Rapid Epidemiological Analysis of Comorbidities and Treatments as risk factors for COVID-19 in Scotland (REACT-SCOT): A population-based case-control study

Complex patterns of multimorbidity associated with severe COVID-19 and long COVID

BACKGROUND

Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses are scarce but may help to understand severe COVID-19 among patients at supposedly low risk.

METHODS

We systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID.

RESULTS

Here we identify 679 diseases associated with an increased risk for severe COVID-19 (n = 672) and/or Long COVID (n = 72) that span almost all clinical specialties and are strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we establish consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This includes a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observe partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis or rheumatoid arthritis, possibly indicating a segregation of disease mechanisms.

CONCLUSIONS

Our results provide a unique reference that demonstrates how 1) complex co-occurrence of multiple - including non-fatal - conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.

Factors associated with SARS-CoV-2 testing, diagnosis and COVID-19 disease among individuals prescribed opioid-agonist treatment: a nationwide retrospective cohort study

OBJECTIVES

Among people receiving opioid-agonist treatment (OAT), the risk of COVID-19 infection and disease may be higher owing to underlying health problems and vulnerable social circumstances. We aimed to determine whether recent OAT, when compared with past exposure, affected the risk of (i) testing for SARS-CoV-2, (ii) testing positive for SARS-CoV-2, and (iii) being hospitalized or dying with COVID-19 disease.

METHODS

We included individuals prescribed OAT in Scotland from 2015 to 2020. We performed record linkage to SARS-CoV-2 PCR testing, vaccination, hospitalization, and mortality data, and followed up from March 2020 to December 2021. We used proportional hazards analysis and multivariate logistic regression to estimate associations between recent OAT prescription (in the previous 2 months), compared with past exposure (off treatment for over a year), and COVID-19 outcomes. Models were adjusted for confounders.

RESULTS

Among 36 093 individuals prescribed OAT, 19 071 (52.9%) were tested for SARS-CoV-2; 2896 (8.3%) tested positive; and 552 (1.5%) were hospitalized or died with COVID-19. Recent OAT, compared with past exposure, was associated with lower odds of testing positive among those tested (aOR, 0.63; 95% CI, 0.57-0.69). However, among those testing positive, recent OAT was associated with two-fold higher odds of hospitalization or death (aOR, 2.04; 95% CI, 1.60-2.59).

DISCUSSION

We found that recent OAT was associated with lower odds of SARS-CoV-2 infection, but with higher odds of disease once diagnosed. Clinical studies are needed to unravel the role of OAT in these associations. An enhanced effort is warranted to increase vaccine coverage among OAT patients to mitigate the severe consequences of COVID-19.

Occurrence of COVID-19 and serum per- and polyfluoroalkyl substances: A case-control study among workers with a wide range of exposures

Per- and polyfluoroalkyl substances (PFAS) are a broad class of synthetic chemicals; some are present in most humans in developed countries. Some studies suggest that certain PFAS may have immunotoxic effects in humans, which could put individuals with high levels of exposure at increased risk for infectious diseases such as COVID-19. We conducted a case-control study to examine the association between COVID-19 diagnosis and PFAS serum concentrations among employees and retirees from two 3 M facilities, one of which historically generated perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and perfluorohexane sulfonic acid (PFHxS). Participants completed enrollment and follow-up study visits in the Spring of 2021. Participants were categorized as cases if they reported a COVID-19 diagnosis or became sick with at least one symptom of COVID-19 when someone else in their household was diagnosed, otherwise they were categorized as a control. COVID-19 diagnosis was modeled in relation to concentration of serum PFAS measured at enrollment after adjusting for covariates. The analytic sample comprised 573 individuals, 111 cases (19.4%) and 462 controls (80.6%). In adjusted models, the odds ratio of COVID-19 was 0.94 per interquartile range (14.3 ng/mL) increase in PFOS (95% confidence interval 0.85, 1.04). Results for PFOA, PFHxS, and perfluorononanoic acid (PFNA) were similar. Other PFAS present at lower concentrations were examined as categorical variables (above the limit of quantification [LOQ], yes vs. no [referent category]), and also showed no positive associations. In our study, which used individual-level data and included people with high occupational exposure, the serum concentrations of all PFAS examined were not associated with an increased odds ratio for COVID-19. At this point, the epidemiologic data supporting no association of COVID-19 occurrence with PFAS exposure are stronger than those suggesting a positive association.

SARS-CoV-2 vaccine uptake and risks of severe COVID-19 disease among people prescribed opioid agonist therapy in Scotland

BACKGROUND

There is limited evidence quantifying the risk of severe COVID-19 disease among people with opioid dependence. We examined vaccine uptake and severe disease (admission to critical care or death with COVID-19) among individuals prescribed opioid agonist therapy (OAT).

METHOD

A case-control design was used to examine vaccine uptake in those prescribed OAT compared with the general population, and the association between severe disease and OAT. In both analyses, 10 controls from the general population were matched (to each OAT recipient and COVID-19 case, respectively) according to socio-demographic factors. Conditional logistic regression was used to estimate rate ratios (RR) for severe disease.

RESULTS

Vaccine uptake was markedly lower in the OAT cohort (dose 1: 67%, dose 2: 53% and dose 3: 31%) compared with matched controls (76%, 72% and 57%, respectively). Those prescribed OAT within the last 5 years, compared with those not prescribed, had increased risk of severe COVID-19 (RR 3.38, 95% CI 2.75 to 4.15), particularly in the fourth wave (RR 6.58, 95% CI 4.20 to 10.32); adjustment for comorbidity and vaccine status attenuated this risk (adjusted RR (aRR) 2.43, 95% CI 1.95 to 3.02; wave 4 aRR 3.78, 95% CI 2.30 to 6.20). Increased risk was also observed for those prescribed OAT previously (>3 months ago) compared with recently (aRR 1.74, 95% CI 1.11 to 2.71).

CONCLUSIONS

The widening gap in vaccine coverage for those prescribed OAT, compared with the general population, is likely to have exacerbated the risk of severe COVID-19 in this population over the pandemic. However, continued OAT use may have provided protection from severe COVID-19 among those with opioid dependence.

Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: a national linked data study

BACKGROUND

We compared the quality of ethnicity coding within the Public Health Scotland Ethnicity Look-up (PHS-EL) dataset, and other National Health Service datasets, with the 2011 Scottish Census.

METHODS

Measures of quality included the level of missingness and misclassification. We examined the impact of misclassification using Cox proportional hazards to compare the risk of severe coronavirus disease (COVID-19) (hospitalization & death) by ethnic group.

RESULTS

Misclassification within PHS-EL was higher for all minority ethnic groups [12.5 to 69.1%] compared with the White Scottish majority [5.1%] and highest in the White Gypsy/Traveller group [69.1%]. Missingness in PHS-EL was highest among the White Other British group [39%] and lowest among the Pakistani group [17%]. PHS-EL data often underestimated severe COVID-19 risk compared with Census data. e.g. in the White Gypsy/Traveller group the Hazard Ratio (HR) was 1.68 [95% Confidence Intervals (CI): 1.03, 2.74] compared with the White Scottish majority using Census ethnicity data and 0.73 [95% CI: 0.10, 5.15] using PHS-EL data; and HR was 2.03 [95% CI: 1.20, 3.44] in the Census for the Bangladeshi group versus 1.45 [95% CI: 0.75, 2.78] in PHS-EL.

CONCLUSIONS

Poor quality ethnicity coding in health records can bias estimates, thereby threatening monitoring and understanding ethnic inequalities in health.

Predictors of mortality among inpatients in COVID-19 treatment centers in the city of Butembo, North Kivu, Democratic Republic of Congo

Determining the risk factors for severe disease and death among hospitalized Covid-19 patients is critical to optimize health outcomes and health services efficiency, especially in resource-constrained and humanitarian settings. This study aimed to identify the predictors of mortality of Covid-19 patients in North Kivu province in the Democratic Republic of Congo.A retrospective cohort study was conducted in 6 Covid-19 treatment centers in the city of Butembo from 1 January to 31 December 2021. The time to event (death), the outcome variable, was visualized by Kaplan-Meier curves and the log-rank test was used to confirm differences in trends. Cox regression was used for all the predictors in the bivariate analysis and multivariate analysis was done using predictors found statistically significant in the bivariate analysis. The following variables were considered for inclusion to the Cox regression model: Age, Sex, Disease length, Treatment site, History of at least one co-morbidity, Body mass index, Stage according to SpO2 and the NEWS-modified score.Among the 303 participants (mean age of 53 years), the fatality rate was 33.8 deaths per 1000 patient-days. Four predictors were independently associated with inpatient death: age category (≥ 60 years) (adjusted HR: 9.90; 95% CI: 2.68-36.27), presence of at least one comorbidity (adjusted HR: 11.39; 95% CI: 3.19-40.71); duration of illness of > 5 days before hospitalization (adjusted HR:1.70, 95% CI: 1.04-2.79) and peripheral capillary oxygen saturation (SpO2) < 90% (adjusted HR = 14.02, 95% CI: 2.23-88.32). In addition to advanced age, comorbidity, and length of disease before hospitalization, ambient air SpO2 measured by healthcare providers using low-tech, affordable and relatively accessible pulse oximetry could inform the care pathways of Covid-19 inpatients in resource-challenged health systems in humanitarian settings.

