS, Russell PB, Seibold MA, Everman JL, Moore CM, Snyder BM, Thompson K, Tregoning GS, Wellford S, Arbes SJ, Bacharier LB, Calatroni A, Camargo CA, Dupont WD, Furuta GT, Gruchalla RS, Gupta RS, Hershey GK, Jackson DJ, Johnson CC, Kattan M, Liu AH, Murrison L, O’Connor GT, Phipatanakul W, Rivera-Spoljaric K, Rothenberg ME, Seroogy CM, Teach SJ, Zoratti EM, Togias A, Hartert TV. Human Epidemiology and RespOnse to SARS-CoV-2 (HEROS): Objectives, Design and Enrollment Results of a 12-City Remote Observational Surveillance Study of Households with Children using Direct-to-Participant Methods. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.07.09.22277457. [PMID: 35860216 PMCID: PMC9298141 DOI: 10.1101/2022.07.09.22277457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The Human Epidemiology and Response to SARS-CoV-2 (HEROS) is a prospective multi-city 6-month incidence study which was conducted from May 2020-February 2021. The objectives were to identify risk factors for SARS-CoV-2 infection and household transmission among children and people with asthma and allergic diseases, and to use the host nasal transcriptome sampled longitudinally to understand infection risk and sequelae at the molecular level. To overcome challenges of clinical study implementation due to the coronavirus pandemic, this surveillance study used direct-to-participant methods to remotely enroll and prospectively follow eligible children who are participants in other NIH-funded pediatric research studies and their household members. Households participated in weekly surveys and biweekly nasal sampling regardless of symptoms. The aim of this report is to widely share the methods and study instruments and to describe the rationale, design, execution, logistics and characteristics of a large, observational, household-based, remote cohort study of SARS-CoV-2 infection and transmission in households with children. The study enrolled a total of 5,598 individuals, including 1,913 principal participants (children), 1,913 primary caregivers, 729 secondary caregivers and 1,043 other household children. This study was successfully implemented without necessitating any in-person research visits and provides an approach for rapid execution of clinical research.
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Affiliation(s)
| | | | - Casper G. Bendixsen
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, WI, USA
| | | | - Tebeb Gebretsadik
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jessica S. Marlin
- Vanderbilt Coordinating Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Patty B. Russell
- Department of Medicine, Center for Asthma Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Max A. Seibold
- Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA
- Department of Pediatrics, National Jewish Health, Denver, CO, USA
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado School of Medicine; Aurora, CO, USA
| | - Jamie L. Everman
- Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA
| | - Camille M. Moore
- Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA
- Department of Biomedical Research, National Jewish Health; Denver, CO, USA
- Department of Biostatistics and Informatics, University of Colorado; Denver, CO, USA
| | - Brittney M. Snyder
- Department of Medicine, Center for Asthma Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kathy Thompson
- National Institute of Allergy and Infectious Diseases, Rockville, MD, USA
| | - George S. Tregoning
- Vanderbilt Coordinating Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Leonard B. Bacharier
- Department of Medicine, Center for Asthma Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Carlos A. Camargo
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - William D. Dupont
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Glenn T. Furuta
- Digestive Health Institute, Children’s Hospital Colorado and Section of Pediatric Gastroenterology, Hepatology and Nutrition, Gastrointestinal Eosinophilic Diseases Program, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Ruchi S. Gupta
- Ann & Robert H. Lurie Hospital of Chicago & Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Gurjit Khurana Hershey
- Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Daniel J. Jackson
- University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | | | - Meyer Kattan
- Columbia University Medical Center, New York, NY, USA
| | - Andrew H. Liu
- Breathing Institute, Children’s Hospital Colorado and Section of Pediatric Pulmonary & Sleep Medicine, University of Colorado School of Medicine, Aurora CO, USA
| | - Liza Murrison
- Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | | | | | | | - Marc E. Rothenberg
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | | | | | - Alkis Togias
- National Institute of Allergy and Infectious Diseases, Rockville, MD, USA
| | - Tina V. Hartert
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Asthma Research, Vanderbilt University Medical Center, Nashville, TN, USA
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Lee TC, Radha Saseendrakumar B, Nayak M, Chan AX, McDermott JJ, Shahrvini B, Ye GY, Sitapati AM, Nebeker C, Baxter SL. Social Determinants of Health Data Availability for Patients with Eye Conditions. OPHTHALMOLOGY SCIENCE 2022; 2:100151. [PMID: 35662804 PMCID: PMC9162036 DOI: 10.1016/j.xops.2022.100151] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/23/2022] [Accepted: 03/30/2022] [Indexed: 11/23/2022]
