Asset Details
Do you wish to reserve the book?
OP60 Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: A national linked data study
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
OP60 Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: A national linked data study
Do you wish to request the book?
OP60 Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: A national linked data study
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
OP60 Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: A national linked data study
2023
Overview
BackgroundHaving high-quality ethnicity data alongside health records is crucial to monitor and redress ethnic inequalities in health. We assessed the quality of ethnicity coding in Scottish health datasets and its implications for assessing ethnic inequalities in severe COVID-19.MethodsWe compared ethnicity coding within the Public Health Scotland Ethnicity Look-up (PHS-EL) dataset, and other NHS datasets, with the 2011 Scottish Census as the ‘gold standard’. Measures of quality included the level of missingness (ethnicity missing compared to the Census) and misclassification (ethnicity miscoded compared to the Census). We examined the implications of misclassification, using age- and sex-adjusted Cox proportional hazards models to estimate the risk of severe COVID-19 (hospitalisation or death) by ethnicity using PHS-EL compared with Census coding.ResultsMisclassification within PHS-EL was higher for all minority ethnic groups [12.5 to 69.1%] compared to the White Scottish majority [5.1%] and highest in the White Gypsy/Traveller group [69.1%]. Missingness in PHS-EL was high overall [30%] but was not higher among ethnic minority groups. PHS-EL data often underestimated severe COVID-19 risk compared to Census data. For example, in the White Gypsy/Traveller group the Hazard Ratio (HR) was 1.68 [95% Confidence Intervals (CI): 1.03, 2.74] compared to the White Scottish majority using Census ethnicity data and 0.73 [95% CI: 0.10, 5.15] using PHS-EL data; and HR was 2.03 [95% CI: 1.20, 3.44] in the Census for the Bangladeshi group versus 1.45 [95% CI: 0.75, 2.78] in PHS-EL.ConclusionThe quality of ethnicity coding in Scottish health datasets is poorer among minority ethnic groups and this can bias estimates, thereby threatening monitoring and understanding ethnic inequalities in health.
Publisher
BMJ Publishing Group LTD
Subject
