Ethnic differences in the indirect impacts of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: An observational cohort study using OpenSAFELY (preprint)

Background: The COVID-19 pandemic disrupted healthcare and may have impacted ethnic inequalities in healthcare. We aimed to describe the impact of pandemic-related disruption on ethnic differences in clinical monitoring and hospital admissions for non-COVID conditions in England. Methods We conducted a cohort study using OpenSAFELY (2018-2022). We grouped ethnicity (exposure), into five categories: White, South Asian, Black, Other, Mixed. We used interrupted time-series regression to estimate ethnic differences in clinical monitoring frequency (e.g., blood pressure measurements) before and after 23rd March 2020. We used multivariable Cox regression to quantify ethnic differences in hospitalisations related to: diabetes, cardiovascular disease, respiratory disease, and mental health before and after 23rd March 2020. Findings Of 14,930,356 adults in 2020 with known ethnicity (92% of sample): 86.6% were White, 7.3% Asian, 2.6% Black, 1.4% Mixed ethnicity, and 2.2% Other ethnicities. Clinical monitoring did not return to pre-pandemic levels for any ethnic group. Ethnic differences were apparent pre-pandemic, except for diabetes monitoring, and remained unchanged, except for blood pressure monitoring in those with mental health conditions where differences narrowed during the pandemic. For those of Black ethnicity, there were seven additional admissions for diabetic ketoacidosis per month during the pandemic, and relative ethnic differences narrowed during the pandemic compared to White. There was increased admissions for heart failure during the pandemic for all ethnic groups, though highest in White ethnicity. Relatively, ethnic differences narrowed for heart failure admission in those of Asian and Black ethnicity compared to White. For other outcomes the pandemic had minimal impact on ethnic differences. Interpretation Our study suggests ethnic differences in clinical monitoring and hospitalisations remained largely unchanged during the pandemic for most conditions. Key exceptions were hospitalisations for diabetic ketoacidosis and heart failure, which warrant further investigation to understand the causes. Funding LSHTM COVID-19 Response Grant (DONAT15912).

Clinical characteristics with inflammation profiling of Long-COVID and association with one-year recovery following hospitalisation in the UK: a prospective observational study (preprint)

Background There are currently no effective pharmacological or non-pharmacological interventions for Long-COVID. To identify potential therapeutic targets, we focussed on previously described four recovery clusters five months after hospital discharge, their underlying inflammatory profiles and relationship with clinical outcomes at one year. Methods PHOSP-COVID is a prospective longitudinal cohort study, recruiting adults hospitalised with COVID-19 across the UK. Recovery was assessed using patient reported outcomes measures (PROMs), physical performance, and organ function at five-months and one-year after hospital discharge. Hierarchical logistic regression modelling was performed for patient-perceived recovery at one-year. Cluster analysis was performed using clustering large applications (CLARA) k-medoids approach using clinical outcomes at five-months. Inflammatory protein profiling from plasma at the five-month visit was performed. Findings 2320 participants have been assessed at five months after discharge and 807 participants have completed both five-month and one-year visits. Of these, 35.6% were female, mean age 58.7 (SD 12.5) years, and 27.8% received invasive mechanical ventilation (IMV). The proportion of patients reporting full recovery was unchanged between five months 501/165 (25.6%) and one year 232/804 (28.9%). Factors associated with being less likely to report full recovery at one year were: female sex OR 0.68 (95% CI 0.46-0.99), obesity OR 0.50 (95%CI 0.34-0.74) and IMV OR 0.42 (95%CI 0.23-0.76). Cluster analysis (n=1636) corroborated the previously reported four clusters: very severe, severe, moderate/cognitive, mild relating to the severity of physical, mental health and cognitive impairments at five months in a larger sample. There was elevation of inflammatory mediators of tissue damage and repair in both the very severe and the moderate/cognitive clusters compared to the mild cluster including interleukin-6 which was elevated in both comparisons. Overall, there was a substantial deficit in median (IQR) EQ5D-5L utility index from pre-COVID (retrospective assessment) 0.88 (0.74-1.00), five months 0.74 (0.60-0.88) to one year: 0.74 (0.59-0.88), with minimal improvements across all outcome measures at one-year after discharge in the whole cohort and within each of the four clusters. Interpretation The sequelae of a hospital admission with COVID-19 remain substantial one year after discharge across a range of health domains with the minority in our cohort feeling fully recovered. Patient perceived health-related quality of life remains reduced at one year compared to pre-hospital admission. Systematic inflammation and obesity are potential treatable traits that warrant further investigation in clinical trials.