Abstract
Purpose To quantify and characterize social determinants of health (SDoH) data coverage using single-center electronic health records (EHRs) and the National Institutes of Health All of Us research program. Design Retrospective cohort study from June 2014 through June 2021. Participants Adults 18 years of age or older with a diagnosis of diabetic retinopathy, glaucoma, cataracts, or age-related macular degeneration. Methods For All of Us, research participants completed online survey forms as part of a nationwide prospective cohort study. In local EHRs, patients were selected based on diagnosis codes. Main Outcome Measures Social determinants of health data coverage, characterized by the proportion of each disease cohort with available data regarding demographics and socioeconomic factors. Results In All of Us, we identified 23 806 unique adult patients, of whom 2246 had a diagnosis of diabetic retinopathy, 13 448 had a diagnosis of glaucoma, 6634 had a diagnosis of cataracts, and 1478 had a diagnosis of age-related macular degeneration. Survey completion rates were high (99.5%-100%) across all cohorts for demographic information, overall health, income, education, and lifestyle. However, health care access (12.7%-29.4%), housing (0.7%-1.1%), social isolation (0.2%-0.3%), and food security (0-0.1%) showed significantly lower response rates. In local EHRs, we identified 80 548 adult patients, of whom 6616 had a diagnosis of diabetic retinopathy, 26 793 had a diagnosis of glaucoma, 40 427 had a diagnosis of cataracts, and 6712 had a diagnosis of age-related macular degeneration. High data coverage was found across all cohorts for variables related to tobacco use (82.84%-89.07%), alcohol use (77.45%-83.66%), and intravenous drug use (84.76%-93.14%). However, low data coverage (< 50% completion) was found for all other variables, including education, finances, social isolation, stress, physical activity, food insecurity, and transportation. We used chi-square testing to assess whether the data coverage varied across different disease cohorts and found that all fields varied significantly (P < 0.001). Conclusions The limited and highly variable data coverage in both local EHRs and All of Us highlights the need for researchers and providers to develop SDoH data collection strategies and to assemble complete datasets.
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Affiliation(s)
- Terrence C. Lee
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
| | - Bharanidharan Radha Saseendrakumar
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
| | - Mahasweta Nayak
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
| | - Alison X. Chan
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
| | - John J. McDermott
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
| | - Bita Shahrvini
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
| | - Gordon Y. Ye
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
| | - Amy M. Sitapati
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
- Department of Medicine, University of California, San Diego, La Jolla, California
| | - Camille Nebeker
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California
| | - Sally L. Baxter
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California
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25
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Hull LE, Natarajan P. Self-rated family health history knowledge among All of Us program participants. Genet Med 2022; 24:955-961. [PMID: 35058155 PMCID: PMC8995381 DOI: 10.1016/j.gim.2021.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/12/2021] [Accepted: 12/14/2021] [Indexed: 10/19/2022] Open
Abstract
PURPOSE Disparities in access to genetics services are well-documented. Family health history is routinely used to determine whether patients should be screened for heritable conditions. We sought to explore variation in levels of self-rated family health history knowledge as a possible contributer to this disparity. METHODS We performed a cross-sectional analysis of survey data from the All of Us Research Program. We compared the characteristics of participants who reported "None," "Some", and "A lot" of family health history knowledge using multinomial logistic regression. RESULTS Self-rated family health history data were available for 116,799 participants. A minority of survey participants (37%) endorsed "A lot" of knowledge about their family health history (n = 43,661). Most participants (60%) endorsed "Some" family health history knowledge (n = 69,914) and 3% (n = 3224) endorsed "None." In adjusted analyses, those who indicated "Some" family health history knowledge or "None" were more likely to be assigned male sex at birth, identify as possible gender and sexual minorities, have a self-reported race other than White, have a lower household annual income (<$25,000), or report lower educational attainment ( CONCLUSION Family health history knowledge may be limited, especially among traditionally underserved populations.
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Affiliation(s)
- Leland E Hull
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA.
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, MA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA; Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA.