Scottish Index of Multiple Deprivation (SIMD) indicators as predictors of mortality among patients hospitalised with COVID-19 disease in the Lothian Region, Scotland during the first wave: a cohort study

BACKGROUND

Sars-CoV-2, the causative agent of COVID-19, has led to more than 226,000 deaths in the UK and multiple risk factors for mortality including age, sex and deprivation have been identified. This study aimed to identify which individual indicators of the Scottish Index of Multiple Deprivation (SIMD), an area-based deprivation index, were predictive of mortality.

METHODS

This was a prospective cohort study of anonymised electronic health records of 710 consecutive patients hospitalised with Covid-19 disease between March and June 2020 in the Lothian Region of Southeast Scotland. Data sources included automatically extracted data from national electronic platforms and manually extracted data from individual admission records. Exposure variables of interest were SIMD quintiles and 12 indicators of deprivation deemed clinically relevant selected from the SIMD. Our primary outcome was mortality. Age and sex adjusted univariable and multivariable analyses were used to determine measures of association between exposures of interest and the primary outcome.

RESULTS

After adjusting for age and sex, we found an increased risk of mortality in the more deprived SIMD quintiles 1 and 3 (OR 1.75, CI 0.99-3.08, p = 0.053 and OR 2.17, CI 1.22-3.86, p = 0.009, respectively), but this association was not upheld in our multivariable model containing age, sex, Performance Status and clinical parameters of severity at admission. Of the 12 pre-selected indicators of deprivation, two were associated with greater mortality in our multivariable analysis: income deprivation rate categorised by quartile (Q4 (most deprived): 2.11 (1.20-3.77) p = 0.011)) and greater than expected hospitalisations due to alcohol per SIMD data zone (1.96 (1.28-3.00) p = 0.002)).

CONCLUSIONS

SIMD as an aggregate measure of deprivation was not predictive of mortality in our cohort when other exposure measures were accounted for. However, we identified a two-fold increased risk of mortality in patients residing in areas with greater income-deprivation and/or number of hospitalisations due to alcohol. In areas where aggregate measures fail to capture pockets of deprivation, exploring the impact of specific SIMD indicators may be helpful in targeting resources to residents at risk of poorer outcomes from Covid-19.

Psychosocial predictors of COVID-19 infection in UK biobank (N = 104 201)

BACKGROUND

Since the outbreak of COVID-19, data on its psychosocial predictors are limited. We therefore aimed to explore psychosocial predictors of COVID-19 infection at the UK Biobank (UKB).

METHODS

This was a prospective cohort study conducted among UKB participants.

RESULTS

The sample size was N = 104 201, out of which 14 852 (14.3%) had a positive COVID-19 test. The whole sample analysis showed significant interactions between sex and several predictor variables. Among females, absence of college/university degree [odds ratio (OR) 1.55, 95% confidence interval (CI) 1.45-1.66] and socioeconomic deprivation (OR 1.16 95% CI 1.11-1.21) were associated with higher odds of COVID-19 infection, while history of psychiatric consultation (OR 0.85 95% CI 0.77-0.94) with lower odds. Among males, absence of college/university degree (OR 1.56, 95% CI 1.45-1.68) and socioeconomic deprivation (OR 1.12, 95% CI 1.07-1.16) were associated with higher odds, while loneliness (OR 0.87, 95% CI 0.78-0.97), irritability (OR 0.91, 95% CI 0.83-0.99) and history of psychiatric consultation (OR 0.85, 95% CI 0.75-0.97) were associated with lower odds.

CONCLUSION

Sociodemographic factors predicted the odds of COVID-19 infection equally among male and female participants, while psychological factors had differential impacts.

Epilepsy-related mortality during the COVID-19 pandemic: A nationwide study of routine Scottish data

OBJECTIVE

To examine whether epilepsy-related deaths increased during the COVID-19 pandemic and if the proportion with COVID-19 listed as the underlying cause is different between people experiencing epilepsy-related deaths and those experiencing deaths unrelated to epilepsy.

METHODS

This was a Scotland-wide, population-based, cross-sectional study of routinely-collected mortality data pertaining to March-August of 2020 (COVID-19 pandemic peak) compared to the corresponding periods in 2015-2019. ICD-10-coded causes of death of deceased people of any age were obtained from a national mortality registry of death certificates in order to identify those experiencing epilepsy-related deaths (coded G40-41), deaths with COVID-19 listed as a cause (coded U07.1-07.2), and deaths unrelated to epilepsy (death without G40-41 coded). The number of epilepsy-related deaths in 2020 were compared to the mean observed through 2015-2019 on an autoregressive integrated moving average (ARIMA) model (overall, men, women). Proportionate mortality and odds ratios (OR) for deaths with COVID-19 listed as the underlying cause were determined for the epilepsy-related deaths compared to deaths unrelated to epilepsy, reporting 95% confidence intervals (CIs).

RESULTS

A mean number of 164 epilepsy-related deaths occurred through March-August of 2015-2019 (of which a mean of 71 were in women and 93 in men). There were subsequently 189 epilepsy-related deaths during the pandemic March-August 2020 (89 women, 100 men). This was 25 more epilepsy-related deaths (18 women, 7 men) compared to the mean through 2015-2019. The increase in women was beyond the mean year-to-year variation seen in 2015-2019. Proportionate mortality with COVID-19 listed as the underlying cause was similar between people experiencing epilepsy-related deaths (21/189, 11.1%, CI 7.0-16.5%) and deaths unrelated to epilepsy (3,879/27,428, 14.1%, CI 13.7-14.6%), OR 0.76 (CI 0.48-1.20). Ten of 18 excess epilepsy-related deaths in women had COVID-19 listed as an additional cause.

CONCLUSIONS

There is little evidence to suggest there have been any major increases in epilepsy-related deaths in Scotland during the COVID-19 pandemic. COVID-19 is a common underlying cause of both epilepsy-related and unrelated deaths.

Population pharmacokinetics and exposure-response analysis of a single dose of sotrovimab in the early treatment of patients with mild to moderate COVID-19

Sotrovimab is a recombinant human monoclonal antibody that has been shown to prevent progression to hospitalization or death in non-hospitalized high-risk patients with mild to moderate coronavirus disease 2019 following either intravenous (i.v.) or intramuscular (i.m.) administration. Population pharmacokinetic (PopPK) and exposure-response (ER) analyses were performed to characterize single dose sotrovimab pharmacokinetics (PK) and the relationship between exposure and response (probability of progression), as well as covariates that may contribute to between-participant variability in sotrovimab PK and efficacy following i.v. or i.m. administration. Sotrovimab PK was described by a two-compartment model with linear elimination; i.m. absorption was characterized by a sigmoid absorption model. PopPK covariate analysis led to the addition of the effect of body weight on systemic clearance and peripheral volume of distribution, sex on i.m. bioavailability and first-order absorption rate (KA), and body mass index on KA. However, the magnitude of covariate effect was not pronounced and was therefore not expected to be clinically relevant based on available data to date. For ER analysis, sotrovimab exposure measures were predicted using the final PopPK model. An ER model was developed using the exposure measure of sotrovimab concentration at 168 h that described the relationship between exposure and probability of progression within the ER dataset for COMET-TAIL. The number of risk factors (≤1 vs. >1) was incorporated as an additive shift on the model-estimated placebo response but had no impact on overall drug response. Limitations in the ER model may prevent generalization of these results to describe the sotrovimab exposure-progression relationship across severe acute respiratory syndrome-coronavirus 2 variants.

A follow up study of cycle threshold values of SARS-CoV-2 in Hunan Province, China

Since the epidemic of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), many governments have used reverse transcription polymerase chain reaction (RT-PCR) to detect the virus. However, there are fewer measures of CT values information based on RT-PCR results, and the relationship between CT values and factors from consecutive tests is not clear enough. So in this study, we analyzed the connection between CT values and the factors based on cohort data from Delta variant of SARS-CoV-2 in Hunan Province. Previous studies have showed that the mean age of the cases was 33.34 years (±18.72 years), with a female predominance (55.03%, n = 71), and the greatest proportion of clinical symptoms were of the common type (60.47%, n = 78). There were statistical differences between the N and ORF1ab genes in the CT values for the cases. Based on the analysis of the association between CT values and the factors, the lowest CT values were obtained for the unvaccinated, older and clinically symptomatic group at 3-10 days, the maximum peak of viral load occurred. Therefore, it is recommended to use patient information to focus on older, clinically symptomatic, unvaccinated patients and to intervene promptly upon admission.

