Consistency, completeness and external validity of ethnicity recording in NHS primary care records: a cohort study in 25 million patients’ records at source using OpenSAFELY
The OpenSAFELY Collaborative, Colm D Andrews, Rohini Mathur, Jon Massey, Robin Park, Lisa Hopcroft, Helen J Curtis, Amir Mehrkar, Seb Bacon, George Hickman, Rebecca Smith, David Evans, Tom Ward, Simon Davy, Peter Inglesby, Iain Dillingham, Steven Maude, Thomas O’Dwyer, Ben Butler-Cole, Lucy Bridges, Chris Bates, John Parry, Frank Hester, Sam Harper, Jonathan Cockburn, Ben Goldacre, Brian MacKenna, Laurie Tomlinson, Alex J Walker, William J Hulme
{"title":"Consistency, completeness and external validity of ethnicity recording in NHS primary care records: a cohort study in 25 million patients’ records at source using OpenSAFELY","authors":"The OpenSAFELY Collaborative, Colm D Andrews, Rohini Mathur, Jon Massey, Robin Park, Lisa Hopcroft, Helen J Curtis, Amir Mehrkar, Seb Bacon, George Hickman, Rebecca Smith, David Evans, Tom Ward, Simon Davy, Peter Inglesby, Iain Dillingham, Steven Maude, Thomas O’Dwyer, Ben Butler-Cole, Lucy Bridges, Chris Bates, John Parry, Frank Hester, Sam Harper, Jonathan Cockburn, Ben Goldacre, Brian MacKenna, Laurie Tomlinson, Alex J Walker, William J Hulme","doi":"10.1101/2023.11.21.23298690","DOIUrl":null,"url":null,"abstract":"<strong>Background</strong> Ethnicity is known to be an important correlate of health outcomes, particularly during the COVID-19 pandemic, where some ethnic groups were shown to be at higher risk of infection and adverse outcomes. The recording of patients’ ethnic groups in primary care can support research and efforts to achieve equity in service provision and outcomes; however the coding of ethnicity is known to present complex challenges. We therefore set out to describe ethnicity coding in detail with a view to supporting the use of this data in a wide range of settings, as part of wider efforts to robustly describe and define methods of using administrative data.","PeriodicalId":501023,"journal":{"name":"medRxiv - Primary Care Research","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Primary Care Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2023.11.21.23298690","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background Ethnicity is known to be an important correlate of health outcomes, particularly during the COVID-19 pandemic, where some ethnic groups were shown to be at higher risk of infection and adverse outcomes. The recording of patients’ ethnic groups in primary care can support research and efforts to achieve equity in service provision and outcomes; however the coding of ethnicity is known to present complex challenges. We therefore set out to describe ethnicity coding in detail with a view to supporting the use of this data in a wide range of settings, as part of wider efforts to robustly describe and define methods of using administrative data.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
