Background There are few primary care studies of the COVID-19 pandemic. We aimed to identify demographic and clinical risk factors for testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre primary care network. MethodsWe analysed routinely collected, pseudonymised data for patients in the RCGP Research and Surveillance Centre primary care sentinel network who were tested for SARS-CoV-2 between Jan 28 and April 4, 2020. We used multivariable logistic regression models with multiple imputation to identify risk factors for positive SARS-CoV-2 tests within this surveillance network. Findings We identified 3802 SARS-CoV-2 test results, of which 587 were positive. In multivariable analysis, male sex was independently associated with testing positive for SARS-CoV-2 (296 [18•4%] of 1612 men vs 291 [13•3%] of 2190 women; adjusted odds ratio [OR] 1•55, 95% CI 1•27-1•89). Adults were at increased risk of testing positive for SARS-CoV-2 compared with children, and people aged 40-64 years were at greatest risk in the multivariable model (243 [18•5%] of 1316 adults aged 40-64 years vs 23 [4•6%] of 499 children; adjusted OR 5•36, 95% CI 3•28-8•76). Compared with white people, the adjusted odds of a positive test were greater in black people (388 [15•5%] of 2497 white people vs 36 [62•1%] of 58 black people; adjusted OR 4•75, 95% CI 2•65-8•51). People living in urban areas versus rural areas (476 [26•2%] of 1816 in urban areas vs 111 [5•6%] of 1986 in rural areas; adjusted OR 4•59, 95% CI 3•57-5•90) and in more deprived areas (197 [29•5%] of 668 in most deprived vs 143 [7•7%] of 1855 in least deprived; adjusted OR 2•03, 95% CI 1•51-2•71) were more likely to test positive. People with chronic kidney disease were more likely to test positive in the adjusted analysis (68 [32•9%] of 207 with chronic kidney disease vs 519 [14•4%] of 3595 without; adjusted OR 1•91, 95% CI 1•31-2•78), but there was no significant association with other chronic conditions in that analysis. We found increased odds of a positive test among people who are obese (142 [20•9%] of 680 people with obesity vs 171 [13•2%] of 1296 normal-weight people; adjusted OR 1•41, 95% CI 1•04-1•91). Notably, active smoking was linked with decreased odds of a positive test result (47 [11•4%] of 413 active smokers vs 201 [17•9%] of 1125 non-smokers; adjusted OR 0•49, 95% CI 0•34-0•71). Interpretation A positive SARS-CoV-2 test result in this primary care cohort was associated with similar risk factors as observed for severe outcomes of COVID-19 in hospital settings, except for smoking. We provide evidence of potential sociodemographic factors associated with a positive test, including deprivation, population density, ethnicity, and chronic kidney disease. Funding Wellcome Trust.

Objectives To report reliable estimates of short term and long term survival rates for people with a diagnosis of heart failure and to assess trends over time by year of diagnosis, hospital admission, and socioeconomic group. Design Population based cohort study. Setting Primary care, United Kingdom. Participants Primary care data for 55 959 patients aged 45 and over with a new diagnosis of heart failure and 278 679 age and sex matched controls in the Clinical Practice Research Datalink from 1 January 2000 to 31 December 2017 and linked to inpatient Hospital Episode Statistics and Office for National Statistics mortality data. Main outcome measures Survival rates at one, five, and 10 years and cause of death for people with and without heart failure; and temporal trends in survival by year of diagnosis, hospital admission, and socioeconomic group. Results Overall, one, five, and 10 year survival rates increased by 6.6% (from 74.2% in 2000 to 80.8% in 2016), 7.2% (from 41.0% in 2000 to 48.2% in 2012), and 6.4% (from 19.8% in 2000 to 26.2% in 2007), respectively. There were 30 906 deaths in the heart failure group over the study period. Heart failure was listed on the death certificate in 13 093 (42.4%) of these patients, and in 2237 (7.2%) it was the primary cause of death. Improvement in survival was greater for patients not requiring admission to hospital around the time of diagnosis (median difference 2.4 years; 5.3 v 2.9 years, P<0.001). There was a deprivation gap in median survival of 0.5 years between people who were least deprived and those who were most deprived (4.6 v 4.1 years, P<0.001). Conclusions Survival after a diagnosis of heart failure has shown only modest improvement in the 21st century and lags behind other serious conditions, such as cancer. New strategies to achieve timely diagnosis and treatment initiation in primary care for all socioeconomic groups should be a priority for future research and policy.