Epidemiology of SARS-CoV-2 during the first three waves in Scotland: a national record linkage study

BACKGROUND

The early COVID-19 pandemic in Scotland-defined as the era before widespread access to vaccination and monoclonal antibody treatment-can be characterised into three distinct waves: March-July 2020, July 2020-April 2021 and May-August 2021. Each wave was met with various societal restrictions in an effort to reduce disease transmission and associated morbidity and mortality. Understanding the epidemiology of infections during these waves can provide valuable insights into future pandemic planning.

METHODS

Scottish RT-PCR testing data reported up until 8 August 2021, the day prior to most restrictions being lifted in Scotland, were included. Demographic characteristics including age, sex and social deprivation associated with transmission, morbidity and mortality were compared across waves. A case-control analysis for each wave was then modelled to further compare risk factors associated with death over time.

RESULTS

Of the 349 904 reported cases, there were 18 099, 197 251 and 134 554 in waves 1, 2 and 3, respectively. Hospitalisations, intensive care unit admissions and deaths appeared highest in wave 2, though risk factors associated with COVID-19 death remained similar across the waves. Higher deprivation and certain comorbidities were associated with higher deaths in all waves.

CONCLUSIONS

Despite the higher number of cases reported in waves 2 and 3, case fatality rates were lower: likely a combination of improved detection of infections in younger age groups, introduction of social measures and vaccination. Higher social deprivation and comorbidities resulted in higher deaths for all waves.

Interplay between demographic, clinical and polygenic risk factors for severe COVID-19

BACKGROUND

We aimed to identify clinical, socio-demographic and genetic risk factors for severe COVID-19 (hospitalization, critical care admission or death) in the general population.

METHODS

In this observational study, we identified 9560 UK Biobank participants diagnosed with COVID-19 during 2020. A polygenic risk score (PRS) for severe COVID-19 was derived and optimized using publicly available European and trans-ethnic COVID-19 genome-wide summary statistics. We estimated the risk of hospital or critical care admission within 28 days or death within 100 days following COVID-19 diagnosis, and assessed associations with socio-demographic factors, immunosuppressant use and morbidities reported at UK Biobank enrolment (2006-2010) and the PRS. To improve biological understanding, pathway analysis was performed using genetic variants comprising the PRS.

RESULTS

We included 9560 patients followed for a median of 61 (interquartile range = 34-88) days since COVID-19 diagnosis. The risk of severe COVID-19 increased with age and obesity, and was higher in men, current smokers, those living in socio-economically deprived areas, those with historic immunosuppressant use and individuals with morbidities and higher co-morbidity count. An optimized PRS, enriched for single-nucleotide polymorphisms in multiple immune-related pathways, including the 'oligoadenylate synthetase antiviral response' and 'interleukin-10 signalling' pathways, was associated with severe COVID-19 (adjusted odds ratio 1.32, 95% CI 1.11-1.58 for the highest compared with the lowest PRS quintile).

CONCLUSION

This study conducted in the pre-SARS-CoV-2-vaccination era, emphasizes the novel insights to be gained from using genetic data alongside commonly considered clinical and socio-demographic factors to develop greater biological understanding of severe COVID-19 outcomes.

Statistical modelling of COVID-19 data: Putting generalized additive models to work

Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter estimates are independent of the (unknown) case-detection ratio, which plays an important role in COVID-19 surveillance data. Second, we model the incidence of hospitalizations, for which data is only available with a temporal delay. We illustrate how correcting for this reporting delay through a nowcasting procedure can be naturally incorporated into the GAM framework as an offset term. Third, we propose a multinomial model for the weekly occupancy of intensive care units (ICU), where we distinguish between the number of COVID-19 patients, other patients and vacant beds. With these three examples, we aim to showcase the practical and ‘off-the-shelf’ applicability of GAMs to gain new insights from real-world data.

Optimising the impact of COVID-19 vaccination on mortality and hospitalisations using an individual additive risk measuring approach based on a risk adjustment scheme

In this population-based cohort study, billing data from German statutory health insurance (BARMER, 10% of population) are used to develop a prioritisation model for COVID-19 vaccinations based on cumulative underlying conditions. Using a morbidity-based classification system, prevalence and risks for COVID-19-related hospitalisations, ventilations and deaths are estimated. Trisomies, behavioural and developmental disorders (relative risk: 2.09), dementia and organic psychoorganic syndromes (POS) (2.23) and (metastasised) malignant neoplasms (1.99) were identified as the most important conditions for escalations of COVID-19 infection. Moreover, optimal vaccination priority schedules for participants are established on the basis of individual cumulative escalation risk and are compared to the prioritisation scheme chosen by the German Government. We estimate how many people would have already received a vaccination prior to escalation. Vaccination schedules based on individual cumulative risk are shown to be 85% faster than random schedules in preventing deaths, and as much as 57% faster than the German approach, which was based primarily on age and specific diseases. In terms of hospitalisation avoidance, the individual cumulative risk approach was 51% and 28% faster. On this basis, it is concluded that using individual cumulative risk-based vaccination schedules, healthcare systems can be relieved and escalations more optimally avoided.

Incidence and severity of SARS-CoV-2 infection in former Q fever patients as compared to the Dutch population, 2020-2021

Surveillance data shows a geographical overlap between the early coronavirus disease 2019 (COVID-19) pandemic and the past Q fever epidemic (2007-2010) in the Netherlands. We investigated the relationship between past Q fever and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 2020/2021, using a retrospective matched cohort study.In January 2021, former Q fever patients received a questionnaire on demographics, SARS-CoV-2 test results and related hospital/intensive care unit (ICU) admissions. SARS-CoV-2 incidence with 95% confidence intervals (CI) in former Q fever patients and standardised incidence ratios (SIR) to compare to the age-standardised SARS-CoV-2 incidence in the general regional population were calculated.Among 890 former Q fever patients (response rate: 68%), 66 had a PCR-confirmed SARS-CoV-2 infection. Of these, nine (14%) were hospitalised and two (3%) were admitted to ICU. From February to June 2020 the SARS-CoV-2 incidence was 1573/100 000 (95% CI 749-2397) in former Q fever patients and 695/100 000 in the general population (SIR 2.26; 95% CI 1.24-3.80). The incidence was not significantly higher from September 2020 to February 2021.We found no sufficient evidence for a difference in SARS-CoV-2 incidence or an increased severity in former Q fever patients vs. the general population during the period with widespread SARS-CoV-2 testing availability (September 2020-February 2021). This indicates that former Q fever patients do not have a higher risk of SARS-CoV-2 infection.

Vaccine efficacy against severe COVID-19 in relation to delta variant (B.1.617.2) and time since second dose in patients in Scotland (REACT-SCOT): a case-control study

BACKGROUND

Reports have suggested that the efficacy of vaccines against COVID-19 might have fallen since the delta (B.1.617.2) SARS-CoV-2 variant replaced the alpha (B.1.1.7) variant as the predominant variant. We aimed to investigate, for the two main classes of vaccine, whether efficacy against severe COVID-19 has decreased since delta became the predominant variant and whether the efficacy of two doses of vaccine against severe COVID-19 wanes with time since second dose.

METHODS

In the REACT-SCOT case-control study, vaccine efficacy was estimated using a matched case-control design that includes all diagnosed cases of COVID-19 in Scotland up to Sept 8, 2021. For every incident case of COVID-19 in the Scottish population, ten controls matched for age rounded to the nearest year, sex, and primary care practice, and alive on the day of presentation of the case that they were matched to were selected using the Community Health Index database. To minimise ascertainment bias we prespecified the primary outcome measure to assess vaccine efficacy as severe COVID-19, defined as diagnosed patients with entry to critical care within 21 days of first positive test, death within 28 days of first positive test, or any death for which COVID-19 was coded as underlying cause. Although the data extracted for this study included cases presenting up to Sept 22, 2021, the analyses reported here are restricted to cases and controls presenting from Dec 1, 2020, to Sept 8, 2021, ensuring follow-up for at least 14 days after presentation date to allow classification of hospitalisation and (for most cases) severity based on entry to critical care or fatal outcome.

