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Hoverd E, Effiom V, Gravesande D, Hollowood L, Kelly T, Mukuka E, Owatemi T, Sargeant I, Ward S, Spencer R, Edge D, Dale J, Staniszewska S. Understanding the inclusion and participation of adults from Black African Diaspora Communities (BAFDC) in health and care research in the UK: a realist review protocol. BMJ Open 2024; 14:e082564. [PMID: 38553075 PMCID: PMC10982753 DOI: 10.1136/bmjopen-2023-082564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
INTRODUCTION People from Black African Diaspora Communities (BAFDC) experience poorer health outcomes, have many long-term conditions and are persistently under-represented in health and care research. There is limited focus on programmes, or interventions that support inclusion and participation of people from BAFDC in research. Through coproduction, this realist review seeks to provide a programme theory explaining what context and mechanisms may be required, to produce outcomes that facilitate inclusion and participation for people from BAFDC in health and care research, in the UK. METHODS AND ANALYSIS A group of people from BAFDC with lived and professional experience, representing all levels of the health and care research system, will coproduce a realist review with a team of African-Caribbean, white British and white British of Polish origin health and care researchers. They will follow Pawson's five steps: (1) shaping the scope of the review; (2) searching for evidence; (3) document selection and appraisal; (4) data extraction and (5) data synthesis. The coproduction group will help to map the current landscape, identifying key issues that may inhibit or facilitate inclusion. Data will be extracted, analysed and synthesised following realist logic analysis, identifying and explaining how context and mechanisms are conceptualised in the literature and the types of contextual factors that exist and impact on inclusion and participation. Findings will be reported in accordance with Realist and Meta-narrative Evidence Synthesis Evolving Standards . ETHICS AND DISSEMINATION The coproduction group will agree an ethical approach considering accountability, responsibility and power dynamics, by establishing a terms of reference, taking a reflexive approach and coproducing an ethical framework. Findings will be disseminated to BAFDC and the research community through arts-based methods, peer-reviewed publications and conference presentations, agreeing a coproduced strategy for dissemination. Ethical review is not required. PROSPERO REGISTRATION NUMBER CRD42024517124.
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Affiliation(s)
- Eleanor Hoverd
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Violet Effiom
- NIHR Clinical Research Network West Midlands, Coventry, UK
| | | | | | | | | | | | | | | | - Rachel Spencer
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Dawn Edge
- University of Manchester, Manchester, UK
| | - Jeremy Dale
- Warwick Medical School, University of Warwick, Coventry, UK
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Craven CK, Highfield L, Basit M, Bernstam EV, Choi BY, Ferrer RL, Gelfond JA, Pruitt SL, Kannan V, Shireman PK, Spratt H, Morales KJT, Wang CP, Wang Z, Zozus MN, Sankary EC, Schmidt S. Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration. J Clin Transl Sci 2024; 8:e17. [PMID: 38384919 PMCID: PMC10880009 DOI: 10.1017/cts.2024.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/12/2023] [Accepted: 12/31/2023] [Indexed: 02/23/2024] Open
Abstract
Introduction The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.
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Affiliation(s)
- Catherine K. Craven
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Division of Clinical Research Informatics, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Linda Highfield
- University of Texas Health Science Center at Houston, School of Public Health, San Antonio, TX, USA
| | - Mujeeb Basit
- Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Elmer V. Bernstam
- D. Bradley McWilliams School of Biomedical Informatics and Division of General Internal Medicine, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Byeong Yeob Choi
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Biostatistics Division, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Robert L. Ferrer
- Department of Community and Family Medicine, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Jonathan A. Gelfond
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Biostatistics Division, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Sandi L. Pruitt
- University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, USA
| | | | - Paula K. Shireman
- Department of Surgery, Division of Vascular and Endovascular Surgery, Texas A&M University School of Medicine, Bryan, TX, USA
- Departments of Primary Care & Rural Medicine and Medical Physiology, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, University of Texas Medical Branch Galveston, Galveston, TX, USA
| | - Kayla J. Torres Morales
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Division of Clinical Research Informatics, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Chen-Pin Wang
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Biostatistics Division, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Zhan Wang
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Division of Clinical Research Informatics, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Meredith N. Zozus
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Division of Clinical Research Informatics, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Edward C. Sankary
- University of Texas Health Science Center San Antonio, UT Health Physicians, San Antonio, TX, USA
| | - Susanne Schmidt
- Department of Population Health Sciences, University of Texas Health Science Center San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
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Espinoza JC, Sehgal S, Phuong J, Bahroos N, Starren J, Wilcox A, Meeker D. Development of a social and environmental determinants of health informatics maturity model. J Clin Transl Sci 2023; 7:e266. [PMID: 38380394 PMCID: PMC10877515 DOI: 10.1017/cts.2023.691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/04/2023] [Accepted: 11/29/2023] [Indexed: 02/22/2024] Open
Abstract
Introduction Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps. Methods We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration's Informatics Enterprise Committee, and a publicly available online self-assessment tool. Results We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels. Conclusion The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities.
