Suchergebnisse

ObjectivesDuring the COVID-19 pandemic, higher mortality among some ethnic minority groups was identified and has become the subject of significant public and government interest, highlighting an urgent requirement to quantify the reliability of ethnicity classification across health administrative data sets, which are utilised in health analysis and pandemic planning.
ApproachThe aim of our work was to assess how ethnicity data recorded in Census and health admin records varied across ethnicities and provide recommendations for how missingness can be accounted for by statisticians. Combining population level data from general practice (GP) records with hospital episode statistics (HES) for patients in England, we created a linked data set with Census 2011 data to reliably assess coverage and missingness between data sources. Most recent and modal ethnicity classifications were derived on a person-level from both HES and GP administrative data for comparison to gold-standard Census 2011 records.
ResultsAgreement rates were calculated to assess the reliability of ethnicity data recorded in health administrative datasets compared to Census data. We found that the agreement rates vary by ethnic group and other demographic characteristics. Furthermore, we highlighted groups of people who exist in one health-admin source, but not Census, and vice versa, illustrating the importance of accounting for the sample bias in health analysis when relying solely on primary or secondary care data sources. Implementation of techniques to account for bias and missingness were tested to propose methodology to improve reliability of ethnicity estimates from both HES and GP data, in order to ensure estimates of health disparities are as accurate as possible.
ConclusionWe have linked GP records to Census 2011 and HES data to provide population-based ethnicity estimates of coverage, missingness and bias between data sources, in order to improve our understanding of ethnicity data quality. This work aims to inform policies tackling ethnic health inequalities in England.

Child mortality is a common measure of the overall health of society. Maternal characteristics, such as ethnicity and socioeconomic status are known to contribute to inequalities in health and mortality in babies and children. Overall, in the UK babies born to non-white mothers have a higher risk of mortality, and there is also a strong association between deprivation and risk of death. However, population data is currently lacking for England and Wales disentangling the various socioeconomic risk factors which are known to contribute to increased risk. We utilise population level data to assess the association between socioeconomic status, ethnicity, and child mortality.
Our cohort consisted of all live singleton births in England and Wales between 2011 and 2016. We linked birth notifications to mothers' 2011 Census records using NHS number, which provides person-level sociodemographic information. Death registration data was linked to identify all-cause and cause-specific deaths in babies. Babies were followed from date-of-birth for up to 10-years, or date-of-death. We report cumulative incidence of all-cause mortality for a 10-year follow-up for neonatal, infant and child deaths based on maternal socioeconomic and ethnic groups. Cox proportional hazard models are used to estimate hazard ratios for neonatal, infant and child deaths, with both minimally adjusted (maternal age and baby sex) and fully-adjusted models (accounting for other maternal, household and birth characteristics).
This presentation will give an overview of our unique linked data sources, the statistical techniques employed, the latest available analytical results, and the emerging implications of the research.