Introduction The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm. Methods and analysis We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell’s C, Brier Score, R 2 and Royston’s D. Ethics and dissemination Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal.
Abstract Aims Cardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both ‘direct’, through infection, and ‘indirect’, through changes in healthcare. Methods and results We used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0). Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60–100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2–3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths. Conclusion Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic.
Since 2008, the Improving Access to Psychological Therapies (IAPT) programme has offered adults in England evidence-based psychological treatments for common mental disorders (CMDs) such as depression and anxiety disorders. However, inequalities in access have not been explored at the national level.Using a unique individual patient dataset that linked 2011 Census information of English residents to national IAPT data collected between April 2017 and March 2018, we estimated the rate of access by a wide range of socio-demographic characteristics that are not routinely available. A large household survey was used to estimate the prevalence of probable CMDs by these socio-demographic characteristics. We estimated the probability of access to IAPT amongst people with CMDs by comparing the rates of access from IAPT data and the estimates of prevalence of CMDs from the household survey. Both unadjusted and adjusted (for important patient characteristics) access rates were estimated in logistic regression models.As a proportion of those with a probable CMD, access to IAPT varied markedly by socio-demographic characteristics. Older adults, males, people born outside of the UK, people with religious beliefs, people from Asian ethnic backgrounds, people reporting a disability and those without any academic or professional qualifications were underrepresented in IAPT services nationally, in adjusted models.The identification of patients who may be underrepresented in IAPT provides an opportunity for services to target outreach and engagement with these groups. Further understanding of barriers to access should help increase equity in access.
We estimated population-level associations between ethnicity and coronavirus disease 2019 (COVID-19) mortality using a newly linked census-based data set and investigated how ethnicity-specific mortality risk evolved during the pandemic.We conducted a retrospective cohort study of respondents to the 2011 Census of England and Wales in private households, linked to death registrations and adjusted for emigration (n = 47 872 412). The outcome of interest was death involving COVID-19 between 2 March 2020 and 15 May 2020. We estimated hazard ratios (HRs) for ethnic-minority groups compared with the White population, controlling for individual, household and area characteristics. HRs were estimated on the full outcome period and separately for pre- and post-lockdown periods.In age-adjusted models, people from all ethnic-minority groups were at elevated risk of COVID-19 mortality; the HRs for Black males and females were 3.13 (95% confidence interval: 2.93 to 3.34) and 2.40 (2.20 to 2.61), respectively. However, in fully adjusted models for females, the HRs were close to unity for all ethnic groups except Black [1.29 (1.18 to 1.42)]. For males, the mortality risk remained elevated for the Black [1.76 (1.63 to 1.90)], Bangladeshi/Pakistani [1.35 (1.21 to 1.49)] and Indian [1.30 (1.19 to 1.43)] groups. The HRs decreased after lockdown for all ethnic groups, particularly Black and Bangladeshi/Pakistani females.Differences in COVID-19 mortality between ethnic groups were largely attenuated by geographical and socio-demographic factors, though some residual differences remained. Lockdown was associated with reductions in excess mortality risk in ethnic-minority populations, which has implications for a second wave of infection.
Background: Demand for Emergency services has been soaring within England. Socio-economically disadvantaged people are more frequent users of healthcare services in general, particularly emergency services, however the underlying reason for this remains unclear. Methods: We estimated the odds of A&E attendance by socioeconomic status using data from the 2021 Census linked with Emergency Care data that included 51,776,958 individuals aged 0 to 95 resident in England. Logistic regression models were used to estimate odds of attendance, and to test whether health explained these differences. Findings: The odds of A&E attendance increased with level of deprivation, with the odds for those in the most deprived decile being 1.69 (95% CI – 1.68 to 1.69) times greater than the least deprived decile. Adjusting for underlying health attenuated but did not fully explain the association, with the odds reducing to 1.41 (95% CI – 1.40 to 1.41). This pattern was similar across age groups but most pronounced for people aged between 30 and 65. Those living in the most deprived decile had 2.26 times (95% CI = 2.23 to 2.28) higher odds of attending A&E for a condition classified as low acuity compared with those in the least deprived decile. This reduced to 2.02 (95% CI = 1.99 to 2.02) after adjusting for health. Interpretation: People living in more deprived areas were more likely to access A&E and these differences are not fully explained by differences in underlying health, and other factors, such as access to primary care services, may explain the remaining differences.Funding: The Office for National Statistics.Declaration of Interest: I confirm that authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported.Ethical Approval: This study was ethically self-assessed against the ethical principles of the National Statistician's Data Ethics Advisory Committee (NSDEC) using NSDEC's ethics self- assessment tool. We engaged with the UK Statistics Authority Data Ethics team, who were satisfied that no further ethical approval was required.