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26
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McDermott JJ, Lee TC, Chan AX, Ye GY, Shahrvini B, Saseendrakumar BR, Ferreyra H, Nudleman E, Baxter SL. Novel Association between Opioid Use and Increased Risk of Retinal Vein Occlusion Using the National Institutes of Health All of Us Research Program. OPHTHALMOLOGY SCIENCE 2022; 2:100099. [PMID: 35721456 PMCID: PMC9205363 DOI: 10.1016/j.xops.2021.100099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 12/19/2022]
Abstract
Purpose To assess for risk factors for retinal vein occlusion (RVO) among participants in the National Institutes of Health All of Us database, particularly social risk factors that have not been well studied, including substance use. Design Retrospective, case-control study. Participants Data were extracted for 380 adult participants with branch retinal vein occlusion (BRVO), 311 adult participants with central retinal vein occlusion (CRVO), and 1520 controls sampled among 311 640 adult participants in the All of Us database. Methods Data were extracted regarding demographics, comorbidities, income, housing, insurance, and substance use. Opioid use was defined by relevant diagnosis and prescription codes, with prescription use > 30 days. Controls were sampled at a 4:1 control to case ratio from a pool of individuals aged > 18 years without a diagnosis of RVO and proportionally matched to the demographic distribution of the 2019 U.S. census. Multivariable logistic regression identified medical and social determinants significantly associated with BRVO or CRVO. Statistical significance was defined as P < 0.05. Main Outcome Measure Development of BRVO or CRVO based on diagnosis codes. Results Among patients with BRVO, the mean (standard deviation) age was 70.1 (10.5) years. The majority (53.7%) were female. Cases were diverse; 23.7% identified as Black, and 18.4% identified as Hispanic or Latino. Medical risk factors including glaucoma (odds ratio [OR], 3.29; 95% confidence interval [CI], 2.22-4.90; P < 0.001), hypertension (OR, 2.15; 95% CI, 1.49-3.11; P < 0.001), and diabetes mellitus (OR, 1.68; 95% CI, 1.18-2.38; P = 0.004) were re-demonstrated to be associated with BRVO. Black race (OR, 2.64; 95% CI, 1.22-6.05; P = 0.017) was found to be associated with increased risk of BRVO. Past marijuana use (OR, 0.68; 95% CI, 0.50-0.92; P = 0.013) was associated with decreased risk of BRVO; however, opioid use (OR, 1.98; 95% CI, 1.41-2.78; P < 0.001) was associated with a significantly increased risk of BRVO. Similar associations were found for CRVO. Conclusions Understanding RVO risk factors is important for primary prevention and improvement in visual outcomes. This study capitalizes on the diversity and scale of a novel nationwide database to elucidate a previously uncharacterized association between RVO and opioid use.
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Affiliation(s)
- John J. McDermott
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Terrence C. Lee
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Alison X. Chan
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Gordon Y. Ye
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Bita Shahrvini
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Bharanidharan Radha Saseendrakumar
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Henry Ferreyra
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
| | - Eric Nudleman
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
| | - Sally L. Baxter
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
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Sulieman L, Cronin RM, Carroll RJ, Natarajan K, Marginean K, Mapes B, Roden D, Harris P, Ramirez A. OUP accepted manuscript. J Am Med Inform Assoc 2022; 29:1131-1141. [PMID: 35396991 PMCID: PMC9196700 DOI: 10.1093/jamia/ocac046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 02/18/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE A participant's medical history is important in clinical research and can be captured from electronic health records (EHRs) and self-reported surveys. Both can be incomplete, EHR due to documentation gaps or lack of interoperability and surveys due to recall bias or limited health literacy. This analysis compares medical history collected in the All of Us Research Program through both surveys and EHRs. MATERIALS AND METHODS The All of Us medical history survey includes self-report questionnaire that asks about diagnoses to over 150 medical conditions organized into 12 disease categories. In each category, we identified the 3 most and least frequent self-reported diagnoses and retrieved their analogues from EHRs. We calculated agreement scores and extracted participant demographic characteristics for each comparison set. RESULTS The 4th All of Us dataset release includes data from 314 994 participants; 28.3% of whom completed medical history surveys, and 65.5% of whom had EHR data. Hearing and vision category within the survey had the highest number of responses, but the second lowest positive agreement with the EHR (0.21). The Infectious disease category had the lowest positive agreement (0.12). Cancer conditions had the highest positive agreement (0.45) between the 2 data sources. DISCUSSION AND CONCLUSION Our study quantified the agreement of medical history between 2 sources-EHRs and self-reported surveys. Conditions that are usually undocumented in EHRs had low agreement scores, demonstrating that survey data can supplement EHR data. Disagreement between EHR and survey can help identify possible missing records and guide researchers to adjust for biases.
