FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308844566> ?p ?o ?g. }

• W4308844566 abstract "Abstract Background The link between ethnicity and healthcare inequity, and the urgency for better data is well-recognised. This study describes ethnicity data in nation-wide electronic health records in England, UK. Methods We conducted a retrospective cohort study using de-identified person-level records for the England population available in the National Health Service (NHS) Digital trusted research environment. Primary care records (GDPPR) were linked to hospital and national mortality records. We assessed completeness, consistency, and granularity of ethnicity records using all available SNOMED-CT concepts for ethnicity and NHS ethnicity categories. Findings From 61.8 million individuals registered with a primary care practice in England, 51.5 (83.3%) had at least one ethnicity record in GDPPR, increasing to 93·9% when linked with hospital records. Approximately 12·0% had at least two conflicting ethnicity codes in primary care records. Women were more likely to have ethnicity recorded than men. Ethnicity was missing most frequently in individuals from 18 to 39 years old and in the southern regions of England. Individuals with an ethnicity record had more comorbidities recorded than those without. Of 489 SNOMED-CT ethnicity concepts available, 255 were used in primary care records. Discrepancies between SNOMED-CT and NHS ethnicity categories were observed, specifically within “Other-” ethnicity groups. Interpretation More than 250 ethnicity sub-groups may be found in health records for the English population, although commonly categorised into “White”, “Black”, “Asian”, “Mixed”, and “Other”. One in ten individuals do not have ethnicity information recorded in primary care or hospital records. SNOMED-CT codes represent more diversity in ethnicity groups than the NHS ethnicity classification. Improved recording of self-reported ethnicity at first point-of-care and consistency in ethnicity classification across healthcare settings can potentially improve the accuracy of ethnicity in research and ultimately care for all ethnicities. Funding British Heart Foundation Data Science Centre led by Health Data Research UK. Research in context Evidence before this study Ethnicity has been highlighted as a significant factor in the disproportionate impact of SARS-CoV-2 infection and mortality. Better knowledge of ethnicity data recorded in real clinical practice is required to improve health research and ultimately healthcare. We searched PubMed from database inception to 14 th July 2022 for publications using the search terms “ethnicity” and “electronic health records” or “EHR,” without language restrictions. 228 publications in 2019, before the COVID-19 pandemic, and 304 publications between 2020 and 2022 were identified. However, none of these publications used or reported any of over 400 available SNOMED-CT concepts for ethnicity to account for more granularity and diversity than captured by traditional high-level classification limited to 5 to 9 ethnicity groups. Added value of this study We provide a comprehensive study of the largest collection of ethnicity records from a national-level electronic health records trusted research environment, exploring completeness, consistency, and granularity. This work can serve as a data resource profile of ethnicity from routinely-collected EHR in England. Implications of all the available evidence To achieve equity in healthcare, we need to understand the differences between individuals, as well as the influence of ethnicity both on health status and on health interventions, including variation in the behaviour of tests and therapies. Thus, there is a need for measurements, thresholds, and risk estimates to be tailored to different ethnic groups. This study presents the different medical concepts describing ethnicity in routinely collected data that are readily available to researchers and highlights key elements for improving their accuracy in research. We aim to encourage researchers to use more granular ethnicity than the than typical approaches which aggregate ethnicity into a limited number of categories, failing to reflect the diversity of underlying populations. Accurate ethnicity data will lead to a better understanding of individual diversity, which will help to address disparities and influence policy recommendations that can translate into better, fairer health for all." @default.
• W4308844566 created "2022-11-17" @default.
• W4308844566 creator A5003770253 @default.
• W4308844566 creator A5005247953 @default.
• W4308844566 creator A5007287776 @default.
• W4308844566 creator A5018384194 @default.
• W4308844566 creator A5029564842 @default.
• W4308844566 creator A5030613053 @default.
• W4308844566 creator A5033011934 @default.
• W4308844566 creator A5036062380 @default.
• W4308844566 creator A5036600654 @default.
• W4308844566 creator A5037523041 @default.
• W4308844566 creator A5051460301 @default.
• W4308844566 creator A5072175850 @default.
• W4308844566 creator A5073010916 @default.
• W4308844566 creator A5074056939 @default.
• W4308844566 creator A5081850154 @default.
• W4308844566 creator A5081911168 @default.
• W4308844566 creator A5083222970 @default.
• W4308844566 creator A5087514519 @default.
• W4308844566 creator A5088305020 @default.
• W4308844566 date "2022-11-11" @default.
• W4308844566 modified "2023-10-18" @default.
• W4308844566 title "Digital ethnicity data in population-wide electronic health records in England: a description of completeness, coverage, and granularity of diversity" @default.
