Suchergebnisse

ObjectivesThis study aims to assess whether ethnic differences in COVID-19 mortality in England have continued into the third wave and to what extent differences in vaccination rates contributed to excess COVID-19 mortality after accounting for other risk factors. 
ApproachThis Cohort study of 28.8 million adults living in private households or communal establishments in England is based on data from the Office for National Statistics (ONS) Public Health Data Asset (PHDA). The ONS PHDA is a linked dataset combining the 2011 Census, mortality records, the General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR), Hospital Episode Statistics (HES) and vaccination data from the National Immunisation Management System (NIMS). We calculated hazard ratios (HRs) for death involving COVID-19 during the second (8 December 2020 to 12 June 2021) and third wave (13 June 2021 to 1 December 2021) of the pandemic separately for males to females to assess the association between ethnic group and death involving COVID-19 in each wave, sequentially adjusting for age, residence type, geographical factors, sociodemographic characteristics, pre-pandemic health, and vaccination status.
ResultsAge-adjusted HRs of death involving COVID-19 were higher for most ethnic minority groups than the White British group during both waves, particularly for groups with lowest vaccination rates (Bangladeshi, Pakistani, Black African and Black Caribbean). In both waves, HRs were attenuated after adjusting for geographical factors, sociodemographic characteristics, and pre-pandemic health. Further adjusting for vaccination status substantially reduced residual HRs for Black African, Black Caribbean, and Pakistani groups in the third wave. The only groups where fully-adjusted HRs remained elevated were the Bangladeshi group (men: 2.19, 95% CI 1.72 to 2.78; women: 2.12, 95% CI 1.58 to 2.86) and men from the Pakistani group (1.24, 95% CI 1.06 to 1.46). 
ConclusionPublic health strategies to increase vaccination uptake in ethnic minority groups could reduce disparities in COVID-19 mortality that cannot be accounted for by pre-existing risk factors.

AbstractThis paper provides novel evidence on how a sharp increase in labor force participation among older women affects the provision of informal care to their older parents. Based on data from Understanding Society – The UK Household Longitudinal Study, we use an instrumental variable approach that exploits a unique reform that increased the female State Pension age by up to six years. Our results provide evidence of a trade‐off between the intensive margin of work and informal care provided outside the household: an increase of 10 hours of work per week reduces the provision of informal care by 2.1 hours a week, which amounts to roughly £2,100 of yearly care‐hours lost. This reduction in caregiving is largest among women working in physically or psychosocially demanding jobs, and "sandwich generation" women who have both a living grandchild and a parent alive. Using data from the English Longitudinal Study of Ageing, we show that older parents whose daughters became ineligible to claim their pensions experienced a significant reduction in the amount of care they receive from their daughters, which was not compensated by an increase in formal care or other sources of support. Our results suggest that policies that increase older workers' labor supply require changes in long‐term care policy that compensate for the loss of informal care.

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.

ObjectivesWith suicide a major public health concern, it is vital research identifies predictors of suicide to support vulnerable groups who should be targeted for intervention. We use a novel linkage of 2011 Census and population level mortality data to assess which risk factors are important predictors of suicide.
MethodsExposures of interest were identified from Census 2011 and were sex, age, ethnicity, marital status, day-to-day impairments, religion, region, National Statistics Socio-economic Classification. Our study population consisted of 35,136,917 people aged 18-to-74; there were 35,928 suicides in our study period (28/03/2011-31/12/2021), with 73.9\% occurring in men. We fitted generalised linear models with a Poisson link function, with suicide being the outcome of interest. The natural logarithm of exposure time was included as an offset term. To estimate rates of suicide per 100,000 people for each level of our exposure, by sex for the average age, we calculated marginal means.
ResultsThe groups with the highest rates of suicide were those who reported an impairment affecting their day-to-day activities, those who were long term unemployed or never had worked, or those who were single or separated. Comparison of minimally adjusted models with models accounting for all other characteristics identified predictors which remain important risk factors after accounting for other characteristics; day-to-day impairments were still found to increase the incidence of suicide relative to those whose activities were not impaired after adjusting for employment status. Additionally, the estimated rates of suicide remained lowest in London compared to other regions in our fully adjusted estimates. Overall, rates of suicide were higher in men compared to females across all ages, with the highest rates in 40- to 50-year-olds.
ConclusionThe findings of this work provide novel population level insights into the risk of suicide by sociodemographic characteristics, this work should pave the way for further research exploring the interaction of factors which lead to suicide and drive policy change for targeted intervention.

Introduction & BackgroundEvidence is mounting that children's physical environment (e.g. in and around the home, school, and neighbourhood) is critical for their long-term health and education. Early life exposure to factors such as indoor and outdoor air pollution, or a lack of access to greenspaces are associated with the development of long-term health conditions such as asthma or mental health problems. Local and central government in England are implementing numerous policies to improve air quality and housing, and mitigate climate change. Further, England has seen large scale changes to local service provision (including childcare and libraries) due to austerity policies and the COVID-19 pandemic. Currently, there is no national, linked data resource for England that allows research into how the local environment impacts children's health and education.
Objectives & ApproachThe Kids' Environment and Health Cohort will be a new, linked national data resource for England currently being developing by researchers from UCL, London School of Hygiene and Tropical Medicine, London School of Economics and Political Science, Brock University, and City, University of London in collaboration with the Office for National Statistics (ONS), and funded by Administrative Data Research-UK (ADR-UK). The Kids' Environment and Health Cohort will be a de-identified and annually updated national birth cohort of all children born in England from 2006 onwards – around 10.5 million children until 2023. The cohort will be constructed using linked administrative data from vital registration (live and stillbirth, and death registration), Census (housing and socio-economic indicators), health (hospital contacts, mental health referrals, and community dispensing data), and education (key stage results, special educational needs, absenteeism). Environmental exposure data can be securely linked to the Cohort via longitudinal residential unique property reference numbers (UPRNs) and postcodes from the Personal Demographic Service, and school location from education records.
Relevance to Digital FootprintsThe Kids' Environment and Health Cohort will, for the first time, link health, education, Census and environmental data at national level in England. It will allow researchers to integrate data on local environments, including physical characteristics (such as temperature, building energy efficiency, or greenspace access) or the social environment (including proximity to food outlets, or services like libraries) with individual level data on health and education outcomes in children. This will be done using the ONS's 5 safes framework, ensuring highest standards of data security and confidentiality.
ResultsThe Kids' Environment and Health Cohort will be constructed using administrative datasets, including national linked vital statistics, health, education and Census data from multiple data providers (ONS, NHS England and Department for Education), combined with small-area level environmental data for England. Together, these datasets allow detailed analyses of the impact of environmental exposures on health and education outcomes in children, with robust confounder adjustment. The Kids' Environment and Health Cohort will be made available in a de-identified format in the ONS Secure Research Service (SRS).
Conclusions & ImplicationsThe Kids' Environment and Health Cohort will provide researchers secure access to a national data resource integrating environmental and administrative health and education data, for child public health research.

IntroductionCOVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society.
ObjectivesTo validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK.
MethodsWe conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January–30th April 2020 and 1st May–28th July 2020) to assess algorithm performance.
Results1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell's C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes.
ConclusionsThe QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.