FINDINGS

During the study period, a total of 5645 severe cases of COVID-19 were recorded; these were matched to 50 096 controls. Of the severe cases, 4495 (80%) were not vaccinated, and of the controls, 36 879 (74%) were not vaccinated. Of the severe cases of COVID-19 who had been vaccinated, 389 had received an mRNA vaccine and 759 had received the ChAdOx1 vaccine. The efficacy of vaccination against severe COVID-19 decreased in May, 2021, coinciding with the replacement of the alpha SARS-CoV-2 variant by the delta variant in Scotland, but this decrease was reversed over the following month. In the most recent time window centred on July 29, 2021, the efficacy of two doses was 91% (95% CI 87-94) for the ChAdOx1 vaccine and 92% (88-95) for mRNA (Pfizer or Moderna) vaccines. The efficacy of the ChAdOx1 vaccine against severe COVID-19 declined with time since second dose to 69% (95% CI 52-80) at 20 weeks from second dose. The efficacy of mRNA vaccines declined in the first ten weeks from second dose but more slowly thereafter to 93% (88-96) at 20 weeks from second dose.

INTERPRETATION

Our results are reassuring with respect to concerns that vaccine efficacy against severe COVID-19 might have fallen since the delta variant became predominant, or that efficacy of mRNA vaccines wanes within the first 5-6 months after second dose. However, the efficacy of the ChAdOx1 vaccine against severe COVID-19 wanes substantially by 20 weeks from second dose. Efficacy of mRNA vaccines after 20 weeks and against newer variants remains to be established. Our findings support the case for additional protective measures for those at risk of severe disease, including, but not limited to, booster doses, at times when transmission rates are high or expected to rise.

FUNDING

None.

Anti-SARS-CoV-2 IgG and Neutralizing Antibody Levels in Patients with Past COVID-19 Infection: A Longitudinal Study

Background

Monitoring the longevity of immunoglobulin G (IgG) responses following severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections is vital to understanding the role of antibodies in preventing infection.

Aims

To determine the quantitative IgG responses specific to the Spike-S1 (S1) receptor-binding domain (S1/RBD) region of the virus in serum samples taken between 4 weeks and 7 months after polymerase chain reaction (PCR) positivity in patients who are diagnosed with coronavirus disease-2019 (COVID-19).

Study Design

A longitudinal study.

Methods

This study included 113 patients with a clinical and molecular diagnosis of COVID-19. The first and second serum samples were taken 1 and 7 months, respectively, after the PCR positivity. S1/RBD-specific IgG antibody response was assayed using anti-SARS-CoV- 2 QuantiVac ELISA (IgG) kit (Euroimmun, Lübeck, Germany). The neutralizing antibodies were investigated in 57 patients whose IgG test results were above the cut-off value.

Results

In 57 patients with SARS-CoV-2 IgG, the anti-SARS-CoV-2 IgG quantitative antibody levels significantly decreased after 7 months (Z = −2.197, p = 0.028). A correlation was detected between the anti-SARS-CoV-2 IgG and nAb percent inhibition (IH%) levels detected in 1 month (rs = 0.496, p < 0.001), but without significant correlation in serum samples taken on 7 months. The nAb IH% levels of the first and second were compared for COVID-19 severity and revealed no statistical difference (p = 0.256). In the second serum sample, the nAb IH%s of patients with moderate COVID-19 showed a statistically significant difference from patients with mild COVID-19 (p = 0.018), but without significant differences between severe and moderate or mild COVID-19.

Conclusion

SARS-CoV-2 quantitative IgG antibody titers are significantly reduced at long-term follow-up (> 6 months). Due to the limited information on seroconversion, comprehensive studies should be conducted for long-term follow-up of the immune response against SARS-CoV-2.

Risk of severe COVID-19 in patients with inflammatory rheumatic diseases treated with immunosuppressive therapy in Scotland

OBJECTIVE

To investigate the association of severe coronavirus disease 2019 (COVID-19) in patients with inflammatory rheumatic diseases (IRDs) treated with immunosuppressive drugs.

METHOD

A list of 4633 patients on targeted - biological or targeted synthetic - DMARDs in March 2020 was linked to a case-control study that includes all cases of COVID-19 in Scotland.

RESULTS

By 22 November 2021, 433 of the 4633 patients treated with targeted DMARDS had been diagnosed with COVID-19, of whom 58 had been hospitalized. With all those in the population not on DMARDs as the reference category, the rate ratio for hospitalized COVID-19 associated with DMARD treatment was 2.14 [95% confidence interval (CI) 2.02-2.26] in those on conventional synthetic (cs) DMARDs, 2.01 (95% CI 1.38-2.91) in those on tumour necrosis factor (TNF) inhibitors as the only targeted agent, and 3.83 (95% CI 2.65-5.56) in those on other targeted DMARDs. Among those on csDMARDs, rate ratios for hospitalized COVID-19 were lowest at 1.66 (95% CI 1.51-1.82) in those on methotrexate and highest at 5.4 (95% CI 4.4-6.7) in those on glucocorticoids at an average dose > 10 mg/day prednisolone equivalent.

CONCLUSION

The risk of hospitalized COVID-19 is elevated in IRD patients treated with immunosuppressive drugs compared with the general population. Of these drugs, methotrexate, hydroxychloroquine, and TNF inhibitors carry the lowest risk. The highest risk is associated with prednisolone. A larger study is needed to estimate reliably the risks associated with each class of targeted DMARD.

Treatment of Moderate to Severe Psoriasis during the COVID-19 Pandemic: Lessons Learned and Opportunities

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, clinicians have been overwhelmed by questions beyond the SARS-CoV-2 infection itself. In dermatology practice, clinicians have been facing difficulties concerning therapeutic management of chronic immune-mediated skin disease, above all psoriasis. Major challenges arisen were to understand the role of immunosuppression or immunomodulation on COVID-19 evolution, the benefit/risk ratio related to discontinuation or modification of ongoing treatment, and the appropriateness of initiating new treatments, the optimization of timing in vaccination administration to patients under immunomodulatory treatments, and finally how to find new strategy of patients’ management through remote assistance. In this comprehensive review, we present the current evidence about the course and management of psoriasis during the COVID-19 pandemic. The general message from dermatologists was that data did not suggest that having PSO or its treatment significantly increased risk of SARS-CoV-2 infection or more severe COVID-19 course, the vaccination is highly recommended in all psoriatic patients, beyond ongoing treatment, and that the telehealth experience was a success overall.

Clinical findings, viral load, and outcomes of COVID-19: Comparison of patients with negative and positive initial chest computed tomography

Reports detailing the clinical characteristics, viral load, and outcomes of patients with normal initial chest CT findings are lacking. We sought to compare the differences in clinical findings, viral loads, and outcomes between patients with confirmed COVID-19 who initially tested negative on chest CT (CT negative) with patients who tested initially positive on chest CT (CT positive). The clinical data, viral loads, and outcomes of initial CT-positive and CT-negative patients examined between January 2020 and April 2020 were retrospectively compared. The efficacy of viral load (cyclic threshold value [Ct value]) in predicting pneumonia was evaluated using receiver operating characteristic (ROC) curve and area under the curve (AUC). In total, 128 patients underwent initial chest CT (mean age, 54.3 ± 19.0 years, 50% male). Of those, 36 were initially CT negative, and 92 were CT positive. The CT-positive patients were significantly older (P < .001) than the CT-negative patients. Only age was significantly associated with the initial presence of pneumonia (odds ratio, 1.060; confidence interval (CI), 1.020-1-102; P = .003). In addition, age (OR, 1.062; CI, 1.014–1.112; P = .011), fever at diagnosis (OR, 6.689; CI, 1.715–26.096; P = .006), and CRP level (OR, 1.393; CI, 1.150–1.687; P = .001) were significantly associated with the need for O₂ therapy. Viral load was significantly higher in the CT-positive group than in the CT-negative group (P = .017). The cutoff Ct value for predicting the presence of pneumonia was 27.71. Outcomes including the mean hospital stay, intensive care unit admission, and O₂ therapy were significantly worse in the CT-positive group than in the CT-negative group (all P < .05). In conclusion, initially CT-negative patients showed better outcomes than initially CT-positive patients. Age was significantly associated with the initial presence of pneumonia, and viral load may help in predicting the initial presence of pneumonia.

Vaccination for SARS-CoV-2 in Patients With Psoriatic Arthritis: Can Therapy Affect the Immunological Response?

Background

A few studies on vaccination in patients with rheumatic diseases, including arthritis, connective tissue diseases, vasculitis, and psoriatic arthropathy (PsA), demonstrated reduced production of neutralizing antibodies to SARS-CoV-2 Spike RBD (receptor-binding domain contained in the N-terminal of the S1 globular head region) when compared to the general population.

Objective

The aim of our study was to observe whether different therapies for PsA [methotrexate, anti-TNF antibodies, soluble TNF receptor (etanercept) or IL-17 inhibitors] have a different impact on SARS-CoV-2 vaccination in a homogeneous population of patients.

