Clinical trial results in context: comparison of baseline characteristics and outcomes of 38,510 RECOVERY trial participants versus a reference population of 346,271 people hospitalised with COVID-19 in England.

BACKGROUND: Randomised trials are essential to reliably assess medical interventions. Nevertheless, interpretation of such studies, particularly when considering absolute effects, is enhanced by understanding how the trial population may differ from the populations it aims to represent. METHODS: We compared baseline characteristics and mortality of RECOVERY participants recruited in England (n = 38,510) with a reference population hospitalised with COVID-19 in England (n = 346,271) from March 2020 to November 2021. We used linked hospitalisation and mortality data for both cohorts to extract demographics, comorbidity/frailty scores, and crude and age- and sex-adjusted 28-day all-cause mortality. RESULTS: Demographics of RECOVERY participants were broadly similar to the reference population, but RECOVERY participants were younger (mean age [standard deviation]: RECOVERY 62.6 [15.3] vs reference 65.7 [18.5] years) and less frequently female (37% vs 45%). Comorbidity and frailty scores were lower in RECOVERY, but differences were attenuated after age stratification. Age- and sex-adjusted 28-day mortality declined over time but was similar between cohorts across the study period (RECOVERY 23.7% [95% confidence interval: 23.3-24.1%]; vs reference 24.8% [24.6-25.0%]), except during the first pandemic wave in the UK (March-May 2020) when adjusted mortality was lower in RECOVERY. CONCLUSIONS: Adjusted 28-day mortality in RECOVERY was similar to a nationwide reference population of patients admitted with COVID-19 in England during the same period but varied substantially over time in both cohorts. Therefore, the absolute effect estimates from RECOVERY were broadly applicable to the target population at the time but should be interpreted in the light of current mortality estimates. TRIAL REGISTRATION: ISRCTN50189673- Feb. 04, 2020, NCT04381936- May 11, 2020.

Accuracy of heart failure ascertainment using routinely collected healthcare data: a systematic review and meta-analysis.

BACKGROUND: Ascertainment of heart failure (HF) hospitalizations in cardiovascular trials is costly and complex, involving processes that could be streamlined by using routinely collected healthcare data (RCD). The utility of coded RCD for HF outcome ascertainment in randomized trials requires assessment. We systematically reviewed studies assessing RCD-based HF outcome ascertainment against "gold standard" (GS) methods to study the feasibility of using such methods in clinical trials. METHODS: Studies assessing International Classification of Disease (ICD) coded RCD-based HF outcome ascertainment against GS methods and reporting at least one agreement statistic were identified by searching MEDLINE and Embase from inception to May 2021. Data on study characteristics, details of RCD and GS data sources and definitions, and test statistics were reviewed. Summary sensitivities and specificities for studies ascertaining acute and prevalent HF were estimated using a bivariate random effects meta-analysis. Heterogeneity was evaluated using I2 statistics and hierarchical summary receiver operating characteristic (HSROC) curves. RESULTS: A total of 58 studies of 48,643 GS-adjudicated HF events were included in this review. Strategies used to improve case identification included the use of broader coding definitions, combining multiple data sources, and using machine learning algorithms to search free text data, but these methods were not always successful and at times reduced specificity in individual studies. Meta-analysis of 17 acute HF studies showed that RCD algorithms have high specificity (96.2%, 95% confidence interval [CI] 91.5-98.3), but lacked sensitivity (63.5%, 95% CI 51.3-74.1) with similar results for 21 prevalent HF studies. There was considerable heterogeneity between studies. CONCLUSIONS: RCD can correctly identify HF outcomes but may miss approximately one-third of events. Methods used to improve case identification should also focus on minimizing false positives.

Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials.

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS. METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status. DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.

Thromboxane biosynthesis and future events in diabetes: the ASCEND trial.

