ObjectiveTo assess the risk of hospital admission for coronavirus disease 2019 (covid-19) among patient facing and non-patient facing healthcare workers and their household members.DesignNationwide linkage cohort study.SettingScotland, UK, 1 March to 6 June 2020.ParticipantsHealthcare workers aged 18-65 years, their households, and other members of the general population.Main outcome measureAdmission to hospital with covid-19.ResultsThe cohort comprised 158 445 healthcare workers, most of them (90 733; 57.3%) being patient facing, and 229 905 household members. Of all hospital admissions for covid-19 in the working age population (18-65 year olds), 17.2% (360/2097) were in healthcare workers or their households. After adjustment for age, sex, ethnicity, socioeconomic deprivation, and comorbidity, the risk of admission due to covid-19 in non-patient facing healthcare workers and their households was similar to the risk in the general population (hazard ratio 0.81 (95% confidence interval 0.52 to 1.26) and 0.86 (0.49 to 1.51), respectively). In models adjusting for the same covariates, however, patient facing healthcare workers, compared with non-patient facing healthcare workers, were at higher risk (hazard ratio 3.30, 2.13 to 5.13), as were household members of patient facing healthcare workers (1.79, 1.10 to 2.91). After sub-division of patient facing healthcare workers into those who worked in “front door,” intensive care, and non-intensive care aerosol generating settings and other, those in front door roles were at higher risk (hazard ratio 2.09, 1.49 to 2.94). For most patient facing healthcare workers and their households, the estimated absolute risk of hospital admission with covid-19 was less than 0.5%, but it was 1% and above in older men with comorbidity.ConclusionsHealthcare workers and their households contributed a sixth of covid-19 cases admitted to hospital. Although the absolute risk of admission was low overall, patient facing healthcare workers and their household members had threefold and twofold increased risks of admission with covid-19.

Background We aimed to ascertain the cumulative risk of fatal or critical care unit-treated COVID-19 in people with diabetes and compare it with that of people without diabetes, and to investigate risk factors for and build a crossvalidated predictive model of fatal or critical care unit-treated COVID-19 among people with diabetes. MethodsIn this cohort study, we captured the data encompassing the first wave of the pandemic in Scotland, from March 1, 2020, when the first case was identified, to July 31, 2020, when infection rates had dropped sufficiently that shielding measures were officially terminated. The participants were the total population of Scotland, including all people with diabetes who were alive 3 weeks before the start of the pandemic in Scotland (estimated Feb 7, 2020). We ascertained how many people developed fatal or critical care unit-treated COVID-19 in this period from the Electronic Communication of Surveillance in Scotland database (on virology), the RAPID database of daily hospitalisations, the Scottish Morbidity Records-01 of hospital discharges, the National Records of Scotland death registrations data, and the Scottish Intensive Care Society and Audit Group database (on critical care). Among people with fatal or critical care unit-treated COVID-19, diabetes status was ascertained by linkage to the national diabetes register, Scottish Care Information Diabetes. We compared the cumulative incidence of fatal or critical care unit-treated COVID-19 in people with and without diabetes using logistic regression. For people with diabetes, we obtained data on potential risk factors for fatal or critical care unit-treated COVID-19 from the national diabetes register and other linked health administrative databases. We tested the association of these factors with fatal or critical care unit-treated COVID-19 in people with diabetes, and constructed a prediction model using stepwise regression and 20-fold cross-validation. Findings Of the total Scottish population onMarch 1, 2020 (n=5 463 300), the population with diabetes was 319 349 (5•8%), 1082 (0•3%) of whom developed fatal or critical care unit-treated COVID-19 by July 31, 2020, of whom 972 (89•8%) were aged 60 years or older. In the population without diabetes, 4081 (0•1%) of 5 143 951 people developed fatal or critical care unit-treated COVID-19. As of July 31, the overall odds ratio (OR) for diabetes, adjusted for age and sex, was 1•395 (95% CI 1•304-1•494; p<0•0001, compared with the risk in those without diabetes. The OR was 2•396 (1•815-3•163; p<0•0001) in type 1 diabetes and 1•369 (1•276-1•468; p<0•0001) in type 2 diabetes. Among people with diabetes, adjusted for age, sex, and diabetes duration and type, those who developed fatal or critical care unit-treated COVID-19 were more likely to be male, live in residential care or a more deprived area, have a COVID-19 risk condition, retinopathy, reduced renal function, or worse glycaemic control, have had a diabetic ketoacidosis or hypoglycaemia hospitalisation in the past 5 years, be on more...

A remarkable excess mortality has coincided with the COVID-19 pandemic in Europe. We present preliminary pooled estimates of all-cause mortality for 24 European countries/federal states participating in the European monitoring of excess mortality for public health action (EuroMOMO) network, for the period March–April 2020. Excess mortality particularly affected  ≥ 65 year olds (91% of all excess deaths), but also 45–64 (8%) and 15–44 year olds (1%). No excess mortality was observed in 0–14 year olds.