• W4308844566 cites W2102393256 @default.
• W4308844566 cites W2144437696 @default.
• W4308844566 cites W2167779416 @default.
• W4308844566 cites W2408942822 @default.
• W4308844566 cites W2912029133 @default.
• W4308844566 cites W3002108456 @default.
• W4308844566 cites W3047144258 @default.
• W4308844566 cites W3094542430 @default.
• W4308844566 cites W3112318891 @default.
• W4308844566 cites W3144274120 @default.
• W4308844566 cites W3149026346 @default.
• W4308844566 cites W3158323486 @default.
• W4308844566 cites W3173667512 @default.
• W4308844566 cites W4243082332 @default.
• W4308844566 doi "https://doi.org/10.1101/2022.11.11.22282217" @default.
• W4308844566 hasPublicationYear "2022" @default.
• W4308844566 type Work @default.
• W4308844566 citedByCount "0" @default.
• W4308844566 crossrefType "posted-content" @default.
• W4308844566 hasAuthorship W4308844566A5003770253 @default.
• W4308844566 hasAuthorship W4308844566A5005247953 @default.
• W4308844566 hasAuthorship W4308844566A5007287776 @default.
• W4308844566 hasAuthorship W4308844566A5018384194 @default.
• W4308844566 hasAuthorship W4308844566A5029564842 @default.
• W4308844566 hasAuthorship W4308844566A5030613053 @default.
• W4308844566 hasAuthorship W4308844566A5033011934 @default.
• W4308844566 hasAuthorship W4308844566A5036062380 @default.
• W4308844566 hasAuthorship W4308844566A5036600654 @default.
• W4308844566 hasAuthorship W4308844566A5037523041 @default.
• W4308844566 hasAuthorship W4308844566A5051460301 @default.
• W4308844566 hasAuthorship W4308844566A5072175850 @default.
• W4308844566 hasAuthorship W4308844566A5073010916 @default.
• W4308844566 hasAuthorship W4308844566A5074056939 @default.
• W4308844566 hasAuthorship W4308844566A5081850154 @default.
• W4308844566 hasAuthorship W4308844566A5081911168 @default.
• W4308844566 hasAuthorship W4308844566A5083222970 @default.
• W4308844566 hasAuthorship W4308844566A5087514519 @default.
• W4308844566 hasAuthorship W4308844566A5088305020 @default.
• W4308844566 hasBestOaLocation W43088445661 @default.
• W4308844566 hasConcept C126838900 @default.
• W4308844566 hasConcept C137403100 @default.
• W4308844566 hasConcept C138885662 @default.
• W4308844566 hasConcept C160735492 @default.
• W4308844566 hasConcept C17744445 @default.
• W4308844566 hasConcept C195910791 @default.
• W4308844566 hasConcept C199539241 @default.
• W4308844566 hasConcept C206497026 @default.
• W4308844566 hasConcept C2908647359 @default.
• W4308844566 hasConcept C2908807203 @default.
• W4308844566 hasConcept C41895202 @default.
• W4308844566 hasConcept C512399662 @default.
• W4308844566 hasConcept C547195049 @default.
• W4308844566 hasConcept C71924100 @default.
• W4308844566 hasConcept C99454951 @default.
• W4308844566 hasConceptScore W4308844566C126838900 @default.
• W4308844566 hasConceptScore W4308844566C137403100 @default.
• W4308844566 hasConceptScore W4308844566C138885662 @default.
• W4308844566 hasConceptScore W4308844566C160735492 @default.
• W4308844566 hasConceptScore W4308844566C17744445 @default.
• W4308844566 hasConceptScore W4308844566C195910791 @default.
• W4308844566 hasConceptScore W4308844566C199539241 @default.
• W4308844566 hasConceptScore W4308844566C206497026 @default.
• W4308844566 hasConceptScore W4308844566C2908647359 @default.
• W4308844566 hasConceptScore W4308844566C2908807203 @default.
• W4308844566 hasConceptScore W4308844566C41895202 @default.
• W4308844566 hasConceptScore W4308844566C512399662 @default.
• W4308844566 hasConceptScore W4308844566C547195049 @default.
• W4308844566 hasConceptScore W4308844566C71924100 @default.
• W4308844566 hasConceptScore W4308844566C99454951 @default.
• W4308844566 hasLocation W43088445661 @default.
• W4308844566 hasOpenAccess W4308844566 @default.
• W4308844566 hasPrimaryLocation W43088445661 @default.
• W4308844566 hasRelatedWork W1600493186 @default.
• W4308844566 hasRelatedWork W1979568804 @default.
• W4308844566 hasRelatedWork W2004410463 @default.
• W4308844566 hasRelatedWork W2168587036 @default.

• next