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Affiliation(s)
- Lina Sulieman
- Corresponding Author: Lina Sulieman, PhD, Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave, Suite 1475, Nashville, TN 37202, USA;
| | - Robert M Cronin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Kayla Marginean
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Brandy Mapes
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Dan Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Paul Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Andrea Ramirez
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Office of data and analytics, All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
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Cronin RM, Halvorson AE, Springer C, Feng X, Sulieman L, Loperena-Cortes R, Mayo K, Carroll RJ, Chen Q, Ahmedani BK, Karnes J, Korf B, O’Donnell CJ, Qian J, Ramirez AH. Comparison of family health history in surveys vs electronic health record data mapped to the observational medical outcomes partnership data model in the All of Us Research Program. J Am Med Inform Assoc 2021; 28:695-703. [PMID: 33404595 PMCID: PMC7973437 DOI: 10.1093/jamia/ocaa315] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/15/2020] [Accepted: 11/14/2020] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVE Family health history is important to clinical care and precision medicine. Prior studies show gaps in data collected from patient surveys and electronic health records (EHRs). The All of Us Research Program collects family history from participants via surveys and EHRs. This Demonstration Project aims to evaluate availability of family health history information within the publicly available data from All of Us and to characterize the data from both sources. MATERIALS AND METHODS Surveys were completed by participants on an electronic portal. EHR data was mapped to the Observational Medical Outcomes Partnership data model. We used descriptive statistics to perform exploratory analysis of the data, including evaluating a list of medically actionable genetic disorders. We performed a subanalysis on participants who had both survey and EHR data. RESULTS There were 54 872 participants with family history data. Of those, 26% had EHR data only, 63% had survey only, and 10.5% had data from both sources. There were 35 217 participants with reported family history of a medically actionable genetic disorder (9% from EHR only, 89% from surveys, and 2% from both). In the subanalysis, we found inconsistencies between the surveys and EHRs. More details came from surveys. When both mentioned a similar disease, the source of truth was unclear. CONCLUSIONS Compiling data from both surveys and EHR can provide a more comprehensive source for family health history, but informatics challenges and opportunities exist. Access to more complete understanding of a person's family health history may provide opportunities for precision medicine.
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Affiliation(s)
- Robert M Cronin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Alese E Halvorson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Cassie Springer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Xiaoke Feng
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lina Sulieman
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Roxana Loperena-Cortes
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kelsey Mayo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Qingxia Chen
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Brian K Ahmedani
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA
| | - Jason Karnes
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tuscon, Arizona, USA
| | - Bruce Korf
- Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Christopher J O’Donnell
- Department of Medicine, Veterans Administration Boston Healthcare System, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jun Qian
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Andrea H Ramirez
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Sullivan MH, Sommer EC, Schlundt D, Shinall JB, Haws KL, Bonnet KR, Burgess LE, Po'e EK, Barkin SL. Development of Brief Child Nutrition and Physical Activity Screening Questions for Electronic Health Record Use. Child Obes 2020; 16:488-498. [PMID: 32721216 PMCID: PMC7575345 DOI: 10.1089/chi.2020.0088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Background: To develop and test brief nutrition and physical activity screening questions for children ages 2-11 years that could be used as a pragmatic screening tool to tailor counseling, track behavior change, and improve population health. Methods: A literature review identified existing validated questions for nutrition and physical activity behaviors in children ages 2-11 years. Response variation and concurrent validity was then assessed using a mechanical Turk (MTurk) crowdsourcing survey employed in 2018. Additionally, cognitive interviews were conducted with both providers and parents of 2- to 11-year-old children to assess screening question priorities and perceived added value. Results: The literature review identified 260 questions, and 20 items were selected with expert guidance based on prespecified criteria (simplicity and potential utility for both clinical interactions during a well-child exam and population health). MTurk surveys yielded 1147 records that met eligibility criteria and revealed 6 items that had adequate response variation and were significantly correlated with parent-reported child BMI or BMI percentile, exhibiting concurrent validity. Cognitive interviews with 10 providers and 20 parents uncovered themes regarding suggestions and usability of the questions, eliminating 3 items due to parent and provider concerns. Combining quantitative and qualitative results, 3 nutrition and physical activity screening items remained for inclusion into the electronic health record (EHR). Conclusions: The three-pronged validation methodology produced a brief, 3-item child nutrition and physical activity screener to incorporate in the EHR, where it can inform tailored counseling for well-child care and be used to test associations with population health outcomes.