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Affiliation(s)
- Juan C. Espinoza
- Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Shruti Sehgal
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jimmy Phuong
- Division of Biomedical and Health Informatics, University of Washington, Seattle, WA, USA
- Harborview Injury Prevention Research Center, University of Washington, Seattle, WA, USA
| | - Neil Bahroos
- University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - Justin Starren
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Adam Wilcox
- Institute for Informatics, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniella Meeker
- Department of Biomedical Informatics & Data Science, Yale University School of Medicine, New Haven, CT, USA
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Acevedo-Sánchez G, Mora-Aguilera G, Coria-Contreras JJ, Álvarez-Maya I. Were metabolic and other chronic diseases the driven onset epidemic forces of COVID-19 in Mexico? Front Public Health 2023; 11:995602. [PMID: 37608984 PMCID: PMC10441236 DOI: 10.3389/fpubh.2023.995602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 07/14/2023] [Indexed: 08/24/2023] Open
Abstract
The underline hypothesis of this study was that SARS-CoV-2 can infect individuals regardless of health condition, sex, and age in opposition to the classical epidemiological assumption of an identifiable susceptible subpopulation for epidemic development. To address this issue, a population cohort with 24.4 million metadata associated with 226,089 official RT-qPCR positive and 283,450 negative cases, including 27,769 deceased, linked putatively to B.1. and B.1.1. SARS-CoV-2 lineages were analyzed. The analysis baseline was to determine the infection and mortality structure of the diseased cohort at the onset-exponential phase of the first epidemic wave in Mexico under the assumption of limited herd immunity. Individuals with nonchronic diseases (NOCDs) were compared with those exhibiting at least one of 10 chronic diseases (CDs) adjusted by age and sex. Risk factors for infection and mortality were estimated with classification and regression tree (CART) and cluster analysis based on Spearman's matrix of rho-values in RStudio®, complemented with two proposed mortality indices. SARS-CoV-2 infection was independent of health condition (52.8% NOCD vs. 47.2% CDs; p = 0.001-0.009) but influenced by age >46 in one risk analysis scenario (p < 0.001). Sex contributed 9.7% to the overall risk. The independent effect was supported by the health structure of negative cases with a similar tendency but a higher proportion of NOCDs (61.4%, p = 0.007). The infection probability in individuals with one CD was determined by the disease type and age, which was higher in those older individuals (≥56 years) exhibiting diabetes (12.3%, cp = 0.0006), hypertension (10.1%, cp < 0.0001), and obesity (7.8%, cp = 0.001). In contrast, the mortality risk was heavily influenced by CD conditioned by sex and age, accounting for 72.3% of total deaths (p = 0.001-0.008). Significant mortality risk (48%) was comprised of women and men (w, m) aged ≥56 years with diabetes (19% w and 27.9% m, cp < 0.0004), hypertension (11.5% w, cp = 0.0001), and CKD (3.5% w and 5.3% m, cp = 0.0009). Older people with diabetes and hypertension comorbidity increased the risk to 60.5% (p = 0.001). Based on a mortality-weighted index, women were more vulnerable to preexisting metabolic or cardiovascular diseases. These findings support our hypothesis and justify the need for surveillance systems at a communitarian level. This is the first study addressing this fundamental epidemiological question.
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Affiliation(s)
- Gerardo Acevedo-Sánchez
- Laboratory of Epidemiological Risk Analysis (LANREF), Postgraduate College, Montecillo Campus, Texcoco, State of Mexico, Mexico
| | - Gustavo Mora-Aguilera
- Laboratory of Epidemiological Risk Analysis (LANREF), Postgraduate College, Montecillo Campus, Texcoco, State of Mexico, Mexico
| | - Juan J. Coria-Contreras
- Laboratory of Epidemiological Risk Analysis (LANREF), Postgraduate College, Montecillo Campus, Texcoco, State of Mexico, Mexico
| | - Ikuri Álvarez-Maya
- Center for Research and Applied Technology in Jalisco (CIATEJ), Guadalajara, Jalisco, Mexico
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Woldemariam SR, Tang AS, Oskotsky TT, Yaffe K, Sirota M. Similarities and differences in Alzheimer's dementia comorbidities in racialized populations identified from electronic medical records. COMMUNICATIONS MEDICINE 2023; 3:50. [PMID: 37031271 PMCID: PMC10082816 DOI: 10.1038/s43856-023-00280-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 03/24/2023] [Indexed: 04/10/2023] Open
Abstract
BACKGROUND Alzheimer's dementia (AD) is a neurodegenerative disease that is disproportionately prevalent in racially marginalized individuals. However, due to research underrepresentation, the spectrum of AD-associated comorbidities that increase AD risk or suggest AD treatment disparities in these individuals is not completely understood. We leveraged electronic medical records (EMR) to explore AD-associated comorbidities and disease networks in racialized individuals identified as Asian, Non-Latine Black, Latine, or Non-Latine White. METHODS We performed low-dimensional embedding, differential analysis, and disease network-based analyses of 5664 patients with AD and 11,328 demographically matched controls across two EMR systems and five medical centers, with equal representation of Asian-, Non-Latine Black-, Latine-, and Non-Latine White-identified individuals. For low-dimensional embedding and disease network comparisons, Mann-Whitney U tests or Kruskal-Wallis tests followed by Dunn's tests were used to compare categories. Fisher's exact or chi-squared tests were used for differential analysis. Spearman's rank correlation coefficients were used to compare results between the two EMR systems. RESULTS Here we show that primarily established AD-associated comorbidities, such as essential hypertension and major depressive disorder, are generally similar across racialized populations. However, a few comorbidities, including respiratory diseases, may be significantly associated with AD in Black- and Latine- identified individuals. CONCLUSIONS Our study revealed similarities and differences in AD-associated comorbidities and disease networks between racialized populations. Our approach could be a starting point for hypothesis-driven studies that can further explore the relationship between these comorbidities and AD in racialized populations, potentially identifying interventions that can reduce AD health disparities.