Covid-19 does not look like what you are looking for: clustering symptoms by nation and multi- morbidities reveal substantial differences to the classical symptom triad (preprint)

COVID-19 is by convention characterised by a triad of symptoms: cough, fever and loss of taste/smell. The aim of this study was to examine clustering of COVID-19 symptoms based on underlying chronic disease and geographical location. Using a large global symptom survey of 78,299 responders in 190 different countries, we examined symptom profiles in relation to geolocation (grouped by country) and underlying chronic disease (single, co- or multi-morbidities) associated with a positive COVID-19 test result using statistical and machine learning methods to group populations by underlying disease, countries, and symptoms. Taking the responses of 7980 responders with a COVID-19 positive test in the top 5 contributing countries, we find that the most frequently reported symptoms differ across the globe: For example, fatigue 4108(51.5%), headache 3640(45.6%) and loss of smell and taste 3563(44.6%) are the most reported symptoms globally. However, symptom patterns differ by continent; India reported a significantly lower proportion of headache (22.8% vs 45.6%, p<0.05) and itchy eyes (7.0% vs. 15.3%, p<0.05) than other countries, as does Pakistan (33.6% vs 45.6%, p<0.05 and 8.6% vs 15.3%, p<0.05). Mexico and Brazil report significantly less of these symptoms. As with geographic location, we find people differed in their reported symptoms, if they suffered from specific underlying diseases. For example, COVID-19 positive responders with asthma or other lung disease were more likely to report shortness of breath as a symptom, compared with COVID-19 positive responders who had no underlying disease (25.3% vs. 13.7%, p<0.05, and 24.2 vs.13.7%, p<0.05). Responders with no underlying chronic diseases were more likely to report loss of smell and tastes as a symptom (46%), compared with the responders with type 1 diabetes (21.3%), Type 2 diabetes (33.5%) lung disease (29.3%), or hypertension (37.8%). Global symptom ranking differs markedly from the well-known and commonly described symptoms for COVID-19, which are based on a few localised studies. None of the five countries studied in depth recorded cough or temperature as the most common symptoms. The most common symptoms reported were fatigue and loss of smell and taste. Amongst responders from Brazil cough was the second most frequently reported symptom, after fatigue. Moreover, we find that across countries and based on underlying chronic diseases, there are significant differences in symptom profiles at presentation, that cannot be fully explained by the different chronic disease profiles of these countries, and may be caused by differences in climate, environment and ethnicities. These factors uncovered by our global comorbidity survey of COVID-19 positive tested people may contribute to the apparent large asymptotic COVID-19 spread and put patients with underlying disease systematically more at risk.

Impact of COVID-19 on People with Asthma: A Mixed Methods Analysis from a UK Wide Survey (preprint)

Background: The impact of COVID-19 on people with asthma is a concern, with evidence that they are more likely to require intensive care if hospitalised. However, data regarding longer-term impacts is limited. Methods: Using data from an online UK-wide survey of 4,500 people with asthma (Median age 50-59 years, 81% female), conducted by the Asthma UK-British Lung Foundation partnership in October 2020, we undertook a mixed methods analysis of the characteristics and experience of those reporting that they had had COVID-19. Findings: The COVID-19 group (n=471, 10·5%) reported increased inhaler use and worse asthma management, compared to those not reporting COVID-19, but did not differ by gender, ethnicity, or household income. Among the COVID-19 group, 56·1% reported having long-COVID, 20·2% were ‘unsure’. Those with long-COVID were more likely than those without long-COVID to describe: their breathing as worse or much worse after their initial illness (73·7% Vs 34·8%, P<0·001), increased inhaler use (67·8% Vs 34·8%, P<0·001), and worse or much worse asthma management (59·6% Vs 25·6%, P<0·001). Having long-COVID was not associated with age, gender, ethnicity, UK nation or household income. Analysis of free text survey responses identified three key themes: 1) variable COVID-19 severity, duration, and recovery; 2) Symptom overlap and interaction between COVID-19 and asthma; 3) Barriers to accessing healthcare. Interpretation: Persisting symptoms are common in people with asthma following COVID-19. Measures are needed to ensure appropriate healthcare access including clinical evaluation and investigation, to distinguish between COVID-19 symptoms and asthma. Funding: KEJP was supported by the Imperial College Clinician Investigator Scholarship. KEJP would like to acknowledge the National Institute for Health Research (NIHR) Biomedical Research Centre based at Imperial College Healthcare NHS Trust and Imperial College London for their support. No other authors have anything to declare. Declaration of Interest: None to declare. Ethical Approval: Ethical approval for this study was granted by the Imperial College Research Governance and Integrity Team (RGIT) (ICREC Ref: 20IC6625).