BACKGROUND AND AIMS: Thromboxane (TX) A2, released by activated platelets, plays an important role in atherothrombosis. Urinary 11-dehydro-TXB2 (U-TXM), a stable metabolite reflecting the whole-body TXA2 biosynthesis, is reduced by ∼70% by daily low-dose aspirin. The U-TXM represents a non-invasive biomarker of in vivo platelet activation and is enhanced in patients with diabetes. This study assessed whether U-TXM is associated with the risk of future serious vascular events or revascularizations (SVE-R), major bleeding, or cancer in patients with diabetes. METHODS: The U-TXM was measured pre-randomization to aspirin or placebo in 5948 people with type 1 or 2 diabetes and no cardiovascular disease, in the ASCEND trial. Associations between log U-TXM and SVE-R (n = 618), major bleed (n = 206), and cancer (n = 700) during 6.6 years of follow-up were investigated by Cox regression; comparisons of these associations with the effects of randomization to aspirin were made. RESULTS: Higher U-TXM was associated with older age, female sex, current smoking, type 2 diabetes, higher body size, urinary albumin/creatinine ratio of ≥3 mg/mmol, and higher estimated glomerular filtration rate. After adjustment for these, U-TXM was marginally statistically significantly associated with SVE-R and major bleed but not cancer [hazard ratios per 1 SD higher log U-TXM (95% confidence interval): 1.09 (1.00-1.18), 1.16 (1.01-1.34), and 1.06 (0.98-1.14)]. The hazard ratio was similar to that implied by the clinical effects of randomization to aspirin for SVE-R but not for major bleed. CONCLUSIONS: The U-TXM was log-linearly independently associated with SVE-R in diabetes. This is consistent with the involvement of platelet TXA2 in diabetic atherothrombosis.

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial.

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome.

Delivering trials in the NHS: more than worth it.

Randomised trials are the best method to determine the efficacy and safety of health technologies. A recent report by Lord O'Shaughnessy highlighted many of the current challenges to delivering trials in the UK and proposed potential solutions. Among these, making trials the business of all NHS institutions and a valued part of all doctors' work, while leveraging the potential of the data that the NHS collects routinely, offers an opportunity to improve NHS efficiency, doctors' job satisfaction and population health simultaneously.

Reliability of major bleeding events in UK routine data versus clinical trial adjudicated follow-up data.

OBJECTIVE: To assess how reliable UK routine data are for ascertaining major bleeding events compared with adjudicated follow-up. METHODS: The ASCEND (A Study of Cardiovascular Events iN Diabetes) primary prevention trial randomised 15 480 UK people with diabetes to aspirin versus matching placebo. The primary safety outcome was major bleeding (including intracranial haemorrhage, sight-threatening eye bleeding, serious gastrointestinal bleeding and other major bleeding (epistaxis, haemoptysis, haematuria, vaginal and other bleeding)) ascertained by direct-participant mail-based follow-up, with >90% of outcomes undergoing adjudication. Nearly all participants were linked to routinely collected hospitalisation and death data (ie, routine data). An algorithm categorised bleeding events from routine data as major/minor. Kappa statistics were used to assess agreement between data sources, and randomised comparisons were re-run using routine data. RESULTS: When adjudicated follow-up and routine data were compared, there was agreement for 318 major bleeding events, with routine data identifying 281 additional-potential events, and not identifying 241 participant-reported events (kappa 0.53, 95% CI 0.49 to 0.57). Repeating ASCEND's randomised comparisons using routine data only found estimated relative and absolute effects of allocation to aspirin versus placebo on major bleeding similar to adjudicated follow-up (adjudicated follow-up: aspirin 314 (4.1%) vs placebo 245 (3.2%); rate ratio (RR) 1.29, 95% CI 1.09 to 1.52; absolute excess +6.3/5000 person-years (mean SE±2.1); vs routine data: 327 (4.2%) vs 272 (3.5%); RR 1.21, 95% CI 1.03 to 1.41; absolute excess +5.0/5000 (±2.2)). CONCLUSIONS: Analyses of the ASCEND randomised trial found that major bleeding events ascertained via UK routine data sources provided relative and absolute treatment effects similar to adjudicated follow-up. TRIAL REGISTRATION NUMBER: ISRCTN60635500; NCT00135226.