Methods

We enrolled 110 PsA patients in remission, assessed with Disease Activity in PSoriatic Arthritis (DAPSA). Of these: 63 were in treatment with anti-TNF-α therapy (26 etanercept, 15 certolizumab, 5 golimumab, 17 adalimumab); 37 with anti-IL17 secukinumab; 10 with methotrexate. All patients underwent vaccination for SARS-CoV-2 with mRNA BNT162b2 vaccine. Assessment of absolute and percentage lymphocyte subsets and anti-SARS-CoV-2 Spike RBD IgG antibody value 3 weeks after the second vaccine dose were performed. In addition, the serum antibody levels of 96 healthy healthcare workers (HCW) were analyzed.

Results

The mean disease activity assessed with DAPSA score was 2.96 (SD = 0.60) with no significant differences between patients under different medications (p = 0.779). Median levels of neutralizing antibodies to SARS-CoV-2 Spike RBD were 928.00 binding antibody unit (BAU)/mL [IQR 329.25, 1632.0]; 1068.00 BAU/ml [IQR 475.00, 1632.00] in patients taking MTX, 846.00 BAU/ml [IQR 125.00, 1632.00] in patients taking etanercept, 908.00 BAU/mL [IQR 396.00, 1632.00] in patients taking anti-IL17 and 1148.00 BAU/ml [IQR 327.00, 1632.00] in patients taking TNF-α inhibitors, without statistically significant differences between these groups. Mean serum antibody level of HCW group was 1562.00 BAU/ml [IQR 975.00, 1632.00], being significantly higher than in the patient group (p = 0.000816). Absolute and percentage count of lymphocyte subsets were not statistically different between the subgroups under different treatments and when compared with HCW.

Conclusions

As for other rheumatic diseases on immunomodulatory treatment, our data showed a reduced humoral response in PsA patients compared to the control group. However, antibody response did not significantly differ between groups treated with different medications.

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.

COVID-19 Pandemic: Public Health Risk Assessment and Risk Mitigation Strategies

A newly emerged respiratory viral disease called severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is also known as pandemic coronavirus disease (COVID-19). This pandemic has resulted an unprecedented global health crisis and devastating impact on several sectors of human lives and economies. Fortunately, the average case fatality ratio for SARS-CoV-2 is below 2%, much lower than that estimated for MERS (34%) and SARS (11%). However, COVID-19 has a much higher transmissibility rate, as evident from the constant increase in the count of infections worldwide. This article explores the reasons behind how COVID-19 was able to cause a global pandemic crisis. The current outbreak scenario and causes of rapid global spread are examined using recent developments in the literature, epidemiological features relevant to public health awareness, and critical perspective of risk assessment and mitigation strategies. Effective pandemic risk mitigation measures have been established and amended against COVID-19 diseases, but there is still much scope for upgrading execution and coordination among authorities in terms of organizational leadership's commitment and diverse range of safety measures, including administrative control measures, engineering control measures, and personal protective equipment (PPE). The significance of containment interventions against the COVID-19 pandemic is now well established; however, there is a need for its effective execution across the globe, and for the improvement of the performance of risk mitigation practices and suppression of future pandemic crises.

A plea for equitable global access to COVID-19 diagnostics, vaccination and therapy: The NeuroCOVID-19 Task Force of the European Academy of Neurology

Coronavirus disease 2019 (COVID-19), a multi-organ disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to challenge health and care systems around the globe. The pandemic has disrupted acute neurology services and routine patient care and has impacted the clinical course in patients with chronic neurological disease. COVID-19 appears to have exposed inequalities of societies and healthcare systems and had a disproportionate impact on already vulnerable communities. The next challenge will be to set up initiatives to stop disparities in all aspects related to COVID-19. From the medical perspective, there is a need to consider inequalities in prevention, treatment and long-term consequences. Some of the issues of direct relevance to neurologists are summarised. With this appraisal, the European Academy of Neurology NeuroCOVID-19 Task Force intends to raise awareness of the potential impact of COVID-19 on inequalities in healthcare and calls for action to prevent disparity at individual, national and supranational levels.

Estimation of viral kinetics model parameters in young and aged SARS-CoV-2 infected macaques

The SARS-CoV-2 virus disproportionately causes serious illness and death in older individuals. In order to have the greatest impact in decreasing the human toll caused by the virus, antiviral treatment should be targeted to older patients. For this, we need a better understanding of the differences in viral dynamics between SARS-CoV-2 infection in younger and older adults. In this study, we use previously published averaged viral titre measurements from the nose and throat of SARS-CoV-2 infection in young and aged cynomolgus macaques to parametrize a viral kinetics model. We find that all viral kinetics parameters differ between young and aged macaques in the nasal passages, but that there are fewer differences in parameter estimates from the throat. We further use our parametrized model to study the antiviral treatment of young and aged animals, finding that early antiviral treatment is more likely to lead to a lengthening of the infection in aged animals, but not in young animals.

Mental and neurological disorders and risk of COVID-19 susceptibility, illness severity and mortality: A systematic review, meta-analysis and call for action

BACKGROUND

Coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic, and has been found to be closely associated with mental and neurological disorders. We aimed to comprehensively quantify the association between mental and neurological disorders, both pre-existing and subsequent, and the risk of susceptibility, severity and mortality of COVID-19.

METHODS

In this systematic review and meta-analysis, we searched PubMed, Web of Science, Embase, PsycINFO, and Cochrane library databases for studies published from the inception up to January 16, 2021 and updated at July 7, 2021. Observational studies including cohort and case-control, cross-sectional studies and case series that reported risk estimates of the association between mental or neurological disorders and COVID-19 susceptibility, illness severity and mortality were included. Two researchers independently extracted data and conducted the quality assessment. Based on I2 heterogeneity, we used a random effects model to calculate pooled odds ratios (OR) and 95% confidence intervals (95% CI). Subgroup analyses and meta-regression analysis were also performed. This study was registered on PROSPERO (registration number: CRD 42021230832).

FINDING

A total of 149 studies (227,351,954 participants, 89,235,737 COVID-19 patients) were included in this analysis, in which 27 reported morbidity (132,727,798), 56 reported illness severity (83,097,968) and 115 reported mortality (88,878,662). Overall, mental and neurological disorders were associated with a significant high risk of infection (pre-existing mental: OR 1·67, 95% CI 1·12-2·49; and pre-existing neurological: 2·05, 1·58-2·67), illness severity (mental: pre-existing, 1·40, 1·25-1·57; sequelae, 4·85, 2·53-9·32; neurological: pre-existing, 1·43, 1·09-1·88; sequelae, 2·17, 1·45-3·24), and mortality (mental: pre-existing, 1·47, 1·26-1·72; neurological: pre-existing, 2·08, 1·61-2·69; sequelae, 2·03, 1·66-2·49) from COVID-19. Subgroup analysis revealed that association with illness severity was stronger among younger COVID-19 patients, and those with subsequent mental disorders, living in low- and middle-income regions. Younger patients with mental and neurological disorders were associated with higher mortality than elders. For type-specific mental disorders, susceptibility to contracting COVID-19 was associated with pre-existing mood disorders, anxiety, and attention-deficit hyperactivity disorder (ADHD); illness severity was associated with both pre-existing and subsequent mood disorders as well as sleep disturbance; and mortality was associated with pre-existing schizophrenia. For neurological disorders, susceptibility was associated with pre-existing dementia; both severity and mortality were associated with subsequent delirium and altered mental status; besides, mortality was associated with pre-existing and subsequent dementia and multiple specific neurological diseases. Heterogeneities were substantial across studies in most analysis.

INTERPRETATION

The findings show an important role of mental and neurological disorders in the context of COVID-19 and provide clues and directions for identifying and protecting vulnerable populations in the pandemic. Early detection and intervention for neurological and mental disorders are urgently needed to control morbidity and mortality induced by the COVID-19 pandemic. However, there was substantial heterogeneity among the included studies, and the results should be interpreted with caution. More studies are needed to explore long-term mental and neurological sequela, as well as the underlying brain mechanisms for the sake of elucidating the causal pathways for these associations.

FUNDING

This study is supported by grants from the National Key Research and Development Program of China, the National Natural Science Foundation of China, Special Research Fund of PKUHSC for Prevention and Control of COVID-19, and the Fundamental Research Funds for the Central Universities.

Covid-19 risk factors: statistical learning from German healthcare claims data

BACKGROUND

Precise individual risk quantification of severe courses of Covid-19 is needed to prioritize protective measures and to assess population risks in a phase of increased immunization. So far, results for the German population are lacking. Furthermore, existing studies pre-specify comorbidity risks by broad categories rather than deriving them from the data using statistical learning techniques.