COVID-19 Collateral: Indirect Acute Effects of the Pandemic on Physical and Mental Health in the UK (preprint)

Background: Concerns have been raised that the response to the UK COVID-19 pandemic may have worsened physical and mental health, and reduced use of health services. However, the scale of the problem is unquantified, impeding development of effective mitigations. We asked what has happened to general practice contacts for acute physical and mental health outcomes during the pandemic?Methods: Using electronic health records from the Clinical Research Practice Datalink (CPRD) Aurum (2017-2020), we calculated weekly primary care contacts for selected acute physical and mental health conditions (including: anxiety, depression, acute alcohol-related events, asthma and chronic obstructive pulmonary disease [COPD] exacerbations, cardiovascular and diabetic emergencies). We used interrupted time series (ITS) analysis to formally quantify changes in conditions after the introduction of population-wide restrictions (‘lockdown’) compared to the period prior to their introduction in March 2020.Findings: The overall population included 9,863,903 individuals on 1st January 2017. Primary care contacts for all conditions dropped dramatically after introduction of population-wide restrictions. By July 2020, except for unstable angina and acute alcohol-related events, contacts for all conditions had not recovered to pre-lockdown levels. The largest reductions were for contacts for: diabetic emergencies (OR: 0.35, 95% CI: 0.25-0.50), depression (OR: 0.53, 95% CI: 0.52-0.53), and self-harm (OR: 0.56, 95% CI: 0.54-0.58).Interpretation: There were substantial reductions in primary care contacts for acute physical and mental conditions with restrictions, with limited recovery by July 2020. It is likely that much of the deficit in care represents unmet need, with implications for subsequent morbidity and premature mortality. The conditions we studied are sufficiently severe that any unmet need will have substantial ramifications for the people experiencing the conditions and healthcare provision. Maintaining access must be a key priority in future public health planning (including further restrictions).Funding: Wellcome Trust Senior Fellowship (SML), Health Data Research UK.Declaration of Interests: All authors have completed the ICMJE uniform disclosure form (www.icmje.org/coi_disclosure.pdf). MM is a member of Independent SAGE, Dr. Warren-Gash reports grants from Wellcome Trust, during the conduct of the study, Liam Smeeth, is a non-executive director of the MHRA. All other authors have nothing to declare. Ethics Approval Statement: The study was approved by the London School of Hygiene and Tropical Medicine Research Ethics Committee (Reference: 22143 /RR/18495) and by the CPRD Independent Scientific Advisory Committee (ISAC Protocol Number: 20_089R2).

COVID-19 collateral: Indirect acute effects of the pandemic on physical and mental health in the UK (preprint)

Background: Concerns have been raised that the response to the UK COVID-19 pandemic may have worsened physical and mental health, and reduced use of health services. However, the scale of the problem is unquantified, impeding development of effective mitigations. We asked what has happened to general practice contacts for acute physical and mental health outcomes during the pandemic? Methods: Using electronic health records from the Clinical Research Practice Datalink (CPRD) Aurum (2017-2020), we calculated weekly primary care contacts for selected acute physical and mental health conditions (including: anxiety, depression, acute alcohol-related events, asthma and chronic obstructive pulmonary disease [COPD] exacerbations, cardiovascular and diabetic emergencies). We used interrupted time series (ITS) analysis to formally quantify changes in conditions after the introduction of population-wide restrictions ('lockdown') compared to the period prior to their introduction in March 2020. Findings: The overall population included 9,863,903 individuals on 1st January 2017. Primary care contacts for all conditions dropped dramatically after introduction of population-wide restrictions. By July 2020, except for unstable angina and acute alcohol-related events, contacts for all conditions had not recovered to pre-lockdown levels. The largest reductions were for contacts for: diabetic emergencies (OR: 0.35, 95% CI: 0.25-0.50), depression (OR: 0.53, 95% CI: 0.52-0.53), and self-harm (OR: 0.56, 95% CI: 0.54-0.58). Interpretation: There were substantial reductions in primary care contacts for acute physical and mental conditions with restrictions, with limited recovery by July 2020. It is likely that much of the deficit in care represents unmet need, with implications for subsequent morbidity and premature mortality. The conditions we studied are sufficiently severe that any unmet need will have substantial ramifications for the people experiencing the conditions and healthcare provision. Maintaining access must be a key priority in future public health planning (including further restrictions). Funding: Wellcome Trust Senior Fellowship (SML), Health Data Research UK.

