{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T14:30:29Z","timestamp":1778509829752,"version":"3.51.4"},"reference-count":20,"publisher":"Oxford University Press (OUP)","funder":[{"DOI":"10.13039\/100006545","name":"National Institute on Minority Health and Health Disparities","doi-asserted-by":"publisher","award":["1ZIAMD000018"],"award-info":[{"award-number":["1ZIAMD000018"]}],"id":[{"id":"10.13039\/100006545","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The UK Biobank (UKB), a large-scale biomedical database that includes demographic and electronic health record data for more than half a million ethnically diverse participants, is a potentially valuable resource for the study of health disparities. However, publicly accessible databases that catalog health disparities in the UKB do not exist. We developed the UKB Health Disparities Browser with the aims of (i) facilitating the exploration of the landscape of health disparities in the UK and (ii) directing the attention to areas of disparities research that might have the greatest public health impact. Health disparities were characterized for UKB participant groups defined by age, country of residence, ethnic group, sex and socioeconomic deprivation. We defined disease cohorts for UKB participants by mapping participant International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes to phenotype codes (phecodes). For each of the population attributes used to define population groups, disease percent prevalence values were computed for all groups from phecode case\u2013control cohorts, and the magnitude of the disparities was calculated by both the difference and ratio of the range of disease prevalence values among groups to identify high- and low-prevalence disparities. We identified numerous diseases and health conditions with disparate prevalence values across population attributes, and we deployed an interactive web browser to visualize the results of our analysis: https:\/\/ukbatlas.health-disparities.org. The interactive browser includes overall and group-specific prevalence data for 1513 diseases based on a cohort of &amp;gt;500\u2009000 participants from the UKB. Researchers can browse and sort by disease prevalence and prevalence differences to visualize health disparities for each of the five population attributes, and users can search for diseases of interest by disease names or codes.<\/jats:p>\n               <jats:p>Database URL https:\/\/ukbatlas.health-disparities.org\/<\/jats:p>","DOI":"10.1093\/database\/baad026","type":"journal-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T19:59:59Z","timestamp":1682539199000},"source":"Crossref","is-referenced-by-count":17,"title":["The landscape of health disparities in the UK Biobank"],"prefix":"10.1093","volume":"2023","author":[{"given":"Shashwat Deepali","family":"Nagar","sequence":"first","affiliation":[{"name":"School of Biological Sciences, Georgia Institute of Technology , Atlanta, GA 30332 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4996-2203","authenticated-orcid":false,"given":"I King","family":"Jordan","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, Georgia Institute of Technology , Atlanta, GA 30332 USA"},{"name":"IHRC-Georgia Tech Applied Bioinformatics Laboratory , Atlanta, GA 30332 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5716-8512","authenticated-orcid":false,"given":"Leonardo","family":"Mari\u00f1o-Ram\u00edrez","sequence":"additional","affiliation":[{"name":"National Institute on Minority Health and Health Disparities, National Institutes of Health , Rockville, MD 20818 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"2023042619594522500_R1","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1146\/annurev.publhealth.29.020907.090852","article-title":"U.S. disparities in health: descriptions, causes, and mechanisms","volume":"29","author":"Adler","year":"2008","journal-title":"Annu. Rev. Public Health"},{"key":"2023042619594522500_R2","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1097\/CCO.0b013e32834161b8","article-title":"Biobanking: the foundation of personalized medicine","volume":"23","author":"Hewitt","year":"2011","journal-title":"Curr. Opin. Oncol."},{"key":"2023042619594522500_R3","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1001\/jama.299.19.2316","article-title":"Tracing biological collections: between books and clinical trials","volume":"299","author":"Kauffmann","year":"2008","journal-title":"JAMA"},{"key":"2023042619594522500_R4","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK Biobank resource with deep phenotyping and genomic data","volume":"562","author":"Bycroft","year":"2018","journal-title":"Nature"},{"key":"2023042619594522500_R5","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1093\/ije\/dym276","article-title":"The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine","volume":"37","author":"Elliott","year":"2008","journal-title":"Int. J. Epidemiol."},{"key":"2023042619594522500_R6","article-title":"Data-Field 21003: age when attended assessment centre","author":"UK Biobank Showcase","year":"2020"},{"key":"2023042619594522500_R7","article-title":"Data-Field 54: UK Biobank assessment centre","author":"UK Biobank Showcase.","year":"2020"},{"key":"2023042619594522500_R8","article-title":"Data-Field 21000: ethnic background","author":"UK Biobank Showcase.","year":"2020"},{"key":"2023042619594522500_R9","article-title":"Data-Field 41270: diagnoses\u2014ICD10","author":"UK Biobank Showcase.","year":"2020"},{"key":"2023042619594522500_R10","article-title":"Data-Field 31: sex","author":"UK Biobank Showcase.","year":"2020"},{"key":"2023042619594522500_R11","article-title":"Data-Field 189: Townsend deprivation index at recruitment","author":"UK Biobank Showcase.","year":"2020"},{"key":"2023042619594522500_R12","volume-title":"Health and Deprivation: Inequality and the North","author":"Townsend","year":"1988"},{"key":"2023042619594522500_R13","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1093\/bioinformatics\/btu197","article-title":"R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment","volume":"30","author":"Carroll","year":"2014","journal-title":"Bioinformatics"},{"key":"2023042619594522500_R14","doi-asserted-by":"crossref","DOI":"10.2196\/14325","article-title":"Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation","volume":"7","author":"Wu","year":"2019","journal-title":"JMIR Med. Inform."},{"key":"2023042619594522500_R15","first-page":"56","article-title":"Data structures for statistical computing in Python","author":"McKinney","year":"2010"},{"key":"2023042619594522500_R16","article-title":"Elegant graphics for data analysis","volume":"35","author":"Wickham","year":"2009","journal-title":"Media"},{"key":"2023042619594522500_R17","article-title":"R: a language and environment for statistical computing","author":"Team","year":"2013"},{"key":"2023042619594522500_R18","first-page":"126","article-title":"Visualization of bioinformatics data with dash bio","author":"Hossain","year":"2019"},{"key":"2023042619594522500_R19","first-page":"118","article-title":"Web-application development using the model\/view\/controller design pattern","author":"Leff","year":"2001"},{"key":"2023042619594522500_R20","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1093\/aje\/kwx246","article-title":"Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population","volume":"186","author":"Fry","year":"2017","journal-title":"Am. J. Epidemiol."}],"container-title":["Database"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/database\/article-pdf\/doi\/10.1093\/database\/baad026\/50103027\/baad026.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/database\/article-pdf\/doi\/10.1093\/database\/baad026\/50103027\/baad026.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T20:00:17Z","timestamp":1682539217000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/database\/article\/doi\/10.1093\/database\/baad026\/7143539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1093\/database\/baad026","relation":{},"ISSN":["1758-0463"],"issn-type":[{"value":"1758-0463","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1,1]]},"published":{"date-parts":[[2023,1,1]]},"article-number":"baad026"}}