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Affiliation(s)
| | - Evan C. Sommer
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David Schlundt
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | | | - Kelly L. Haws
- Vanderbilt University Owen Graduate School of Management, Nashville, TN, USA
| | | | - Laura E. Burgess
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eli K. Po'e
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shari L. Barkin
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
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Opening doors to clinical trial participation among Hispanics: Lessons learned from the Spanish translation of ResearchMatch. J Clin Transl Sci 2020; 5:e46. [PMID: 33948268 PMCID: PMC8057389 DOI: 10.1017/cts.2020.539] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Introduction: Clinical trial participation among US Hispanics remains low, despite a significant effort by research institutions nationwide. ResearchMatch, a national online platform, has matched 113,372 individuals interested in participating in research with studies conducted by 8778 researchers. To increase accessibility to Spanish speakers, we translated the ResearchMatch platform into Spanish by implementing tenets of health literacy and respecting linguistic and cultural diversity across the US Hispanic population. We describe this multiphase process, preliminary results, and lessons learned. Methods: Translation of the ResearchMatch site consisted of several activities including: (1) improving the English language site’s reading level, removing jargon, and using plain language; (2) obtaining a professional Spanish translation of the site and incorporating iterative revisions by a panel of bilingual community members from diverse Hispanic backgrounds; (3) technical development and launch; and (4) initial promotion. Results: The Spanish language version was launched in August 2018, after 11 months of development. Community input improved the initial translation, and early registration and use by researchers demonstrate the utility of Spanish ResearchMatch in engaging Hispanics. Over 12,500 volunteers in ResearchMatch self-identify as Hispanic (8.5%). From August 2018 to March 2020, 162 volunteers registered through the Spanish language version of ResearchMatch, and over 500 new and existing volunteers have registered a preference to receive messages about studies in Spanish. Conclusion: By applying the principles of health literacy and cultural competence, we developed a Spanish language translation of ResearchMatch. Our multiphase approach to translation included key principles of community engagement that should prove informative to other multilingual web-based platforms.
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Batty GD, Gale CR, Kivimäki M, Deary IJ, Bell S. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ 2020; 368:m131. [PMID: 32051121 PMCID: PMC7190071 DOI: 10.1136/bmj.m131] [Citation(s) in RCA: 313] [Impact Index Per Article: 78.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/16/2019] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To compare established associations between risk factors and mortality in UK Biobank, a study with an exceptionally low rate of response to its baseline survey, against those from representative studies that have conventional response rates. DESIGN Prospective cohort study alongside individual participant meta-analysis of other cohort studies. SETTING United Kingdom. PARTICIPANTS Analytical sample of 499 701 people (response rate 5.5%) in analyses in UK Biobank; pooled data from the Health Surveys for England (HSE) and the Scottish Health Surveys (SHS), including 18 studies and 89 895 people (mean response rate 68%). Both study populations were linked to the same nationwide mortality registries, and the baseline age range was aligned at 40-69 years. MAIN OUTCOME MEASURE Death from cardiovascular disease, selected malignancies, and suicide. To quantify the difference between hazard ratios in the two studies, a ratio of the hazard ratios was used with HSE-SHS as the referent. RESULTS Risk factor levels and mortality rates were typically more favourable in UK Biobank participants relative to the HSE-SHS consortium. For the associations between risk factors and mortality endpoints, however, close agreement was seen between studies. Based on 14 288 deaths during an average of 7.0 years of follow-up in UK Biobank and 7861 deaths over 10 years of mortality surveillance in HSE-SHS, for cardiovascular disease mortality, for instance, the age and sex adjusted hazard ratio for ever having smoked cigarettes (versus never) was 2.04 (95% confidence interval 1.87 to 2.24) in UK Biobank and 1.99 (1.78 to 2.23) in HSE-SHS, yielding a ratio of hazard ratios close to unity (1.02, 0.88 to 1.19). The overall pattern of agreement between studies was essentially unchanged when results were compared separately by sex and when baseline years and censoring dates were aligned. CONCLUSION Despite a very low response rate, risk factor associations in the UK Biobank seem to be generalisable.
