Excess years of life lost to COVID-19 and other causes of death by sex, neighbourhood deprivation, and region in England and Wales during 2020: A registry-based study.

BACKGROUND: Deaths in the first year of the Coronavirus Disease 2019 (COVID-19) pandemic in England and Wales were unevenly distributed socioeconomically and geographically. However, the full scale of inequalities may have been underestimated to date, as most measures of excess mortality do not adequately account for varying age profiles of deaths between social groups. We measured years of life lost (YLL) attributable to the pandemic, directly or indirectly, comparing mortality across geographic and socioeconomic groups. METHODS AND FINDINGS: We used national mortality registers in England and Wales, from 27 December 2014 until 25 December 2020, covering 3,265,937 deaths. YLLs (main outcome) were calculated using 2019 single year sex-specific life tables for England and Wales. Interrupted time-series analyses, with panel time-series models, were used to estimate expected YLL by sex, geographical region, and deprivation quintile between 7 March 2020 and 25 December 2020 by cause: direct deaths (COVID-19 and other respiratory diseases), cardiovascular disease and diabetes, cancer, and other indirect deaths (all other causes). Excess YLL during the pandemic period were calculated by subtracting observed from expected values. Additional analyses focused on excess deaths for region and deprivation strata, by age-group. Between 7 March 2020 and 25 December 2020, there were an estimated 763,550 (95% CI: 696,826 to 830,273) excess YLL in England and Wales, equivalent to a 15% (95% CI: 14 to 16) increase in YLL compared to the equivalent time period in 2019. There was a strong deprivation gradient in all-cause excess YLL, with rates per 100,000 population ranging from 916 (95% CI: 820 to 1,012) for the least deprived quintile to 1,645 (95% CI: 1,472 to 1,819) for the most deprived. The differences in excess YLL between deprivation quintiles were greatest in younger age groups; for all-cause deaths, a mean of 9.1 years per death (95% CI: 8.2 to 10.0) were lost in the least deprived quintile, compared to 10.8 (95% CI: 10.0 to 11.6) in the most deprived; for COVID-19 and other respiratory deaths, a mean of 8.9 years per death (95% CI: 8.7 to 9.1) were lost in the least deprived quintile, compared to 11.2 (95% CI: 11.0 to 11.5) in the most deprived. For all-cause mortality, estimated deaths in the most deprived compared to the most affluent areas were much higher in younger age groups, but similar for those aged 85 or over. There was marked variability in both all-cause and direct excess YLL by region, with the highest rates in the North West. Limitations include the quasi-experimental nature of the research design and the requirement for accurate and timely recording. CONCLUSIONS: In this study, we observed strong socioeconomic and geographical health inequalities in YLL, during the first calendar year of the COVID-19 pandemic. These were in line with long-standing existing inequalities in England and Wales, with the most deprived areas reporting the largest numbers in potential YLL.

Excess deaths from COVID-19 and other causes by region, neighbourhood deprivation level and place of death during the first 30 weeks of the pandemic in England and Wales: A retrospective registry study.

BACKGROUND: Excess deaths during the COVID-19 pandemic compared with those expected from historical trends have been unequally distributed, both geographically and socioeconomically. Not all excess deaths have been directly related to COVID-19 infection. We investigated geographical and socioeconomic patterns in excess deaths for major groups of underlying causes during the pandemic. METHODS: Weekly mortality data from 27/12/2014 to 2/10/2020 for England and Wales were obtained from the Office of National Statistics. Negative binomial regressions were used to model death counts based on pre-pandemic trends for deaths caused directly by COVID-19 (and other respiratory causes) and those caused indirectly by it (cardiovascular disease or diabetes, cancers, and all other indirect causes) over the first 30 weeks of the pandemic (7/3/2020-2/10/2020). FINDINGS: There were 62,321 (95% CI: 58,849 to 65,793) excess deaths in England and Wales in the first 30 weeks of the pandemic. Of these, 46,221 (95% CI: 45,439 to 47,003) were attributable to respiratory causes, including COVID-19, and 16,100 (95% CI: 13,410 to 18,790) to other causes. Rates of all-cause excess mortality ranged from 78 per 100,000 in the South West of England and in Wales to 130 per 100,000 in the West Midlands; and from 93 per 100,000 in the most affluent fifth of areas to 124 per 100,000 in the most deprived. The most deprived areas had the highest rates of death attributable to COVID-19 and other indirect deaths, but there was no socioeconomic gradient for excess deaths from cardiovascular disease/diabetes and cancer. INTERPRETATION: During the first 30 weeks of the COVID-19 pandemic there was significant geographic and socioeconomic variation in excess deaths for respiratory causes, but not for cardiovascular disease, diabetes and cancer. Pandemic recovery plans, including vaccination programmes, should take account of individual characteristics including health, socioeconomic status and place of residence. FUNDING: None.

Indirect Impact of the COVID-19 Pandemic on Activity and Outcomes of Transcatheter and Surgical Treatment of Aortic Stenosis in England.

[Figure: see text].

Revascularisation strategies in patients with significant left main coronary disease during the COVID-19 pandemic.

BACKGROUND: There are limited data on the impact of the COVID-19 pandemic on left main (LM) coronary revascularisation activity, choice of revascularisation strategy, and post-procedural outcomes. METHODS: All patients with LM disease (≥50% stenosis) undergoing coronary revascularisation in England between January 1, 2017 and August 19, 2020 were included (n = 22,235), stratified by time-period (pre-COVID: 01/01/2017-29/2/2020; COVID: 1/3/2020-19/8/2020) and revascularisation strategy (percutaneous coronary intervention (PCI) vs. coronary artery bypass grafting (CABG). Logistic regression models were performed to examine odds ratio (OR) of 1) receipt of CABG (vs. PCI) and 2) in-hospital and 30-day postprocedural mortality, in the COVID-19 period (vs. pre-COVID). RESULTS: There was a decline of 1,354 LM revascularisation procedures between March 1, 2020 and July 31, 2020 compared with previous years' (2017-2019) averages (-48.8%). An increased utilization of PCI over CABG was observed in the COVID period (receipt of CABG vs. PCI: OR 0.46 [0.39, 0.53] compared with 2017), consistent across all age groups. No difference in adjusted in-hospital or 30-day mortality was observed between pre-COVID and COVID periods for both PCI (odds ratio (OR): 0.72 [0.51. 1.02] and 0.83 [0.62, 1.11], respectively) and CABG (OR 0.98 [0.45, 2.14] and 1.51 [0.77, 2.98], respectively) groups. CONCLUSION: LM revascularisation activity has significantly declined during the COVID period, with a shift towards PCI as the preferred strategy. Postprocedural mortality within each revascularisation group was similar in the pre-COVID and COVID periods, reflecting maintenance in quality of outcomes during the pandemic. Future measures are required to safely restore LM revascularisation activity to pre-COVID levels.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE: To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES: PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION: Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS: 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.