Abstract Background Evidence on the long-term employment consequences of SARS-CoV-2 infection is lacking. We used data from a large, community-based sample in the UK to estimate associations between Long Covid and employment outcomes. Methods This was an observational, longitudinal study using a pre–post design. We included survey participants from 3 February 2021 to 30 September 2022 when they were aged 16–64 years and not in education. Using conditional logit modelling, we explored the time-varying relationship between Long Covid status ≥12 weeks after a first test-confirmed SARS-CoV-2 infection (reference: pre-infection) and labour market inactivity (neither working nor looking for work) or workplace absence lasting ≥4 weeks. Results Of 206 299 participants (mean age 45 years, 54% female, 92% white), 15% were ever labour market inactive and 10% were ever long-term absent during follow-up. Compared with pre-infection, inactivity was higher in participants reporting Long Covid 30 to <40 weeks [adjusted odds ratio (aOR): 1.45; 95% CI: 1.17–1.81] or 40 to <52 weeks (aOR: 1.34; 95% CI: 1.05–1.72) post-infection. Combining with official statistics on Long Covid prevalence, and assuming a correct statistical model, our estimates translate to 27 000 (95% CI: 6000–47 000) working-age adults in the UK being inactive because of Long Covid in July 2022. Conclusions Long Covid is likely to have contributed to reduced participation in the UK labour market, though it is unlikely to be the sole driver. Further research is required to quantify the contribution of other factors, such as indirect health effects of the pandemic.
Abstract Objectives To estimate population-level associations between ethnicity and coronavirus disease 2019 (COVID-19) mortality, and to investigate how ethnicity-specific mortality risk evolved over the course of the pandemic. Design Retrospective cohort study using linked administrative data. Setting England and Wales, deaths occurring 2 March to 15 May 2020. Participants Respondents to the 2011 Census of England and Wales aged ≤100 years and enumerated in private households, linked to death registrations and adjusted to account for emigration before the outcome period, who were alive on 1 March 2020 ( n =47,872,412). Main outcome measure Death related to COVID-19, registered by 29 May 2020. Statistical methods We estimated hazard ratios (HRs) for ethnic minority groups compared with the White population using Cox regression models, controlling for geographical, demographic, socio-economic, occupational, and self-reported health factors. HRs were estimated on the full outcome period and separately for pre- and post-lockdown periods in the UK. Results In the age-adjusted models, people from all ethnic minority groups were at elevated risk of COVID-19 mortality; the HRs for Black males and females were 3.13 [95% confidence interval: 2.93 to 3.34] and 2.40 [2.20 to 2.61] respectively. However, in the fully adjusted model for females, the HRs were close to unity for all ethnic groups except Black (1.29 [1.18 to 1.42]). For males, COVID-19 mortality risk remained elevated for the Black (1.76 [1.63 to 1.90]), Bangladeshi/Pakistani (1.35 [1.21 to 1.49]) and Indian (1.30 [1.19 to 1.43]) groups. The HRs decreased after lockdown for all ethnic groups, particularly Black and Bangladeshi/Pakistani females. Conclusions Differences in COVID-19 mortality between ethnic groups were largely attenuated by geographical and socio-economic factors, although some residual differences remained. Lockdown was associated with reductions in excess mortality risk in ethnic minority populations, which has major implications for a second wave of infection or local spikes. Further research is needed to understand the causal mechanisms underpinning observed differences in COVID-19 mortality between ethnic groups.
We investigated long COVID incidence by vaccination status in a random sample of UK adults from April 2020 to November 2021. Persistent symptoms were reported by 9.5% of 3090 breakthrough severe acute respiratory syndrome coronavirus 2 infections and 14.6% of unvaccinated controls (adjusted odds ratio, 0.59 [95% confidence interval, .50-.69]), emphasizing the need for public health initiatives to increase population-level vaccine uptake.
BackgroundUrban greenspaces could reduce non-communicable disease (NCD) risk. The links between greenspaces and NCD-related mortality remain unclear. We aimed to estimate associations between residential greenspace quantity and access and all-cause mortality, cardiovascular disease mortality, cancer mortality, respiratory mortality, and type 2 diabetes mortality.MethodsWe linked 2011 UK Census data of London-dwelling adults (aged ≥18 years) to data from the UK death registry and the Greenspace Information for Greater London resource. We calculated percentage greenspace area, access point density (access points per km2), and distance in metres to the nearest access point for each respondent's residential neighbourhood (defined as 1000 m street network buffers) for greenspaces overall and by park type using a geographic information system. We estimated associations using Cox proportional hazards models, adjusted for a range of confounders.FindingsData were available for 4 645 581 individuals between March 27, 2011, and Dec 31, 2019. Respondents were followed up for a mean of 8·4 years (SD 1·4). All-cause mortality did not differ with overall greenspace coverage (hazard ratio [HR] 1·0004, 95% CI 0·9996–1·0012), increased with increasing access point density (1·0076, 1·0031–1·0120), and decreased slightly with increasing distance to the nearest access point (HR 0·9993, 0·9987–0·9998). A 1 percentage point (pp) increase in pocket park (areas for rest and recreation under 0·4 hectares) coverage was associated with a decrease in all-cause mortality risk (0·9441, 0·9213–0·9675), and an increase of ten pocket park access points per km2 was associated with a decreased respiratory mortality risk (0·9164, 0·8457–0·9931). Other associations were observed, but the estimated effects were small (eg, all-cause mortality risk for increases of 1 pp in regional park area were 0·9913, 0·9861–0·9966 and increases of ten small open space access points per km2 were 1·0247, 1·0151–1·0344).InterpretationIncreasing the quantity of, and access to, pocket parks might help mitigate mortality risk. More research is needed to elucidate the mechanisms that could explain these associations.FundingHealth Data Research UK (HDRUK).
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)