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COVID-19 - Resources

Keeping up to date with all the emerging Covid-19 research is no easy task! To try and help, we’ve created a resources page focusing specifically on research related to Covid-19 outcomes and risk factors. We hope this provides a quick and easy platform for you to read and understand all the important news being published in this area!

Date Published 08/01/2021
OpenSAFELY NHS Service Restoration Observatory 1: describing trends and variation in primary care clinical activity for 23.3 million patients in England during the first wave of COVID-19
  • Much activity recorded in general practice declined to some extent during the pandemic, but largely recovered by September 2020.
  • There was a large drop in coded activity for commonly used laboratory tests, with broad recovery to pre-pandemic levels by September.
  • The overall pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as no change from the previous year.
  • Respiratory tract infections exhibited a sustained drop compared with pre-pandemic levels, not returning to pre-pandemic levels by September 2020.
  • Various COVID-19 codes increased through the period.
  • Observed a small decline associated with high level codes for long-term respiratory conditions such as chronic obstructive pulmonary disease (COPD) and asthma.
  • Asthma annual reviews experienced a small drop but since recovered, while COPD annual reviews remain below baseline.
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Date Published 14/12/2020
A Predictive Model for Severe Covid-19 in the Medicare Population: A Tool for Prioritizing Scarce Vaccine Supply
  • Developed a predictive model for severe COVID-19 using clinical data from de-identified Medicare claims for 16 million Medicare fee-for-service beneficiaries, including 1 million COVID-19 cases, and socio-economic data from the CDC Social Vulnerability Index.
  • Identified risk factors for severe COVID-19, using multivariate logistic regression and random forest modeling. Predicted individual probabilities of COVID-19 hospitalization were then calculated for population risk stratification and COVID-19 vaccine prioritization, and for mapping of population risk levels at the county and zip code levels on a nationwide dashboard.
  • The leading Covid-19 hospitalization risk factors driving the risk model were: Non-white ethnicity (particularly North American Native, Black, and Hispanic), end-stage renal disease, advanced age (particularly age over 85), prior hospitalization, leukemia, morbid obesity, chronic kidney disease, lung cancer, chronic liver disease, pulmonary fibrosis or pulmonary hypertension, and chemotherapy.
  • Previously reported risk factors such as chronic obstructive pulmonary disease and diabetes conferred modest hospitalization risk. Among all social vulnerability factors analyzed, residence in a low-income zip code was the only risk factor independently predicting Covid-19 hospitalization.
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Date Published 18/11/2020
Comparison of COVID-19 outcomes among shielded and non-shielded populations: A general population cohort study of 1.3 million
  • General population study was conducted using linked primary care, prescribing, laboratory, hospital and death records up to end of May 2020.
  • Poisson regression models and population attributable fractions were used to compare COVID-19 outcomes by overall risk category, and individual risk criteria: confirmed infection, hospitalisation, intensive care unit (ICU) admission, population mortality and case-fatality.
  • Of the 1.3 million population, 32,533 (2.47%) had been advised to shield, a further 347,374 (26.41%) were classified as moderate risk.
  • Testing for COVID-19 was more common in the shielded (6.75%) and moderate (1.99%) than low (0.72%) risk categories.
  • Referent to low-risk, the shielded group had higher risk of confirmed infection (RR 7.91, 95% 7.01-8.92), case-fatality (RR 5.19, 95% CI 4.12-6.53) and population mortality (RR 48.64, 95% 37.23-63.56).
  • The moderate risk had intermediate risk of confirmed infection (RR 4.11, 95% CI 3.82-4.42) and population mortality (RR 26.10, 95% CI 20.89-32.60), but had comparable case-fatality (RR 5.13, 95% CI 4.24-6.21) to the shielded, and accounted for a higher proportion of deaths (PAF 75.27% vs 13.38%).
  • Age ≥70 years made the largest contribution to deaths (49.53%) and was associated with an 8-fold risk of infection, 7-fold case-fatality and 74-fold mortality.
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Date Published 12/11/2020
Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis
  • Performed a systematic review and meta-analysis to explore the relationship between ethnicity and clinical outcomes in COVID-19.
  • Databases (MEDLINE, EMBASE, PROSPERO, Cochrane library and MedRxiv) were searched up to 31st August 2020, for studies reporting COVID-19 data disaggregated by ethnicity.
  • Outcomes were: risk of infection; intensive therapy unit (ITU) admission and death. PROSPERO ID: 180654.