METHODS

Risk factors for severe, critical and lethal courses of Covid-19 are identified from a large German claims dataset covering more than 4 million individuals. To avoid prior grouping and pre-selection of risk factors, fine-grained hierarchical information from medical classification systems for diagnoses, pharmaceuticals and procedures are used, resulting in more than 33,000 covariates. These are processed using a LASSO approach.

RESULTS

We identify relevant risk factors, among which hypertensive diseases, heart disease and the corresponding medications are most relevant at population level. Prior use of diuretics is the strongest single medical predictor for severe course (e.g. Torasemide, odds ratio (OR) 1.801), but also for a critical course (OR 2.304) and death (OR 2.523). To assess risk profiles at the individual level, our approach sums up many such factors and has better predictive ability than using pre-specified morbidity groups (AUC for predicting critical course 0.875 versus AUC ≤ 0.865).

CONCLUSIONS

The proposed method can help to identify risk factors and assess risk at the individual level for other infectious diseases. The results can be used by administrative data holders to guide protective policies, while a risk index can be applied in clinical studies with a narrower focus.

Mental disorders and risk of COVID-19-related mortality, hospitalisation, and intensive care unit admission: a systematic review and meta-analysis

BACKGROUND

Mental disorders might be a risk factor for severe COVID-19. We aimed to assess the specific risks of COVID-19-related mortality, hospitalisation, and intensive care unit (ICU) admission associated with any pre-existing mental disorder, and specific diagnostic categories of mental disorders, and exposure to psychopharmacological drug classes.

METHODS

In this systematic review and meta-analysis, we searched Web of Science, Cochrane, PubMed, and PsycINFO databases between Jan 1, 2020, and March 5, 2021, for original studies reporting data on COVID-19 outcomes in patients with psychiatric disorders compared with controls. We excluded studies with overlapping samples, studies that were not peer-reviewed, and studies written in languages other than English, Danish, Dutch, French, German, Italian, and Portuguese. We modelled random-effects meta-analyses to estimate crude odds ratios (OR) for mortality after SARS-CoV-2 infection as the primary outcome, and hospitalisation and ICU admission as secondary outcomes. We calculated adjusted ORs for available data. Heterogeneity was assessed using the I² statistic, and publication bias was tested with Egger regression and visual inspection of funnel plots. We used the GRADE approach to assess the overall strength of the evidence and the Newcastle Ottawa Scale to assess study quality. We also did subgroup analyses and meta-regressions to assess the effects of baseline COVID-19 treatment setting, patient age, country, pandemic phase, quality assessment score, sample sizes, and adjustment for confounders. This study is registered with PROSPERO, CRD42021233984.

FINDINGS

841 studies were identified by the systematic search, of which 33 studies were included in the systematic review and 23 studies in the meta-analysis, comprising 1 469 731 patients with COVID-19, of whom 43 938 had mental disorders. The sample included 130 807 females (8·9% of the whole sample) and 130 373 males (8·8%). Nine studies provided data on patient race and ethnicity, and 22 studies were rated as high quality. The presence of any mental disorder was associated with an increased risk of COVID-19 mortality (OR 2·00 [95% CI 1·58-2·54]; I²=92·66%). This association was also observed for psychotic disorders (2·05 [1·37-3·06]; I²=80·81%), mood disorders (1·99 [1·46-2·71]; I²=68·32%), substance use disorders (1·76 [1·27-2·44]; I²=47·90%), and intellectual disabilities and developmental disorders (1·73 [1·29-2·31]; I²=90·15%) but not for anxiety disorders (1·07 [0·73-1·56]; I²=11·05%). COVID-19 mortality was associated with exposure to antipsychotics (3·71 [1·74-7·91]; I²=90·31%), anxiolytics (2·58 [1·22-5·44]; I²=96·42%), and antidepressants (2·23 [1·06-4·71]; I²=95·45%). For psychotic disorders, mood disorders, antipsychotics, and anxiolytics, the association remained significant after adjustment for age, sex, and other confounders. Mental disorders were associated with increased risk of hospitalisation (2·24 [1·70-2·94]; I²=88·80%). No significant associations with mortality were identified for ICU admission. Subgroup analyses and meta-regressions showed significant associations of baseline COVID-19 treatment setting (p=0·013) and country (p<0·0001) with mortality. No significant associations with mortality were identified for other covariates. No evidence of publication bias was found. GRADE assessment indicated high certainty for crude mortality and hospitalisation, and moderate certainty for crude ICU admission.

INTERPRETATION

Pre-existing mental disorders, in particular psychotic and mood disorders, and exposure to antipsychotics and anxiolytics were associated with COVID-19 mortality in both crude and adjusted models. Although further research is required to determine the underlying mechanisms, our findings highlight the need for targeted approaches to manage and prevent COVID-19 in at-risk patient groups identified in this study.

FUNDING

None.

TRANSLATIONS

For the Italian, French and Portuguese translations of the abstract see Supplementary Materials section.

Risk of hospital admission with covid-19 among teachers compared with healthcare workers and other adults of working age in Scotland, March 2020 to July 2021: population based case-control study

OBJECTIVE

To determine the risk of hospital admission with covid-19 and severe covid-19 among teachers and their household members, overall and compared with healthcare workers and adults of working age in the general population.

DESIGN

Population based nested case-control study.

SETTING

Scotland, March 2020 to July 2021, during defined periods of school closures and full openings in response to covid-19.

PARTICIPANTS

All cases of covid-19 in adults aged 21 to 65 (n=132 420) and a random sample of controls matched on age, sex, and general practice (n=1 306 566). Adults were identified as actively teaching in a Scottish school by the General Teaching Council for Scotland, and their household members were identified through the unique property reference number. The comparator groups were adults identified as healthcare workers in Scotland, their household members, and the remaining general population of working age.

MAIN OUTCOME MEASURES

The primary outcome was hospital admission with covid-19, defined as having a positive test result for SARS-CoV-2 during hospital admission, being admitted to hospital within 28 days of a positive test result, or receiving a diagnosis of covid-19 on discharge from hospital. Severe covid-19 was defined as being admitted to intensive care or dying within 28 days of a positive test result or assigned covid-19 as a cause of death.

RESULTS

Most teachers were young (mean age 42), were women (80%), and had no comorbidities (84%). The risk (cumulative incidence) of hospital admission with covid-19 was <1% for all adults of working age in the general population. Over the study period, in conditional logistic regression models adjusted for age, sex, general practice, race/ethnicity, deprivation, number of comorbidities, and number of adults in the household, teachers showed a lower risk of hospital admission with covid-19 (rate ratio 0.77, 95% confidence interval 0.64 to 0.92) and of severe covid-19 (0.56, 0.33 to 0.97) than the general population. In the first period when schools in Scotland reopened, in autumn 2020, the rate ratio for hospital admission in teachers was 1.20 (0.89 to 1.61) and for severe covid-19 was 0.45 (0.13 to 1.55). The corresponding findings for household members of teachers were 0.91 (0.67 to 1.23) and 0.73 (0.37 to 1.44), and for patient facing healthcare workers were 2.08 (1.73 to 2.50) and 2.26 (1.43 to 3.59). Similar risks were seen for teachers in the second period, when schools reopened in summer 2021. These values were higher than those seen in spring/summer 2020, when schools were mostly closed.

CONCLUSION

Compared with adults of working age who are otherwise similar, teachers and their household members were not found to be at increased risk of hospital admission with covid-19 and were found to be at lower risk of severe covid-19. These findings should reassure those who are engaged in face-to-face teaching.

Ethnic and social inequalities in COVID-19 outcomes in Scotland: protocol for early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II)

INTRODUCTION

Evidence from previous pandemics, and the current COVID-19 pandemic, has found that risk of infection/severity of disease is disproportionately higher for ethnic minority groups, and those in lower socioeconomic positions. It is imperative that interventions to prevent the spread of COVID-19 are targeted towards high-risk populations. We will investigate the associations between social characteristics (such as ethnicity, occupation and socioeconomic position) and COVID-19 outcomes and the extent to which characteristics/risk factors might explain observed relationships in Scotland.The primary objective of this study is to describe the epidemiology of COVID-19 by social factors. Secondary objectives are to (1) examine receipt of treatment and prevention of COVID-19 by social factors; (2) quantify ethnic/social differences in adverse COVID-19 outcomes; (3) explore potential mediators of relationships between social factors and SARS-CoV-2 infection/COVID-19 prognosis; (4) examine whether occupational COVID-19 differences differ by other social factors and (5) assess quality of ethnicity coding within National Health Service datasets.