Physical distancing is an important part of measures to control covid-19, but exactly how far away and for how long contact is safe in different contexts is unclear. Rules that stipulate a single specific physical distance (1 or 2 metres) between individuals to reduce transmission of SARS-CoV-2, the virus causing covid-19, are based on an outdated, dichotomous notion of respiratory droplet size. This overlooks the physics of respiratory emissions, where droplets of all sizes are trapped and moved by the exhaled moist and hot turbulent gas cloud that keeps them concentrated as it carries them over metres in a few seconds. 1 2 After the cloud slows sufficiently, ventilation, specific patterns of airflow, and type of activity become important. Viral load of the emitter, duration of exposure, and susceptibility of an individual to infection are also important. Instead of single, fixed physical distance rules, we propose graded recommendations that better reflect the multiple factors that combine to determine risk. This would provide greater protection in the highest risk settings but also greater freedom in lower risk settings, potentially enabling a return towards normality in some aspects of social and economic life.

Aim To provide reliable survival estimates for people with chronic heart failure and explain variation in survival by key factors including age at diagnosis, left ventricular ejection fraction, decade of diagnosis, and study setting. Methods and results We searched in relevant databases from inception to August 2018 for non‐interventional studies reporting survival rates for patients with chronic or stable heart failure in any ambulatory setting. Across the 60 included studies, there was survival data for 1.5 million people with heart failure. In our random effects meta‐analyses the pooled survival rates at 1 month, 1, 2, 5 and 10 years were 95.7% (95% confidence interval 94.3–96.9), 86.5% (85.4–87.6), 72.6% (67.0–76.6), 56.7% (54.0–59.4) and 34.9% (24.0–46.8), respectively. The 5‐year survival rates improved between 1970–1979 and 2000–2009 across healthcare settings, from 29.1% (25.5–32.7) to 59.7% (54.7–64.6). Increasing age at diagnosis was significantly associated with a reduced survival time. Mortality was lowest in studies conducted in secondary care, where there were higher reported prescribing rates of key heart failure medications. There was significant heterogeneity among the included studies in terms of heart failure diagnostic criteria, participant co‐morbidities, and treatment rates. Conclusion These results can inform health policy and individual patient advanced care planning. Mortality associated with chronic heart failure remains high despite steady improvements in survival. There remains significant scope to improve prognosis through greater implementation of evidence‐based treatments. Further research exploring the barriers and facilitators to treatment is recommended.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has resulted in a rapid change in workload across healthcare systems. Factors related to this adaptation in UK primary care have not yet been examined.AimTo assess the responsiveness and prioritisation of primary care consultation type for older adults during the COVID-19 pandemic.Design and settingA cross-sectional database study examining consultations between 17 February and 10 May 2020 for patients aged ≥65 years, drawn from primary care practices within the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) sentinel network, UK.MethodThe authors reported the proportion of consultation type across five categories: clinical administration, electronic/video, face-to-face, telephone, and home visits. Temporal trends in telephone and face-to-face consultations were analysed by polypharmacy, frailty status, and socioeconomic group using incidence rate ratios (IRR).ResultsAcross 3 851 304 consultations, the population median age was 75 years (interquartile range [IQR] 70–82); and 46% (n = 82 926) of the cohort (N = 180 420) were male. The rate of telephone and electronic/video consultations more than doubled across the study period (106.0% and 102.8%, respectively). Face-to-face consultations fell by 64.6% and home visits by 62.6%. This predominantly occurred across week 11 (week commencing 9 March 2020), coinciding with national policy change. Polypharmacy and frailty were associated with a relative increase in consultations. The greatest relative increase was among people taking ≥10 medications compared with those taking none (face-to-face IRR 9.90, 95% CI = 9.55 to 10.26; telephone IRR 17.64, 95% CI = 16.89 to 18.41).ConclusionPrimary care has undergone an unprecedented in-pandemic reorganisation while retaining focus on patients with increased complexity.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and prevalence is predicted to double over the next 30 years due to changing demographics and the rise in prevalence of risk factors such as hypertension and diabetes. Atrial fibrillation is associated with a five-fold increased stroke risk, but anticoagulation in eligible patients can reduce this risk by around 65%. Many people with AF currently go undetected and therefore untreated, either because they are asymptomatic or because they have paroxysmal AF. Screening has been suggested as one approach to increase AF detection rates and reduce the incidence of ischaemic stroke by earlier initiation of anticoagulation therapy. However, international taskforces currently recommend against screening, citing the cost implications and uncertainty over the benefits of a systematic screening programme compared to usual care. A number of large randomized controlled trials have commenced to determine the cost-effectiveness and clinical benefit of screening using a range of devices and across different populations. The recent AppleWatch study demonstrates how advances in technology are providing the public with self-screening devices that are increasingly affordable and accessible. Health care professionals should be aware of the implications of these emerging data for diagnostic pathways and treatment. This review provides an overview of the gaps in the current evidence and a summary of the arguments for and against screening.

Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n = 28); study 2: soccer players (n = 39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P = 0.0005) and Aero3 (P = 0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-intensity trained with power genotype) demonstrated non-significant improvements in CMJ (P = 0.175) and less prominent results in Aero3 (P = 0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P < 0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P < 0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective resistance training. The developed algorithm may be used to guide individualised resistance-training interventions.

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