  • 18,728,893 patients from 50 studies were included; 26 were peer-reviewed; 42 were from the United States of America and 8 from the United Kingdom.
  • Individuals from Black and Asian ethnicities had a higher risk of COVID-19 infection compared to White individuals. This was consistent in both the main analysis (pooled adjusted RR for Black: 2.02, 95% CI 1.672.44; pooled adjusted RR for Asian: 1.50, 95% CI 1.241.83) and sensitivity analyses examining peer-reviewed studies only (pooled adjusted RR for Black: 1.85, 95%CI: 1.462.35; pooled adjusted RR for Asian: 1.51, 95% CI 1.221.88). Individuals of Asian ethnicity may also be at higher risk of ITU admission (pooled adjusted RR 1.97 95% CI 1.342.89) (but no studies had yet been peer-reviewed) and death (pooled adjusted RR/HR 1.22 [0.991.50]).
  • Individuals of Black and Asian ethnicity are at increased risk of COVID-19 infection compared to White individuals; Asians may be at higher risk of ITU admission and death.
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Date Published 23/09/2020
Diagnosis of physical and mental health conditions in primary care during the COVID-19 pandemic: a retrospective cohort study
  • Aimed to investigate the indirect effect of the COVID-19 pandemic on general practice health-care usage, and the subsequent diagnoses of common physical and mental health conditions in a deprived UK population.
  • Retrospective cohort study using routinely collected primary care data that was recorded in the Salford Integrated Record between Jan 1, 2010, and May 31, 2020.
  • Between March 1 and May 31, 2020, 1073 first diagnoses of common mental health problems were reported compared with 2147 expected cases (95% CI 1821 to 2489) based on preceding years, representing a 50·0% reduction (95% CI 41·1 to 56·9).
  • Compared with expected numbers, 456 fewer diagnoses of circulatory system diseases (43·3% reduction, 95% CI 29·6 to 53·5), and 135 fewer type 2 diabetes diagnoses (49·0% reduction, 23·8 to 63·1) were observed. The number of first prescriptions of associated medications was also lower than expected for the same time period. However, the gap between observed and expected cancer diagnoses (31 fewer; 16·0% reduction, −18·1 to 36·6) during this time period was not statistically significant.
  • In this deprived urban population, diagnoses of common conditions decreased substantially between March and May 2020, suggesting a large number of patients have undiagnosed conditions.
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Date Published 20/08/2020
Multimorbidity, polypharmacy, and COVID-19 infection within the UK Biobank cohort
  • Examined the relationships between multimorbidity, polypharmacy (a proxy of multimorbidity), and COVID-19; and how these differ by sociodemographic, lifestyle, and physiological prognostic factors.
  • Studied data from UK Biobank (428,199 participants; aged 37–73; recruited 2006–2010) on self-reported LTCs, medications, sociodemographic, lifestyle, and physiological measures which were linked to COVID-19 test data.
  • Poisson regression models examined risk of COVID-19 by multimorbidity/polypharmacy and effect modification by COVID-19 prognostic factors (age/sex/ethnicity/socioeconomic status/smoking/physical activity/BMI/systolic blood pressure/renal function).
  • 4,498 (1.05%) participants were tested; 1,324 (0.31%) tested positive for COVID-19.
  • Compared with no LTCs, relative risk (RR) of COVID-19 in those with 1 LTC was no higher (RR 1.12 (CI 0.96–1.30)), whereas those with ≥2 LTCs had 48% higher risk; RR 1.48 (1.28–1.71). Compared with no cardiometabolic LTCs, having 1 and ≥2 cardiometabolic LTCs had a higher risk of COVID-19; RR 1.28 (1.12–1.46) and 1.77 (1.46–2.15), respectively.
  • Polypharmacy was associated with a dose response higher risk of COVID-19. All prognostic factors were associated with a higher risk of COVID-19 infection in multimorbidity; being non-white, most socioeconomically deprived, BMI ≥40 kg/m2, and reduced renal function were associated with the highest risk of COVID-19 infection: RR 2.81 (2.09–3.78); 2.79 (2.00–3.90); 2.66 (1.88–3.76); 2.13 (1.46–3.12), respectively.
  • Increasing multimorbidity, especially cardiometabolic multimorbidity, and polypharmacy are associated with a higher risk of developing COVID-19. Those with multimorbidity and additional factors, such as non-white ethnicity, are at heightened risk of COVID-19.
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Date Published 08/07/2020
Factors associated with COVID-19-related death using OpenSAFELY
  • OpenSAFELY is a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records.
  • OpenSAFELY was used to examine factors associated with COVID-19-related death.
  • Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths.
  • COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53–1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions.
  • Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29–1.69) and 1.45 (1.32–1.58), respectively).
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Date Published 17/06/2020
Ethnicity and Outcomes from COVID-19: The ISARIC CCP-UK Prospective Observational Cohort Study of Hospitalised Patients