METHODS AND ANALYSIS

We will use a national cohort comprising the adult population of Scotland who completed the 2011 Census and were living in Scotland on 31 March 2020 (~4.3 million people). Census data will be linked to the Early Assessment of Vaccine and Anti-Viral Effectiveness II cohort consisting of primary/secondary care, laboratory data and death records. Sensitivity/specificity and positive/negative predictive values will be used to assess coding quality of ethnicity. Descriptive statistics will be used to examine differences in treatment and prevention of COVID-19. Poisson/Cox regression analyses and mediation techniques will examine ethnic and social differences, and drivers of inequalities in COVID-19. Effect modification (on additive and multiplicative scales) between key variables (such as ethnicity and occupation) will be assessed.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the National Research Ethics Committee, South East Scotland 02. We will present findings of this study at international conferences, in peer-reviewed journals and to policy-makers.

Impact of diabetes mellitus on in-hospital mortality in adult patients with COVID-19: a systematic review and meta-analysis

BACKGROUND

The novel coronavirus disease 2019 (COVID-19) has spread worldwide since the beginning of 2020, placing the heavy burden on the health systems all over the world. The population that particularly has been affected by the pandemic is the group of patients suffering from diabetes mellitus. Having taken the public health in considerations, we have decided to perform a systematic review and meta-analysis of diabetes mellitus on in-hospital mortality in patients with COVID-19.

METHODS

A systematic literature review (MEDLINE, EMBASE, Web of Science, Scopus, Cochrane) including all published clinical trials or observational studies published till December 10, 2020, was performed using following terms "diabetes mellitus" OR "diabetes" OR "DM" AND "survival" OR "mortality" AND "SARS-CoV-2" OR "COVID-19".

RESULTS

Nineteen studies were included out of the 7327 initially identified studies. Mortality of DM patients vs non-DM patients was 21.3 versus 6.1%, respectively (OR = 2.39; 95%CI: 1.65, 3.64; P < 0.001), while severe disease in DM and non-DM group varied and amounted to 34.8% versus 22.8% (OR = 1.43; 95%CI: 0.82, 2.50; P = 0.20). In the DM group, the complications were observed far more often when compared with non-DM group, both in acute respiratory distress (31.4 vs. 17.2%; OR = 2.38; 95%CI:1.80, 3.13; P < 0.001), acute cardiac injury (22.0% vs. 12.8%; OR = 2.59; 95%CI: 1.81, 3.73; P < 0.001), and acute kidney injury (19.1 vs. 10.2%; OR = 1.97; 95%CI: 1.36, 2.85; P < 0.001).

CONCLUSIONS

Based on the findings, we shall conclude that diabetes is an independent risk factor of the severity of COVID-19 in-hospital settings; therefore, patients with diabetes shall aim to reduce the exposure to the potential infection of COVID-19.

COVID-19: decision-making in public health

Against a background of stalling UK life expectancy, the COVID-19 pandemic necessitated a different way of working for public health to respond quickly to new and many demands. At the same time, public health teams had to ensure they did not concentrate on the immediate crisis at the expense of mitigating longer-term impacts of the pandemic. This was, and is, a major challenge with additional demands on an already hard-pressed workforce. This paper discusses the experience of a local public health department in responding to the pandemic and raises four key areas that influenced decisions and need to be considered in future. These are care homes issues, addressing all four harms of the pandemic, lessons for behaviour change and the need to strengthen Scotland's public health workforce.

The role of T-cell immunity in COVID-19 severity amongst people living with type II diabetes

The COVID‐19 pandemic has highlighted the vulnerability of people with diabetes mellitus (DM) to respiratory viral infections. Despite the short history of COVID‐19, various studies have shown that patients with DM are more likely to have increased hospitalisation and mortality rates as compared to patients without. At present, the mechanisms underlying this susceptibility are unclear. However, prior studies show that the course of COVID‐19 disease is linked to the efficacy of the host’s T‐cell responses. Healthy individuals who can elicit a robust T‐cell response are more likely to limit the severity of COVID‐19. Here, we investigate the hypothesis that an impaired T‐cell response in patients with type 2 diabetes mellitus (T2DM) drives the severity of COVID‐19 in this patient population. While there is currently a limited amount of information that specifically addresses T‐cell responses in COVID‐19 patients with T2DM, there is a wealth of evidence from other infectious diseases that T‐cell immunity is impaired in patients with T2DM. The reasons for this are likely multifactorial, including the presence of hyperglycaemia, glycaemic variability and metformin use. This review emphasises the need for further research into T‐cell responses of COVID‐19 patients with T2DM in order to better inform our response to COVID‐19 and future disease outbreaks.

Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections-Upper Silesia pilot study

In the DECODE project, data were collected from 3,114 surveys filled by symptomatic patients RT-qPCR tested for SARS-CoV-2 in a single university centre in March-September 2020. The population demonstrated balanced sex and age with 759 SARS-CoV-2( +) patients. The most discriminative symptoms in SARS-CoV-2( +) patients at early infection stage were loss of taste/smell (OR = 3.33, p < 0.0001), body temperature above 38℃ (OR = 1.67, p < 0.0001), muscle aches (OR = 1.30, p = 0.0242), headache (OR = 1.27, p = 0.0405), cough (OR = 1.26, p = 0.0477). Dyspnea was more often reported among SARS-CoV-2(-) (OR = 0.55, p < 0.0001). Cough and dyspnea were 3.5 times more frequent among SARS-CoV-2(-) (OR = 0.28, p < 0.0001). Co-occurrence of cough, muscle aches, headache, loss of taste/smell (OR = 4.72, p = 0.0015) appeared significant, although co-occurrence of two symptoms only, cough and loss of smell or taste, means OR = 2.49 (p < 0.0001). Temperature > 38℃ with cough was most frequent in men (20%), while loss of taste/smell with cough in women (17%). For younger people, taste/smell impairment is sufficient to characterise infection, whereas in older patients co-occurrence of fever and cough is necessary. The presented study objectifies the single symptoms and interactions significance in COVID-19 diagnoses and demonstrates diverse symptomatology in patient groups.

Impact of COVID-19 on care-home mortality and life expectancy in Scotland

BACKGROUND

COVID-19 deaths are commoner among care-home residents, but the mortality burden has not been quantified.

METHODS

Care-home residency was identified via a national primary care registration database linked to mortality data. Life expectancy was estimated using Makeham-Gompertz models to (i) describe yearly life expectancy from November 2015 to October 2020 (ii) compare life expectancy (during 2016-18) between care-home residents and the wider population and (iii) apply care-home life expectancy estimates to COVID-19 death counts to estimate years of life lost (YLL).

RESULTS

Among care-home residents, life expectancy in 2015/16 to 2019/20 ranged from 2.7 to 2.3 years for women and 2.3 to 1.8 years for men. Age-sex-specific life expectancy in 2016-18 in care-home residents was lower than in the Scottish population (10 and 2.5 years in those aged 70 and 90, respectively). Applying care home-specific life expectancies to COVID-19 deaths yield mean YLLs for care-home residents of 2.6 and 2.2 for women and men, respectively. In total YLL care-home residents have lost 3,560 years in women and 2,046 years in men. Approximately half of deaths and a quarter of YLL attributed to COVID-19 were accounted for by the 5% of over-70s who were care-home residents.

CONCLUSION

COVID-19 infection has led to the loss of substantial years of life in care-home residents aged 70 years and over in Scotland. Prioritising the 5% of older adults who are care-home residents for vaccination is justified not only in terms of total deaths, but also in terms of YLL.

Relation of severe COVID-19 in Scotland to transmission-related factors and risk conditions eligible for shielding support: REACT-SCOT case-control study

BACKGROUND

Clinically vulnerable individuals have been advised to shield themselves during the COVID-19 epidemic. The objectives of this study were to investigate (1) the rate ratio of severe COVID-19 associated with eligibility for the shielding programme in Scotland across the first and second waves of the epidemic and (2) the relation of severe COVID-19 to transmission-related factors in those in shielding and the general population.

METHODS

In a matched case-control design, all 178,578 diagnosed cases of COVID-19 in Scotland from 1 March 2020 to 18 February 2021 were matched for age, sex and primary care practice to 1,744,283 controls from the general population. This dataset (REACT-SCOT) was linked to the list of 212,702 individuals identified as eligible for shielding. Severe COVID-19 was defined as cases that entered critical care or were fatal. Rate ratios were estimated by conditional logistic regression.

RESULTS

With those without risk conditions as reference category, the univariate rate ratio for severe COVID-19 was 3.21 (95% CI 3.01 to 3.41) in those with moderate risk conditions and 6.3 (95% CI 5.8 to 6.8) in those eligible for shielding. The highest rate was in solid organ transplant recipients: rate ratio 13.4 (95% CI 9.6 to 18.8). Risk of severe COVID-19 increased with the number of adults but decreased with the number of school-age children in the household. Severe COVID-19 was strongly associated with recent exposure to hospital (defined as 5 to 14 days before presentation date): rate ratio 12.3 (95% CI 11.5 to 13.2) overall. The population attributable risk fraction for recent exposure to hospital peaked at 50% in May 2020 and again at 65% in December 2020.