Effect of low-dose aspirin on urinary 11-dehydro-thromboxane B2 in the ASCEND (A Study of Cardiovascular Events iN Diabetes) randomized controlled trial.

BACKGROUND: Aspirin is widely used for cardioprotection with its antiplatelet effects due to the blocking of thromboxane A2 production. However, it has been suggested that platelet abnormalities in those with diabetes prevent adequate suppression with once daily aspirin. METHODS: In the ASCEND randomized double-blind trial of aspirin 100 mg once daily versus placebo in participants with diabetes but no history of cardiovascular disease, suppression was assessed by measuring 11-dehydro-thromboxane B2 excretion in urine (U-TXM) in a randomly selected sample of 152 participants (76 aspirin arm, 74 placebo arm), plus 198 (93 aspirin arm, 105 placebo arm) adherent to study drugs and selected to maximize the numbers ingesting their last tablet 12-24 h before urine sampling. U-TXM was assayed using a competitive ELISA assay in samples mailed a mean of 2 years after randomization, with time since taking last aspirin/placebo tablet recorded at the time of sample provision. Effective suppression (U-TXM < 1500 pg/mg creatinine) and percentage reductions in U-TXM by aspirin allocation were compared. RESULTS: In the random sample, U-TXM was 71% (95% CI 64-76%) lower among aspirin vs placebo-allocated participants. Among adherent participants in the aspirin arm, U-TXM was 72% (95% CI 69-75%) lower than in the placebo arm and 77% achieved effective suppression overall. Suppression was similar among those who ingested their last tablet more than 12 h before urine sampling with levels in the aspirin arm 72% (95% CI 67-77%) lower than in the placebo arm and 70% achieving effective suppression. CONCLUSIONS: Daily aspirin significantly reduces U-TXM in participants with diabetes, including at 12-24 h after ingestion. TRIAL REGISTRATION: ISRCTN ISRCTN60635500. Registered on 1 Sept 2005; ClinicalTrials.gov NCT00135226. Registered on 24 Aug 2005.

A DELPHI study priority setting the remaining challenges for the use of routinely collected data in trials: COMORANT-UK.

BACKGROUND: Researchers are increasingly seeking to use routinely collected data to support clinical trials. This approach has the potential to transform the way clinical trials are conducted in the future. The availability of routinely collected data for research, whether healthcare or administrative, has increased, and infrastructure funding has enabled much of this. However, challenges remain at all stages of a trial life cycle. This study, COMORANT-UK, aimed to systematically identify, with key stakeholders across the UK, the ongoing challenges related to trials that seek to use routinely collected data. METHODS: This three-step Delphi method consisted of two rounds of anonymous web-based surveys and a virtual consensus meeting. Stakeholders included trialists, data infrastructures, funders of trials, regulators, data providers and the public. Stakeholders identified research questions or challenges that they considered were of particular importance and then selected their top 10 in the second survey. The ranked questions were taken forward to the consensus meeting for discussion with representatives invited from the stakeholder groups. RESULTS: In the first survey, 66 respondents yielded over 260 questions or challenges. These were thematically grouped and merged into a list of 40 unique questions. Eighty-eight stakeholders then ranked their top ten from the 40 questions in the second survey. The most common 14 questions were brought to the virtual consensus meeting in which stakeholders agreed a top list of seven questions. We report these seven questions which are within the following domains: trial design, Patient and Public Involvement, trial set-up, trial open and trial data. These questions address both evidence gaps (requiring further methodological research) and implementation gaps (requiring training and/or service re-organisation). CONCLUSION: This prioritised list of seven questions should inform the direction of future research in this area and should direct efforts to ensure that the benefits in major infrastructure for routinely collected data are achieved and translated. Without this and future work to address these questions, the potential societal benefits of using routinely collected data to help answer important clinical questions will not be realised.

Coronial postmortem reports and indirect COVID-19 pandemic-related mortality.

