In total, 17,278,392 adults were included (Fig. 1; cohort description in Table 1). Eleven per cent of individuals (1,851,868) had ethnicity recorded as mixed, South Asian, Black or other (hereafter referred to

anzctr.org.au Identifier: ACTRN12605000658617; isrctn.org Identifier: ISRCTN83409867.

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Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R 2 ); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

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Abstract:Propensity scores (PS) are an increasingly popular method to adjust for confounding in observational studies. PS methods have theoretical advantages over traditional covariate adjustment, but their relative performance in real-word scenarios is poorly characterized. We used datasets from four large-scale cardiovascular observational studies (PROMETHEUS, ADAPT-DES, THIN, and CHARM) to compare the performance of traditional covariate adjustment and four commonly used PS methods: matching, stratification, inverse probability weighting and use of propensity score as a covariate. We found that stratification performed poorly with few outcome events, and inverse probability weighting gave imprecise estimates of treatment effect and undue influence to a small number of observations when substantial confounding was present. Covariate adjustment and matching performed well in all of our examples, although matching tended to give less precise estimates in some cases. PS methods are not necessarily superior to traditional covariate adjustment, and care should be taken to select the most suitable method.

Background COVID-19 has disproportionately affected minority ethnic populations in the UK. Our aim was to quantify ethnic differences in SARS-CoV-2 infection and COVID-19 outcomes during the first and second waves of the COVID-19 pandemic in England. MethodsWe conducted an observational cohort study of adults (aged ≥18 years) registered with primary care practices in England for whom electronic health records were available through the OpenSAFELY platform, and who had at least 1 year of continuous registration at the start of each study period (Feb 1 to Aug 3, 2020 [wave 1], and Sept 1 to Dec 31, 2020 [wave 2]). Individual-level primary care data were linked to data from other sources on the outcomes of interest: SARS-CoV-2 testing and positive test results and COVID-19-related hospital admissions, intensive care unit (ICU) admissions, and death. The exposure was self-reported ethnicity as captured on the primary care record, grouped into five high-level census categories (White, South Asian, Black, other, and mixed) and 16 subcategories across these five categories, as well as an unknown ethnicity category. We used multivariable Cox regression to examine ethnic differences in the outcomes of interest. Models were adjusted for age, sex, deprivation, clinical factors and comorbidities, and household size, with stratification by geographical region. FindingsOf 17 288 532 adults included in the study (excluding care home residents), 10 877 978 (62•9%) were White, 1 025 319 (5•9%) were South Asian, 340 912 (2•0%) were Black, 170 484 (1•0%) were of mixed ethnicity, 320 788 (1•9%) were of other ethnicity, and 4 553 051 (26•3%) were of unknown ethnicity. In wave 1, the likelihood of being tested for SARS-CoV-2 infection was slightly higher in the South Asian group (adjusted hazard ratio 1•08 [95% CI 1•07-1•09]), Black group (1•08 [1•06-1•09]), and mixed ethnicity group (1•04 [1•02-1•05]) and was decreased in the other ethnicity group (0•77 [0•76-0•78]) relative to the White group. The risk of testing positive for SARS-CoV-2 infection was higher in the South Asian group (1•99 [1•94-2•04]), Black group (1•69 [1•62-1•77]), mixed ethnicity group (1•49 [1•39-1•59]), and other ethnicity group (1•20 [1•14-1•28]). Compared with the White group, the four remaining high-level ethnic groups had an increased risk of COVID-19-related hospitalisation (

Background: Establishing who is at risk from a novel rapidly arising cause of death, and why, requires a new approach to epidemiological research with very large datasets and timely data. Working on behalf of NHS England we therefore set out to deliver a secure and pseudonymised analytics platform inside the data centre of a major primary care electronic health records vendor establishing coverage across detailed primary care records for a substantial proportion of all patients in England. The following results are preliminary.