CONCLUSIONS

The effectiveness of shielding vulnerable individuals was limited by the inability to control transmission in hospital and from other adults in the household. Mitigating the impact of the epidemic requires control of nosocomial transmission.

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values

Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases.

Identifying the predictors of Covid-19 infection outcomes and development of prediction models

BACKGROUND

The infection of Corona Virus Disease (Covid-19) is challenging health problems worldwide. COVID-19 pandemic is spreading all over the world with the number of infected cases increased to 54.4 million with 1.32 million deaths. Different types of statistical models have been developed to predict viral infection and multiple studies have compared the performance of these predictive models, but results were not consistent. This study aimed to develop and provide easy to use model to predict the Covid-19 infection severity in the patients and to help understanding the patient's condition.

METHODS

This study analyzed simulated data obtained from the large database for 340 patients with an active Covid-19 infection. The study identified predictors of Covid-19 outcomes that may be measured in two different ways: the total T-cell levels in the blood with T-cell subsets and number of cells in the blood infected with virus. All measures are relatively unobtrusive as they only require a blood sample, however there is a significant laboratory cost implications for measuring the number of cells infected with virus. This study used methodological approach using two different methods showing how multiple regression and logistic regression can be used in the context of Covid-19 longitudinal data to develop the prediction models.

RESULTS

This study has identified the predictors of Covid-19 infection outcomes and developed prediction models. In the regression model of Total_T Cell, the predictors BMI, comorbidity and Total_Tcell were all associated with increased levels of infection severity (p < 0.001). For BMI, the mean % of unhealthy cells increased by 0.42 (95% CI 0.24 to 0.60) and comorbidity predictor has on average 8.3% more unhealthy liver cells than without comorbidity (95% CI - 2.9%-1.29%). The results of multivariate logistic regression model predicting the Covid-19 Infection severity were promising. The significant predictors were observed such as Age (OR 0.95, p = 0.02, 95% CI: 0.91-0.99), Helper T_cells (OR O.93, p = 0.03, 95% CI: 0.87-0.99), Basic_Tcell (OR 1.11, p = 0.001, 95% CI: 1.06-1.71) and Comorbidity (OR 0.41, p = 0.05, 95% CI: 0.16-1.07).

CONCLUSIONS

In this study recommendation has been provided to clinical researchers on the best way to use the various Covid-19 infections measures along with identifying other possible predictors of Covid-19 infection. It is imperative to monitor closely the T-cell subsets using prediction models that might provide valuable information about the patient's condition during the treatment process.

A risk score based on procalcitonin for predicting acute kidney injury in COVID-19 patients

Background

Acute kidney injury (AKI) has been reported developing commonly in coronavirus disease 2019 (COVID‐19) patients and could increase the risk of poor outcomes in these patients. We design this study to explore the value of serum procalcitonin (PCT) on predicting AKI and construct risk score for predicting AKI in COVID‐19 patients.

Methods

Patients diagnosed with COVID‐19 and hospitalized in Renmin Hospital of Wuhan University between January 30 and February 24, 2020, were included. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the strongest predictors of AKI. Multivariate logistic regression analysis was conducted to find independent risk factors for AKI and construct risk score using odds ratio (OR) value of those risk factors. Receiver operating characteristics (ROC) curves were plotted, and area under the ROC curve (AUC) value was calculated to evaluate the predictive value of single PCT level and the constructed risk score.

Results

Among 389 included COVID‐19 patients, 28 (7.2%) patients developed AKI. LASSO regression showed hypertension, saturation of arterial oxygen (SaO₂), PCT, and blood urea nitrogen (BUN) were the strongest predictors for AKI. After multivariate logistic regression analysis, only SaO₂ (<0.001), PCT (p = 0.004), and BUN (p = 0.005) were independently associated with development of AKI in COVID‐19 patients. The AUC of single PCT and constructed risk score was 0. 881 and 0.928, respectively.

Conclusion

PCT level is correlated with AKI in COVID‐19 patients. The efficient risk score consisted of SaO₂, PCT, and BUN is readily accessible for physicians to evaluate the possibility of AKI in COVID‐19 patients.

National Psoriasis Foundation COVID-19 Task Force guidance for management of psoriatic disease during the pandemic: Version 2-Advances in psoriatic disease management, COVID-19 vaccines, and COVID-19 treatments

OBJECTIVE

To update guidance regarding the management of psoriatic disease during the COVID-19 pandemic.

STUDY DESIGN

The task force (TF) includes 18 physician voting members with expertise in dermatology, rheumatology, epidemiology, infectious diseases, and critical care. The TF was supplemented by nonvoting members, which included fellows and National Psoriasis Foundation staff. Clinical questions relevant to the psoriatic disease community were informed by inquiries received by the National Psoriasis Foundation. A Delphi process was conducted.

RESULTS

The TF updated evidence for the original 22 statements and added 5 new recommendations. The average of the votes was within the category of agreement for all statements, 13 with high consensus and 14 with moderate consensus.

LIMITATIONS

The evidence behind many guidance statements is variable in quality and/or quantity.

CONCLUSIONS

These statements provide guidance for the treatment of patients with psoriatic disease on topics including how the disease and its treatments affect COVID-19 risk, how medical care can be optimized during the pandemic, what patients should do to lower their risk of getting infected with severe acute respiratory syndrome coronavirus 2 (including novel vaccination), and what they should do if they develop COVID-19. The guidance is a living document that is continuously updated by the TF as data emerge.

Relation of severe COVID-19 to polypharmacy and prescribing of psychotropic drugs: the REACT-SCOT case-control study

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 EUPAS35558.

Risks of and risk factors for COVID-19 disease in people with diabetes: a cohort study of the total population of Scotland

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 cross-validated predictive model of fatal or critical care unit-treated COVID-19 among people with diabetes.

METHODS

In 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 on March 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 anti-diabetic and other medication (all p<0·0001), and have been a smoker (p=0·0011). The cross-validated predictive model of fatal or critical care unit-treated COVID-19 in people with diabetes had a C-statistic of 0·85 (0·83-0·86).

INTERPRETATION

Overall risks of fatal or critical care unit-treated COVID-19 were substantially elevated in those with type 1 and type 2 diabetes compared with the background population. The risk of fatal or critical care unit-treated COVID-19, and therefore the need for special protective measures, varies widely among those with diabetes but can be predicted reasonably well using previous clinical history.

FUNDING

None.

Costs of COVID-19 pandemic associated with diabetes in Europe: a health care cost model

OBJECTIVE

Diabetes is associated with progression to severe COVID-19. The objective of this study was to estimate to what extent the increased risk among people with diabetes could impact the secondary care costs of COVID-19 throughout Europe during the first wave of the COVID-19 pandemic from January to June 2020.

METHODS

Applying a health care cost model based on inputs from data published in international peer-reviewed journals, identified via a rapid literature review this study aimed to estimate the total secondary sector costs of COVID-19. Estimates of unit costs were based on data from Denmark, France, Spain and the UK. We calculated average costs per patient without diabetes and according to four diabetes categories based on risk of hospitalization, admission to intensive care unit, ventilator support and length of hospital stay.

RESULTS

The estimated cost per hospital admission during the first wave of COVID-19 in Europe ranged between EUR 25,018 among people with type 2 diabetes in good glycaemic control to EUR 57,244 among people with type 1 diabetes in poor glycaemic control, reflecting higher risk of intensive care, ventilator support and longer hospital stay according to diabetes category, while the corresponding cost for people without diabetes was estimated at EUR 16,993. The total direct costs of secondary care of COVID-19 in Europe were estimated at EUR 13.9 billion. Thus, 23.5% of the total costs accounted for treating people with diabetes.

CONCLUSIONS

This study highlights the importance of a greater focus on prevention and adequate treatment of diabetes and the need for special attention to avoid infection with COVID-19 to the extent possible among those already diagnosed with diabetes.

Risk of hospital admission with coronavirus disease 2019 in healthcare workers and their households: nationwide linkage cohort study

OBJECTIVE

To 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.

DESIGN

Nationwide linkage cohort study.

SETTING

Scotland, UK, 1 March to 6 June 2020.

PARTICIPANTS

Healthcare workers aged 18-65 years, their households, and other members of the general population.

MAIN OUTCOME MEASURE

Admission to hospital with covid-19.

RESULTS

The 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.

CONCLUSIONS

Healthcare 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.