AIMS: Widespread disruption of healthcare services and excess mortality not directly attributed to COVID-19 occurred between March and May 2020. We undertook the first UK multicentre study of coroners' autopsies before and during this period using postmortem reports. METHODS: We reviewed reports of non-forensic coroners' autopsies performed during the first COVID-19 lockdown (23 March to 8 May 2020), and the same period in 2018. Deaths were categorised as natural non-COVID-19, COVID-19-related, non-natural (suicide, drug and alcohol-related, traumatic, other). We provided opinion regarding whether delayed access to medical care or changes in behaviour due to lockdown were a potential factor in deaths. RESULTS: Seven centres covering nine coronial jurisdictions submitted a total of 1100 coroners' autopsies (498 in 2018, 602 in 2020). In only 54 autopsies was death attributed to COVID-19 (9%). We identified a significant increase in cases where delays in accessing medical care potentially contributed to death (10 in 2018, 44 in 2020). Lockdown was a contributing factor in a proportion of suicides (24%) and drug and alcohol-related deaths (12%). CONCLUSIONS: Postmortem reports have considerable utility in evaluating excess mortality due to healthcare and wider societal disruption during a pandemic. They provide information at an individual case level that is not available from assessment of death certification data. Detailed evaluation of coroners' autopsy reports, supported by appropriate regulatory oversight, is recommended to mitigate disruption and indirect causes of mortality in future pandemics. Maintaining access to healthcare, including substance misuse and mental health services, is an important consideration.

Weight loss in a cardiovascular trial population identifies people at future risk of dementia.

Introduction: Populations at increased risk of dementia need to be identified for well-powered trials of preventive interventions. Weight loss, which often occurs in pre-clinical dementia, could identify a population at sufficiently high dementia risk. Methods: In 12,975 survivors in the Heart Protection Study statin trial of people with, or at high risk of, cardiovascular disease, the association of weight change over 5 years during the trial with post-trial dementia recorded in electronic hospital admission and death records (n = 784) was assessed, after adjustment for age, sex, treatment allocation, and deprivation measures. Results: Among the 60% without substantial weight gain (≤2 kg weight gain), each 1 kg weight loss was associated with a risk ratio for dementia of 1.04 (95% confidence interval, 1.02-1.07). Weight loss ≥4 kg and cognitive function below the mean identified participants aged ≥67 years with a 13% 10-year dementia risk. Discussion: The combination of weight loss and high vascular risk identified individuals at high risk of dementia who could be recruited to dementia prevention trials.

Effects of aspirin on dementia and cognitive function in diabetic patients: the ASCEND trial.

AIMS: Aspirin is widely used in cardiovascular disease prevention but is also associated with an increased risk of bleeding. The net effect of aspirin on dementia and cognitive impairment is uncertain. METHODS AND RESULTS: In the ASCEND trial, 15 480 people from the UK with diabetes and no history of cardiovascular disease were randomized to aspirin 100 mg daily or matching placebo for a mean of 7.4 years. The 15 427 ASCEND participants with no recorded dementia prior to baseline were included in this cognitive study with a primary pre-specified outcome of 'broad dementia', comprising dementia, cognitive impairment, or confusion. This was ascertained through participant, carer, or general practitioner report or hospital admission diagnosis, by 31 March 2019 (∼2 years beyond the scheduled treatment period). The broad dementia outcome occurred in a similar percentage of participants in the aspirin group and placebo group: 548 participants (7.1%) vs. 598 (7.8%), rate ratio 0.91 [95% confidence interval (CI), 0.81-1.02]. Thus, the CI excluded proportional hazards of >2% and proportional benefits of >19%. CONCLUSION: Aspirin does not have a large proportional effect on the risk of dementia. Trials or meta-analyses with larger total numbers of incident dementia cases to increase statistical power are needed to assess whether any modest proportional 10-15% benefits of 5-7 years of aspirin use on dementia exist. CLINICAL TRIAL REGISTRATION: Current Controlled Trials number, ISRCTN60635500; ClinicalTrials.gov number: NCT00135226.