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Affiliation(s)
- G David Batty
- Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK
- School of Biological and Population Health Sciences, Oregon State University, Corvallis, OR, USA
| | - Catharine R Gale
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Steven Bell
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, Cambridge, UK
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Mapes BM, Foster CS, Kusnoor SV, Epelbaum MI, AuYoung M, Jenkins G, Lopez-Class M, Richardson-Heron D, Elmi A, Surkan K, Cronin RM, Wilkins CH, Pérez-Stable EJ, Dishman E, Denny JC, Rutter JL. Diversity and inclusion for the All of Us research program: A scoping review. PLoS One 2020; 15:e0234962. [PMID: 32609747 PMCID: PMC7329113 DOI: 10.1371/journal.pone.0234962] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/01/2020] [Indexed: 12/21/2022] Open
Abstract
The All of Us Research Program (All of Us) is a national effort to accelerate health research by exploring the relationship between lifestyle, environment, and genetics. It is set to become one of the largest research efforts in U.S. history, aiming to build a national resource of data from at least one million participants. All of Us aims to address the need for more diversity in research and set the stage for that diversity to be leveraged in precision medicine research to come. This paper describes how the program assessed demographic characteristics of participants who have enrolled in other U.S. biomedical research cohorts to better understand which groups are traditionally represented or underrepresented in biomedical research. We 1) reviewed the enrollment characteristics of national cohort studies like All of Us, and 2) surveyed the literature, focusing on key diversity categories essential to the program's enrollment aims. Based on these efforts, All of Us emphasizes enrollment of racial and ethnic minorities, and has formally designated the following additional groups as historically underrepresented: individuals-with inadequate access to medical care; under the age of 18 or over 65; with an annual household income at or below 200% of the federal poverty level; who have a cognitive or physical disability; have less than a high school education or equivalent; are intersex; identify as a sexual or gender minority; or live in rural or non-metropolitan areas. Research accounting for wider demographic variability is critical. Only by ensuring diversity and by addressing the very barriers that limit it, can we position All of Us to better understand and tackle health disparities.
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Affiliation(s)
- Brandy M. Mapes
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- * E-mail: (BMM); (JLR)
| | - Christopher S. Foster
- Office of the Director, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sheila V. Kusnoor
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States of American
| | - Marcia I. Epelbaum
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States of American
| | - Mona AuYoung
- Scripps Whittier Diabetes Institute, Scripps Health, San Diego, California, United States of American
| | - Gwynne Jenkins
- Office of the Director, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Maria Lopez-Class
- Office of the Director, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Dara Richardson-Heron
- Office of the Director, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Ahmed Elmi
- Office of the Director, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Karl Surkan
- Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America and All of Us Research Program Participant Representative
| | - Robert M. Cronin
- Department of Biomedical Informatics, Medicine, and Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Consuelo H. Wilkins
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Eliseo J. Pérez-Stable
- National Institute on Minority Health and Health Disparities, Bethesda, Maryland, United States of America
| | - Eric Dishman
- Office of the Director, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Joshua C. Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Joni L. Rutter
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (BMM); (JLR)
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Denny JC, Rutter JL, Goldstein DB, Philippakis A, Smoller JW, Jenkins G, Dishman E. The "All of Us" Research Program. N Engl J Med 2019; 381:668-676. [PMID: 31412182 PMCID: PMC8291101 DOI: 10.1056/nejmsr1809937] [Citation(s) in RCA: 822] [Impact Index Per Article: 164.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Knowledge gained from observational cohort studies has dramatically advanced the prevention and treatment of diseases. Many of these cohorts, however, are small, lack diversity, or do not provide comprehensive phenotype data. The All of Us Research Program plans to enroll a diverse group of at least 1 million persons in the United States in order to accelerate biomedical research and improve health. The program aims to make the research results accessible to participants, and it is developing new approaches to generate, access, and make data broadly available to approved researchers. All of Us opened for enrollment in May 2018 and currently enrolls participants 18 years of age or older from a network of more than 340 recruitment sites. Elements of the program protocol include health questionnaires, electronic health records (EHRs), physical measurements, the use of digital health technology, and the collection and analysis of biospecimens. As of July 2019, more than 175,000 participants had contributed biospecimens. More than 80% of these participants are from groups that have been historically underrepresented in biomedical research. EHR data on more than 112,000 participants from 34 sites have been collected. The All of Us data repository should permit researchers to take into account individual differences in lifestyle, socioeconomic factors, environment, and biologic characteristics in order to advance precision diagnosis, prevention, and treatment.