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Affiliation(s)
- Sarah R Woldemariam
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA
| | - Alice S Tang
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA
- School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Kristine Yaffe
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA.
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA.
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Phuong J, Riches NO, Calzoni L, Datta G, Duran D, Lin AY, Singh RP, Solomonides AE, Whysel NY, Kavuluru R. Toward informatics-enabled preparedness for natural hazards to minimize health impacts of climate change. J Am Med Inform Assoc 2022; 29:2161-2167. [PMID: 36094062 PMCID: PMC9667167 DOI: 10.1093/jamia/ocac162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/21/2022] [Accepted: 08/30/2022] [Indexed: 09/14/2023] Open
Abstract
Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.
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Affiliation(s)
- Jimmy Phuong
- University of Washington, School of Medicine, Research Information Technologies, Seattle, Washington, USA
- University of Washington, Harborview Injury Prevention and Research Center, Seattle, Washington, USA
| | - Naomi O Riches
- University of Utah School of Medicine, Obstetrics and Gynecology Research Network, Salt Lake City, Utah, USA
| | - Luca Calzoni
- National Institute on Minority Health and Health Disparities (NIMHD), National Institutes of Health, Bethesda, Maryland, USA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gora Datta
- Department of Civil & Environmental Engineering, University of California at Berkeley, Berkeley, California, USA
| | - Deborah Duran
- National Institute on Minority Health and Health Disparities (NIMHD), National Institutes of Health, Bethesda, Maryland, USA
| | - Asiyah Yu Lin
- National Human Genome Research Institute (NHGRI), National Institutes of Health, Bethesda, Maryland, USA
| | - Ramesh P Singh
- School of Life and Earth Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, USA
| | - Anthony E Solomonides
- Department of Communication Design, NorthShore University Health System, Outcomes Research Network, Research Institute, Evanston, Illinois, USA
| | - Noreen Y Whysel
- New York City College of Technology, CUNY, Brooklyn, New York, USA
| | - Ramakanth Kavuluru
- Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, Kentucky, USA
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Khan AT, Gogarten SM, McHugh CP, Stilp AM, Sofer T, Bowers ML, Wong Q, Cupples LA, Hidalgo B, Johnson AD, McDonald MLN, McGarvey ST, Taylor MR, Fullerton SM, Conomos MP, Nelson SC. Recommendations on the use and reporting of race, ethnicity, and ancestry in genetic research: Experiences from the NHLBI TOPMed program. CELL GENOMICS 2022; 2:100155. [PMID: 36119389 PMCID: PMC9481067 DOI: 10.1016/j.xgen.2022.100155] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
How race, ethnicity, and ancestry are used in genomic research has wide-ranging implications for how research is translated into clinical care and incorporated into public understanding. Correlation between race and genetic ancestry contributes to unresolved complexity for the scientific community, as illustrated by heterogeneous definitions and applications of these variables. Here, we offer commentary and recommendations on the use of race, ethnicity, and ancestry across the arc of genetic research, including data harmonization, analysis, and reporting. While informed by our experiences as researchers affiliated with the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, these recommendations are applicable to basic and translational genomic research in diverse populations with genome-wide data. Moving forward, considerable collaborative effort will be required to ensure that race, ethnicity, and ancestry are described and used appropriately to generate scientific knowledge that yields broad and equitable benefit.
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Affiliation(s)
- Alyna T. Khan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Institute for Public Health Genetics, University of Washington, Seattle, WA, USA
| | | | - Caitlin P. McHugh
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Adrienne M. Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Michael L. Bowers
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Bertha Hidalgo
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Andrew D. Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Merry-Lynn N. McDonald
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Genetics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Stephen T. McGarvey
- Department of Epidemiology and International Health Institute, Brown University School of Public Health, Providence, RI, USA
- Department of Anthropology, Brown University, Providence, RI, USA
| | - Matthew R.G. Taylor
- Department of Medicine, Adult Medical Genetics Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | | | - Sarah C. Nelson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Institute for Public Health Genetics, University of Washington, Seattle, WA, USA
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