Performance of the UK Prospective Diabetes Study Outcomes Model 2 in a Contemporary UK Type 2 Diabetes Trial Cohort.

OBJECTIVES: The UK Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS-OM) developed using 30-year (1977-2007) data from the UKPDS is widely used for health outcomes' projections and economic evaluations of therapies for patients with type 2 diabetes (T2D). Nevertheless, its reliability for contemporary UK T2D populations is unclear. We assessed the performance of version 2 of the model (UKPDS-OM2) using data from A Study of Cardiovascular Events in Diabetes (ASCEND), which followed participants with diabetes in the UK between 2005 and 2017. METHODS: The UKPDS-OM2 was used to predict the occurrence of myocardial infarction (MI), other ischemic heart disease, stroke, cardiovascular (CV) death, and other death among the 14 569 participants with T2D in ASCEND, all without previous CV disease at study entry. Calibration (comparison of predicted and observed year-on-year cumulative incidence over 10 years) and discrimination (c-statistics) of the model were assessed for each endpoint. The percentage error in event rates at year 7 (mean duration of follow up) was used to quantify model bias. RESULTS: The UKPDS-OM2 substantially overpredicted MI, stroke, CV death, and other death over the 10-year follow-up period (by 149%, 42%, 269%, and 52%, respectively, at year 7). Discrimination of the model for MI and other ischemic heart disease (c-statistics 0.58 and 0.60, respectively) was poorer than that for other outcomes (c-statistics ranging from 0.66 to 0.72). CONCLUSIONS: The UKPDS-OM2 substantially overpredicted risks of key CV outcomes and death in people with T2D in ASCEND. Appropriate adjustments or a new model may be required for assessments of long-term effects of treatments in contemporary T2D cohorts.

Comparison of the Accuracy and Completeness of Records of Serious Vascular Events in Routinely Collected Data vs Clinical Trial-Adjudicated Direct Follow-up Data in the UK: Secondary Analysis of the ASCEND Randomized Clinical Trial.

Importance: Routinely collected data could substantially decrease the cost of conducting trials. Objective: To assess the accuracy and completeness of UK routine data for ascertaining serious vascular events (SVEs) compared with adjudicated follow-up data. Design, Setting, and Participants: This was a secondary analysis of a randomized clinical trial. From June 24, 2005, to July 28, 2011, the ASCEND (A Study of Cardiovascular Events in Diabetes) primary prevention trial used mail-based methods to randomize people with diabetes without evidence of atherosclerotic vascular disease using a 2 × 2 factorial design to aspirin and/or ω-fatty acids vs matching placebo in the UK. Direct participant mail-based follow-up was the main source of outcome data, with more than 90% of the primary outcome events undergoing adjudication. Follow-up was completed on July 31, 2017. In parallel, more than 99% of participants were linked to routinely collected hospital admission and death registry data (ie, routine data), enabling post hoc randomized comparisons of different sources of outcome data (conducted from September 1, 2018, to October 1, 2021). Interventions: Random allocation to 100 mg of aspirin once daily vs matching placebo and separately to 1 g of ω-3 fatty acids once daily vs placebo. Main Outcomes and Measures: The primary outcome consisted of SVEs (a composite of nonfatal myocardial infarction, ischemic stroke, transient ischemic attack [TIA], or vascular death, excluding hemorrhagic stroke). Results: A total of 15 480 participants were randomized (mean [SD] age, 63 [9] years; 9684 [62.6%] men) and followed up for a mean (SD) of 7.4 (1.8) years. For SVEs, agreement between adjudicated direct follow-up and routine data sources was strong (1401 vs 1127 events; κ = 0.78 [95% CI, 0.76-0.80]; sensitivity, 72.0% [95% CI, 69.7%-74.4%]; specificity, 99.2% [95% CI, 99.0%-99.3%]), and sensitivity improved for SVEs excluding transient ischemic attack (1129 vs 1026 events; sensitivity, 80.6% [95% CI, 78.3%-82.9%]). Rate ratios for the aspirin-randomized comparison for adjudicated direct follow-up vs follow-up solely through routine data alone were 0.88 (95% CI, 0.79-0.97) vs 0.91 (95% CI, 0.81-1.02) for the primary outcome and 0.92 (95% CI, 0.82-1.03) vs 0.91 (95% CI, 0.80-1.02) for SVEs excluding TIA. Results were similar for the ω-3 fatty acid comparison, and adjudication did not seem to markedly change rate ratios. Conclusions and Relevance: Post hoc analyses of the ASCEND trial suggest that routinely collected hospital admission and death registry data in the UK could be used as the sole method of follow-up for myocardial infarction, ischemic stroke resulting in hospitalization, vascular death, and arterial revascularization in primary prevention cardiovascular trials, without the need for verification by clinical adjudication.