Summary:

  • Prospective cohort study in which hospitalised patients with suspected/confirmed COVID-19 were recruited from 260 hospitals across England, Scotland and Wales, collecting data directly and from records between 6th February and 8th May 2020 with follow-up until 22nd May 2020.
  • Analysis used hierarchical regression models accounting for confounding, competing risks, and clustering of patients in hospitals.
  • Findings: Of 34,986 patients enrolled, 30,693 (88%) had ethnicity recorded: South Asian (1,388, 5%), East Asian (266, 1%), Black (1,094, 4%), Other Ethnic Minority (2,398, 8%) (collectively Ethnic Minorities), and White groups (25,547, 83%).
  • No difference was seen between ethnic groups in the time from symptom onset to hospital admission, nor in illness severity at admission.
  • Critical care admission was more common in South Asian (odds ratio 1.28, 95% confidence interval 1.09 to 1.52), Black (1.36, 1.14 to 1.62), and Other Ethnic Minority (1.29, 1.13 to 1.47) groups compared to the White group, after adjusting for age, sex and location. This was broadly unchanged after adjustment for deprivation and comorbidities.
  • Higher adjusted mortality was seen in the South Asian (hazard ratio 1.19, 1.05 to 1.36), but not East Asian (1.00, 0.74 to 1.35), Black (1.05, 0.91 to 1.26) or Other Ethnic Minority (0.99, 0.89 to 1.10) groups, compared to the White group.
  • 18% (95% CI, 9% to 56%) of the excess mortality in South Asians was mediated by pre-existing diabetes.
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Date Published 01/06/2020
The impact of ethnicity on clinical outcomes in COVID-19: A systematic review
  • Systematic review searching EMBASE, MEDLINE, Cochrane Library and PROSPERO for English-language citations on ethnicity and COVID-19 (1st December 2019-15th May 2020).
  • COVID-19 articles in NEJM, Lancet, BMJ, JAMA, clinical trial protocols, grey literature, surveillance data and preprint articles on COVID-19 in MedRxiv were reviewed to evaluate if the association between ethnicity and clinical outcomes were reported and what they showed.
  • Of 207 articles in the database search, five reported ethnicity; two reported no association between ethnicity and mortality.
  • Of 690 articles identified from medical journals, 12 reported ethnicity; three reported no association between ethnicity and mortality.
  • Of 209 preprints, 34 reported ethnicity – 13 found Black, Asian and Minority Ethnic (BAME) individuals had an increased risk of infection with SARS-CoV-2 and 12 reported worse clinical outcomes, including ITU admission and mortality, in BAME patients compared to White patients. Of 12 grey literature reports, seven with original data reported poorer clinical outcomes in BAME groups compared to White groups.
  • Data on ethnicity in patients with COVID-19 in the published medical literature remains limited.
  • However, emerging data from the grey literature and preprint articles suggest BAME individuals are at an increased risk of acquiring SARS-CoV-2 infection compared to White individuals and also worse clinical outcomes from COVID-19.
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Date Published 20/05/2020
Type 1 and Type 2 diabetes and COVID-19 related mortality in England: a whole population study

Summary:

  • A population cohort study assessing risks of in-hospital death with COVID-19 between 1st March and 11th May 2020, including individuals registered with a General Practice in England and alive on February 19th 2020. Multivariate logistic regression examined diabetes status, by type, and associations with in-hospital death, adjusting for demographic factors and comorbidities.
  • Of the 61,414,470 individuals registered, 263,830 (0∙4%) had a recorded diagnosis of Type 1 and 2,864,670 (4∙7%) of Type 2 diabetes. There were 23,804 COVID-19 related deaths.
  • One third occurred in people with diabetes:7,466 (31∙4%) with Type 2 and 365 (1∙5%) with Type 1 diabetes.
  • Crude mortality rates per 100,000 persons over the 72 days for the overall population and for those with Type 1 and Type 2 diabetes were 38∙8 (38∙3-39∙3), 138∙3 (124∙5-153∙3), and 260∙6 (254∙7-266∙6) respectively.
  • Adjusted for age, sex, deprivation, ethnicity and geographical region, people with Type 1 and Type 2 diabetes had 3∙50 (3∙15-3∙89) and 2∙03 (1∙97- 2∙09) times the odds respectively of dying in hospital with COVID-19 compared to those without diabetes, attenuated to 2∙86 and 1∙81 respectively when also adjusted for previous hospital admissions with coronary heart disease, cerebrovascular disease or heart failure.
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Date Published 12/05/2020
Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study
  • Estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease.
  • Used linked primary and secondary care electronic health records from England (Health Data Research UK–CALIBER).
  • Included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men).
  • Estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition.
  • 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41–4·51).
  • Age and underlying conditions combined to influence background risk, varying markedly across conditions.
  • In a full suppression scenario in the UK population, estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0.
  • Results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions.
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Date Published 07/05/2020
OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients.

Summary:

  • Death from COVID-19 was strongly associated with: being male (hazard ratio 1.99, 95%CI 1.88-2.10); older age and deprivation (both with a strong gradient); uncontrolled diabetes (HR 2.36 95% CI 2.18-2.56); severe asthma (HR 1.25 CI 1.08-1.44); and various other prior medical conditions.
  • Compared to people with ethnicity recorded as white, black people were at higher risk of death, with only partial attenuation in hazard ratios from the fully adjusted model (age-sex adjusted HR 2.17 95% CI 1.84-2.57; fully adjusted HR 1.71 95% CI 1.44-2.02); with similar findings for Asian people (age-sex adjusted HR 1.95 95% CI 1.73-2.18; fully adjusted HR 1.62 95% CI 1.43-1.82).
  • People from Asian and black groups are at markedly increased risk of in-hospital death from COVID19, and contrary to some prior speculation this is only partially attributable to pre-existing clinical risk factors or deprivation; further research into the drivers of this association is therefore urgently required.
  • Deprivation is also a major risk factor with, again, little of the excess risk explained by comorbidity or other risk factors.
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