Outcome of hospitalisation for COVID-19 in patients with Interstitial Lung Disease: An international multicentre study. (preprint)

Rationale: The impact of COVID-19 on patients with Interstitial Lung Disease (ILD) has not been established. Objectives: To assess outcomes following COVID-19 in patients with ILD versus those without in a contemporaneous age, sex and comorbidity matched population. Methods: An international multicentre audit of patients with a prior diagnosis of ILD admitted to hospital with COVID-19 between 1 March and 1 May 2020 was undertaken and compared with patients, without ILD obtained from the ISARIC 4C cohort, admitted with COVID-19 over the same period. The primary outcome was survival. Secondary analysis distinguished IPF from non-IPF ILD and used lung function to determine the greatest risks of death. Measurements and Main Results: Data from 349 patients with ILD across Europe were included, of whom 161 were admitted to hospital with laboratory or clinical evidence of COVID-19 and eligible for propensity-score matching. Overall mortality was 49% (79/161) in patients with ILD with COVID-19. After matching ILD patients with COVID-19 had higher mortality (HR 1.60, Confidence Intervals 1.17-2.18 p=0.003) compared with age, sex and co-morbidity matched controls without ILD. Patients with a Forced Vital Capacity (FVC) of <80% had an increased risk of death versus patients with FVC [≥]80% (HR 1.72, 1.05-2.83). Furthermore, obese patients with ILD had an elevated risk of death (HR 1.98, 1.13-3.46). Conclusions: Patients with ILD are at increased risk of death from COVID-19, particularly those with poor lung function and obesity. Stringent precautions should be taken to avoid COVID-19 in patients with ILD.

Inhaled corticosteroid use and risk COVID-19 related death among 966,461 patients with COPD or asthma: an OpenSAFELY analysis (preprint)

Background: Early descriptions of the coronavirus outbreak showed a lower prevalence of asthma and COPD than was expected for people diagnosed with COVID-19, leading to speculation that inhaled corticosteroids (ICS) may protect against infection with SARS-CoV-2, and development of serious sequelae. We evaluated the association between ICS and COVID-19 related death using linked electronic health records in the UK. Methods: We conducted cohort studies on two groups of people (COPD and asthma) using the OpenSAFELY platform to analyse data from primary care practices linked to national death registrations. People receiving an ICS were compared to those receiving alternative respiratory medications. Our primary outcome was COVID-19 related death. Findings: We identified 148,588 people with COPD and 817,973 people with asthma receiving relevant respiratory medications in the four months prior to 01 March 2020. People with COPD receiving ICS were at a greater risk of COVID-19 related death compared to those receiving a long-acting beta agonist (LABA) and a long-acting muscarinic antagonist (LAMA) (adjusted HR = 1.38, 95% CI = 1.08 - 1.75). People with asthma receiving high dose ICS were at an increased risk of death compared to those receiving a short-acting beta agonist (SABA) only (adjusted HR = 1.52, 95%CI = 1.08 - 2.14); the adjusted HR for those receiving low-medium dose ICS was 1.10 (95% CI = 0.82 - 1.49). Quantitative bias analyses indicated that an unmeasured confounder of only moderate strength of association with exposure and outcome could explain the observed associations in both populations. Interpretation: These results do not support a major role of ICS in protecting against COVID-19 related deaths. Observed increased risks of COVID-19 related death among people with COPD and asthma receiving ICS can be plausibly explained by unmeasured confounding due to disease severity.

A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems (preprint)

Objectives The UK Biobank (UKB) is making primary care Electronic Health Records (EHR) for 500,000 participants available for COVID-19-related research. Data are extracted from four sources, recorded using five clinical terminologies and stored in different schemas. The aims of our research were to: a) develop a semi-supervised approach for bootstrapping EHR phenotyping algorithms in UKB EHR, and b) to evaluate our approach by implementing and evaluating phenotypes for 31 common biomarkers. Materials and Methods We describe an algorithmic approach to phenotyping biomarkers in primary care EHR involving a) bootstrapping definitions using existing phenotypes, b) excluding generic, rare or semantically distant terms, c) forward-mapping terminology terms, d) expert review, and e) data extraction. We evaluated the phenotypes by assessing the ability to reproduce known epidemiological associations with all-cause mortality using Cox proportional hazards models. Results We created and evaluated phenotyping algorithms for 31 biomarkers many of which are directly related to COVID–19 complications e.g. diabetes, cardiovascular disease, respiratory disease. Our algorithm identified 1651 Read v2 and Clinical Terms Version 3 terms and automatically excluded 1228 terms. Clinical review excluded 103 terms and included 44 terms, resulting in 364 terms for data extraction (sensitivity 0.89, specificity 0.92). We extracted 38,190,682 events and identified 220,978 participants with at least one biomarker measured. Discussion and conclusion Bootstrapping phenotyping algorithms from similar EHR can potentially address pre-existing methodological concerns that undermine the outputs of biomarker discovery pipelines and provide research-quality phenotyping algorithms.