Making trials part of good clinical care: lessons from the RECOVERY trial.

When COVID-19 hit the UK in early 2020, there were no known treatments for a condition that results in the death of around one in four patients hospitalised with this disease. Around the world, possible treatments were administered to huge numbers of patients, without any reliable assessments of safety and efficacy. The rapid generation of high-quality evidence was vital. RECOVERY is a streamlined, pragmatic, randomised controlled trial, which was set up in response to this challenge. As of April 2021, over 39,000 patients have been enrolled from 178 hospital sites in the UK. Within 100 days of its initiation, RECOVERY demonstrated that dexamethasone improves survival for patients with severe disease; a result that was rapidly implemented in the UK and internationally saving hundreds of thousands of lives. Importantly, it also showed that other widely used treatments (such as hydroxychloroquine and azithromycin) have no meaningful benefit for hospitalised patients. This was only possible through randomisation of large numbers of patients and the adoption of streamlined and pragmatic procedures focused on quality, together with widespread collaboration focused on a single goal. RECOVERY illustrates how clinical trials and healthcare can be integrated, even in a pandemic. This approach provides new opportunities to generate the evidence needed for high-quality healthcare not only for a pandemic but for the many other conditions that place a burden on patients and the healthcare system.

A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions.

BACKGROUND: Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention. METHODS: A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40-70 recruited in 2006-2010, and followed up for 6-12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models. RESULTS: Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for >65% and >84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p < .001) and hospital admissions (p < 1 × 10-10) over chronological age alone. CONCLUSIONS: This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.

Place and Underlying Cause of Death During the COVID-19 Pandemic: Retrospective Cohort Study of 3.5 Million Deaths in England and Wales, 2014 to 2020.

OBJECTIVE: To describe the place and cause of death during the coronavirus disease 2019 (COVID-19) pandemic to assess its impact on excess mortality. METHODS: This national death registry included all adult (aged ≥18 years) deaths in England and Wales between January 1, 2014, and June 30, 2020. Daily deaths during the COVID-19 pandemic were compared against the expected daily deaths, estimated with use of the Farrington surveillance algorithm for daily historical data between 2014 and 2020 by place and cause of death. RESULTS: Between March 2 and June 30, 2020, there was an excess mortality of 57,860 (a proportional increase of 35%) compared with the expected deaths, of which 50,603 (87%) were COVID-19 related. At home, only 14% (2267) of the 16,190 excess deaths were related to COVID-19, with 5963 deaths due to cancer and 2485 deaths due to cardiac disease, few of which involved COVID-19. In care homes or hospices, 61% (15,623) of the 25,611 excess deaths were related to COVID-19, 5539 of which were due to respiratory disease, and most of these (4315 deaths) involved COVID-19. In the hospital, there were 16,174 fewer deaths than expected that did not involve COVID-19, with 4088 fewer deaths due to cancer and 1398 fewer deaths due to cardiac disease than expected. CONCLUSION: The COVID-19 pandemic has resulted in a large excess of deaths in care homes that were poorly characterized and likely to be the result of undiagnosed COVID-19. There was a smaller but important and ongoing excess in deaths at home, particularly from cancer and cardiac disease, suggesting public avoidance of hospital care for non-COVID-19 conditions.