Background The objectives of this study were to identify risk factors for severe coronavirus disease 2019 (COVID-19) and to lay the basis for risk stratification based on demographic data and health records. Methods and findings The design was a matched case-control study. Severe COVID-19 was defined as either a positive nucleic acid test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the national database followed by entry to a critical care unit or death within 28 days or a death certificate with COVID-19 as underlying cause. Up to 10 controls per case matched for sex, age, and primary care practice were selected from the national population register. For this analysis-based on ascertainment of positive test results up to 6 June 2020, entry to critical care up to 14 June 2020, and deaths registered up to 14 June 2020-there were 36,948 controls and 4,272 cases, of which 1,894 (44%) were care home residents. All diagnostic codes from the past 5 years of hospitalisation records and all drug codes from prescriptions dispensed during the past 240 days were extracted. Rate ratios for severe COVID-19 were estimated by conditional logistic regression. In a logistic regression using the age-sex distribution of the national population, the odds ratios for severe disease were 2.

Background The objective of this study was to investigate the relation of severe COVID-19 to prior drug prescribing. Methods Severe cases were defined by entry to critical care or fatal outcome. For this matched case-control study (REACT-SCOT), all 4251 cases of severe COVID-19 in Scotland since the start of the epidemic were matched for age, sex and primary care practice to 36,738 controls from the population register. Records were linked to hospital discharges since June 2015 and dispensed prescriptions issued in primary care during the last 240 days. Results Severe COVID-19 was strongly associated with the number of non-cardiovascular drug classes dispensed. This association was strongest in those not resident in a care home, in whom the rate ratio (95% CI) associated with dispensing of 12 or more drug classes versus none was 10.8 (8.8, 13.3), and in those without any of the conditions designated as conferring increased risk of COVID-19. Of 17 drug classes postulated at the start of the epidemic to be “medications compromising COVID”, all were associated with increased risk of severe COVID-19 and these associations were present in those without any of the designated risk conditions. The fraction of cases in the population attributable to exposure to these drug classes was 38%. The largest effect was for antipsychotic agents: rate ratio 4.18 (3.42, 5.11). Other drug classes with large effects included proton pump inhibitors (rate ratio 2.20 (1.72, 2.83) for = 2 defined daily doses/day), opioids (3.66 (2.68, 5.01) for = 50 mg morphine equivalent/day) and gabapentinoids. These associations persisted after adjusting for covariates and were stronger with recent than with non-recent exposure. Conclusions Severe COVID-19 is associated with polypharmacy and with drugs that cause sedation, respiratory depression, or dyskinesia; have anticholinergic effects; or affect the gastrointestinal system. These associations are not easily explained by co-morbidity. Measures to reduce the burden of mortality and morbidity from COVID-19 should include reinforcing existing guidance on reducing overprescribing of these drug classes and limiting inappropriate polypharmacy. Registration ENCEPP number https://EUPAS35558

The European monitoring of excess mortality for public health action (EuroMOMO) network monitors weekly excess all-cause mortality in 27 European countries or subnational areas. During the first wave of the coronavirus disease (COVID-19) pandemic in Europe in spring 2020, several countries experienced extraordinarily high levels of excess mortality. Europe is currently seeing another upsurge in COVID-19 cases, and EuroMOMO is again witnessing a substantial excess all-cause mortality attributable to COVID-19.

Background The COVID-19 pandemic and ensuing national lockdowns have dramatically changed the healthcare landscape. The pandemic’s impact on people with chronic obstructive pulmonary disease (COPD) remains poorly understood. We hypothesised that the UK-wide lockdown restrictions were associated with reductions in severe COPD exacerbations. We provide the first national level analyses of the impact of the COVID-19 pandemic and first lockdown on severe COPD exacerbations resulting in emergency hospital admissions and/or leading to death as well as those recorded in primary care or emergency departments. Methods Using data from Public Health Scotland and the Secure Anonymised Information Linkage Databank in Wales, we accessed weekly counts of emergency hospital admissions and deaths due to COPD over the first 30 weeks of 2020 and compared these to the national averages over the preceding 5 years. For both Scotland and Wales, we undertook interrupted time-series analyses to model the impact of instigating lockdown on these outcomes. Using fixed-effect meta-analysis, we derived pooled estimates of the overall changes in trends across the two nations. Results Lockdown was associated with 48% pooled reduction in emergency admissions for COPD in both countries (incidence rate ratio, IRR 0.52, 95% CI 0.46 to 0.58), relative to the 5-year averages. There was no statistically significant change in deaths due to COPD (pooled IRR 1.08, 95% CI 0.87 to 1.33). In Wales, lockdown was associated with 39% reduction in primary care consultations for acute exacerbation of COPD (IRR 0.61, 95% CI 0.52 to 0.71) and 46% reduction in COPD-related emergency department attendances (IRR 0.54, 95% CI 0.36 to 0.81). Conclusions The UK-wide lockdown was associated with the most substantial reductions in COPD exacerbations ever seen across Scotland and Wales, with no corresponding increase in COPD deaths. This may have resulted from reduced transmission of respiratory infections, reduced exposure to outdoor air pollution and/or improved COPD self-management.

highest incidence specialties were intensive care, renal medicine, and cardiothoracic surgery. HAI occurred at a median of 9 days (interquartile range: 4e19) after admission. Incidence data were extrapolated to provide an annual national estimate of HAI in NHS Scotland of 7437 (95% confidence interval: 7021e7849) cases. Conclusion:This study provides a unique overview of incidence of HAI and identifies the burden of HAI at the national level for the first time. Understanding the incidence in different clinical settings, at different times, will allow targeting of IPC measures to those patients who would benefit the most.