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Affiliation(s)
- Joshua C Denny
- From the Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville (J.C.D.); the National Center for Advancing Translational Sciences (J.L.R.) and the All of Us Research Program (G.J., E.D.), National Institutes of Health, Bethesda, MD; the Institute for Genomic Medicine and Department of Neurology, Columbia University Irving Medical Center, New York (D.B.G.); and the Broad Institute, Cambridge (A.P., J.W.S.), and the Center for Genomic Medicine and Department of Psychiatry, Massachusetts General Hospital, Boston (J.W.S.) - both in Massachusetts
| | - Joni L Rutter
- From the Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville (J.C.D.); the National Center for Advancing Translational Sciences (J.L.R.) and the All of Us Research Program (G.J., E.D.), National Institutes of Health, Bethesda, MD; the Institute for Genomic Medicine and Department of Neurology, Columbia University Irving Medical Center, New York (D.B.G.); and the Broad Institute, Cambridge (A.P., J.W.S.), and the Center for Genomic Medicine and Department of Psychiatry, Massachusetts General Hospital, Boston (J.W.S.) - both in Massachusetts
| | - David B Goldstein
- From the Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville (J.C.D.); the National Center for Advancing Translational Sciences (J.L.R.) and the All of Us Research Program (G.J., E.D.), National Institutes of Health, Bethesda, MD; the Institute for Genomic Medicine and Department of Neurology, Columbia University Irving Medical Center, New York (D.B.G.); and the Broad Institute, Cambridge (A.P., J.W.S.), and the Center for Genomic Medicine and Department of Psychiatry, Massachusetts General Hospital, Boston (J.W.S.) - both in Massachusetts
| | - Anthony Philippakis
- From the Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville (J.C.D.); the National Center for Advancing Translational Sciences (J.L.R.) and the All of Us Research Program (G.J., E.D.), National Institutes of Health, Bethesda, MD; the Institute for Genomic Medicine and Department of Neurology, Columbia University Irving Medical Center, New York (D.B.G.); and the Broad Institute, Cambridge (A.P., J.W.S.), and the Center for Genomic Medicine and Department of Psychiatry, Massachusetts General Hospital, Boston (J.W.S.) - both in Massachusetts
| | - Jordan W Smoller
- From the Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville (J.C.D.); the National Center for Advancing Translational Sciences (J.L.R.) and the All of Us Research Program (G.J., E.D.), National Institutes of Health, Bethesda, MD; the Institute for Genomic Medicine and Department of Neurology, Columbia University Irving Medical Center, New York (D.B.G.); and the Broad Institute, Cambridge (A.P., J.W.S.), and the Center for Genomic Medicine and Department of Psychiatry, Massachusetts General Hospital, Boston (J.W.S.) - both in Massachusetts
| | - Gwynne Jenkins
- From the Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville (J.C.D.); the National Center for Advancing Translational Sciences (J.L.R.) and the All of Us Research Program (G.J., E.D.), National Institutes of Health, Bethesda, MD; the Institute for Genomic Medicine and Department of Neurology, Columbia University Irving Medical Center, New York (D.B.G.); and the Broad Institute, Cambridge (A.P., J.W.S.), and the Center for Genomic Medicine and Department of Psychiatry, Massachusetts General Hospital, Boston (J.W.S.) - both in Massachusetts
| | - Eric Dishman
- From the Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville (J.C.D.); the National Center for Advancing Translational Sciences (J.L.R.) and the All of Us Research Program (G.J., E.D.), National Institutes of Health, Bethesda, MD; the Institute for Genomic Medicine and Department of Neurology, Columbia University Irving Medical Center, New York (D.B.G.); and the Broad Institute, Cambridge (A.P., J.W.S.), and the Center for Genomic Medicine and Department of Psychiatry, Massachusetts General Hospital, Boston (J.W.S.) - both in Massachusetts
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